system

Abstract

Provide a system. A means for a user to set project objectives and requirements; a means to specify the skills and experience required; A means of listing AI agents based on specified criteria; means for a user to select an AI agent from the list; means for directing a task to the selected AI agent; A means for AI agents to share information with each other and refine their answers; A system including a means for providing a refined answer to a user.

Description

[Technical Field]

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

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

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

[0004] In modern project management and entrepreneurial activities, efficiently selecting individuals with different specialized skills and experience and forming teams is extremely important. However, finding the right people and ensuring smooth communication takes time and effort. Furthermore, poor communication between team members and difficulty in sharing and coordinating tasks can cause project delays and failures during the project. There is a need for a system that can resolve these issues and progress projects efficiently and effectively. [Means for solving the problem]

[0005] The present invention provides a means for a user to set the project's objectives and requirements and specify the required skills and experience. It also includes a means for listing AI agents based on the specified conditions and allowing the user to select one of these AI agents. The selected AI agents can receive task instructions from the user and refine their answers while sharing information with each other. It also includes a means for providing the refined answers to the user, thereby supporting the efficient progress of the project. Specifically, the system includes means for saving the project's objectives and requirements in a database, associating the selected AI agent with the project, and transferring task instructions to the AI ​​agent. It also provides an interface for the user to check the task results and give further instructions.

[0006] A "user" is a person or entity that accesses the system and sets up projects and gives instructions to AI agents.

[0007] A "project" is a set of activities or tasks planned and set up to achieve a specific purpose or goal.

[0008] "Skills" are the abilities, knowledge, and techniques required to perform a particular job or task.

[0009] "Career" refers to the past work experience and history of a particular person or agent.

[0010] An "AI agent" is a software program based on artificial intelligence that can autonomously perform specific tasks or operations.

[0011] "Listing" means making a list of items or candidates that meet certain criteria.

[0012] A "task" is a specific task or activity that is part of a project.

[0013] "Communication" refers to the means and process of exchanging and sharing information.

[0014] An "answer" is a solution or result that an AI agent provides in response to a user instruction or task.

[0015] A "database" is a system or structure for storing and managing specific information in an organized manner.

[0016] An "interface" is a screen or means by which a user interacts with a system. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention relates to a system for project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents.

[0039] overview

[0040] It provides a means for users to set the project's objectives and requirements and specify the necessary skills and background. Based on this information, the server lists AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. Finally, it has a means to provide the refined answers to the user.

[0041] System configuration

[0042] 1. User project settings

[0043] A user logs into the system and accesses the dashboard.

[0044] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0045] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0046] 2. AI Agent Selection

[0047] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0048] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0049] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0050] The terminal transmits the selected agent information to the server, which associates it with the project.

[0051] 3. Project progress instructions

[0052] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0053] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0054] 4. Communication between AI agents

[0055] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0056] AI agents share information with each other, communicate as needed, and refine their responses.

[0057] 5. Providing results

[0058] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0059] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0060] Specific examples

[0061] Example 1: Health management app development project

[0062] 1. Project Settings

[0063] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0064] 2. AI Agent Selection

[0065] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0066] The server lists AI agents that meet the criteria, and the user selects from them.

[0067] 3. Task Instructions

[0068] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0069] 4. Cooperation between agents

[0070] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0071] 5. Providing results

[0072] The server provides the compiled results to the user, who then confirms and gives further instructions.

[0073] As described above, the system of the present invention supports the efficient progress of projects by combining project management and AI agent functions.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] A user logs into the system and accesses the dashboard.

[0077] Step 2:

[0078] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0079] Step 3:

[0080] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0081] Step 4:

[0082] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0083] Step 5:

[0084] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0085] Step 6:

[0086] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0087] Step 7:

[0088] The terminal transmits the selected agent information to the server, which associates it with the project.

[0089] Step 8:

[0090] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0091] Step 9:

[0092] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0093] Step 10:

[0094] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0095] Step 11:

[0096] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0097] Step 12:

[0098] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[0099] Step 13:

[0100] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[0101] Step 14:

[0102] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0103] Step 15:

[0104] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[0105] Example 1

[0106] 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."

[0107] Conventional project management systems have problems with delays in project progress due to inefficient tasks such as setting project objectives and requirements, selecting AI agents with the necessary skills and experience, distributing and completing tasks, and checking results.Furthermore, there is a lack of cooperation and communication between AI agents, which makes it difficult to guarantee the quality of the final answer.

[0108] 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.

[0109] In this invention, the server includes: means for a user to set the project's objectives and requirements; means for specifying required skills and experience; means for saving the set project information and conditions in a database; means for automatically listing AI agents based on the specified conditions; means for a user to select a listed AI agent; means for associating information about the selected AI agent with the project; means for a user to create a new task and input the task; means for automatically allocating tasks to AI agents; means for the AI ​​agents to share information with each other and refine their answers; means for transmitting the refined answer to the server and providing it to the user; and interface means for the user to check the results and give further instructions. This enables the project to proceed efficiently and quickly, and enables the AI ​​agents to cooperate to provide high-quality answers.

[0110] "User" means a person or end user who accesses and operates the System.

[0111] "Means for setting project objectives and requirements" refers to the interface that allows users to input and record specific project goals and conditions within the system.

[0112] "Means for specifying required skills and experience" refers to an interface that allows users to specify AI agents within the system that have the skills and experience required for a project.

[0113] "Means for saving set project information and conditions in a database" refers to a function by which the server records project information and conditions set by the user in a database.

[0114] "Means for listing AI agents" refers to a function that automatically searches for and displays relevant AI agents based on conditions specified by the user.

[0115] "Means for associating AI agent information with a project" refers to a function for associating an AI agent selected by a user with a set project.

[0116] "A means for creating and entering new tasks" refers to the interface through which a user can create and enter new tasks within a project.

[0117] "Means for automatically allocating tasks to AI agents" refers to a function that automatically assigns input tasks to the corresponding AI agents.

[0118] "Means for AI agents to share information with each other and refine answers" refers to the ability for multiple AI agents to communicate with each other and collaboratively optimize answers to tasks.

[0119] "Means for sending refined answers to the server and providing them to the user" refers to the function by which the AI ​​agent sends the optimized answer to the server and displays it to the user.

[0120] "Interface means for the user to check the results and give further instructions" refers to an interface for the user to check the results provided in the system and input further instructions.

[0121] The following describes an embodiment of the present invention. The present invention is a system for streamlining project management and using appropriate AI agents to accomplish tasks. The main elements of the system include a user, a terminal, a server, and an AI agent.

[0122] System configuration

[0123] 1. User project settings

[0124] A user logs into the system and accesses the dashboard, which involves entering credentials and being authenticated by the server.

[0125] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0126] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0127] 2. AI Agent Selection

[0128] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0129] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0130] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0131] The terminal transmits the selected agent information to the server, which associates it with the project.

[0132] 3. Task creation and instructions

[0133] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0134] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0135] 4. Communication between AI agents

[0136] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0137] AI agents share information with each other, communicate as needed, and refine their answers.

[0138] 5. Providing results

[0139] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0140] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0141] Specific examples

[0142] Example 1: Health management app development project

[0143] 1. Project Settings

[0144] A user logs into the system and clicks the "Create a new project" button on the dashboard.

[0145] A user creates a project called "Developing a Health Management App" and enters the purpose as "Tracking user health data and providing advice."

[0146] The terminal sends the project information to the server, which stores the information in a database.

[0147] 2. AI Agent Selection

[0148] In a form where users can specify the required skill set and experience, they enter "engineer with app development experience," "UI / UX designer," "data scientist," and "marketing specialist."

[0149] The terminal sends the conditions to the server, and the server searches the database for the corresponding AI agent.

[0150] The server generates a list of AI agents and sends it to the device.

[0151] The user selects an agent from the list, and the terminal transmits the selection to the server.

[0152] 3. Task creation and instructions

[0153] The user enters a new task: "Make a list of the app's main features and propose a design."

[0154] The terminal sends task information to the server, and the server distributes tasks to AI agents.

[0155] 4. Cooperation between agents

[0156] AI agents receive tasks and share information to compile feature lists and design proposals.

[0157] The AI ​​agent refines the answer and sends the final answer to the server.

[0158] 5. Providing results

[0159] The server receives the final answer and sends it to the device.

[0160] The user checks the results on the terminal and gives the next instruction.

[0161] Prompt Sentence Examples

[0162] By using prompts to set up a project, the system can accurately and quickly grasp the project requirements. Below are some examples of specific prompts:

[0163] "Make a list of the main features of a health management app and come up with a design proposal."

[0164] "List agents with data science expertise."

[0165] As described above, the system of the present invention provides a series of processes for users to set up a project, select an appropriate AI agent, and efficiently complete the task, thereby streamlining the progress of the project and improving the quality of the deliverables.

[0166] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0167] Step 1: User logs into the system

[0168] Input: The user enters authentication information (user ID and password).

[0169] How it works: The device sends authentication information to the server.

[0170] Data processing: The server searches the database for the relevant user information and verifies the authentication information.

[0171] Output: If authentication is successful, the user dashboard will be displayed on the terminal. If authentication is unsuccessful, an error message will be displayed.

[0172] Step 2: Create a new project

[0173] Input: A user clicks the "Create a new project" button on the dashboard and fills out a form with the project's name, purpose, detailed requirements, etc.

[0174] Operation: The device sends the entered project information to the server.

[0175] Data processing: The server saves the entered project information in a database.

[0176] Output: A project creation confirmation message is displayed in the terminal.

[0177] Step 3: Specify the required skill sets and experience

[0178] Input: Users fill out a form on the project page to specify the required skill set and experience.

[0179] Operation: The device sends the specified conditions to the server.

[0180] Data processing: The server searches the database for AI agents that match the conditions.

[0181] Output: The server generates a list of applicable AI agents and sends it to the device.

[0182] Step 4: Selecting an AI Agent

[0183] Input: The user selects a specific AI agent from a list.

[0184] Operation: The terminal sends the selected agent information to the server.

[0185] Data processing: The server associates the selected AI agent information with the project.

[0186] Output: The agent information associated with the project is saved in the database.

[0187] Step 5: Create a new task

[0188] Input: A user clicks the "Create new task" button on a project page and enters task details.

[0189] Operation: The device sends the entered task information to the server.

[0190] Data processing: The server distributes tasks to the appropriate AI agents.

[0191] Output: The task is sent to the AI ​​agent.

[0192] Step 6: Communication between AI agents

[0193] How it works: The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0194] Data processing: AI agents share information with each other and communicate to refine their answers.

[0195] Output: The refined answer is finally sent to the server.

[0196] Step 7: Delivering results

[0197] Input: The server receives the final answer from the AI ​​agent.

[0198] Action: The server sends the final answer to the device.

[0199] Output: The terminal displays the final result to the user.

[0200] Action: The user interacts with the interface to confirm the results and give further instructions.

[0201] The above is a detailed flow of each processing step.

[0202] (Application example 1)

[0203] 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."

[0204] Conventional project management systems require users to manually assign tasks and monitor progress, limiting their ability to improve productivity and efficiently manage projects. Furthermore, manually optimizing production line operations, scheduling robot operations, and planning equipment maintenance is extremely labor-intensive and prone to errors. Furthermore, there was a lack of a system that allowed AI agents to efficiently share information with each other and provide optimal solutions.

[0205] 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.

[0206] In this invention, the server includes a means for allowing a user to optimize the operation of a factory's production line, a means for managing the operation schedule of factory robots, and a means for formulating equipment maintenance plans. This makes it possible to automatically optimize the operation status of a factory's production line, efficiently manage the operation schedule of robots, and easily formulate equipment maintenance plans. In project management, the AI ​​agent can also automatically assign tasks and provide optimal answers, thereby improving project efficiency and facilitating management.

[0207] A "user" is a person who manages a project and gives instructions on tasks, or a user of the system.

[0208] A "means for setting project objectives and requirements" is a mechanism that allows users to specify project details, goals, and required skills and experience.

[0209] An "AI agent" is an artificial intelligence program designed to efficiently perform a specific task.

[0210] "Means for transferring task instructions to an AI agent" refers to a mechanism for delivering task instructions from the user to an AI agent.

[0211] "Means for sharing information and refining answers" refers to the methods by which AI agents exchange information with each other to arrive at optimal solutions.

[0212] "Means for providing refined answers to users" refers to a mechanism that presents the optimal answers generated by the AI ​​agent to the user.

[0213] "Means for optimizing factory production line operations" are automated process management tools that maximize factory production efficiency.

[0214] "Means for managing the operation schedules of factory robots" refers to a system that automatically adjusts and manages the working hours and work content of robots used in factories.

[0215] A "means for creating equipment maintenance plans" is a tool for creating schedules for efficiently inspecting and repairing equipment within a factory.

[0216] The "interface means for checking task results and giving instructions for the next step" is a user interface that allows the user to check the results provided by the AI ​​agent and give instructions for the next action.

[0217] "Means for monitoring the operating status of factory robots" refers to a system that monitors the operation and status of robots in real time while they are actually working.

[0218] The present invention provides a system that allows users to efficiently manage projects and optimize factory production lines. A specific embodiment for realizing this system will be described below.

[0219] System Overview

[0220] The system helps users set up a project, specify the required skills and background, and select an AI agent from a list. The selected AI agents then share information with each other to complete the task and provide a final answer to the user. The system also includes functions for optimizing factory production lines, managing robot operation schedules, and creating equipment maintenance plans.

[0221] Hardware and Software Configuration

[0222] Hardware: smartphones, servers, factory robots

[0223] Software: iOS / ANDROID® app, server-side database management system (e.g., MongoDB or MySQL®), AI agent management system (e.g., using TENSORFLOW® or PyTorch)

[0224] Data processing and calculation

[0225] User Project Settings

[0226] Users log in to the system using their smartphones, access the dashboard, enter project details (project name, objectives, detailed requirements, etc.), and send them to the server, which then stores the received information in a database.

[0227] AI Agent Selection

[0228] The user specifies the required skill set and experience and sends the criteria to the server. The server searches the database for AI agents that match the criteria and lists them for the user. The user selects an appropriate AI agent from the list and sends it to the server. The server associates the selected AI agent with the project.

[0229] Task assignment and execution

[0230] A user inputs the details of a new task and submits it to the server, which distributes the task instructions to AI agents. Each AI agent receives the task, shares information with each other, communicates as needed, and creates the optimal answer.

[0231] Providing results

[0232] Each AI agent sends its final answer to the server, which then provides it to the user, who can check the results on their smartphone and give further instructions.

[0233] Specific examples

[0234] For example, if a user is starting a new product line optimization project, they might enter the prompt statement as follows:

[0235] "Set up launch dates and production targets for the new product line and create a schedule for the robots we need. Also, create a maintenance plan for the equipment."

[0236] Based on this, the AI ​​agent will perform the following tasks:

[0237] 1. Proposing a working schedule based on production targets.

[0238] 2. Creating and proposing optimal operating schedules for each terminal robot.

[0239] 3. Planning and proposing regular maintenance plans for equipment.

[0240] This system automatically optimizes the operation status of factory production lines, enabling efficient management. It also reduces the effort required for project management, allowing users to quickly take the next action.

[0241] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0242] Step 1:

[0243] User Project Settings

[0244] Input: The user sets the project name, purpose, detailed requirements, etc. via their smartphone.

[0245] What happens: The device sends this information to the server.

[0246] Specific data processing: The server stores the received project information in a database.

[0247] Output: The project is saved to the database and the project setup is complete.

[0248] Step 2:

[0249] AI Agent Selection

[0250] Input: The user specifies the required skill set and experience and enters the requirements into the terminal.

[0251] Operation: The device sends the specified conditions to the server.

[0252] Specific data processing: The server searches the database for AI agents that match the conditions and lists them.

[0253] Output: The AI ​​agents are displayed on the device as a list that the user can view.

[0254] Step 3:

[0255] AI Agent Selection

[0256] Input: The user selects an appropriate AI agent from a list.

[0257] Action: The device sends the selected AI agent information to the server.

[0258] Specific data processing: The server associates the selected AI agent with a project.

[0259] Output: The project will have an associated AI agent.

[0260] Step 4:

[0261] Task Instructions

[0262] Input: The user enters the details of a new task (e.g., "Set launch dates and production targets for a new product line").

[0263] Operation: The device sends task information to the server.

[0264] Specific data processing: The server allocates tasks to the appropriate AI agents.

[0265] Output: Task information is delivered to the AI ​​agent.

[0266] Step 5:

[0267] Information sharing and task execution among AI agents

[0268] Input: Each AI agent receives a task to process.

[0269] How it works: AI agents share information with each other, communicate as needed, and use generative AI models to derive optimal solutions.

[0270] Specific data processing: AI agents use relevant information to analyze data, make predictions, and formulate optimal answers.

[0271] Output: The answers generated by each AI agent are sent to the server.

[0272] Step 6:

[0273] Aggregating and providing results

[0274] Input: The best answer from the AI ​​agent.

[0275] Action: The server aggregates these responses.

[0276] Specific data processing: The server aggregates the received responses and processes them into a format that is easy for the user to understand.

[0277] Output: The final result is the best answer displayed on the user's device.

[0278] This process flow allows users to optimize factory production line operations, efficiently manage robot operation schedules, and easily create equipment maintenance plans. Furthermore, the AI ​​agent efficiently completes tasks and provides optimal answers to users.

[0279] 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.

[0280] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[0281] overview

[0282] It provides a means for users to set project objectives and requirements and specify the necessary skills and background. Based on this information, the server creates a list of AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. It also incorporates an emotion engine that recognizes user emotions and adjusts the system's behavior based on that emotional data, making it possible to progress a project while taking into account the user's stress level and satisfaction.

[0283] System configuration

[0284] 1. User project settings

[0285] A user logs into the system and accesses the dashboard.

[0286] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0287] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0288] 2. AI Agent Selection

[0289] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0290] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0291] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0292] The terminal transmits the selected agent information to the server, which associates it with the project.

[0293] 3. Operation of the Emotion Engine

[0294] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[0295] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[0296] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[0297] 4. Project progress instructions

[0298] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0299] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0300] 5. Communication between AI agents

[0301] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0302] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0303] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[0304] 6. Providing Results

[0305] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0306] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0307] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[0308] Specific examples

[0309] Example 1: Using an emotion engine in a health management app development project

[0310] 1. Project Settings

[0311] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0312] 2. AI Agent Selection

[0313] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0314] The server lists AI agents that meet the criteria, and the user selects from them.

[0315] 3. Operation of the Emotion Engine

[0316] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[0317] The server optimizes the settings based on the emotional data.

[0318] 4. Task Instructions

[0319] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0320] 5. Cooperation between agents

[0321] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0322] If necessary, adjust communication methods based on the user's emotional data.

[0323] 6. Providing results

[0324] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[0325] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[0326] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[0327] The processing flow will be explained below.

[0328] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[0329] Processing flow

[0330] Step 1:

[0331] A user logs into the system and accesses the dashboard.

[0332] Step 2:

[0333] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0334] Step 3:

[0335] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0336] Step 4:

[0337] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0338] Step 5:

[0339] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0340] Step 6:

[0341] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0342] Step 7:

[0343] The terminal transmits the selected agent information to the server, which associates it with the project.

[0344] Step 8:

[0345] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[0346] Step 9:

[0347] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[0348] Step 10:

[0349] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[0350] Step 11:

[0351] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0352] Step 12:

[0353] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0354] Step 13:

[0355] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0356] Step 14:

[0357] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0358] Step 15:

[0359] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[0360] Step 16:

[0361] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[0362] Step 17:

[0363] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[0364] Step 18:

[0365] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0366] Step 19:

[0367] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[0368] Step 20:

[0369] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[0370] Specific examples

[0371] Example 1: Using an emotion engine in a health management app development project

[0372] 1. Project Settings

[0373] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0374] 2. AI Agent Selection

[0375] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0376] The server lists AI agents that meet the criteria, and the user selects from them.

[0377] 3. Operation of the Emotion Engine

[0378] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[0379] The server optimizes the settings based on the emotional data.

[0380] 4. Task Instructions

[0381] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0382] 5. Cooperation between agents

[0383] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0384] If necessary, adjust communication methods based on the user's emotional data.

[0385] 6. Providing results

[0386] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[0387] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[0388] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[0389] Example 2

[0390] 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."

[0391] Conventional project management systems can cause users to feel emotionally stressed and their satisfaction levels to decline. Especially in large-scale projects, interpersonal stress can negatively impact project progress. Furthermore, the process for users to select AI agents with appropriate skill sets is complicated, leading to issues such as poor communication between teams. To solve these problems, a system that integrates the management of AI agents with the monitoring of users' emotional states is needed.

[0392] 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.

[0393] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and experience, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for the user to instruct the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for collecting and evaluating the user's emotions in real time, and means for adjusting the system's operation based on the emotion data, thereby enabling the project to proceed efficiently while taking the user's emotional state into consideration.

[0394] A "project" is a set of planned activities designed to achieve a specific purpose or goal.

[0395] "Requirements" are the conditions or standards necessary to carry out a project or task.

[0396] "Skills" are the specialized abilities and knowledge required to perform a specific job or task.

[0397] "Experience" refers to the history of past jobs and projects you have undertaken, as well as the skills and qualifications you have acquired.

[0398] An "AI agent" is software that uses artificial intelligence technology to automate specific tasks and work in collaboration with other agents and users to complete tasks.

[0399] A "task" is a specific task or activity that is performed to achieve the project's objectives.

[0400] "Emotion" in "Tarza" refers to the user's psychological state, specifically emotional responses such as stress, satisfaction, and anxiety.

[0401] An "emotion engine" is a system that analyzes and evaluates a user's emotional state in real time based on input such as facial expressions and voice.

[0402] A "system" is a collective term for a set of hardware and software components designed to work together to perform a specific function.

[0403] A "user interface" is the input and output means by which a user interacts with a system.

[0404] A "database" is a system for efficiently managing and storing large amounts of data.

[0405] "Real-time" refers to information and data being processed and analyzed as soon as it is generated.

[0406] "Communication" is the process of exchanging information and opinions, and here it specifically refers to sharing information between AI agents and with users.

[0407] "Analysis" is the process of collecting data and interpreting it statistically or logically.

[0408] This invention is a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents. The system incorporates an emotion engine that recognizes user emotions in real time and reflects them in the user interface and project progress.

[0409] System configuration

[0410] The system consists of the following main components:

[0411] 1. User project settings

[0412] 2. AI Agent Selection

[0413] 3. Operation of the Emotion Engine

[0414] 4. Project progress instructions

[0415] 5. Communication between AI agents

[0416] 6. Providing Results

[0417] Hardware and software used

[0418] Terminal: A device directly operated by a user, equipped with a camera and microphone for collecting emotional data, typically running a commercial operating system (e.g., Windows, macOS, Linux).

[0419] Server: A computer system with powerful computing resources that manages project data, lists AI agents, evaluates emotional data, etc. Servers are often operated on commercial cloud services (e.g., AWS (registered trademark), Google (registered trademark), Microsoft (registered trademark) Azure (registered trademark)).

[0420] Emotion Engine: Uses machine learning models (e.g., TensorFlow, PyTorch) to recognize emotions in real time from the user's facial expressions and tone of voice.

[0421] Project Setup and Data Management

[0422] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button, which displays a form where the user can enter the project name, purpose, and detailed requirements. The terminal sends the entered project information to the server, which then stores the project information in a database.

[0423] AI Agent Selection

[0424] The user navigates to a project page and is presented with a form to specify the required skill set and experience. The device sends the specified criteria to the server, which searches its database for and lists AI agents that match the criteria. The list of applicable AI agents is sent to the device, and the user can view and select these agents. The device sends the selected agent information to the server, which associates them with the project.

[0425] Emotion Engine Operation

[0426] While the user is using the system, the device's built-in camera and microphone analyze facial expressions and tone of voice to collect emotional data. The device then transmits the emotional data to a server in real time, which then uses an AI model to evaluate the emotion. The server then automatically fine-tunes project settings and optimizes instructions to the AI ​​agent based on the evaluated emotional data.

[0427] Project progress instructions

[0428] The user clicks the "Create a new task" button on the project page and fills in the task details. The device sends the entered task information to the server, which then assigns the task to the appropriate AI agent.

[0429] Communication between AI agents

[0430] The server assigns tasks to each AI agent, and the AI ​​agent receives the tasks. The AI ​​agent shares information with other agents as needed and progresses with the task with a common understanding. The AI ​​agent adjusts its communication method and response expression based on the user's emotional data.

[0431] Providing results

[0432] The AI ​​agent sends the final answer to the server, which provides it to the user. The terminal displays the final result, and the user confirms it and gives instructions. The emotion engine monitors the user's reaction and reflects it in the next step.

[0433] Example: Prompt sentence for generative AI model

[0434] The following example shows how an emotion engine is used in a health management app development project.

[0435] Example: Health management app development project

[0436] Prompt statement

[0437] "I'd like to create a project to develop a health management app. The goal is to track users' health data and provide advice. I'd like to assign an engineer with app development experience, a UI / UX designer, a data scientist, and a marketing specialist. I'd like the system to use an emotion engine to assess my stress level and satisfaction level and optimize the project's progress."

[0438] The above is a specific example of a system for implementing the present invention. This system enables efficient project management by linking project management with user emotion management.

[0439] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0440] Step 1: Log in and create a project

[0441] Input: User authentication information, project setting information

[0442] Specific behavior:

[0443] The user enters their authentication information (such as user ID and password) and logs in to the system.

[0444] The device sends the authentication information to the server, which checks the authentication. If the authentication is successful, the dashboard is displayed.

[0445] The user clicks the "Create New Project" button on the dashboard and enters the project name, purpose, detailed requirements, etc.

[0446] Output: Send and save project setting information

[0447] Step 2: Save the project information

[0448] Input: Project setting information

[0449] Specific behavior:

[0450] The terminal transmits the input project information to the server, which stores it in a database.

[0451] The server generates a project ID and records it in a database along with the entered project information.

[0452] Output: Saving project information to a database

[0453] Step 3: Selecting an AI agent

[0454] Input: User-specified skill sets and experience

[0455] Specific behavior:

[0456] A user visits a project page and fills out a form specifying the required skill set and experience.

[0457] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0458] Output: List of AI agents

[0459] Step 4: Selecting and Associating an AI Agent

[0460] Input: A list of AI agents

[0461] Specific behavior:

[0462] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0463] The terminal transmits the selected agent information to the server, and the server associates the selected agent with the project.

[0464] Output: Associating an AI agent with a project

[0465] Step 5: Collect and evaluate emotion data

[0466] Input: User facial expressions and tone of voice

[0467] Specific behavior:

[0468] While the user is using the system, the device's built-in camera and microphone analyze the user's facial expressions and tone of voice in real time.

[0469] The device sends emotional data to a server, which then uses an AI model to evaluate the emotion.

[0470] Output: Generate emotion evaluation results

[0471] Step 6: Optimize project progress

[0472] Input: Emotion evaluation results, project setting information

[0473] Specific behavior:

[0474] Based on the evaluated emotional data, the server automatically fine-tunes project settings and optimizes instructions to AI agents.

[0475] The server updates the project task information and sends the information to the terminal.

[0476] Output: Sending updated project configuration information

[0477] Step 7: Creating and Distributing Tasks

[0478] Input: User task instructions, project information

[0479] Specific behavior:

[0480] A user clicks the "Create new task" button on a project page and fills in the task details.

[0481] The terminal transmits the input task information to the server.

[0482] The server stores task information in a database and distributes tasks to appropriate AI agents.

[0483] Output: Tasks are stored in a database and distributed to AI agents.

[0484] Step 8: Information sharing between AI agents

[0485] Input: Task information for each AI agent

[0486] Specific behavior:

[0487] The server distributes tasks to each AI agent and shares relevant information.

[0488] AI agents communicate with each other and progress through tasks with a common understanding.

[0489] Output: Information sharing between AI agents

[0490] Step 9: Providing task results

[0491] Input: Task results sent by the AI ​​agent

[0492] Specific behavior:

[0493] The AI ​​agent sends the final answer to the server.

[0494] The server transmits the received results to the terminal and provides them to the user.

[0495] Output: Providing the final task results to the user

[0496] Step 10: User feedback and next steps

[0497] Input: User feedback

[0498] Specific behavior:

[0499] The terminal displays the final result, which the user confirms.

[0500] The emotion engine monitors user reactions and reflects them in the next project steps.

[0501] Output: Project information reflected in the next step

[0502] Through the above steps, project management and user emotion management are linked, enabling projects to proceed efficiently and flexibly.

[0503] (Application example 2)

[0504] 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."

[0505] Conventional project management systems are unable to consider the emotions and stress levels of managers when progressing tasks, limiting their usability and productivity. Furthermore, in factory management, robot operations are not adjusted based on real-time emotional data from managers, making it difficult to provide an optimal work environment. This creates a need for efficient project progress management and flexible responses.

[0506] 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.

[0507] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and background, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their responses, means for providing the refined responses to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotion data. This enables the project to proceed while taking the manager's emotions into consideration, thereby providing an efficient and flexible work environment.

[0508] A "user" is a person or organization that operates the system and sets project objectives and requirements, selects AI agents, etc.

[0509] A "project" is a set of tasks or activities planned and set out to achieve a specific purpose.

[0510] "Skills" refer to the abilities and knowledge required to perform a specific task or role.

[0511] "History" refers to the past experience and achievements of a user or AI agent.

[0512] An "AI agent" is an autonomous program that uses artificial intelligence to perform specific tasks and refines its answers by sharing information with other agents.

[0513] A "task" refers to an individual activity or work that has a specific role and purpose within a project.

[0514] The "system" is a platform that supports users in project management and integrates a series of functions and tools, including AI agents and emotion engines.

[0515] "Emotions" refer to psychological states and changes that can be recognized from the user's facial expressions, tone of voice, etc.

[0516] A "robot" is an automated machine or device designed to perform designated tasks in a factory.

[0517] An "emotion engine" is a combination of software or hardware that recognizes a user's emotions in real time and adjusts the system's behavior accordingly.

[0518] A "manager" is a person or position that oversees the progress of a factory or project and operates the system to manage the progress of tasks.

[0519] A specific system configuration and its operation procedure will be described below for the embodiment of the present invention.

[0520] The server includes means for a user to set the objectives and requirements of the project, means for specifying the necessary skills and background, means for listing AI agents based on specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotional data.

[0521] A user logs into the system and sets up a project by entering the project's objectives and requirements into a dashboard. The user then specifies the appropriate skills and background, and the server generates a list of AI agents based on this information. The user then selects an appropriate AI agent from the list and sends it to the server.

[0522] In this system, factory managers are equipped with cameras and microphones on their devices to recognize emotions from facial expressions and tone of voice while working on a project. Specifically, OpenCV is used to capture the manager's facial expressions in real time and analyze them with a pre-trained emotion recognition model. This emotional data is sent to a server, and the AI ​​agent adjusts its behavior based on that data while performing the task.

[0523] For example, if a manager is feeling stressed, the server will use this emotional data to monitor the progress of the task and issue new instructions to the AI ​​agent if necessary. Also, if the manager is feeling happy or satisfied, the server will reflect this emotional data to ensure the project progresses smoothly.

[0524] As a concrete example, the server can perform the following processing based on an example prompt: "Recognize faces using a camera, recognize emotions in real time using an emotion model, and send that data to the server to generate Python code that will optimize project progress. The server URL is http: / / factory-management-system.example.com." By inputting this prompt into a generative AI model, task management that takes user emotions into account can be achieved.

[0525] This allows managers to understand their emotions in real time and flexibly progress projects accordingly. It also allows for efficient adjustment of robot operations, optimizing the work environment.

[0526] With the above configuration, the system of the present invention realizes project management that improves usability and productivity.

[0527] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0528] Step 1:

[0529] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button and enters the project name, purpose, detailed requirements, etc. This input data is sent from the terminal to the server, which then stores the project information in a database.

[0530] Input: Project name, purpose, detailed requirements

[0531] Processing: The terminal sends the input data to the server, and the server saves it in a database

[0532] Output: Project information stored in a database

[0533] Step 2:

[0534] The user fills out a form to specify appropriate skills and experience. The specified criteria are sent from the device to the server, which searches and lists AI agents that match the criteria from its database. The user selects an AI agent from the list, and the selection information is sent from the device to the server, which associates it with the project.

[0535] Input: Required skills, experience

[0536] Processing: The device sends the specified conditions to the server, and the server lists the corresponding AI agents from the database.

[0537] Output: Information about the AI ​​agent selected by the user.

[0538] Step 3:

[0539] While the user is working on a project, the device's built-in camera and microphone are used to capture the manager's facial expressions and tone of voice in real time. OpenCV is used to recognize facial expressions and generate emotional data. The generated emotional data is then sent from the device to the server.

[0540] Input: facial expression data, voice data

[0541] Processing: Data capture with camera and microphone, facial expression recognition and emotion data generation with OpenCV

[0542] Output: Emotion data sent to the server

[0543] Step 4:

[0544] The server analyzes the received emotional data and adjusts project progress and task priorities based on that data, such as issuing new instructions to AI agents or rescheduling tasks.

[0545] Input: Emotion data

[0546] Processing: Analyzing emotion data on the server and adjusting project progress

[0547] Output: Coordinated task instructions and schedules

[0548] Step 5:

[0549] When the AI ​​agents receive new instructions, they execute the task and share information with each other to refine their answers. They also take into account the user's emotional data and respond flexibly according to their emotions. The completed answer is then sent back to the server.

[0550] Input: adjusted task instructions, emotion data

[0551] Processing: Sharing information and executing tasks between AI agents

[0552] Output: Refined answer (sent to server)

[0553] Step 6:

[0554] The server then provides the answers received from the AI ​​agent to the user, who can review the final results on their device and issue new instructions if necessary. User reactions and emotional data are also collected again and used to inform the next steps of the project.

[0555] Input: Final response from the AI ​​agent, new instructions from the user

[0556] Processing: The server provides the final answer and recollects the user's instructions and emotion data.

[0557] Output: The final result provided to the user, along with new emotion data and instructions.

[0558] Through these steps, the system can achieve efficient project management while taking into account the user's feelings.

[0559] 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.

[0560] 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.

[0561] 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.

[0562] [Second embodiment]

[0563] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0564] 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.

[0565] 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).

[0566] 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.

[0567] 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.

[0568] 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).

[0569] 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.

[0570] 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.

[0571] 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.

[0572] 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.

[0573] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0574] 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."

[0575] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention relates to a system for project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents.

[0576] overview

[0577] It provides a means for users to set the project's objectives and requirements and specify the necessary skills and background. Based on this information, the server lists AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. Finally, it has a means to provide the refined answers to the user.

[0578] System configuration

[0579] 1. User project settings

[0580] A user logs into the system and accesses the dashboard.

[0581] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0582] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0583] 2. AI Agent Selection

[0584] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0585] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0586] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0587] The terminal transmits the selected agent information to the server, which associates it with the project.

[0588] 3. Project progress instructions

[0589] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0590] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0591] 4. Communication between AI agents

[0592] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0593] AI agents share information with each other, communicate as needed, and refine their responses.

[0594] 5. Providing results

[0595] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0596] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0597] Specific examples

[0598] Example 1: Health management app development project

[0599] 1. Project Settings

[0600] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0601] 2. AI Agent Selection

[0602] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0603] The server lists AI agents that meet the criteria, and the user selects from them.

[0604] 3. Task Instructions

[0605] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0606] 4. Cooperation between agents

[0607] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0608] 5. Providing results

[0609] The server provides the compiled results to the user, who then confirms and gives further instructions.

[0610] As described above, the system of the present invention supports the efficient progress of projects by combining project management and AI agent functions.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] A user logs into the system and accesses the dashboard.

[0614] Step 2:

[0615] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0616] Step 3:

[0617] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0618] Step 4:

[0619] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0620] Step 5:

[0621] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0622] Step 6:

[0623] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0624] Step 7:

[0625] The terminal transmits the selected agent information to the server, which associates it with the project.

[0626] Step 8:

[0627] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0628] Step 9:

[0629] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0630] Step 10:

[0631] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0632] Step 11:

[0633] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0634] Step 12:

[0635] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[0636] Step 13:

[0637] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[0638] Step 14:

[0639] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0640] Step 15:

[0641] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[0642] Example 1

[0643] 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."

[0644] Conventional project management systems have problems with delays in project progress due to inefficient tasks such as setting project objectives and requirements, selecting AI agents with the necessary skills and experience, distributing and completing tasks, and checking results.Furthermore, there is a lack of cooperation and communication between AI agents, which makes it difficult to guarantee the quality of the final answer.

[0645] 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.

[0646] In this invention, the server includes: means for a user to set the project's objectives and requirements; means for specifying required skills and experience; means for saving the set project information and conditions in a database; means for automatically listing AI agents based on the specified conditions; means for a user to select a listed AI agent; means for associating information about the selected AI agent with the project; means for a user to create a new task and input the task; means for automatically allocating tasks to AI agents; means for the AI ​​agents to share information with each other and refine their answers; means for transmitting the refined answer to the server and providing it to the user; and interface means for the user to check the results and give further instructions. This enables the project to proceed efficiently and quickly, and enables the AI ​​agents to cooperate to provide high-quality answers.

[0647] "User" means a person or end user who accesses and operates the System.

[0648] "Means for setting project objectives and requirements" refers to the interface that allows users to input and record specific project goals and conditions within the system.

[0649] "Means for specifying required skills and experience" refers to an interface that allows users to specify AI agents within the system that have the skills and experience required for a project.

[0650] "Means for saving set project information and conditions in a database" refers to a function by which the server records project information and conditions set by the user in a database.

[0651] "Means for listing AI agents" refers to a function that automatically searches for and displays relevant AI agents based on conditions specified by the user.

[0652] "Means for associating AI agent information with a project" refers to a function for associating an AI agent selected by a user with a set project.

[0653] "A means for creating and entering new tasks" refers to the interface through which a user can create and enter new tasks within a project.

[0654] "Means for automatically allocating tasks to AI agents" refers to a function that automatically assigns input tasks to the corresponding AI agents.

[0655] "Means for AI agents to share information with each other and refine answers" refers to the ability for multiple AI agents to communicate with each other and collaboratively optimize answers to tasks.

[0656] "Means for sending refined answers to the server and providing them to the user" refers to the function by which the AI ​​agent sends the optimized answer to the server and displays it to the user.

[0657] "Interface means for the user to check the results and give further instructions" refers to an interface for the user to check the results provided in the system and input further instructions.

[0658] The following describes an embodiment of the present invention. The present invention is a system for streamlining project management and using appropriate AI agents to accomplish tasks. The main elements of the system include a user, a terminal, a server, and an AI agent.

[0659] System configuration

[0660] 1. User project settings

[0661] A user logs into the system and accesses the dashboard, which involves entering credentials and being authenticated by the server.

[0662] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0663] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0664] 2. AI Agent Selection

[0665] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0666] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0667] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0668] The terminal transmits the selected agent information to the server, which associates it with the project.

[0669] 3. Task creation and instructions

[0670] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0671] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0672] 4. Communication between AI agents

[0673] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0674] AI agents share information with each other, communicate as needed, and refine their answers.

[0675] 5. Providing results

[0676] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0677] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0678] Specific examples

[0679] Example 1: Health management app development project

[0680] 1. Project Settings

[0681] A user logs into the system and clicks the "Create a new project" button on the dashboard.

[0682] A user creates a project called "Developing a Health Management App" and enters the purpose as "Tracking user health data and providing advice."

[0683] The terminal sends the project information to the server, which stores the information in a database.

[0684] 2. AI Agent Selection

[0685] In a form where users can specify the required skill set and experience, they enter "engineer with app development experience," "UI / UX designer," "data scientist," and "marketing specialist."

[0686] The terminal sends the conditions to the server, and the server searches the database for the corresponding AI agent.

[0687] The server generates a list of AI agents and sends it to the device.

[0688] The user selects an agent from the list, and the terminal transmits the selection to the server.

[0689] 3. Task creation and instructions

[0690] The user enters a new task: "Make a list of the app's main features and propose a design."

[0691] The terminal sends task information to the server, and the server distributes tasks to AI agents.

[0692] 4. Cooperation between agents

[0693] AI agents receive tasks and share information to compile feature lists and design proposals.

[0694] The AI ​​agent refines the answer and sends the final answer to the server.

[0695] 5. Providing results

[0696] The server receives the final answer and sends it to the device.

[0697] The user checks the results on the terminal and gives the next instruction.

[0698] Prompt Sentence Examples

[0699] By using prompts to set up a project, the system can accurately and quickly grasp the project requirements. Below are some examples of specific prompts:

[0700] "Make a list of the main features of a health management app and come up with a design proposal."

[0701] "List agents with data science expertise."

[0702] As described above, the system of the present invention provides a series of processes for users to set up a project, select an appropriate AI agent, and efficiently complete the task, thereby streamlining the progress of the project and improving the quality of the deliverables.

[0703] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0704] Step 1: User logs into the system

[0705] Input: The user enters authentication information (user ID and password).

[0706] How it works: The device sends authentication information to the server.

[0707] Data processing: The server searches the database for the relevant user information and verifies the authentication information.

[0708] Output: If authentication is successful, the user dashboard will be displayed on the terminal. If authentication is unsuccessful, an error message will be displayed.

[0709] Step 2: Create a new project

[0710] Input: A user clicks the "Create a new project" button on the dashboard and fills out a form with the project's name, purpose, detailed requirements, etc.

[0711] Operation: The device sends the entered project information to the server.

[0712] Data processing: The server saves the entered project information in a database.

[0713] Output: A project creation confirmation message is displayed in the terminal.

[0714] Step 3: Specify the required skill sets and experience

[0715] Input: Users fill out a form on the project page to specify the required skill set and experience.

[0716] Operation: The device sends the specified conditions to the server.

[0717] Data processing: The server searches the database for AI agents that match the conditions.

[0718] Output: The server generates a list of applicable AI agents and sends it to the device.

[0719] Step 4: Selecting an AI Agent

[0720] Input: The user selects a specific AI agent from a list.

[0721] Operation: The terminal sends the selected agent information to the server.

[0722] Data processing: The server associates the selected AI agent information with the project.

[0723] Output: The agent information associated with the project is saved in the database.

[0724] Step 5: Create a new task

[0725] Input: A user clicks the "Create new task" button on a project page and enters task details.

[0726] Operation: The device sends the entered task information to the server.

[0727] Data processing: The server distributes tasks to the appropriate AI agents.

[0728] Output: The task is sent to the AI ​​agent.

[0729] Step 6: Communication between AI agents

[0730] How it works: The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0731] Data processing: AI agents share information with each other and communicate to refine their answers.

[0732] Output: The refined answer is finally sent to the server.

[0733] Step 7: Delivering results

[0734] Input: The server receives the final answer from the AI ​​agent.

[0735] Action: The server sends the final answer to the device.

[0736] Output: The terminal displays the final result to the user.

[0737] Action: The user interacts with the interface to confirm the results and give further instructions.

[0738] The above is a detailed flow of each processing step.

[0739] (Application example 1)

[0740] 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."

[0741] Conventional project management systems require users to manually assign tasks and monitor progress, limiting their ability to improve productivity and efficiently manage projects. Furthermore, manually optimizing production line operations, scheduling robot operations, and planning equipment maintenance is extremely labor-intensive and prone to errors. Furthermore, there was a lack of a system that allowed AI agents to efficiently share information with each other and provide optimal solutions.

[0742] 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.

[0743] In this invention, the server includes a means for allowing a user to optimize the operation of a factory's production line, a means for managing the operation schedule of factory robots, and a means for formulating equipment maintenance plans. This makes it possible to automatically optimize the operation status of a factory's production line, efficiently manage the operation schedule of robots, and easily formulate equipment maintenance plans. In project management, the AI ​​agent can also automatically assign tasks and provide optimal answers, thereby improving project efficiency and facilitating management.

[0744] A "user" is a person who manages a project and gives instructions on tasks, or a user of the system.

[0745] A "means for setting project objectives and requirements" is a mechanism that allows users to specify project details, goals, and required skills and experience.

[0746] An "AI agent" is an artificial intelligence program designed to efficiently perform a specific task.

[0747] "Means for transferring task instructions to an AI agent" refers to a mechanism for delivering task instructions from the user to an AI agent.

[0748] "Means for sharing information and refining answers" refers to the methods by which AI agents exchange information with each other to arrive at optimal solutions.

[0749] "Means for providing refined answers to users" refers to a mechanism that presents the optimal answers generated by the AI ​​agent to the user.

[0750] "Means for optimizing factory production line operations" are automated process management tools that maximize factory production efficiency.

[0751] "Means for managing the operation schedules of factory robots" refers to a system that automatically adjusts and manages the working hours and work content of robots used in factories.

[0752] A "means for creating equipment maintenance plans" is a tool for creating schedules for efficiently inspecting and repairing equipment within a factory.

[0753] The "interface means for checking task results and giving instructions for the next step" is a user interface that allows the user to check the results provided by the AI ​​agent and give instructions for the next action.

[0754] "Means for monitoring the operating status of factory robots" refers to a system that monitors the operation and status of robots in real time while they are actually working.

[0755] The present invention provides a system that allows users to efficiently manage projects and optimize factory production lines. A specific embodiment for realizing this system will be described below.

[0756] System Overview

[0757] The system helps users set up a project, specify the required skills and background, and select an AI agent from a list. The selected AI agents then share information with each other to complete the task and provide a final answer to the user. The system also includes functions for optimizing factory production lines, managing robot operation schedules, and creating equipment maintenance plans.

[0758] Hardware and Software Configuration

[0759] Hardware: smartphones, servers, factory robots

[0760] Software: iOS / Android apps, server-side database management systems (e.g., MongoDB or MySQL), AI agent management systems (e.g., using TensorFlow or PyTorch)

[0761] Data processing and calculation

[0762] User Project Settings

[0763] Users log in to the system using their smartphones, access the dashboard, enter project details (project name, objectives, detailed requirements, etc.), and send them to the server, which then stores the received information in a database.

[0764] AI Agent Selection

[0765] The user specifies the required skill set and experience and sends the criteria to the server. The server searches the database for AI agents that match the criteria and lists them for the user. The user selects an appropriate AI agent from the list and sends it to the server. The server associates the selected AI agent with the project.

[0766] Task assignment and execution

[0767] A user inputs the details of a new task and submits it to the server, which distributes the task instructions to AI agents. Each AI agent receives the task, shares information with each other, communicates as needed, and creates the optimal answer.

[0768] Providing results

[0769] Each AI agent sends its final answer to the server, which then provides it to the user, who can check the results on their smartphone and give further instructions.

[0770] Specific examples

[0771] For example, if a user is starting a new product line optimization project, they might enter the prompt statement as follows:

[0772] "Set up launch dates and production targets for the new product line and create a schedule for the robots we need. Also, create a maintenance plan for the equipment."

[0773] Based on this, the AI ​​agent will perform the following tasks:

[0774] 1. Proposing a working schedule based on production targets.

[0775] 2. Creating and proposing optimal operating schedules for each terminal robot.

[0776] 3. Planning and proposing regular maintenance plans for equipment.

[0777] This system automatically optimizes the operation status of factory production lines, enabling efficient management. It also reduces the effort required for project management, allowing users to quickly take the next action.

[0778] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0779] Step 1:

[0780] User Project Settings

[0781] Input: The user sets the project name, purpose, detailed requirements, etc. via their smartphone.

[0782] What happens: The device sends this information to the server.

[0783] Specific data processing: The server stores the received project information in a database.

[0784] Output: The project is saved to the database and the project setup is complete.

[0785] Step 2:

[0786] AI Agent Selection

[0787] Input: The user specifies the required skill set and experience and enters the requirements into the terminal.

[0788] Operation: The device sends the specified conditions to the server.

[0789] Specific data processing: The server searches the database for AI agents that match the conditions and lists them.

[0790] Output: The AI ​​agents are displayed on the device as a list that the user can view.

[0791] Step 3:

[0792] AI Agent Selection

[0793] Input: The user selects an appropriate AI agent from a list.

[0794] Action: The device sends the selected AI agent information to the server.

[0795] Specific data processing: The server associates the selected AI agent with a project.

[0796] Output: The project will have an associated AI agent.

[0797] Step 4:

[0798] Task Instructions

[0799] Input: The user enters the details of a new task (e.g., "Set launch dates and production targets for a new product line").

[0800] Operation: The device sends task information to the server.

[0801] Specific data processing: The server allocates tasks to the appropriate AI agents.

[0802] Output: Task information is delivered to the AI ​​agent.

[0803] Step 5:

[0804] Information sharing and task execution among AI agents

[0805] Input: Each AI agent receives a task to process.

[0806] How it works: AI agents share information with each other, communicate as needed, and use generative AI models to derive optimal solutions.

[0807] Specific data processing: AI agents use relevant information to analyze data, make predictions, and formulate optimal answers.

[0808] Output: The answers generated by each AI agent are sent to the server.

[0809] Step 6:

[0810] Aggregating and providing results

[0811] Input: The best answer from the AI ​​agent.

[0812] Action: The server aggregates these responses.

[0813] Specific data processing: The server aggregates the received responses and processes them into a format that is easy for the user to understand.

[0814] Output: The final result is the best answer displayed on the user's device.

[0815] This process flow allows users to optimize factory production line operations, efficiently manage robot operation schedules, and easily create equipment maintenance plans. Furthermore, the AI ​​agent efficiently completes tasks and provides optimal answers to users.

[0816] 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.

[0817] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[0818] overview

[0819] It provides a means for users to set project objectives and requirements and specify the necessary skills and background. Based on this information, the server creates a list of AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. It also incorporates an emotion engine that recognizes user emotions and adjusts the system's behavior based on that emotional data, making it possible to progress a project while taking into account the user's stress level and satisfaction.

[0820] System configuration

[0821] 1. User project settings

[0822] A user logs into the system and accesses the dashboard.

[0823] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0824] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0825] 2. AI Agent Selection

[0826] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0827] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0828] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0829] The terminal transmits the selected agent information to the server, which associates it with the project.

[0830] 3. Operation of the Emotion Engine

[0831] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[0832] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[0833] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[0834] 4. Project progress instructions

[0835] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0836] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0837] 5. Communication between AI agents

[0838] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0839] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0840] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[0841] 6. Providing Results

[0842] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[0843] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0844] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[0845] Specific examples

[0846] Example 1: Using an emotion engine in a health management app development project

[0847] 1. Project Settings

[0848] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0849] 2. AI Agent Selection

[0850] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0851] The server lists AI agents that meet the criteria, and the user selects from them.

[0852] 3. Operation of the Emotion Engine

[0853] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[0854] The server optimizes the settings based on the emotional data.

[0855] 4. Task Instructions

[0856] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0857] 5. Cooperation between agents

[0858] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0859] If necessary, adjust communication methods based on the user's emotional data.

[0860] 6. Providing results

[0861] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[0862] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[0863] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[0864] The processing flow will be explained below.

[0865] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[0866] Processing flow

[0867] Step 1:

[0868] A user logs into the system and accesses the dashboard.

[0869] Step 2:

[0870] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[0871] Step 3:

[0872] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[0873] Step 4:

[0874] The user navigates to a project page and sees a form to specify the required skill set and experience.

[0875] Step 5:

[0876] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0877] Step 6:

[0878] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[0879] Step 7:

[0880] The terminal transmits the selected agent information to the server, which associates it with the project.

[0881] Step 8:

[0882] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[0883] Step 9:

[0884] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[0885] Step 10:

[0886] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[0887] Step 11:

[0888] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[0889] Step 12:

[0890] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[0891] Step 13:

[0892] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[0893] Step 14:

[0894] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[0895] Step 15:

[0896] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[0897] Step 16:

[0898] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[0899] Step 17:

[0900] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[0901] Step 18:

[0902] The terminal displays the final result, and the user confirms it and gives the next instruction.

[0903] Step 19:

[0904] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[0905] Step 20:

[0906] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[0907] Specific examples

[0908] Example 1: Using an emotion engine in a health management app development project

[0909] 1. Project Settings

[0910] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[0911] 2. AI Agent Selection

[0912] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[0913] The server lists AI agents that meet the criteria, and the user selects from them.

[0914] 3. Operation of the Emotion Engine

[0915] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[0916] The server optimizes the settings based on the emotional data.

[0917] 4. Task Instructions

[0918] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[0919] 5. Cooperation between agents

[0920] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[0921] If necessary, adjust communication methods based on the user's emotional data.

[0922] 6. Providing results

[0923] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[0924] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[0925] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[0926] Example 2

[0927] 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."

[0928] Conventional project management systems can cause users to feel emotionally stressed and their satisfaction levels to decline. Especially in large-scale projects, interpersonal stress can negatively impact project progress. Furthermore, the process for users to select AI agents with appropriate skill sets is complicated, leading to issues such as poor communication between teams. To solve these problems, a system that integrates the management of AI agents with the monitoring of users' emotional states is needed.

[0929] 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.

[0930] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and experience, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for the user to instruct the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for collecting and evaluating the user's emotions in real time, and means for adjusting the system's operation based on the emotion data, thereby enabling the project to proceed efficiently while taking the user's emotional state into consideration.

[0931] A "project" is a set of planned activities designed to achieve a specific purpose or goal.

[0932] "Requirements" are the conditions or standards necessary to carry out a project or task.

[0933] "Skills" are the specialized abilities and knowledge required to perform a specific job or task.

[0934] "Experience" refers to the history of past jobs and projects you have undertaken, as well as the skills and qualifications you have acquired.

[0935] An "AI agent" is software that uses artificial intelligence technology to automate specific tasks and work in collaboration with other agents and users to complete tasks.

[0936] A "task" is a specific task or activity that is performed to achieve the project's objectives.

[0937] "Emotion" in "Tarza" refers to the user's psychological state, specifically emotional responses such as stress, satisfaction, and anxiety.

[0938] An "emotion engine" is a system that analyzes and evaluates a user's emotional state in real time based on input such as facial expressions and voice.

[0939] A "system" is a collective term for a set of hardware and software components designed to work together to perform a specific function.

[0940] A "user interface" is the input and output means by which a user interacts with a system.

[0941] A "database" is a system for efficiently managing and storing large amounts of data.

[0942] "Real-time" refers to information and data being processed and analyzed as soon as it is generated.

[0943] "Communication" is the process of exchanging information and opinions, and here it specifically refers to sharing information between AI agents and with users.

[0944] "Analysis" is the process of collecting data and interpreting it statistically or logically.

[0945] This invention is a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents. The system incorporates an emotion engine that recognizes user emotions in real time and reflects them in the user interface and project progress.

[0946] System configuration

[0947] The system consists of the following main components:

[0948] 1. User project settings

[0949] 2. AI Agent Selection

[0950] 3. Operation of the Emotion Engine

[0951] 4. Project progress instructions

[0952] 5. Communication between AI agents

[0953] 6. Providing Results

[0954] Hardware and software used

[0955] Device: A device directly operated by a user, equipped with a camera and microphone to collect emotional data, typically running a commercial operating system (e.g., Windows, macOS, Linux).

[0956] Server: A computer system with powerful computing resources that manages project data, lists AI agents, evaluates emotional data, etc. Servers are often operated on commercial cloud services (e.g., AWS, Google Cloud, Microsoft Azure).

[0957] Emotion Engine: Uses machine learning models (e.g., TensorFlow, PyTorch) to recognize emotions in real time from the user's facial expressions and tone of voice.

[0958] Project Setup and Data Management

[0959] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button, which displays a form where the user can enter the project name, purpose, and detailed requirements. The terminal sends the entered project information to the server, which then stores the project information in a database.

[0960] AI Agent Selection

[0961] The user navigates to a project page and is presented with a form to specify the required skill set and experience. The device sends the specified criteria to the server, which searches its database for and lists AI agents that match the criteria. The list of applicable AI agents is sent to the device, and the user can view and select these agents. The device sends the selected agent information to the server, which associates them with the project.

[0962] Emotion Engine Operation

[0963] While the user is using the system, the device's built-in camera and microphone analyze facial expressions and tone of voice to collect emotional data. The device then transmits the emotional data to a server in real time, which then uses an AI model to evaluate the emotion. The server then automatically fine-tunes project settings and optimizes instructions to the AI ​​agent based on the evaluated emotional data.

[0964] Project progress instructions

[0965] The user clicks the "Create a new task" button on the project page and fills in the task details. The device sends the entered task information to the server, which then assigns the task to the appropriate AI agent.

[0966] Communication between AI agents

[0967] The server assigns tasks to each AI agent, and the AI ​​agent receives the tasks. The AI ​​agent shares information with other agents as needed and progresses with the task with a common understanding. The AI ​​agent adjusts its communication method and response expression based on the user's emotional data.

[0968] Providing results

[0969] The AI ​​agent sends the final answer to the server, which provides it to the user. The terminal displays the final result, and the user confirms it and gives instructions. The emotion engine monitors the user's reaction and reflects it in the next step.

[0970] Example: Prompt sentence for generative AI model

[0971] The following example shows how an emotion engine is used in a health management app development project.

[0972] Example: Health management app development project

[0973] Prompt statement

[0974] "I'd like to create a project to develop a health management app. The goal is to track users' health data and provide advice. I'd like to assign an engineer with app development experience, a UI / UX designer, a data scientist, and a marketing specialist. I'd like the system to use an emotion engine to assess my stress level and satisfaction level and optimize the project's progress."

[0975] The above is a specific example of a system for implementing the present invention. This system enables efficient project management by linking project management with user emotion management.

[0976] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0977] Step 1: Log in and create a project

[0978] Input: User authentication information, project setting information

[0979] Specific behavior:

[0980] The user enters their authentication information (such as user ID and password) and logs in to the system.

[0981] The device sends the authentication information to the server, which checks the authentication. If the authentication is successful, the dashboard is displayed.

[0982] The user clicks the "Create New Project" button on the dashboard and enters the project name, purpose, detailed requirements, etc.

[0983] Output: Send and save project setting information

[0984] Step 2: Save the project information

[0985] Input: Project setting information

[0986] Specific behavior:

[0987] The terminal transmits the input project information to the server, which stores it in a database.

[0988] The server generates a project ID and records it in a database along with the entered project information.

[0989] Output: Saving project information to a database

[0990] Step 3: Selecting an AI agent

[0991] Input: User-specified skill sets and experience

[0992] Specific behavior:

[0993] A user visits a project page and fills out a form specifying the required skill set and experience.

[0994] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[0995] Output: List of AI agents

[0996] Step 4: Selecting and Associating an AI Agent

[0997] Input: A list of AI agents

[0998] Specific behavior:

[0999] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1000] The terminal transmits the selected agent information to the server, and the server associates the selected agent with the project.

[1001] Output: Associating an AI agent with a project

[1002] Step 5: Collect and evaluate emotion data

[1003] Input: User facial expressions and tone of voice

[1004] Specific behavior:

[1005] While the user is using the system, the device's built-in camera and microphone analyze the user's facial expressions and tone of voice in real time.

[1006] The device sends emotional data to a server, which then uses an AI model to evaluate the emotion.

[1007] Output: Generate emotion evaluation results

[1008] Step 6: Optimize project progress

[1009] Input: Emotion evaluation results, project setting information

[1010] Specific behavior:

[1011] Based on the evaluated emotional data, the server automatically fine-tunes project settings and optimizes instructions to AI agents.

[1012] The server updates the project task information and sends the information to the terminal.

[1013] Output: Sending updated project configuration information

[1014] Step 7: Creating and Distributing Tasks

[1015] Input: User task instructions, project information

[1016] Specific behavior:

[1017] A user clicks the "Create new task" button on a project page and fills in the task details.

[1018] The terminal transmits the input task information to the server.

[1019] The server stores task information in a database and distributes tasks to appropriate AI agents.

[1020] Output: Tasks are stored in a database and distributed to AI agents.

[1021] Step 8: Information sharing between AI agents

[1022] Input: Task information for each AI agent

[1023] Specific behavior:

[1024] The server distributes tasks to each AI agent and shares relevant information.

[1025] AI agents communicate with each other and progress through tasks with a common understanding.

[1026] Output: Information sharing between AI agents

[1027] Step 9: Providing task results

[1028] Input: Task results sent by the AI ​​agent

[1029] Specific behavior:

[1030] The AI ​​agent sends the final answer to the server.

[1031] The server transmits the received results to the terminal and provides them to the user.

[1032] Output: Providing the final task results to the user

[1033] Step 10: User feedback and next steps

[1034] Input: User feedback

[1035] Specific behavior:

[1036] The terminal displays the final result, which the user confirms.

[1037] The emotion engine monitors user reactions and reflects them in the next project steps.

[1038] Output: Project information reflected in the next step

[1039] Through the above steps, project management and user emotion management are linked, enabling projects to proceed efficiently and flexibly.

[1040] (Application example 2)

[1041] 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."

[1042] Conventional project management systems are unable to consider the emotions and stress levels of managers when progressing tasks, limiting their usability and productivity. Furthermore, in factory management, robot operations are not adjusted based on real-time emotional data from managers, making it difficult to provide an optimal work environment. This creates a need for efficient project progress management and flexible responses.

[1043] 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.

[1044] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and background, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their responses, means for providing the refined responses to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotion data. This enables the project to proceed while taking the manager's emotions into consideration, thereby providing an efficient and flexible work environment.

[1045] A "user" is a person or organization that operates the system and sets project objectives and requirements, selects AI agents, etc.

[1046] A "project" is a set of tasks or activities planned and set out to achieve a specific purpose.

[1047] "Skills" refer to the abilities and knowledge required to perform a specific task or role.

[1048] "History" refers to the past experience and achievements of a user or AI agent.

[1049] An "AI agent" is an autonomous program that uses artificial intelligence to perform specific tasks and refines its answers by sharing information with other agents.

[1050] A "task" refers to an individual activity or work that has a specific role and purpose within a project.

[1051] The "system" is a platform that supports users in project management and integrates a series of functions and tools, including AI agents and emotion engines.

[1052] "Emotions" refer to psychological states and changes that can be recognized from the user's facial expressions, tone of voice, etc.

[1053] A "robot" is an automated machine or device designed to perform designated tasks in a factory.

[1054] An "emotion engine" is a combination of software or hardware that recognizes a user's emotions in real time and adjusts the system's behavior accordingly.

[1055] A "manager" is a person or position that oversees the progress of a factory or project and operates the system to manage the progress of tasks.

[1056] A specific system configuration and its operation procedure will be described below for the embodiment of the present invention.

[1057] The server includes means for a user to set the objectives and requirements of the project, means for specifying the necessary skills and background, means for listing AI agents based on specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotional data.

[1058] A user logs into the system and sets up a project by entering the project's objectives and requirements into a dashboard. The user then specifies the appropriate skills and background, and the server generates a list of AI agents based on this information. The user then selects an appropriate AI agent from the list and sends it to the server.

[1059] In this system, factory managers are equipped with cameras and microphones on their devices to recognize emotions from facial expressions and tone of voice while working on a project. Specifically, OpenCV is used to capture the manager's facial expressions in real time and analyze them with a pre-trained emotion recognition model. This emotional data is sent to a server, and the AI ​​agent adjusts its behavior based on that data while performing the task.

[1060] For example, if a manager is feeling stressed, the server will use this emotional data to monitor the progress of the task and issue new instructions to the AI ​​agent if necessary. Also, if the manager is feeling happy or satisfied, the server will reflect this emotional data to ensure the project progresses smoothly.

[1061] As a concrete example, the server can perform the following processing based on an example prompt: "Recognize faces using a camera, recognize emotions in real time using an emotion model, and send that data to the server to generate Python code that will optimize project progress. The server URL is http: / / factory-management-system.example.com." By inputting this prompt into a generative AI model, task management that takes user emotions into account can be achieved.

[1062] This allows managers to understand their emotions in real time and flexibly progress projects accordingly. It also allows for efficient adjustment of robot operations, optimizing the work environment.

[1063] With the above configuration, the system of the present invention realizes project management that improves usability and productivity.

[1064] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1065] Step 1:

[1066] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button and enters the project name, purpose, detailed requirements, etc. This input data is sent from the terminal to the server, which then stores the project information in a database.

[1067] Input: Project name, purpose, detailed requirements

[1068] Processing: The terminal sends the input data to the server, and the server saves it in a database

[1069] Output: Project information stored in a database

[1070] Step 2:

[1071] The user fills out a form to specify appropriate skills and experience. The specified criteria are sent from the device to the server, which searches and lists AI agents that match the criteria from its database. The user selects an AI agent from the list, and the selection information is sent from the device to the server, which associates it with the project.

[1072] Input: Required skills, experience

[1073] Processing: The device sends the specified conditions to the server, and the server lists the corresponding AI agents from the database.

[1074] Output: Information about the AI ​​agent selected by the user.

[1075] Step 3:

[1076] While the user is working on a project, the device's built-in camera and microphone are used to capture the manager's facial expressions and tone of voice in real time. OpenCV is used to recognize facial expressions and generate emotional data. The generated emotional data is then sent from the device to the server.

[1077] Input: facial expression data, voice data

[1078] Processing: Data capture with camera and microphone, facial expression recognition and emotion data generation with OpenCV

[1079] Output: Emotion data sent to the server

[1080] Step 4:

[1081] The server analyzes the received emotional data and adjusts project progress and task priorities based on that data, such as issuing new instructions to AI agents or rescheduling tasks.

[1082] Input: Emotion data

[1083] Processing: Analyzing emotion data on the server and adjusting project progress

[1084] Output: Coordinated task instructions and schedules

[1085] Step 5:

[1086] When the AI ​​agents receive new instructions, they execute the task and share information with each other to refine their answers. They also take into account the user's emotional data and respond flexibly according to their emotions. The completed answer is then sent back to the server.

[1087] Input: adjusted task instructions, emotion data

[1088] Processing: Sharing information and executing tasks between AI agents

[1089] Output: Refined answer (sent to server)

[1090] Step 6:

[1091] The server then provides the answers received from the AI ​​agent to the user, who can review the final results on their device and issue new instructions if necessary. User reactions and emotional data are also collected again and used to inform the next steps of the project.

[1092] Input: Final response from the AI ​​agent, new instructions from the user

[1093] Processing: The server provides the final answer and recollects the user's instructions and emotion data.

[1094] Output: The final result provided to the user, along with new emotion data and instructions.

[1095] Through these steps, the system can achieve efficient project management while taking into account the user's feelings.

[1096] 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.

[1097] 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.

[1098] 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.

[1099] [Third embodiment]

[1100] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1101] 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.

[1102] 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).

[1103] 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.

[1104] 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.

[1105] 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).

[1106] 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.

[1107] 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.

[1108] 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.

[1109] 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.

[1110] 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.

[1111] 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."

[1112] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention relates to a system for project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents.

[1113] overview

[1114] It provides a means for users to set the project's objectives and requirements and specify the necessary skills and background. Based on this information, the server lists AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. Finally, it has a means to provide the refined answers to the user.

[1115] System configuration

[1116] 1. User project settings

[1117] A user logs into the system and accesses the dashboard.

[1118] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1119] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1120] 2. AI Agent Selection

[1121] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1122] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1123] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1124] The terminal transmits the selected agent information to the server, which associates it with the project.

[1125] 3. Project progress instructions

[1126] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1127] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1128] 4. Communication between AI agents

[1129] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1130] AI agents share information with each other, communicate as needed, and refine their responses.

[1131] 5. Providing results

[1132] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1133] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1134] Specific examples

[1135] Example 1: Health management app development project

[1136] 1. Project Settings

[1137] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1138] 2. AI Agent Selection

[1139] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1140] The server lists AI agents that meet the criteria, and the user selects from them.

[1141] 3. Task Instructions

[1142] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1143] 4. Cooperation between agents

[1144] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1145] 5. Providing results

[1146] The server provides the compiled results to the user, who then confirms and gives further instructions.

[1147] As described above, the system of the present invention supports the efficient progress of projects by combining project management and AI agent functions.

[1148] The processing flow will be explained below.

[1149] Step 1:

[1150] A user logs into the system and accesses the dashboard.

[1151] Step 2:

[1152] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1153] Step 3:

[1154] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1155] Step 4:

[1156] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1157] Step 5:

[1158] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1159] Step 6:

[1160] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1161] Step 7:

[1162] The terminal transmits the selected agent information to the server, which associates it with the project.

[1163] Step 8:

[1164] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1165] Step 9:

[1166] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1167] Step 10:

[1168] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1169] Step 11:

[1170] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1171] Step 12:

[1172] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[1173] Step 13:

[1174] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[1175] Step 14:

[1176] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1177] Step 15:

[1178] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[1179] Example 1

[1180] 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."

[1181] Conventional project management systems have problems with delays in project progress due to inefficient tasks such as setting project objectives and requirements, selecting AI agents with the necessary skills and experience, distributing and completing tasks, and checking results.Furthermore, there is a lack of cooperation and communication between AI agents, which makes it difficult to guarantee the quality of the final answer.

[1182] 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.

[1183] In this invention, the server includes: means for a user to set the project's objectives and requirements; means for specifying required skills and experience; means for saving the set project information and conditions in a database; means for automatically listing AI agents based on the specified conditions; means for a user to select a listed AI agent; means for associating information about the selected AI agent with the project; means for a user to create a new task and input the task; means for automatically allocating tasks to AI agents; means for the AI ​​agents to share information with each other and refine their answers; means for transmitting the refined answer to the server and providing it to the user; and interface means for the user to check the results and give further instructions. This enables the project to proceed efficiently and quickly, and enables the AI ​​agents to cooperate to provide high-quality answers.

[1184] "User" means a person or end user who accesses and operates the System.

[1185] "Means for setting project objectives and requirements" refers to the interface that allows users to input and record specific project goals and conditions within the system.

[1186] "Means for specifying required skills and experience" refers to an interface that allows users to specify AI agents within the system that have the skills and experience required for a project.

[1187] "Means for saving set project information and conditions in a database" refers to a function by which the server records project information and conditions set by the user in a database.

[1188] "Means for listing AI agents" refers to a function that automatically searches for and displays relevant AI agents based on conditions specified by the user.

[1189] "Means for associating AI agent information with a project" refers to a function for associating an AI agent selected by a user with a set project.

[1190] "A means for creating and entering new tasks" refers to the interface through which a user can create and enter new tasks within a project.

[1191] "Means for automatically allocating tasks to AI agents" refers to a function that automatically assigns input tasks to the corresponding AI agents.

[1192] "Means for AI agents to share information with each other and refine answers" refers to the ability for multiple AI agents to communicate with each other and collaboratively optimize answers to tasks.

[1193] "Means for sending refined answers to the server and providing them to the user" refers to the function by which the AI ​​agent sends the optimized answer to the server and displays it to the user.

[1194] "Interface means for the user to check the results and give further instructions" refers to an interface for the user to check the results provided in the system and input further instructions.

[1195] The following describes an embodiment of the present invention. The present invention is a system for streamlining project management and using appropriate AI agents to accomplish tasks. The main elements of the system include a user, a terminal, a server, and an AI agent.

[1196] System configuration

[1197] 1. User project settings

[1198] A user logs into the system and accesses the dashboard, which involves entering credentials and being authenticated by the server.

[1199] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1200] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1201] 2. AI Agent Selection

[1202] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1203] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1204] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1205] The terminal transmits the selected agent information to the server, which associates it with the project.

[1206] 3. Task creation and instructions

[1207] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1208] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1209] 4. Communication between AI agents

[1210] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1211] AI agents share information with each other, communicate as needed, and refine their answers.

[1212] 5. Providing results

[1213] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1214] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1215] Specific examples

[1216] Example 1: Health management app development project

[1217] 1. Project Settings

[1218] A user logs into the system and clicks the "Create a new project" button on the dashboard.

[1219] A user creates a project called "Developing a Health Management App" and enters the purpose as "Tracking user health data and providing advice."

[1220] The terminal sends the project information to the server, which stores the information in a database.

[1221] 2. AI Agent Selection

[1222] In a form where users can specify the required skill set and experience, they enter "engineer with app development experience," "UI / UX designer," "data scientist," and "marketing specialist."

[1223] The terminal sends the conditions to the server, and the server searches the database for the corresponding AI agent.

[1224] The server generates a list of AI agents and sends it to the device.

[1225] The user selects an agent from the list, and the terminal transmits the selection to the server.

[1226] 3. Task creation and instructions

[1227] The user enters a new task: "Make a list of the app's main features and propose a design."

[1228] The terminal sends task information to the server, and the server distributes tasks to AI agents.

[1229] 4. Cooperation between agents

[1230] AI agents receive tasks and share information to compile feature lists and design proposals.

[1231] The AI ​​agent refines the answer and sends the final answer to the server.

[1232] 5. Providing results

[1233] The server receives the final answer and sends it to the device.

[1234] The user checks the results on the terminal and gives the next instruction.

[1235] Prompt Sentence Examples

[1236] By using prompts to set up a project, the system can accurately and quickly grasp the project requirements. Below are some examples of specific prompts:

[1237] "Make a list of the main features of a health management app and come up with a design proposal."

[1238] "List agents with data science expertise."

[1239] As described above, the system of the present invention provides a series of processes for users to set up a project, select an appropriate AI agent, and efficiently complete the task, thereby streamlining the progress of the project and improving the quality of the deliverables.

[1240] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1241] Step 1: User logs into the system

[1242] Input: The user enters authentication information (user ID and password).

[1243] How it works: The device sends authentication information to the server.

[1244] Data processing: The server searches the database for the relevant user information and verifies the authentication information.

[1245] Output: If authentication is successful, the user dashboard will be displayed on the terminal. If authentication is unsuccessful, an error message will be displayed.

[1246] Step 2: Create a new project

[1247] Input: A user clicks the "Create a new project" button on the dashboard and fills out a form with the project's name, purpose, detailed requirements, etc.

[1248] Operation: The device sends the entered project information to the server.

[1249] Data processing: The server saves the entered project information in a database.

[1250] Output: A project creation confirmation message is displayed in the terminal.

[1251] Step 3: Specify the required skill sets and experience

[1252] Input: Users fill out a form on the project page to specify the required skill set and experience.

[1253] Operation: The device sends the specified conditions to the server.

[1254] Data processing: The server searches the database for AI agents that match the conditions.

[1255] Output: The server generates a list of applicable AI agents and sends it to the device.

[1256] Step 4: Selecting an AI Agent

[1257] Input: The user selects a specific AI agent from a list.

[1258] Operation: The terminal sends the selected agent information to the server.

[1259] Data processing: The server associates the selected AI agent information with the project.

[1260] Output: The agent information associated with the project is saved in the database.

[1261] Step 5: Create a new task

[1262] Input: A user clicks the "Create new task" button on a project page and enters task details.

[1263] Operation: The device sends the entered task information to the server.

[1264] Data processing: The server distributes tasks to the appropriate AI agents.

[1265] Output: The task is sent to the AI ​​agent.

[1266] Step 6: Communication between AI agents

[1267] How it works: The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1268] Data processing: AI agents share information with each other and communicate to refine their answers.

[1269] Output: The refined answer is finally sent to the server.

[1270] Step 7: Delivering results

[1271] Input: The server receives the final answer from the AI ​​agent.

[1272] Action: The server sends the final answer to the device.

[1273] Output: The terminal displays the final result to the user.

[1274] Action: The user interacts with the interface to confirm the results and give further instructions.

[1275] The above is a detailed flow of each processing step.

[1276] (Application example 1)

[1277] 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."

[1278] Conventional project management systems require users to manually assign tasks and monitor progress, limiting their ability to improve productivity and efficiently manage projects. Furthermore, manually optimizing production line operations, scheduling robot operations, and planning equipment maintenance is extremely labor-intensive and prone to errors. Furthermore, there was a lack of a system that allowed AI agents to efficiently share information with each other and provide optimal solutions.

[1279] 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.

[1280] In this invention, the server includes a means for allowing a user to optimize the operation of a factory's production line, a means for managing the operation schedule of factory robots, and a means for formulating equipment maintenance plans. This makes it possible to automatically optimize the operation status of a factory's production line, efficiently manage the operation schedule of robots, and easily formulate equipment maintenance plans. In project management, the AI ​​agent can also automatically assign tasks and provide optimal answers, thereby improving project efficiency and facilitating management.

[1281] A "user" is a person who manages a project and gives instructions on tasks, or a user of the system.

[1282] A "means for setting project objectives and requirements" is a mechanism that allows users to specify project details, goals, and required skills and experience.

[1283] An "AI agent" is an artificial intelligence program designed to efficiently perform a specific task.

[1284] "Means for transferring task instructions to an AI agent" refers to a mechanism for delivering task instructions from the user to an AI agent.

[1285] "Means for sharing information and refining answers" refers to the methods by which AI agents exchange information with each other to arrive at optimal solutions.

[1286] "Means for providing refined answers to users" refers to a mechanism that presents the optimal answers generated by the AI ​​agent to the user.

[1287] "Means for optimizing factory production line operations" are automated process management tools that maximize factory production efficiency.

[1288] "Means for managing the operation schedules of factory robots" refers to a system that automatically adjusts and manages the working hours and work content of robots used in factories.

[1289] A "means for creating equipment maintenance plans" is a tool for creating schedules for efficiently inspecting and repairing equipment within a factory.

[1290] The "interface means for checking task results and giving instructions for the next step" is a user interface that allows the user to check the results provided by the AI ​​agent and give instructions for the next action.

[1291] "Means for monitoring the operating status of factory robots" refers to a system that monitors the operation and status of robots in real time while they are actually working.

[1292] The present invention provides a system that allows users to efficiently manage projects and optimize factory production lines. A specific embodiment for realizing this system will be described below.

[1293] System Overview

[1294] The system helps users set up a project, specify the required skills and background, and select an AI agent from a list. The selected AI agents then share information with each other to complete the task and provide a final answer to the user. The system also includes functions for optimizing factory production lines, managing robot operation schedules, and creating equipment maintenance plans.

[1295] Hardware and Software Configuration

[1296] Hardware: smartphones, servers, factory robots

[1297] Software: iOS / Android apps, server-side database management systems (e.g., MongoDB or MySQL), AI agent management systems (e.g., using TensorFlow or PyTorch)

[1298] Data processing and calculation

[1299] User Project Settings

[1300] Users log in to the system using their smartphones, access the dashboard, enter project details (project name, objectives, detailed requirements, etc.), and send them to the server, which then stores the received information in a database.

[1301] AI Agent Selection

[1302] The user specifies the required skill set and experience and sends the criteria to the server. The server searches the database for AI agents that match the criteria and lists them for the user. The user selects an appropriate AI agent from the list and sends it to the server. The server associates the selected AI agent with the project.

[1303] Task assignment and execution

[1304] A user inputs the details of a new task and submits it to the server, which distributes the task instructions to AI agents. Each AI agent receives the task, shares information with each other, communicates as needed, and creates the optimal answer.

[1305] Providing results

[1306] Each AI agent sends its final answer to the server, which then provides it to the user, who can check the results on their smartphone and give further instructions.

[1307] Specific examples

[1308] For example, if a user is starting a new product line optimization project, they might enter the prompt statement as follows:

[1309] "Set up launch dates and production targets for the new product line and create a schedule for the robots we need. Also, create a maintenance plan for the equipment."

[1310] Based on this, the AI ​​agent will perform the following tasks:

[1311] 1. Proposing a working schedule based on production targets.

[1312] 2. Creating and proposing optimal operating schedules for each terminal robot.

[1313] 3. Planning and proposing regular maintenance plans for equipment.

[1314] This system automatically optimizes the operation status of factory production lines, enabling efficient management. It also reduces the effort required for project management, allowing users to quickly take the next action.

[1315] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1316] Step 1:

[1317] User Project Settings

[1318] Input: The user sets the project name, purpose, detailed requirements, etc. via their smartphone.

[1319] What happens: The device sends this information to the server.

[1320] Specific data processing: The server stores the received project information in a database.

[1321] Output: The project is saved to the database and the project setup is complete.

[1322] Step 2:

[1323] AI Agent Selection

[1324] Input: The user specifies the required skill set and experience and enters the requirements into the terminal.

[1325] Operation: The device sends the specified conditions to the server.

[1326] Specific data processing: The server searches the database for AI agents that match the conditions and lists them.

[1327] Output: The AI ​​agents are displayed on the device as a list that the user can view.

[1328] Step 3:

[1329] AI Agent Selection

[1330] Input: The user selects an appropriate AI agent from a list.

[1331] Action: The device sends the selected AI agent information to the server.

[1332] Specific data processing: The server associates the selected AI agent with a project.

[1333] Output: The project will have an associated AI agent.

[1334] Step 4:

[1335] Task Instructions

[1336] Input: The user enters the details of a new task (e.g., "Set launch dates and production targets for a new product line").

[1337] Operation: The device sends task information to the server.

[1338] Specific data processing: The server allocates tasks to the appropriate AI agents.

[1339] Output: Task information is delivered to the AI ​​agent.

[1340] Step 5:

[1341] Information sharing and task execution among AI agents

[1342] Input: Each AI agent receives a task to process.

[1343] How it works: AI agents share information with each other, communicate as needed, and use generative AI models to derive optimal solutions.

[1344] Specific data processing: AI agents use relevant information to analyze data, make predictions, and formulate optimal answers.

[1345] Output: The answers generated by each AI agent are sent to the server.

[1346] Step 6:

[1347] Aggregating and providing results

[1348] Input: The best answer from the AI ​​agent.

[1349] Action: The server aggregates these responses.

[1350] Specific data processing: The server aggregates the received responses and processes them into a format that is easy for the user to understand.

[1351] Output: The final result is the best answer displayed on the user's device.

[1352] This process flow allows users to optimize factory production line operations, efficiently manage robot operation schedules, and easily create equipment maintenance plans. Furthermore, the AI ​​agent efficiently completes tasks and provides optimal answers to users.

[1353] 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.

[1354] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[1355] overview

[1356] It provides a means for users to set project objectives and requirements and specify the necessary skills and background. Based on this information, the server creates a list of AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. It also incorporates an emotion engine that recognizes user emotions and adjusts the system's behavior based on that emotional data, making it possible to progress a project while taking into account the user's stress level and satisfaction.

[1357] System configuration

[1358] 1. User project settings

[1359] A user logs into the system and accesses the dashboard.

[1360] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1361] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1362] 2. AI Agent Selection

[1363] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1364] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1365] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1366] The terminal transmits the selected agent information to the server, which associates it with the project.

[1367] 3. Operation of the Emotion Engine

[1368] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[1369] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[1370] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[1371] 4. Project progress instructions

[1372] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1373] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1374] 5. Communication between AI agents

[1375] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1376] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1377] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[1378] 6. Providing Results

[1379] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1380] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1381] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[1382] Specific examples

[1383] Example 1: Using an emotion engine in a health management app development project

[1384] 1. Project Settings

[1385] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1386] 2. AI Agent Selection

[1387] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1388] The server lists AI agents that meet the criteria, and the user selects from them.

[1389] 3. Operation of the Emotion Engine

[1390] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[1391] The server optimizes the settings based on the emotional data.

[1392] 4. Task Instructions

[1393] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1394] 5. Cooperation between agents

[1395] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1396] If necessary, adjust communication methods based on the user's emotional data.

[1397] 6. Providing results

[1398] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[1399] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[1400] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[1401] The processing flow will be explained below.

[1402] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[1403] Processing flow

[1404] Step 1:

[1405] A user logs into the system and accesses the dashboard.

[1406] Step 2:

[1407] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1408] Step 3:

[1409] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1410] Step 4:

[1411] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1412] Step 5:

[1413] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1414] Step 6:

[1415] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1416] Step 7:

[1417] The terminal transmits the selected agent information to the server, which associates it with the project.

[1418] Step 8:

[1419] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[1420] Step 9:

[1421] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[1422] Step 10:

[1423] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[1424] Step 11:

[1425] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1426] Step 12:

[1427] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1428] Step 13:

[1429] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1430] Step 14:

[1431] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1432] Step 15:

[1433] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[1434] Step 16:

[1435] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[1436] Step 17:

[1437] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[1438] Step 18:

[1439] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1440] Step 19:

[1441] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[1442] Step 20:

[1443] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[1444] Specific examples

[1445] Example 1: Using an emotion engine in a health management app development project

[1446] 1. Project Settings

[1447] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1448] 2. AI Agent Selection

[1449] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1450] The server lists AI agents that meet the criteria, and the user selects from them.

[1451] 3. Operation of the Emotion Engine

[1452] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[1453] The server optimizes the settings based on the emotional data.

[1454] 4. Task Instructions

[1455] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1456] 5. Cooperation between agents

[1457] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1458] If necessary, adjust communication methods based on the user's emotional data.

[1459] 6. Providing results

[1460] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[1461] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[1462] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[1463] Example 2

[1464] 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."

[1465] Conventional project management systems can cause users to feel emotionally stressed and their satisfaction levels to decline. Especially in large-scale projects, interpersonal stress can negatively impact project progress. Furthermore, the process for users to select AI agents with appropriate skill sets is complicated, leading to issues such as poor communication between teams. To solve these problems, a system that integrates the management of AI agents with the monitoring of users' emotional states is needed.

[1466] 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.

[1467] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and experience, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for the user to instruct the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for collecting and evaluating the user's emotions in real time, and means for adjusting the system's operation based on the emotion data, thereby enabling the project to proceed efficiently while taking the user's emotional state into consideration.

[1468] A "project" is a set of planned activities designed to achieve a specific purpose or goal.

[1469] "Requirements" are the conditions or standards necessary to carry out a project or task.

[1470] "Skills" are the specialized abilities and knowledge required to perform a specific job or task.

[1471] "Experience" refers to the history of past jobs and projects you have undertaken, as well as the skills and qualifications you have acquired.

[1472] An "AI agent" is software that uses artificial intelligence technology to automate specific tasks and work in collaboration with other agents and users to complete tasks.

[1473] A "task" is a specific task or activity that is performed to achieve the project's objectives.

[1474] "Emotion" in "Tarza" refers to the user's psychological state, specifically emotional responses such as stress, satisfaction, and anxiety.

[1475] An "emotion engine" is a system that analyzes and evaluates a user's emotional state in real time based on input such as facial expressions and voice.

[1476] A "system" is a collective term for a set of hardware and software components designed to work together to perform a specific function.

[1477] A "user interface" is the input and output means by which a user interacts with a system.

[1478] A "database" is a system for efficiently managing and storing large amounts of data.

[1479] "Real-time" refers to information and data being processed and analyzed as soon as it is generated.

[1480] "Communication" is the process of exchanging information and opinions, and here it specifically refers to sharing information between AI agents and with users.

[1481] "Analysis" is the process of collecting data and interpreting it statistically or logically.

[1482] This invention is a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents. The system incorporates an emotion engine that recognizes user emotions in real time and reflects them in the user interface and project progress.

[1483] System configuration

[1484] The system consists of the following main components:

[1485] 1. User project settings

[1486] 2. AI Agent Selection

[1487] 3. Operation of the Emotion Engine

[1488] 4. Project progress instructions

[1489] 5. Communication between AI agents

[1490] 6. Providing Results

[1491] Hardware and software used

[1492] Device: A device directly operated by a user, equipped with a camera and microphone to collect emotional data, typically running a commercial operating system (e.g., Windows, macOS, Linux).

[1493] Server: A computer system with powerful computing resources that manages project data, lists AI agents, evaluates emotional data, etc. Servers are often operated on commercial cloud services (e.g., AWS, Google Cloud, Microsoft Azure).

[1494] Emotion Engine: Uses machine learning models (e.g., TensorFlow, PyTorch) to recognize emotions in real time from the user's facial expressions and tone of voice.

[1495] Project Setup and Data Management

[1496] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button, which displays a form where the user can enter the project name, purpose, and detailed requirements. The terminal sends the entered project information to the server, which then stores the project information in a database.

[1497] AI Agent Selection

[1498] The user navigates to a project page and is presented with a form to specify the required skill set and experience. The device sends the specified criteria to the server, which searches its database for and lists AI agents that match the criteria. The list of applicable AI agents is sent to the device, and the user can view and select these agents. The device sends the selected agent information to the server, which associates them with the project.

[1499] Emotion Engine Operation

[1500] While the user is using the system, the device's built-in camera and microphone analyze facial expressions and tone of voice to collect emotional data. The device then transmits the emotional data to a server in real time, which then uses an AI model to evaluate the emotion. The server then automatically fine-tunes project settings and optimizes instructions to the AI ​​agent based on the evaluated emotional data.

[1501] Project progress instructions

[1502] The user clicks the "Create a new task" button on the project page and fills in the task details. The device sends the entered task information to the server, which then assigns the task to the appropriate AI agent.

[1503] Communication between AI agents

[1504] The server assigns tasks to each AI agent, and the AI ​​agent receives the tasks. The AI ​​agent shares information with other agents as needed and progresses with the task with a common understanding. The AI ​​agent adjusts its communication method and response expression based on the user's emotional data.

[1505] Providing results

[1506] The AI ​​agent sends the final answer to the server, which provides it to the user. The terminal displays the final result, and the user confirms it and gives instructions. The emotion engine monitors the user's reaction and reflects it in the next step.

[1507] Example: Prompt sentence for generative AI model

[1508] The following example shows how an emotion engine is used in a health management app development project.

[1509] Example: Health management app development project

[1510] Prompt statement

[1511] "I'd like to create a project to develop a health management app. The goal is to track users' health data and provide advice. I'd like to assign an engineer with app development experience, a UI / UX designer, a data scientist, and a marketing specialist. I'd like the system to use an emotion engine to assess my stress level and satisfaction level and optimize the project's progress."

[1512] The above is a specific example of a system for implementing the present invention. This system enables efficient project management by linking project management with user emotion management.

[1513] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1514] Step 1: Log in and create a project

[1515] Input: User authentication information, project setting information

[1516] Specific behavior:

[1517] The user enters their authentication information (such as user ID and password) and logs in to the system.

[1518] The device sends the authentication information to the server, which checks the authentication. If the authentication is successful, the dashboard is displayed.

[1519] The user clicks the "Create New Project" button on the dashboard and enters the project name, purpose, detailed requirements, etc.

[1520] Output: Send and save project setting information

[1521] Step 2: Save the project information

[1522] Input: Project setting information

[1523] Specific behavior:

[1524] The terminal transmits the input project information to the server, which stores it in a database.

[1525] The server generates a project ID and records it in a database along with the entered project information.

[1526] Output: Saving project information to a database

[1527] Step 3: Selecting an AI agent

[1528] Input: User-specified skill sets and experience

[1529] Specific behavior:

[1530] A user visits a project page and fills out a form specifying the required skill set and experience.

[1531] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1532] Output: List of AI agents

[1533] Step 4: Selecting and Associating an AI Agent

[1534] Input: A list of AI agents

[1535] Specific behavior:

[1536] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1537] The terminal transmits the selected agent information to the server, and the server associates the selected agent with the project.

[1538] Output: Associating an AI agent with a project

[1539] Step 5: Collect and evaluate emotion data

[1540] Input: User facial expressions and tone of voice

[1541] Specific behavior:

[1542] While the user is using the system, the device's built-in camera and microphone analyze the user's facial expressions and tone of voice in real time.

[1543] The device sends emotional data to a server, which then uses an AI model to evaluate the emotion.

[1544] Output: Generate emotion evaluation results

[1545] Step 6: Optimize project progress

[1546] Input: Emotion evaluation results, project setting information

[1547] Specific behavior:

[1548] Based on the evaluated emotional data, the server automatically fine-tunes project settings and optimizes instructions to AI agents.

[1549] The server updates the project task information and sends the information to the terminal.

[1550] Output: Sending updated project configuration information

[1551] Step 7: Creating and Distributing Tasks

[1552] Input: User task instructions, project information

[1553] Specific behavior:

[1554] A user clicks the "Create new task" button on a project page and fills in the task details.

[1555] The terminal transmits the input task information to the server.

[1556] The server stores task information in a database and distributes tasks to appropriate AI agents.

[1557] Output: Tasks are stored in a database and distributed to AI agents.

[1558] Step 8: Information sharing between AI agents

[1559] Input: Task information for each AI agent

[1560] Specific behavior:

[1561] The server distributes tasks to each AI agent and shares relevant information.

[1562] AI agents communicate with each other and progress through tasks with a common understanding.

[1563] Output: Information sharing between AI agents

[1564] Step 9: Providing task results

[1565] Input: Task results sent by the AI ​​agent

[1566] Specific behavior:

[1567] The AI ​​agent sends the final answer to the server.

[1568] The server transmits the received results to the terminal and provides them to the user.

[1569] Output: Providing the final task results to the user

[1570] Step 10: User feedback and next steps

[1571] Input: User feedback

[1572] Specific behavior:

[1573] The terminal displays the final result, which the user confirms.

[1574] The emotion engine monitors user reactions and reflects them in the next project steps.

[1575] Output: Project information reflected in the next step

[1576] Through the above steps, project management and user emotion management are linked, enabling projects to proceed efficiently and flexibly.

[1577] (Application example 2)

[1578] 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."

[1579] Conventional project management systems are unable to consider the emotions and stress levels of managers when progressing tasks, limiting their usability and productivity. Furthermore, in factory management, robot operations are not adjusted based on real-time emotional data from managers, making it difficult to provide an optimal work environment. This creates a need for efficient project progress management and flexible responses.

[1580] 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.

[1581] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and background, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their responses, means for providing the refined responses to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotion data. This enables the project to proceed while taking the manager's emotions into consideration, thereby providing an efficient and flexible work environment.

[1582] A "user" is a person or organization that operates the system and sets project objectives and requirements, selects AI agents, etc.

[1583] A "project" is a set of tasks or activities planned and set out to achieve a specific purpose.

[1584] "Skills" refer to the abilities and knowledge required to perform a specific task or role.

[1585] "History" refers to the past experience and achievements of a user or AI agent.

[1586] An "AI agent" is an autonomous program that uses artificial intelligence to perform specific tasks and refines its answers by sharing information with other agents.

[1587] A "task" refers to an individual activity or work that has a specific role and purpose within a project.

[1588] The "system" is a platform that supports users in project management and integrates a series of functions and tools, including AI agents and emotion engines.

[1589] "Emotions" refer to psychological states and changes that can be recognized from the user's facial expressions, tone of voice, etc.

[1590] A "robot" is an automated machine or device designed to perform designated tasks in a factory.

[1591] An "emotion engine" is a combination of software or hardware that recognizes a user's emotions in real time and adjusts the system's behavior accordingly.

[1592] A "manager" is a person or position that oversees the progress of a factory or project and operates the system to manage the progress of tasks.

[1593] A specific system configuration and its operation procedure will be described below for the embodiment of the present invention.

[1594] The server includes means for a user to set the objectives and requirements of the project, means for specifying the necessary skills and background, means for listing AI agents based on specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotional data.

[1595] A user logs into the system and sets up a project by entering the project's objectives and requirements into a dashboard. The user then specifies the appropriate skills and background, and the server generates a list of AI agents based on this information. The user then selects an appropriate AI agent from the list and sends it to the server.

[1596] In this system, factory managers are equipped with cameras and microphones on their devices to recognize emotions from facial expressions and tone of voice while working on a project. Specifically, OpenCV is used to capture the manager's facial expressions in real time and analyze them with a pre-trained emotion recognition model. This emotional data is sent to a server, and the AI ​​agent adjusts its behavior based on that data while performing the task.

[1597] For example, if a manager is feeling stressed, the server will use this emotional data to monitor the progress of the task and issue new instructions to the AI ​​agent if necessary. Also, if the manager is feeling happy or satisfied, the server will reflect this emotional data to ensure the project progresses smoothly.

[1598] As a concrete example, the server can perform the following processing based on an example prompt: "Recognize faces using a camera, recognize emotions in real time using an emotion model, and send that data to the server to generate Python code that will optimize project progress. The server URL is http: / / factory-management-system.example.com." By inputting this prompt into a generative AI model, task management that takes user emotions into account can be achieved.

[1599] This allows managers to understand their emotions in real time and flexibly progress projects accordingly. It also allows for efficient adjustment of robot operations, optimizing the work environment.

[1600] With the above configuration, the system of the present invention realizes project management that improves usability and productivity.

[1601] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1602] Step 1:

[1603] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button and enters the project name, purpose, detailed requirements, etc. This input data is sent from the terminal to the server, which then stores the project information in a database.

[1604] Input: Project name, purpose, detailed requirements

[1605] Processing: The terminal sends the input data to the server, and the server saves it in a database

[1606] Output: Project information stored in a database

[1607] Step 2:

[1608] The user fills out a form to specify appropriate skills and experience. The specified criteria are sent from the device to the server, which searches and lists AI agents that match the criteria from its database. The user selects an AI agent from the list, and the selection information is sent from the device to the server, which associates it with the project.

[1609] Input: Required skills, experience

[1610] Processing: The device sends the specified conditions to the server, and the server lists the corresponding AI agents from the database.

[1611] Output: Information about the AI ​​agent selected by the user.

[1612] Step 3:

[1613] While the user is working on a project, the device's built-in camera and microphone are used to capture the manager's facial expressions and tone of voice in real time. OpenCV is used to recognize facial expressions and generate emotional data. The generated emotional data is then sent from the device to the server.

[1614] Input: facial expression data, voice data

[1615] Processing: Data capture with camera and microphone, facial expression recognition and emotion data generation with OpenCV

[1616] Output: Emotion data sent to the server

[1617] Step 4:

[1618] The server analyzes the received emotional data and adjusts project progress and task priorities based on that data, such as issuing new instructions to AI agents or rescheduling tasks.

[1619] Input: Emotion data

[1620] Processing: Analyzing emotion data on the server and adjusting project progress

[1621] Output: Coordinated task instructions and schedules

[1622] Step 5:

[1623] When the AI ​​agents receive new instructions, they execute the task and share information with each other to refine their answers. They also take into account the user's emotional data and respond flexibly according to their emotions. The completed answer is then sent back to the server.

[1624] Input: adjusted task instructions, emotion data

[1625] Processing: Sharing information and executing tasks between AI agents

[1626] Output: Refined answer (sent to server)

[1627] Step 6:

[1628] The server then provides the answers received from the AI ​​agent to the user, who can review the final results on their device and issue new instructions if necessary. User reactions and emotional data are also collected again and used to inform the next steps of the project.

[1629] Input: Final response from the AI ​​agent, new instructions from the user

[1630] Processing: The server provides the final answer and recollects the user's instructions and emotion data.

[1631] Output: The final result provided to the user, along with new emotion data and instructions.

[1632] Through these steps, the system can achieve efficient project management while taking into account the user's feelings.

[1633] 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.

[1634] 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.

[1635] 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.

[1636] [Fourth embodiment]

[1637] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1638] 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.

[1639] 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).

[1640] 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.

[1641] 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.

[1642] 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).

[1643] 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.

[1644] 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.

[1645] 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.

[1646] 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.

[1647] 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.

[1648] 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.

[1649] 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."

[1650] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention relates to a system for project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents.

[1651] overview

[1652] It provides a means for users to set the project's objectives and requirements and specify the necessary skills and background. Based on this information, the server lists AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. Finally, it has a means to provide the refined answers to the user.

[1653] System configuration

[1654] 1. User project settings

[1655] A user logs into the system and accesses the dashboard.

[1656] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1657] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1658] 2. AI Agent Selection

[1659] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1660] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1661] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1662] The terminal transmits the selected agent information to the server, which associates it with the project.

[1663] 3. Project progress instructions

[1664] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1665] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1666] 4. Communication between AI agents

[1667] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1668] AI agents share information with each other, communicate as needed, and refine their responses.

[1669] 5. Providing results

[1670] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1671] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1672] Specific examples

[1673] Example 1: Health management app development project

[1674] 1. Project Settings

[1675] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1676] 2. AI Agent Selection

[1677] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1678] The server lists AI agents that meet the criteria, and the user selects from them.

[1679] 3. Task Instructions

[1680] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1681] 4. Cooperation between agents

[1682] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1683] 5. Providing results

[1684] The server provides the compiled results to the user, who then confirms and gives further instructions.

[1685] As described above, the system of the present invention supports the efficient progress of projects by combining project management and AI agent functions.

[1686] The processing flow will be explained below.

[1687] Step 1:

[1688] A user logs into the system and accesses the dashboard.

[1689] Step 2:

[1690] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1691] Step 3:

[1692] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1693] Step 4:

[1694] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1695] Step 5:

[1696] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1697] Step 6:

[1698] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1699] Step 7:

[1700] The terminal transmits the selected agent information to the server, which associates it with the project.

[1701] Step 8:

[1702] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1703] Step 9:

[1704] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1705] Step 10:

[1706] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1707] Step 11:

[1708] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1709] Step 12:

[1710] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[1711] Step 13:

[1712] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[1713] Step 14:

[1714] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1715] Step 15:

[1716] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[1717] Example 1

[1718] 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."

[1719] Conventional project management systems have problems with delays in project progress due to inefficient tasks such as setting project objectives and requirements, selecting AI agents with the necessary skills and experience, distributing and completing tasks, and checking results.Furthermore, there is a lack of cooperation and communication between AI agents, which makes it difficult to guarantee the quality of the final answer.

[1720] 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.

[1721] In this invention, the server includes: means for a user to set the project's objectives and requirements; means for specifying required skills and experience; means for saving the set project information and conditions in a database; means for automatically listing AI agents based on the specified conditions; means for a user to select a listed AI agent; means for associating information about the selected AI agent with the project; means for a user to create a new task and input the task; means for automatically allocating tasks to AI agents; means for the AI ​​agents to share information with each other and refine their answers; means for transmitting the refined answer to the server and providing it to the user; and interface means for the user to check the results and give further instructions. This enables the project to proceed efficiently and quickly, and enables the AI ​​agents to cooperate to provide high-quality answers.

[1722] "User" means a person or end user who accesses and operates the System.

[1723] "Means for setting project objectives and requirements" refers to the interface that allows users to input and record specific project goals and conditions within the system.

[1724] "Means for specifying required skills and experience" refers to an interface that allows users to specify AI agents within the system that have the skills and experience required for a project.

[1725] "Means for saving set project information and conditions in a database" refers to a function by which the server records project information and conditions set by the user in a database.

[1726] "Means for listing AI agents" refers to a function that automatically searches for and displays relevant AI agents based on conditions specified by the user.

[1727] "Means for associating AI agent information with a project" refers to a function for associating an AI agent selected by a user with a set project.

[1728] "A means for creating and entering new tasks" refers to the interface through which a user can create and enter new tasks within a project.

[1729] "Means for automatically allocating tasks to AI agents" refers to a function that automatically assigns input tasks to the corresponding AI agents.

[1730] "Means for AI agents to share information with each other and refine answers" refers to the ability for multiple AI agents to communicate with each other and collaboratively optimize answers to tasks.

[1731] "Means for sending refined answers to the server and providing them to the user" refers to the function by which the AI ​​agent sends the optimized answer to the server and displays it to the user.

[1732] "Interface means for the user to check the results and give further instructions" refers to an interface for the user to check the results provided in the system and input further instructions.

[1733] The following describes an embodiment of the present invention. The present invention is a system for streamlining project management and using appropriate AI agents to accomplish tasks. The main elements of the system include a user, a terminal, a server, and an AI agent.

[1734] System configuration

[1735] 1. User project settings

[1736] A user logs into the system and accesses the dashboard, which involves entering credentials and being authenticated by the server.

[1737] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1738] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1739] 2. AI Agent Selection

[1740] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1741] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1742] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1743] The terminal transmits the selected agent information to the server, which associates it with the project.

[1744] 3. Task creation and instructions

[1745] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1746] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1747] 4. Communication between AI agents

[1748] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1749] AI agents share information with each other, communicate as needed, and refine their answers.

[1750] 5. Providing results

[1751] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1752] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1753] Specific examples

[1754] Example 1: Health management app development project

[1755] 1. Project Settings

[1756] A user logs into the system and clicks the "Create a new project" button on the dashboard.

[1757] A user creates a project called "Developing a Health Management App" and enters the purpose as "Tracking user health data and providing advice."

[1758] The terminal sends the project information to the server, which stores the information in a database.

[1759] 2. AI Agent Selection

[1760] In a form where users can specify the required skill set and experience, they enter "engineer with app development experience," "UI / UX designer," "data scientist," and "marketing specialist."

[1761] The terminal sends the conditions to the server, and the server searches the database for the corresponding AI agent.

[1762] The server generates a list of AI agents and sends it to the device.

[1763] The user selects an agent from the list, and the terminal transmits the selection to the server.

[1764] 3. Task creation and instructions

[1765] The user enters a new task: "Make a list of the app's main features and propose a design."

[1766] The terminal sends task information to the server, and the server distributes tasks to AI agents.

[1767] 4. Cooperation between agents

[1768] AI agents receive tasks and share information to compile feature lists and design proposals.

[1769] The AI ​​agent refines the answer and sends the final answer to the server.

[1770] 5. Providing results

[1771] The server receives the final answer and sends it to the device.

[1772] The user checks the results on the terminal and gives the next instruction.

[1773] Prompt Sentence Examples

[1774] By using prompts to set up a project, the system can accurately and quickly grasp the project requirements. Below are some examples of specific prompts:

[1775] "Make a list of the main features of a health management app and come up with a design proposal."

[1776] "List agents with data science expertise."

[1777] As described above, the system of the present invention provides a series of processes for users to set up a project, select an appropriate AI agent, and efficiently complete the task, thereby streamlining the progress of the project and improving the quality of the deliverables.

[1778] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1779] Step 1: User logs into the system

[1780] Input: The user enters authentication information (user ID and password).

[1781] How it works: The device sends authentication information to the server.

[1782] Data processing: The server searches the database for the relevant user information and verifies the authentication information.

[1783] Output: If authentication is successful, the user dashboard will be displayed on the terminal. If authentication is unsuccessful, an error message will be displayed.

[1784] Step 2: Create a new project

[1785] Input: A user clicks the "Create a new project" button on the dashboard and fills out a form with the project's name, purpose, detailed requirements, etc.

[1786] Operation: The device sends the entered project information to the server.

[1787] Data processing: The server saves the entered project information in a database.

[1788] Output: A project creation confirmation message is displayed in the terminal.

[1789] Step 3: Specify the required skill sets and experience

[1790] Input: Users fill out a form on the project page to specify the required skill set and experience.

[1791] Operation: The device sends the specified conditions to the server.

[1792] Data processing: The server searches the database for AI agents that match the conditions.

[1793] Output: The server generates a list of applicable AI agents and sends it to the device.

[1794] Step 4: Selecting an AI Agent

[1795] Input: The user selects a specific AI agent from a list.

[1796] Operation: The terminal sends the selected agent information to the server.

[1797] Data processing: The server associates the selected AI agent information with the project.

[1798] Output: The agent information associated with the project is saved in the database.

[1799] Step 5: Create a new task

[1800] Input: A user clicks the "Create new task" button on a project page and enters task details.

[1801] Operation: The device sends the entered task information to the server.

[1802] Data processing: The server distributes tasks to the appropriate AI agents.

[1803] Output: The task is sent to the AI ​​agent.

[1804] Step 6: Communication between AI agents

[1805] How it works: The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1806] Data processing: AI agents share information with each other and communicate to refine their answers.

[1807] Output: The refined answer is finally sent to the server.

[1808] Step 7: Delivering results

[1809] Input: The server receives the final answer from the AI ​​agent.

[1810] Action: The server sends the final answer to the device.

[1811] Output: The terminal displays the final result to the user.

[1812] Action: The user interacts with the interface to confirm the results and give further instructions.

[1813] The above is a detailed flow of each processing step.

[1814] (Application example 1)

[1815] 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."

[1816] Conventional project management systems require users to manually assign tasks and monitor progress, limiting their ability to improve productivity and efficiently manage projects. Furthermore, manually optimizing production line operations, scheduling robot operations, and planning equipment maintenance is extremely labor-intensive and prone to errors. Furthermore, there was a lack of a system that allowed AI agents to efficiently share information with each other and provide optimal solutions.

[1817] 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.

[1818] In this invention, the server includes a means for allowing a user to optimize the operation of a factory's production line, a means for managing the operation schedule of factory robots, and a means for formulating equipment maintenance plans. This makes it possible to automatically optimize the operation status of a factory's production line, efficiently manage the operation schedule of robots, and easily formulate equipment maintenance plans. In project management, the AI ​​agent can also automatically assign tasks and provide optimal answers, thereby improving project efficiency and facilitating management.

[1819] A "user" is a person who manages a project and gives instructions on tasks, or a user of the system.

[1820] A "means for setting project objectives and requirements" is a mechanism that allows users to specify project details, goals, and required skills and experience.

[1821] An "AI agent" is an artificial intelligence program designed to efficiently perform a specific task.

[1822] "Means for transferring task instructions to an AI agent" refers to a mechanism for delivering task instructions from the user to an AI agent.

[1823] "Means for sharing information and refining answers" refers to the methods by which AI agents exchange information with each other to arrive at optimal solutions.

[1824] "Means for providing refined answers to users" refers to a mechanism that presents the optimal answers generated by the AI ​​agent to the user.

[1825] "Means for optimizing factory production line operations" are automated process management tools that maximize factory production efficiency.

[1826] "Means for managing the operation schedules of factory robots" refers to a system that automatically adjusts and manages the working hours and work content of robots used in factories.

[1827] A "means for creating equipment maintenance plans" is a tool for creating schedules for efficiently inspecting and repairing equipment within a factory.

[1828] The "interface means for checking task results and giving instructions for the next step" is a user interface that allows the user to check the results provided by the AI ​​agent and give instructions for the next action.

[1829] "Means for monitoring the operating status of factory robots" refers to a system that monitors the operation and status of robots in real time while they are actually working.

[1830] The present invention provides a system that allows users to efficiently manage projects and optimize factory production lines. A specific embodiment for realizing this system will be described below.

[1831] System Overview

[1832] The system helps users set up a project, specify the required skills and background, and select an AI agent from a list. The selected AI agents then share information with each other to complete the task and provide a final answer to the user. The system also includes functions for optimizing factory production lines, managing robot operation schedules, and creating equipment maintenance plans.

[1833] Hardware and Software Configuration

[1834] Hardware: smartphones, servers, factory robots

[1835] Software: iOS / Android apps, server-side database management systems (e.g., MongoDB or MySQL), AI agent management systems (e.g., using TensorFlow or PyTorch)

[1836] Data processing and calculation

[1837] User Project Settings

[1838] Users log in to the system using their smartphones, access the dashboard, enter project details (project name, objectives, detailed requirements, etc.), and send them to the server, which then stores the received information in a database.

[1839] AI Agent Selection

[1840] The user specifies the required skill set and experience and sends the criteria to the server. The server searches the database for AI agents that match the criteria and lists them for the user. The user selects an appropriate AI agent from the list and sends it to the server. The server associates the selected AI agent with the project.

[1841] Task assignment and execution

[1842] A user inputs the details of a new task and submits it to the server, which distributes the task instructions to AI agents. Each AI agent receives the task, shares information with each other, communicates as needed, and creates the optimal answer.

[1843] Providing results

[1844] Each AI agent sends its final answer to the server, which then provides it to the user, who can check the results on their smartphone and give further instructions.

[1845] Specific examples

[1846] For example, if a user is starting a new product line optimization project, they might enter the prompt statement as follows:

[1847] "Set up launch dates and production targets for the new product line and create a schedule for the robots we need. Also, create a maintenance plan for the equipment."

[1848] Based on this, the AI ​​agent will perform the following tasks:

[1849] 1. Proposing a working schedule based on production targets.

[1850] 2. Creating and proposing optimal operating schedules for each terminal robot.

[1851] 3. Planning and proposing regular maintenance plans for equipment.

[1852] This system automatically optimizes the operation status of factory production lines, enabling efficient management. It also reduces the effort required for project management, allowing users to quickly take the next action.

[1853] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1854] Step 1:

[1855] User Project Settings

[1856] Input: The user sets the project name, purpose, detailed requirements, etc. via their smartphone.

[1857] What happens: The device sends this information to the server.

[1858] Specific data processing: The server stores the received project information in a database.

[1859] Output: The project is saved to the database and the project setup is complete.

[1860] Step 2:

[1861] AI Agent Selection

[1862] Input: The user specifies the required skill set and experience and enters the requirements into the terminal.

[1863] Operation: The device sends the specified conditions to the server.

[1864] Specific data processing: The server searches the database for AI agents that match the conditions and lists them.

[1865] Output: The AI ​​agents are displayed on the device as a list that the user can view.

[1866] Step 3:

[1867] AI Agent Selection

[1868] Input: The user selects an appropriate AI agent from a list.

[1869] Action: The device sends the selected AI agent information to the server.

[1870] Specific data processing: The server associates the selected AI agent with a project.

[1871] Output: The project will have an associated AI agent.

[1872] Step 4:

[1873] Task Instructions

[1874] Input: The user enters the details of a new task (e.g., "Set launch dates and production targets for a new product line").

[1875] Operation: The device sends task information to the server.

[1876] Specific data processing: The server allocates tasks to the appropriate AI agents.

[1877] Output: Task information is delivered to the AI ​​agent.

[1878] Step 5:

[1879] Information sharing and task execution among AI agents

[1880] Input: Each AI agent receives a task to process.

[1881] How it works: AI agents share information with each other, communicate as needed, and use generative AI models to derive optimal solutions.

[1882] Specific data processing: AI agents use relevant information to analyze data, make predictions, and formulate optimal answers.

[1883] Output: The answers generated by each AI agent are sent to the server.

[1884] Step 6:

[1885] Aggregating and providing results

[1886] Input: The best answer from the AI ​​agent.

[1887] Action: The server aggregates these responses.

[1888] Specific data processing: The server aggregates the received responses and processes them into a format that is easy for the user to understand.

[1889] Output: The final result is the best answer displayed on the user's device.

[1890] This process flow allows users to optimize factory production line operations, efficiently manage robot operation schedules, and easily create equipment maintenance plans. Furthermore, the AI ​​agent efficiently completes tasks and provides optimal answers to users.

[1891] 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.

[1892] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[1893] overview

[1894] It provides a means for users to set project objectives and requirements and specify the necessary skills and background. Based on this information, the server creates a list of AI agents, and the user has a means to select one of these AI agents. The selected AI agents receive task instructions from the user and can refine their answers by sharing information with each other. It also incorporates an emotion engine that recognizes user emotions and adjusts the system's behavior based on that emotional data, making it possible to progress a project while taking into account the user's stress level and satisfaction.

[1895] System configuration

[1896] 1. User project settings

[1897] A user logs into the system and accesses the dashboard.

[1898] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1899] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1900] 2. AI Agent Selection

[1901] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1902] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1903] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1904] The terminal transmits the selected agent information to the server, which associates it with the project.

[1905] 3. Operation of the Emotion Engine

[1906] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[1907] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[1908] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[1909] 4. Project progress instructions

[1910] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1911] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1912] 5. Communication between AI agents

[1913] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1914] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1915] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[1916] 6. Providing Results

[1917] The AI ​​agent sends the final answer to the server, which then serves it to the user.

[1918] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1919] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[1920] Specific examples

[1921] Example 1: Using an emotion engine in a health management app development project

[1922] 1. Project Settings

[1923] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1924] 2. AI Agent Selection

[1925] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1926] The server lists AI agents that meet the criteria, and the user selects from them.

[1927] 3. Operation of the Emotion Engine

[1928] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[1929] The server optimizes the settings based on the emotional data.

[1930] 4. Task Instructions

[1931] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1932] 5. Cooperation between agents

[1933] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1934] If necessary, adjust communication methods based on the user's emotional data.

[1935] 6. Providing results

[1936] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[1937] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[1938] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[1939] The processing flow will be explained below.

[1940] The following describes an embodiment of the present invention. The present invention combines a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents, with an emotion engine that recognizes the user's emotions.

[1941] Processing flow

[1942] Step 1:

[1943] A user logs into the system and accesses the dashboard.

[1944] Step 2:

[1945] The user clicks the "Create New Project" button, which displays a form for entering the project's name, purpose, detailed requirements, etc.

[1946] Step 3:

[1947] The terminal transmits the input project information to the server, and the server stores the project information in a database.

[1948] Step 4:

[1949] The user navigates to a project page and sees a form to specify the required skill set and experience.

[1950] Step 5:

[1951] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[1952] Step 6:

[1953] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[1954] Step 7:

[1955] The terminal transmits the selected agent information to the server, which associates it with the project.

[1956] Step 8:

[1957] While the user is using the system, sensors such as cameras and microphones installed on the device analyze the user's facial expressions and tone of voice to collect emotional data.

[1958] Step 9:

[1959] The device transmits emotion data to the server in real time, and the server evaluates the user's emotion.

[1960] Step 10:

[1961] The server adjusts project settings and instructions to AI agents based on the evaluated emotional data.

[1962] Step 11:

[1963] A user clicks the "Create New Task" button on a project page, which displays a form for entering task details.

[1964] Step 12:

[1965] The terminal sends the input task information to the server, and the server distributes the task to the corresponding AI agent.

[1966] Step 13:

[1967] The server distributes tasks to each AI agent, and the AI ​​agent receives the tasks.

[1968] Step 14:

[1969] AI agents share information with other agents as needed and proceed with tasks with a common understanding.

[1970] Step 15:

[1971] The AI ​​agent adjusts communication methods and response expressions as needed based on the user's emotional data.

[1972] Step 16:

[1973] The AI ​​agent compiles the results of the tasks and sends the optimized answer to the server.

[1974] Step 17:

[1975] The server verifies the results sent by the AI ​​agent and provides the results to the user.

[1976] Step 18:

[1977] The terminal displays the final result, and the user confirms it and gives the next instruction.

[1978] Step 19:

[1979] The emotion engine monitors the user's reaction to the task results and reflects this in the next step.

[1980] Step 20:

[1981] If necessary, the user creates a new task and returns to the process of issuing instructions again.

[1982] Specific examples

[1983] Example 1: Using an emotion engine in a health management app development project

[1984] 1. Project Settings

[1985] Create a project called "Develop a health management app" and enter the purpose as "Tracking user health data and providing advice."

[1986] 2. AI Agent Selection

[1987] The user specifies "engineer with experience in app development," "UI / UX designer," "data scientist," and "marketing specialist."

[1988] The server lists AI agents that meet the criteria, and the user selects from them.

[1989] 3. Operation of the Emotion Engine

[1990] When setting up a project, the emotion engine analyzes the user's facial expressions and tone of voice to assess their stress level and satisfaction in real time.

[1991] The server optimizes the settings based on the emotional data.

[1992] 4. Task Instructions

[1993] The user is instructed to complete an initial task, such as "create a list of the app's main features and propose a design."

[1994] 5. Cooperation between agents

[1995] AI agents receive tasks and share information with each other to compile a feature list and design proposals.

[1996] If necessary, adjust communication methods based on the user's emotional data.

[1997] 6. Providing results

[1998] The server provides the user with an optimized answer based on the results received from the AI ​​agent.

[1999] The user reviews the results, and the emotion engine monitors their reactions and uses them to guide the next steps.

[2000] As described above, the system of the present invention supports more individualized and flexible project progress by taking into account the user's emotions in addition to the functions of project management and AI agents.

[2001] Example 2

[2002] 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."

[2003] Conventional project management systems can cause users to feel emotionally stressed and their satisfaction levels to decline. Especially in large-scale projects, interpersonal stress can negatively impact project progress. Furthermore, the process for users to select AI agents with appropriate skill sets is complicated, leading to issues such as poor communication between teams. To solve these problems, a system that integrates the management of AI agents with the monitoring of users' emotional states is needed.

[2004] 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.

[2005] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and experience, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for the user to instruct the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for collecting and evaluating the user's emotions in real time, and means for adjusting the system's operation based on the emotion data, thereby enabling the project to proceed efficiently while taking the user's emotional state into consideration.

[2006] A "project" is a set of planned activities designed to achieve a specific purpose or goal.

[2007] "Requirements" are the conditions or standards necessary to carry out a project or task.

[2008] "Skills" are the specialized abilities and knowledge required to perform a specific job or task.

[2009] "Experience" refers to the history of past jobs and projects you have undertaken, as well as the skills and qualifications you have acquired.

[2010] An "AI agent" is software that uses artificial intelligence technology to automate specific tasks and work in collaboration with other agents and users to complete tasks.

[2011] A "task" is a specific task or activity that is performed to achieve the project's objectives.

[2012] "Emotion" in "Tarza" refers to the user's psychological state, specifically emotional responses such as stress, satisfaction, and anxiety.

[2013] An "emotion engine" is a system that analyzes and evaluates a user's emotional state in real time based on input such as facial expressions and voice.

[2014] A "system" is a collective term for a set of hardware and software components designed to work together to perform a specific function.

[2015] A "user interface" is the input and output means by which a user interacts with a system.

[2016] A "database" is a system for efficiently managing and storing large amounts of data.

[2017] "Real-time" refers to information and data being processed and analyzed as soon as it is generated.

[2018] "Communication" is the process of exchanging information and opinions, and here it specifically refers to sharing information between AI agents and with users.

[2019] "Analysis" is the process of collecting data and interpreting it statistically or logically.

[2020] This invention is a system that allows project leaders and entrepreneurs to efficiently manage projects and accomplish tasks using appropriate AI agents. The system incorporates an emotion engine that recognizes user emotions in real time and reflects them in the user interface and project progress.

[2021] System configuration

[2022] The system consists of the following main components:

[2023] 1. User project settings

[2024] 2. AI Agent Selection

[2025] 3. Operation of the Emotion Engine

[2026] 4. Project progress instructions

[2027] 5. Communication between AI agents

[2028] 6. Providing Results

[2029] Hardware and software used

[2030] Device: A device directly operated by a user, equipped with a camera and microphone to collect emotional data, typically running a commercial operating system (e.g., Windows, macOS, Linux).

[2031] Server: A computer system with powerful computing resources that manages project data, lists AI agents, evaluates emotional data, etc. Servers are often operated on commercial cloud services (e.g., AWS, Google Cloud, Microsoft Azure).

[2032] Emotion Engine: Uses machine learning models (e.g., TensorFlow, PyTorch) to recognize emotions in real time from the user's facial expressions and tone of voice.

[2033] Project Setup and Data Management

[2034] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button, which displays a form where the user can enter the project name, purpose, and detailed requirements. The terminal sends the entered project information to the server, which then stores the project information in a database.

[2035] AI Agent Selection

[2036] The user navigates to a project page and is presented with a form to specify the required skill set and experience. The device sends the specified criteria to the server, which searches its database for and lists AI agents that match the criteria. The list of applicable AI agents is sent to the device, and the user can view and select these agents. The device sends the selected agent information to the server, which associates them with the project.

[2037] Emotion Engine Operation

[2038] While the user is using the system, the device's built-in camera and microphone analyze facial expressions and tone of voice to collect emotional data. The device then transmits the emotional data to a server in real time, which then uses an AI model to evaluate the emotion. The server then automatically fine-tunes project settings and optimizes instructions to the AI ​​agent based on the evaluated emotional data.

[2039] Project progress instructions

[2040] The user clicks the "Create a new task" button on the project page and fills in the task details. The device sends the entered task information to the server, which then assigns the task to the appropriate AI agent.

[2041] Communication between AI agents

[2042] The server assigns tasks to each AI agent, and the AI ​​agent receives the tasks. The AI ​​agent shares information with other agents as needed and progresses with the task with a common understanding. The AI ​​agent adjusts its communication method and response expression based on the user's emotional data.

[2043] Providing results

[2044] The AI ​​agent sends the final answer to the server, which provides it to the user. The terminal displays the final result, and the user confirms it and gives instructions. The emotion engine monitors the user's reaction and reflects it in the next step.

[2045] Example: Prompt sentence for generative AI model

[2046] The following example shows how an emotion engine is used in a health management app development project.

[2047] Example: Health management app development project

[2048] Prompt statement

[2049] "I'd like to create a project to develop a health management app. The goal is to track users' health data and provide advice. I'd like to assign an engineer with app development experience, a UI / UX designer, a data scientist, and a marketing specialist. I'd like the system to use an emotion engine to assess my stress level and satisfaction level and optimize the project's progress."

[2050] The above is a specific example of a system for implementing the present invention. This system enables efficient project management by linking project management with user emotion management.

[2051] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2052] Step 1: Log in and create a project

[2053] Input: User authentication information, project setting information

[2054] Specific behavior:

[2055] The user enters their authentication information (such as user ID and password) and logs in to the system.

[2056] The device sends the authentication information to the server, which checks the authentication. If the authentication is successful, the dashboard is displayed.

[2057] The user clicks the "Create New Project" button on the dashboard and enters the project name, purpose, detailed requirements, etc.

[2058] Output: Send and save project setting information

[2059] Step 2: Save the project information

[2060] Input: Project setting information

[2061] Specific behavior:

[2062] The terminal transmits the input project information to the server, which stores it in a database.

[2063] The server generates a project ID and records it in a database along with the entered project information.

[2064] Output: Saving project information to a database

[2065] Step 3: Selecting an AI agent

[2066] Input: User-specified skill sets and experience

[2067] Specific behavior:

[2068] A user visits a project page and fills out a form specifying the required skill set and experience.

[2069] The terminal sends the specified conditions to the server, and the server searches and lists AI agents that match the conditions from its database.

[2070] Output: List of AI agents

[2071] Step 4: Selecting and Associating an AI Agent

[2072] Input: A list of AI agents

[2073] Specific behavior:

[2074] The server sends a list of relevant AI agents to the device, and the user can view and select these agents.

[2075] The terminal transmits the selected agent information to the server, and the server associates the selected agent with the project.

[2076] Output: Associating an AI agent with a project

[2077] Step 5: Collect and evaluate emotion data

[2078] Input: User facial expressions and tone of voice

[2079] Specific behavior:

[2080] While the user is using the system, the device's built-in camera and microphone analyze the user's facial expressions and tone of voice in real time.

[2081] The device sends emotional data to a server, which then uses an AI model to evaluate the emotion.

[2082] Output: Generate emotion evaluation results

[2083] Step 6: Optimize project progress

[2084] Input: Emotion evaluation results, project setting information

[2085] Specific behavior:

[2086] Based on the evaluated emotional data, the server automatically fine-tunes project settings and optimizes instructions to AI agents.

[2087] The server updates the project task information and sends the information to the terminal.

[2088] Output: Sending updated project configuration information

[2089] Step 7: Creating and Distributing Tasks

[2090] Input: User task instructions, project information

[2091] Specific behavior:

[2092] A user clicks the "Create new task" button on a project page and fills in the task details.

[2093] The terminal transmits the input task information to the server.

[2094] The server stores task information in a database and distributes tasks to appropriate AI agents.

[2095] Output: Tasks are stored in a database and distributed to AI agents.

[2096] Step 8: Information sharing between AI agents

[2097] Input: Task information for each AI agent

[2098] Specific behavior:

[2099] The server distributes tasks to each AI agent and shares relevant information.

[2100] AI agents communicate with each other and progress through tasks with a common understanding.

[2101] Output: Information sharing between AI agents

[2102] Step 9: Providing task results

[2103] Input: Task results sent by the AI ​​agent

[2104] Specific behavior:

[2105] The AI ​​agent sends the final answer to the server.

[2106] The server transmits the received results to the terminal and provides them to the user.

[2107] Output: Providing the final task results to the user

[2108] Step 10: User feedback and next steps

[2109] Input: User feedback

[2110] Specific behavior:

[2111] The terminal displays the final result, which the user confirms.

[2112] The emotion engine monitors user reactions and reflects them in the next project steps.

[2113] Output: Project information reflected in the next step

[2114] Through the above steps, project management and user emotion management are linked, enabling projects to proceed efficiently and flexibly.

[2115] (Application example 2)

[2116] 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."

[2117] Conventional project management systems are unable to consider the emotions and stress levels of managers when progressing tasks, limiting their usability and productivity. Furthermore, in factory management, robot operations are not adjusted based on real-time emotional data from managers, making it difficult to provide an optimal work environment. This creates a need for efficient project progress management and flexible responses.

[2118] 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.

[2119] In this invention, the server includes means for a user to set the project's objectives and requirements, means for specifying required skills and background, means for listing AI agents based on the specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their responses, means for providing the refined responses to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotion data. This enables the project to proceed while taking the manager's emotions into consideration, thereby providing an efficient and flexible work environment.

[2120] A "user" is a person or organization that operates the system and sets project objectives and requirements, selects AI agents, etc.

[2121] A "project" is a set of tasks or activities planned and set out to achieve a specific purpose.

[2122] "Skills" refer to the abilities and knowledge required to perform a specific task or role.

[2123] "History" refers to the past experience and achievements of a user or AI agent.

[2124] An "AI agent" is an autonomous program that uses artificial intelligence to perform specific tasks and refines its answers by sharing information with other agents.

[2125] A "task" refers to an individual activity or work that has a specific role and purpose within a project.

[2126] The "system" is a platform that supports users in project management and integrates a series of functions and tools, including AI agents and emotion engines.

[2127] "Emotions" refer to psychological states and changes that can be recognized from the user's facial expressions, tone of voice, etc.

[2128] A "robot" is an automated machine or device designed to perform designated tasks in a factory.

[2129] An "emotion engine" is a combination of software or hardware that recognizes a user's emotions in real time and adjusts the system's behavior accordingly.

[2130] A "manager" is a person or position that oversees the progress of a factory or project and operates the system to manage the progress of tasks.

[2131] A specific system configuration and its operation procedure will be described below for the embodiment of the present invention.

[2132] The server includes means for a user to set the objectives and requirements of the project, means for specifying the necessary skills and background, means for listing AI agents based on specified conditions, means for a user to select an AI agent from the list, means for instructing the selected AI agent on a task, means for the AI ​​agents to share information with each other and refine their answers, means for providing the refined answers to the user, means for a factory manager to recognize the emotions of the manager as the project progresses, and means for adjusting the robot's behavior and task progress based on the manager's emotional data.

[2133] A user logs into the system and sets up a project by entering the project's objectives and requirements into a dashboard. The user then specifies the appropriate skills and background, and the server generates a list of AI agents based on this information. The user then selects an appropriate AI agent from the list and sends it to the server.

[2134] In this system, factory managers are equipped with cameras and microphones on their devices to recognize emotions from facial expressions and tone of voice while working on a project. Specifically, OpenCV is used to capture the manager's facial expressions in real time and analyze them with a pre-trained emotion recognition model. This emotional data is sent to a server, and the AI ​​agent adjusts its behavior based on that data while performing the task.

[2135] For example, if a manager is feeling stressed, the server will use this emotional data to monitor the progress of the task and issue new instructions to the AI ​​agent if necessary. Also, if the manager is feeling happy or satisfied, the server will reflect this emotional data to ensure the project progresses smoothly.

[2136] As a concrete example, the server can perform the following processing based on an example prompt: "Recognize faces using a camera, recognize emotions in real time using an emotion model, and send that data to the server to generate Python code that will optimize project progress. The server URL is http: / / factory-management-system.example.com." By inputting this prompt into a generative AI model, task management that takes user emotions into account can be achieved.

[2137] This allows managers to understand their emotions in real time and flexibly progress projects accordingly. It also allows for efficient adjustment of robot operations, optimizing the work environment.

[2138] With the above configuration, the system of the present invention realizes project management that improves usability and productivity.

[2139] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2140] Step 1:

[2141] A user logs in to the system and accesses the dashboard. The user clicks the "Create a new project" button and enters the project name, purpose, detailed requirements, etc. This input data is sent from the terminal to the server, which then stores the project information in a database.

[2142] Input: Project name, purpose, detailed requirements

[2143] Processing: The terminal sends the input data to the server, and the server saves it in a database

[2144] Output: Project information stored in a database

[2145] Step 2:

[2146] The user fills out a form to specify appropriate skills and experience. The specified criteria are sent from the device to the server, which searches and lists AI agents that match the criteria from its database. The user selects an AI agent from the list, and the selection information is sent from the device to the server, which associates it with the project.

[2147] Input: Required skills, experience

[2148] Processing: The device sends the specified conditions to the server, and the server lists the corresponding AI agents from the database.

[2149] Output: Information about the AI ​​agent selected by the user.

[2150] Step 3:

[2151] While the user is working on a project, the device's built-in camera and microphone are used to capture the manager's facial expressions and tone of voice in real time. OpenCV is used to recognize facial expressions and generate emotional data. The generated emotional data is then sent from the device to the server.

[2152] Input: facial expression data, voice data

[2153] Processing: Data capture with camera and microphone, facial expression recognition and emotion data generation with OpenCV

[2154] Output: Emotion data sent to the server

[2155] Step 4:

[2156] The server analyzes the received emotional data and adjusts project progress and task priorities based on that data, such as issuing new instructions to AI agents or rescheduling tasks.

[2157] Input: Emotion data

[2158] Processing: Analyzing emotion data on the server and adjusting project progress

[2159] Output: Coordinated task instructions and schedules

[2160] Step 5:

[2161] When the AI ​​agents receive new instructions, they execute the task and share information with each other to refine their answers. They also take into account the user's emotional data and respond flexibly according to their emotions. The completed answer is then sent back to the server.

[2162] Input: adjusted task instructions, emotion data

[2163] Processing: Sharing information and executing tasks between AI agents

[2164] Output: Refined answer (sent to server)

[2165] Step 6:

[2166] The server then provides the answers received from the AI ​​agent to the user, who can review the final results on their device and issue new instructions if necessary. User reactions and emotional data are also collected again and used to inform the next steps of the project.

[2167] Input: Final response from the AI ​​agent, new instructions from the user

[2168] Processing: The server provides the final answer and recollects the user's instructions and emotion data.

[2169] Output: The final result provided to the user, along with new emotion data and instructions.

[2170] Through these steps, the system can achieve efficient project management while taking into account the user's feelings.

[2171] 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.

[2172] 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.

[2173] 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.

[2174] 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.

[2175] 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.

[2176] 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.

[2177] 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).

[2178] 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.

[2179] 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."

[2180] 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.

[2181] 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).

[2182] 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.

[2183] 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.

[2184] 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.

[2185] 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.

[2186] 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.

[2187] 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.

[2188] 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.

[2189] 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.

[2190] 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.

[2191] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[2192] The following is further disclosed regarding the above embodiment.

[2193] (Claim 1)

[2194] A means for users to define project goals and requirements;

[2195] a means to specify the skills and experience required;

[2196] A means of listing AI agents based on specified criteria;

[2197] means for a user to select an AI agent from the list;

[2198] means for directing a task to the selected AI agent;

[2199] A means for AI agents to share information with each other and refine their answers;

[2200] A system including a means for providing a refined answer to a user.

[2201] (Claim 2)

[2202] A means of storing project objectives and requirements in a database;

[2203] a means for associating the selected AI agent with a project;

[2204] 10. The system of claim 1, further comprising means for forwarding task instructions to the AI ​​agent.

[2205] (Claim 3)

[2206] 2. The system according to claim 1, further comprising an interface means for a user to check task results and give further instructions.

[2207] "Example 1"

[2208] (Claim 1)

[2209] A means for users to define project goals and requirements;

[2210] a means to specify the skills and experience required;

[2211] A means to save the set project information and conditions in a database,

[2212] A means of automatically listing AI agents based on specified criteria;

[2213] means for a user to select an AI agent from the list;

[2214] means for associating selected AI agent information with a project;

[2215] a means for a user to create and enter new tasks;

[2216] A means of automatically allocating tasks to AI agents,

[2217] A means for AI agents to share information with each other and refine their answers;

[2218] a means for transmitting the final refined answer to a server for presentation to the user;

[2219] The system includes an interface means for the user to confirm the results and give further instructions.

[2220] (Claim 2)

[2221] A means of storing project objectives and requirements in a database;

[2222] a means for associating the selected AI agent with a project;

[2223] 10. The system of claim 1, further comprising means for forwarding task instructions to the AI ​​agent.

[2224] (Claim 3)

[2225] 2. The system according to claim 1, further comprising an interface means for a user to check task results and give further instructions.

[2226] "Application Example 1"

[2227] (Claim 1)

[2228] A means for users to define project goals and requirements;

[2229] a means to specify the skills and experience required;

[2230] A means of listing AI agents based on specified criteria;

[2231] means for a user to select an AI agent from the list;

[2232] means for directing a task to the selected AI agent;

[2233] A means for AI agents to share information with each other and refine their answers;

[2234] a means for providing a refined response to the user;

[2235] A means for users to optimize the operation of factory production lines;

[2236] A means for managing the operation schedule of the factory robot;

[2237] A system including a means for creating a maintenance plan for equipment.

[2238] (Claim 2)

[2239] A means of storing project objectives and requirements in a database;

[2240] a means for associating the selected AI agent with a project;

[2241] a means for transmitting task instructions to the AI ​​agent;

[2242] A means of optimizing factory production planning;

[2243] 10. The system of claim 1, further comprising means for developing a maintenance schedule for the equipment.

[2244] (Claim 3)

[2245] an interface means for a user to check the task result and give a next instruction;

[2246] 10. The system according to claim 1, further comprising means for monitoring the operational status of the factory robot.

[2247] "Example 2: Combining Emotion Engines"

[2248] (Claim 1)

[2249] A means for users to define project goals and requirements;

[2250] a means to specify the skills and experience required;

[2251] A means of listing AI agents based on specified criteria;

[2252] means for a user to select an AI agent from the list;

[2253] means for directing a task to the selected AI agent;

[2254] A means for AI agents to share information with each other and refine their answers;

[2255] a means for providing a refined response to the user;

[2256] A means for collecting and evaluating user emotions in real time;

[2257] A system including means for adjusting system behavior based on emotion data.

[2258] (Claim 2)

[2259] A means of storing project objectives and requirements in a database;

[2260] a means for associating the selected AI agent with a project;

[2261] a means for transmitting task instructions to the AI ​​agent;

[2262] 10. The system of claim 1, further comprising means for transmitting and evaluating emotion data in real time.

[2263] (Claim 3)

[2264] an interface means for a user to check the task result and give a next instruction;

[2265] 10. The system of claim 1, further comprising means for optimizing system operation based on emotion data.

[2266] "Application example 2 when combining emotion engines"

[2267] (Claim 1)

[2268] A means for users to define project goals and requirements;

[2269] a means to specify the skills and experience required;

[2270] A means of listing AI agents based on specified criteria;

[2271] means for a user to select an AI agent from the list;

[2272] means for directing a task to the selected AI agent;

[2273] A means for AI agents to share information with each other and refine their answers;

[2274] a means for providing a refined response to the user;

[2275] A way for factory managers to recognize these emotions during the project, and

[2276] A system including a means for adjusting the robot's behavior and task progress based on the manager's emotional data.

[2277] (Claim 2)

[2278] A means of storing project objectives and requirements in a database;

[2279] a means for associating the selected AI agent with a project;

[2280] A means to recognize the emotions of factory managers in real time and send them to a server,

[2281] A means for optimizing project progress based on sentiment data;

[2282] 10. The system of claim 1, further comprising means for forwarding task instructions to the AI ​​agent.

[2283] (Claim 3)

[2284] an interface means for a user to check the task result and give a next instruction;

[2285] 2. The system according to claim 1, further comprising means for the emotion engine to monitor the user's reaction obtained through the interface means and to utilize the data in the next step. [Explanation of symbols]

[2286] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for users to define project goals and requirements; a means to specify the skills and experience required; A means of listing AI agents based on specified criteria; means for a user to select an AI agent from the list; means for directing a task to the selected AI agent; A means for AI agents to share information with each other and refine their answers; A system including a means for providing a refined answer to a user.

2. A means of storing project objectives and requirements in a database; a means for associating the selected AI agent with a project; 10. The system of claim 1, further comprising means for transferring task instructions to the AI ​​agent.

3. 2. The system according to claim 1, further comprising an interface means for a user to confirm a task result and give a next instruction.

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