system

Abstract

Provide a system. [Solution] A means to centralize project information using generative AI models, and A way to enter project tasks and deadlines and send reminders, A way to input details of a problem or risk and have a generative AI model provide a solution; A system that includes a means to manage messages between parties and provide appropriate responses using generative AI models.

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] Project management requires many stakeholders to share information and respond in a timely manner. However, there are challenges in centralizing information management, coordinating schedules, resolving problems and risks, and smooth communication between stakeholders, and if these elements are lacking, the efficiency and success rate of the project may decrease. Therefore, there is a need for methods to effectively resolve these challenges and improve the success rate of projects. [Means for solving the problem]

[0005] This invention is a project management system that uses a generative AI model and includes the following means: a means for centrally managing project information, a means for inputting project tasks and deadlines and sending reminder notifications, a means for inputting details of problems and risks and providing solutions using a generative AI model, and a means for managing messages between stakeholders and providing appropriate answers using a generative AI model. This system can effectively solve issues in project management such as information sharing, schedule management, problem solving and risk management, and communication support.

[0006] "Generative AI model" refers to artificial intelligence technology that manages information in project management and generates solutions and advice for tasks and risks.

[0007] "Project Information" refers to detailed data required to execute a project, such as project status, progress, task assignments, and resource reservations.

[0008] "Centralized management" refers to a method of centrally managing information so that relevant parties can quickly and easily access the information they need.

[0009] A "reminder" is a notification that notifies a user of a specific task or event at a set date and time.

[0010] "Issues and risks" refer to obstacles and uncertainties that may arise during the project.

[0011] "Solution" refers to providing appropriate solutions or countermeasures for problems or risks that have arisen.

[0012] "Stakeholder messages" refer to the exchange of information between multiple people involved in a project to communicate with each other.

[0013] An "appropriate answer" refers to the accurate and useful information provided by a generative AI model in response to a question or request from a stakeholder. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[0036] Managing and sharing project information

[0037] 1. Enter and manage project information

[0038] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[0039] The terminal converts the input information into an appropriate format and transmits it to the server.

[0040] The server stores the received information in a database and manages it centrally.

[0041] Examples:

[0042] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[0043] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[0044] Scheduling and Reminders

[0045] 1. Enter tasks and deadlines and set reminders

[0046] The user inputs task details and deadlines through the terminal interface.

[0047] The terminal transmits the input information to the server.

[0048] The server stores task and deadline information in a database and schedules reminders.

[0049] 2. Sending reminders

[0050] The server prepares a reminder notification when the set date and time approaches.

[0051] The server sends a reminder notification to the terminal.

[0052] The terminal displays the reminder to the user.

[0053] Examples:

[0054] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[0055] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[0056] The user can check the reminder on the device.

[0057] Problem solving and risk management

[0058] 1. Enter and manage issues and risks

[0059] Users use the terminal interface to enter details of problems and risks that arise in the project.

[0060] The terminal transmits the input information to the server.

[0061] The server stores the details of the received issues and risks in a database.

[0062] 2. Solution generation and delivery

[0063] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0064] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0065] The server transmits the generated solution to the terminal.

[0066] The terminal displays the solution to the user.

[0067] Examples:

[0068] The user enters the problem "out of resources" into the terminal.

[0069] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[0070] The server sends the solution to the terminal, and the user confirms the solution.

[0071] Communication Support

[0072] 1. Sending and Managing Messages

[0073] Users use the terminal interface to enter questions and messages about the project.

[0074] The terminal transmits the input message to the server.

[0075] The server stores the received message in a database and requests an answer from the generative AI model.

[0076] 2. Generating and Providing Answers

[0077] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0078] The server sends the generated response to the terminal.

[0079] The terminal displays the answer to the user.

[0080] Examples:

[0081] The user inputs a "question about the progress of the project" into the terminal.

[0082] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[0083] The server sends the answer to the terminal, and the user confirms the answer.

[0084] The above is an embodiment of the present invention. This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving the efficiency and success rate of projects.

[0085] The processing flow will be explained below.

[0086] Managing and sharing project information

[0087] Step 1:

[0088] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[0089] Step 2:

[0090] The terminal converts the information entered by the user into an appropriate format and transmits it to the server.

[0091] Step 3:

[0092] The server stores the received information in a database and manages it centrally. The stored information can be viewed by related parties.

[0093] Scheduling and Reminders

[0094] Step 1:

[0095] The user inputs task details and deadlines through the terminal interface.

[0096] Step 2:

[0097] The terminal transmits the task information input by the user to the server.

[0098] Step 3:

[0099] The server stores the received task and deadline information in a database and schedules reminders.

[0100] Step 4:

[0101] The server prepares a reminder notification when the set date and time approaches.

[0102] Step 5:

[0103] The server sends a reminder notification to the terminal.

[0104] Step 6:

[0105] The terminal displays the received reminder notification to the user.

[0106] Problem solving and risk management

[0107] Step 1:

[0108] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[0109] Step 2:

[0110] The terminal transmits the problem and risk information entered by the user to the server.

[0111] Step 3:

[0112] The server stores the details of the received issues and risks in a database.

[0113] Step 4:

[0114] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0115] Step 5:

[0116] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0117] Step 6:

[0118] The server transmits the generated solution to the terminal.

[0119] Step 7:

[0120] The terminal displays the solution to the user.

[0121] Communication Support

[0122] Step 1:

[0123] Users use the terminal interface to enter questions and messages about the project.

[0124] Step 2:

[0125] The terminal transmits the message entered by the user to the server.

[0126] Step 3:

[0127] The server stores the received message in a database and requests an answer from the generative AI model.

[0128] Step 4:

[0129] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0130] Step 5:

[0131] The server sends the generated response to the terminal.

[0132] Step 6:

[0133] The terminal displays the answer to the user.

[0134] Specific examples

[0135] Examples of adding tasks and reminders

[0136] Step 1:

[0137] The user inputs the task "Create meeting materials" into the interface of the terminal.

[0138] Step 2:

[0139] The terminal sends detailed information about the task (task name, deadline, etc.) to the server.

[0140] Step 3:

[0141] The server stores the received information in a database and sets a reminder to "create meeting materials" for 10:00 AM next Friday.

[0142] Step 4:

[0143] The server prepares a reminder notification when the set date and time (Friday 10:00 AM) approaches.

[0144] Step 5:

[0145] The server sends a reminder notification to the terminal.

[0146] Step 6:

[0147] The terminal displays a reminder for the task "Create meeting materials" to the user.

[0148] Example 1

[0149] 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."

[0150] In project management, a lot of information is scattered, making it difficult to centrally manage information such as status, progress, task assignment, and resource reservations. There are also challenges such as missing task deadlines and finding quick and appropriate solutions when problems or risks arise. Furthermore, there is a need to improve the efficiency of communication between stakeholders.

[0151] 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.

[0152] In this invention, the server includes means for centrally managing project information using a generative AI model, means for transmitting information entered by a user, such as project status, progress, task assignment, and resource reservation, to the server via a terminal and storing the information in a database, means for inputting project tasks and deadlines and sending reminder notifications, means for generating reminder notifications when a set date and time approaches and sending them to the user's terminal, means for transmitting information on problems and risks entered by the user to the generative AI model and providing solutions, and means for managing messages between parties and providing appropriate answers using the generative AI model. This enables centralized management of project information, prevention of missed deadlines, faster problem resolution, and efficient communication between parties.

[0153] A "generative AI model" is a model generated using artificial intelligence technology, and is a system that analyzes and infers based on input data to provide optimal solutions and answers.

[0154] A "server" is a centralized computer that receives, stores, processes, and transmits information from multiple terminals over a network.

[0155] A "user" is a person or group who uses this system to input, manage, and view project information.

[0156] A "terminal" is a device used by a user to input information, and includes a PC, tablet, smartphone, etc.

[0157] "Project status" is information that indicates the current state and progress of a project.

[0158] "Progress" is an indicator of how far tasks and activities within a project have progressed.

[0159] "Task assignment" refers to assigning a specific task to a specific user.

[0160] "Resource reservation" refers to reserving resources (people, equipment, materials, etc.) required for a project in advance for use during a specific period of time.

[0161] A "database" is a system for organizing large amounts of data and making it easy to search, update, and manage.

[0162] "Reminder notification" is a function that notifies the user of important events or tasks at a set date and time.

[0163] "Issues and risks" refer to obstacles or uncertainties that could impede the progress of a project.

[0164] A "solution" refers to a specific means or method for resolving a specific problem or risk.

[0165] "Messages" are a means of communication for users to exchange project-related information and questions.

[0166] "Answer" refers to the appropriate information or explanation provided by the generative AI model in response to a received message.

[0167] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[0168] Managing and sharing project information

[0169] 1. Enter and manage project information

[0170] Through the terminal interface, users input details such as project status, progress, task assignments, and resource reservations. The terminal converts the input information into an appropriate format and sends it to the server, which stores the received information in a database for centralized management.

[0171] For example, a user can add a new task, "Create a design document," by entering the task name, deadline, and assignee into a terminal. This information is converted to JSON format on the terminal and sent to the server. The server stores this information in a database, making it accessible to other parties.

[0172] Scheduling and Reminders

[0173] 2. Enter tasks and deadlines and set reminders

[0174] The user enters task details and deadlines through the device interface, and the device sends the information to the server, which stores the task and deadline information in a database and schedules reminders.

[0175] 3. Send reminders

[0176] When the set date and time approaches, the server prepares a reminder notification and sends it to the user's device, which then displays the reminder to the user.

[0177] For example, a user can schedule a "weekly meeting" on their device and set the date and time of the meeting. The server stores the information in a database and sends a reminder notification to the device one hour before the meeting. The user can then check the reminder on their device.

[0178] Problem solving and risk management

[0179] 4. Enter and manage issues and risks

[0180] The user enters details of problems and risks that have arisen in the project using the terminal interface. The terminal sends the entered information to the server, which stores the received details of problems and risks in a database.

[0181] 5. Solution generation and delivery

[0182] The server sends information about problems and risks stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device and displays it to the user.

[0183] For example, a user inputs a problem called "lack of resources" into a device. The server sends this problem to a generative AI model and receives a solution to "resource reallocation" from the AI ​​model. The server then sends the solution to the device, where the user confirms it.

[0184] Communication Support

[0185] 6. Sending and Managing Messages

[0186] Users use the device interface to input questions or messages about the project. The device then sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[0187] 7. Generating and Providing Answers

[0188] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The server then sends the generated response to the device, where the user can confirm the response.

[0189] For example, a user enters a question about the progress of a project into a device. The server sends the question to a generative AI model and receives the latest progress report from the AI ​​model. The server then sends the answer to the device, and the user confirms the answer.

[0190] This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving project efficiency and success rates.

[0191] Prompt Sentence Examples

[0192] Creating Design Documentation

[0193] Weekly Meeting

[0194] "Lack of resources"

[0195] "Please tell me about the progress of the project."

[0196] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0197] Managing and sharing project information

[0198] Entering and managing project information

[0199] Step 1:

[0200] The user uses the terminal interface to input project status, progress, task assignments, and resource reservations. The input data includes project information. For example, the user might input "Task name: Create design document," "Deadline: 2023-10-15," and "Responsible person: Tanaka."

[0201] Step 2:

[0202] The terminal receives the input information and converts it into an appropriate format (for example, JSON format). The input data is the project information entered by the user, and the output data is the formatted project information. Specifically, it converts it into {"task_name": "Create design document", "deadline": "2023-10-15", "assignee": "Tanaka"}.

[0203] Step 3:

[0204] The terminal sends the formatted information to the server, where the input data is the project information formatted by the terminal and the output data is the project information sent to the server.

[0205] Step 4:

[0206] The server validates the received information and stores it in a database. The input data is the project information sent to the server, and the output data is the project information stored in the database. A database system such as MySQL (registered trademark) is used to store the data.

[0207] Scheduling and Reminders

[0208] Enter tasks and deadlines and set reminders

[0209] Step 1:

[0210] The user enters task details and deadlines through the device interface. Specifically, the user enters the task name, deadline, reminder settings, etc. For example, the input data might be "Task name: Weekly meeting" and "Date and time: 2023-10-20 10:00."

[0211] Step 2:

[0212] The terminal transmits the input information to the server. The input data is the task information input by the user, and the output data is the task information transmitted to the server.

[0213] Step 3:

[0214] The server stores task and deadline information in a database and sets reminder schedules. The input data is the task information sent to the server, and the output data is the task information stored in the database and the set reminder schedule.

[0215] Sending reminders

[0216] Step 4:

[0217] The server prepares the reminder notification when the set date and time approaches. The input data is the reminder schedule stored in the database, and the output data is the prepared reminder notification.

[0218] Step 5:

[0219] The server sends the reminder notification to the terminal, where the input data is the prepared reminder notification and the output data is the reminder notification sent to the terminal.

[0220] Step 6:

[0221] The terminal displays the reminder to the user. The input data is the reminder notification sent to the terminal, and the output data is the reminder displayed to the user.

[0222] Problem solving and risk management

[0223] Enter and manage issues and risks

[0224] Step 1:

[0225] The user inputs details of problems and risks that have occurred in the project using the terminal interface. The input data includes information about the problems and risks. For example, the user inputs "Problem: Lack of resources."

[0226] Step 2:

[0227] The terminal sends the input information to the server. The input data is the problem or risk information entered by the user, and the output data is the problem or risk information sent to the server.

[0228] Step 3:

[0229] The server stores the received problem and risk information in a database. The input data is the problem and risk information sent to the server, and the output data is the problem and risk information stored in the database.

[0230] Solution generation and delivery

[0231] Step 4:

[0232] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution. The input data is the problem and risk information stored in the database, and the output data is the request sent to the generative AI model.

[0233] Step 5:

[0234] The generative AI model generates solutions based on the received information and sends them to the server. The input data is the problem and risk information sent to the generative AI model, and the output data is the generated solution.

[0235] Step 6:

[0236] The server sends the generated solution to the terminal. The input data is the solution received from the generative AI model, and the output data is the solution sent to the terminal.

[0237] Step 7:

[0238] The terminal displays the solution to the user. The input data is the solution sent to the terminal, and the output data is the solution displayed to the user.

[0239] Communication Support

[0240] Sending and Managing Messages

[0241] Step 1:

[0242] The user inputs a question or message about the project using the terminal interface. The input data includes the message content. For example, the user inputs "Please tell me about the progress of the project."

[0243] Step 2:

[0244] The terminal sends the input message to the server. The input data is the message input by the user, and the output data is the message sent to the server.

[0245] Step 3:

[0246] The server stores the received message in a database and requests a response from the generative AI model. The input data is the message sent to the server, and the output data is the request sent to the generative AI model.

[0247] Generate and provide answers

[0248] Step 4:

[0249] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The input data is the message information sent to the generative AI model, and the output data is the generated response.

[0250] Step 5:

[0251] The server sends the generated answer to the terminal. The input data is the answer received from the generative AI model, and the output data is the answer sent to the terminal.

[0252] Step 6:

[0253] The terminal displays the answer to the user. The input data is the answer sent to the terminal, and the output data is the answer displayed to the user.

[0254] (Application example 1)

[0255] 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."

[0256] Conventional project management systems make information management cumbersome, making it difficult to manage task progress, resources, and problem-solving, especially in large-scale projects. Similarly, in factory production processes, robots often fail to properly manage production and detect anomalies, resulting in problems that reduce production efficiency and safety. The purpose of this invention is to solve these problems and efficiently manage projects and factory robot production.

[0257] 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.

[0258] In this invention, the server includes means for centrally managing information about the project using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for inputting production information and transmitting it to factory robots, means for sending information about abnormalities detected by the robots to the server and providing solutions from the generative AI model, and means for managing robot maintenance and cleaning schedules and sending reminders. This enables efficient centralized management of information and task progress management, as well as rapid management of the production process and problem resolution using robots.

[0259] A "generative AI model" is an artificial intelligence algorithm that automatically generates solutions to problems or appropriate answers based on information entered by the user.

[0260] "Project Information" means all detailed information related to a Project, including but not limited to Project status, progress, task assignments, and resource reservations.

[0261] A "task" refers to a specific task or activity required to move a project forward.

[0262] A "reminder notification" is a notification that notifies the user of a deadline for an event or task when the set date and time approaches.

[0263] "Issues and risks" refer to challenges and uncertainties that may arise in a project or production process.

[0264] "Solution" refers to the specific response to an encountered problem or risk provided by the generative AI model.

[0265] "Messages" refer to information and questions exchanged between project participants.

[0266] "Production information" refers to detailed information related to the production tasks performed by factory robots.

[0267] "Factory robot" refers to an automated machine used to carry out production processes within a factory.

[0268] "Anomaly information" refers to details of errors or abnormalities detected by factory robots during the production process.

[0269] "Resources" refers to the people, equipment, materials, etc. needed to carry out a project or production process.

[0270] This invention is a project management support tool that uses a generative AI model to support information management, task management, problem solving, and communication. It can also be applied to production management of factory robots, realizing an efficient production process.

[0271] System Overview

[0272] The system consists of three main components: the user, the device, and the server. It centralizes information about projects and production tasks managed by the user and provides solutions using generative AI models.

[0273] Hardware and Software

[0274] Hardware:

[0275] Administrator device (tablet, smartphone)

[0276] Factory robot (control terminal)

[0277] Server (central management system)

[0278] software:

[0279] Database management system (MySQL)

[0280] AI model (GPT-4 (registered trademark))

[0281] Interface tool (React Native)

[0282] Data processing and calculation

[0283] Manage and share project information:

[0284] Users input information such as project status, progress, task assignments, and resource reservations from their tablets or smartphones. The information is converted into an appropriate format by the interface tool and sent to the server, which stores the information in a database and makes it accessible to stakeholders.

[0285] Tasks and Reminders:

[0286] Users input tasks and deadlines on their tablets or smartphones, which are then converted by the interface tool and sent to the server, which schedules reminders based on the information entered and sends notifications to the user's device when the specified date and time approaches.

[0287] Problem solving and risk management:

[0288] When a user or robot detects and inputs information about a problem or risk, the information is sent to the server via the interface tool. The server then sends the information to the generative AI model, which then generates a solution. The generated solution is then provided to the user's device or robot via the server.

[0289] Communication support:

[0290] The user enters a question about the project on a tablet or smartphone. The question is sent to the server via the interface tool, and the server sends the question to the generative AI model. The AI ​​model generates an appropriate answer, which is then provided to the user's device via the server.

[0291] Specific examples

[0292] A user adds a "Create design documentation" task by entering the task name, due date, and assignee on a tablet. The interface tool converts the information into the appropriate format and sends it to the server. The server stores it in a database and sets a reminder notification.

[0293] A factory robot detects a "delay in material supply" and sends that information to a server. The server sends a prompt message to the generative AI model informing it of the "delay in material supply" and receives a "suggestion for an alternative supply route" from the AI ​​model. The server sends the suggestion to the robot, which displays it on a display.

[0294] Example prompt sentence:

[0295] There has been a delay in the supply of materials. Please suggest an alternative supply route.

[0296] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0297] Step 1:

[0298] A user inputs project information (e.g., task name, deadline, and assignee) from a tablet or smartphone. The input information is sent to the server through an interface tool (e.g., React Native). This operation allows the user to enter detailed project information into the device, and the interface tool converts the data into an appropriate format and sends it to a database management system (e.g., MySQL).

[0299] Step 2:

[0300] The server stores the received project information in a database management system. This process centralizes project information and makes it accessible to stakeholders. The server creates a new entry in the database and registers the input information, allowing other users and robots to act on this information.

[0301] Step 3:

[0302] The user inputs information to set reminders for tasks and deadlines from their device and sends it to the server. The server receives this information and creates a reminder schedule. The server prepares reminder notifications when the date and time are approaching and sends the notifications to the user's device.

[0303] Step 4:

[0304] A factory robot detects a delay in material supply during the production process and sends the abnormality information to a server. The robot's built-in sensors and control system detect the abnormality and generate detailed error information. The data is then sent to the server via the robot's control terminal.

[0305] Step 5:

[0306] Based on the received anomaly information, the server sends a prompt to the generative AI model (e.g., GPT-4) requesting a solution. The prompt includes details of the anomaly and the required measures. Example: "There has been a delay in the supply of materials. Please propose an alternative supply route."

[0307] Step 6:

[0308] The generative AI model analyzes the prompt and generates an appropriate solution. The generated solution is sent back to the server. The AI ​​model analyzes past data and knowledge bases to propose a rational response.

[0309] Step 7:

[0310] The server sends the solutions received from the generative AI model to the factory robot, which displays the solutions on a display and takes optimal action to avoid unnecessary downtime.

[0311] Step 8:

[0312] A user types a question about a project into a tablet or smartphone and sends it to the server, which then forwards it to the generative AI model and requests an appropriate answer.

[0313] Step 9:

[0314] The generative AI model analyzes the user's question and generates an appropriate answer. The generated answer is sent to the user's device via the server. The device displays the answer to the user, supporting timely information provision.

[0315] Through the above processing steps, project management and factory robot production management are carried out efficiently, and unified information management, problem solving, and communication support are achieved.

[0316] 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.

[0317] This invention is a project management support tool that uses a generative AI model and an emotion engine, and enables project information management, schedule management, problem solving and risk management, communication support, as well as task allocation and advice provision that takes user emotions into consideration. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[0318] Managing and sharing project information

[0319] 1. Enter and manage project information

[0320] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[0321] The terminal converts the input information into an appropriate format and transmits it to the server.

[0322] The server stores the received information in a database and manages it centrally.

[0323] Examples:

[0324] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[0325] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[0326] Scheduling and Reminders

[0327] 1. Enter tasks and deadlines and set reminders

[0328] The user inputs task details and deadlines through the terminal interface.

[0329] The terminal transmits the input information to the server.

[0330] The server stores task and deadline information in a database and schedules reminders.

[0331] 2. Sending reminders

[0332] The server prepares a reminder notification when the set date and time approaches.

[0333] The server sends a reminder notification to the terminal.

[0334] The terminal displays the received reminder notification to the user.

[0335] Examples:

[0336] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[0337] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[0338] The user can check the reminder on the device.

[0339] Problem solving and risk management

[0340] 1. Enter and manage issues and risks

[0341] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[0342] The terminal transmits the input information to the server.

[0343] The server stores the details of the received issues and risks in a database.

[0344] 2. Solution generation and delivery

[0345] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0346] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0347] The server transmits the generated solution to the terminal.

[0348] The terminal displays the solution to the user.

[0349] Examples:

[0350] The user enters the problem "out of resources" into the terminal.

[0351] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[0352] The server sends the solution to the terminal, and the user confirms the solution.

[0353] Communication Support

[0354] 1. Sending and Managing Messages

[0355] Users use the terminal interface to enter questions and messages about the project.

[0356] The terminal transmits the input message to the server.

[0357] The server stores the received message in a database and requests an answer from the generative AI model.

[0358] 2. Generating and Providing Answers

[0359] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0360] The server sends the generated response to the terminal.

[0361] The terminal displays the answer to the user.

[0362] Examples:

[0363] The user inputs a "question about the progress of the project" into the terminal.

[0364] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[0365] The server sends the answer to the terminal, and the user confirms the answer.

[0366] Utilizing the Emotion Engine

[0367] 1. Considering emotions when assigning tasks

[0368] Based on the user's input and actions, the emotion engine recognizes the user's emotions.

[0369] The terminal transmits the output of the emotion engine to the server.

[0370] The server integrates information from the generative AI model and the emotion engine to assign and adjust tasks according to the user's emotional state.

[0371] 2. Providing encouragement and advice based on progress

[0372] The emotion engine continuously assesses the user's emotions as the project progresses.

[0373] The server combines data from the emotion engine with generative AI models to generate appropriate encouragement and advice.

[0374] The terminal displays the generated encouragement or advice to the user.

[0375] 3. Monitoring user stress levels

[0376] The emotion engine monitors stress levels through user input and behavior.

[0377] The server uses this data to rebalance tasks and re-allocate resources as needed.

[0378] Examples:

[0379] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[0380] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[0381] The device displays this suggestion to the user, helping the user to reduce stress.

[0382] In this way, by combining a generative AI model and an emotion engine, the project management support tool of the present invention can realize information management, schedule management, problem solving, communication support, and task management that takes into account the user's emotions, thereby further improving the efficiency and success rate of projects.

[0383] The processing flow will be explained below.

[0384] Utilizing the Emotion Engine

[0385] Considering emotions when assigning tasks

[0386] Step 1:

[0387] The user inputs data related to his / her emotional state (for example, diary entries, questionnaire responses, etc.) through the terminal interface.

[0388] Step 2:

[0389] The terminal transmits the data input by the user to the emotion engine.

[0390] Step 3:

[0391] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0392] Step 4:

[0393] The server integrates the analysis results received from the emotion engine with the generative AI model.

[0394] Step 5:

[0395] The server uses a generative AI model to generate optimal task allocations based on the user's emotional state and sends the results to the device.

[0396] Step 6:

[0397] The terminal displays the task assignments received from the server to the user.

[0398] Providing encouragement and advice based on progress

[0399] Step 1:

[0400] The emotion engine continuously monitors the user's emotional state as the project progresses.

[0401] Step 2:

[0402] Users report project progress and emotional state to the terminal.

[0403] Step 3:

[0404] The terminal sends the reported data to the emotion engine.

[0405] Step 4:

[0406] The emotion engine analyzes the user's emotional state and sends the results to the server.

[0407] Step 5:

[0408] The server uses the output data of the emotion engine and the generative AI model to generate encouragement and advice for the user.

[0409] Step 6:

[0410] The server transmits the generated encouragement and advice to the terminal.

[0411] Step 7:

[0412] The terminal displays encouragement and advice to the user.

[0413] Monitoring user stress levels

[0414] Step 1:

[0415] The emotion engine monitors stress levels through user input and behavior.

[0416] Step 2:

[0417] The user inputs the task progress and emotional state into the terminal.

[0418] Step 3:

[0419] The terminal transmits the input data to the emotion engine.

[0420] Step 4:

[0421] The emotion engine analyzes the user's stress level and sends the results to the server.

[0422] Step 5:

[0423] The server uses the data from the emotion engine to rebalance tasks and reallocate resources as needed.

[0424] Step 6:

[0425] The server sends the proposed task rebalancing and resource allocation to the terminal.

[0426] Step 7:

[0427] The terminal displays the rebalancing proposal received from the server to the user.

[0428] Examples:

[0429] Step 1: The user enters "relaxed" as the emotional state on a day when they are not feeling particularly stressed.

[0430] Step 2: The device sends this information to the emotion engine.

[0431] Step 3: The emotion engine analyzes the emotion based on the user's input data and sends the result to the server.

[0432] Step 4: The server integrates the results from the emotion engine with the generative AI model and selects a suitable task for the user.

[0433] Step 5: The server sends the generated task assignment to the terminal.

[0434] Step 6: The terminal displays the task assignment to the user.

[0435] Step 7: The user confirms the new task and completes it.

[0436] In this way, the project management support tool of the present invention combines a generative AI model and an emotion engine to realize information management, schedule management, problem solving, communication support, as well as task management and advice that take the user's emotions into consideration, thereby further improving the efficiency and success rate of projects.

[0437] Example 2

[0438] 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."

[0439] Project management requires efficient centralized information management, task scheduling, problem solving, risk management, and communication support, but conventional systems lack consideration for the user's emotional state and stress level, which can result in a decline in project efficiency and success rate.

[0440] 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.

[0441] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating a user's emotional state using an emotion engine and assigning and adjusting tasks, and means for providing encouragement and advice using a generative AI model according to the user's emotional state. This enables project management that takes into account the user's emotional state and stress level, thereby improving the efficiency and success rate of projects.

[0442] A "generative AI model" is an artificial intelligence system that uses artificial intelligence technology to automatically generate solutions and suggestions based on input data.

[0443] "Project information" refers to all data necessary for managing and operating a project, such as project status, progress, task assignments, resource reservations, details of issues and risks, and communications.

[0444] "Centralized management" is a method of consolidating all project-related information in one place and managing it consistently.

[0445] "Tasks and deadlines" refer to the specific tasks that need to be completed within a project and the dates and times by which they should be completed.

[0446] "Reminder notification" is a notification function that notifies users of approaching task deadlines or important events.

[0447] "Issues and risks" refer to factors including obstacles and uncertainties that could impede the progress of a project.

[0448] "Providing a solution" means presenting appropriate ways and means to deal with problems or risks that have arisen.

[0449] "Messages between participants" refers to the content of communications and correspondence exchanged between members involved in a project.

[0450] An "emotion engine" is a system that analyzes the emotional state of a user based on their input and behavior and identifies those emotions.

[0451] "Emotional state" refers to a user's psychological state or mood, and includes measures such as stress level and satisfaction.

[0452] "Task allocation and coordination" refers to distributing work within a project to the appropriate people and changing the schedule and content as necessary.

[0453] "Providing encouragement and advice" refers to sending advice and support messages to increase motivation and improve work efficiency according to the user's emotional state.

[0454] This invention is a project management support system that uses a generative AI model and an emotion engine. This system is mainly composed of four main components: a server, a terminal, a user, and an emotion engine.

[0455] Managing and sharing project information

[0456] The system centralizes project information through the following processes:

[0457] 1. The user provides details about the project status, progress, task assignments, resource reservations, etc. through the device interface, which includes input using text boxes and drop-down menus.

[0458] 2. The terminal receives the information entered by the user, converts it into an appropriate format (for example, JSON format), and sends it to the server.

[0459] 3. The server centralizes the information by storing the received data in a database, which can use a common database management system such as PostgreSQL or MySQL.

[0460] Examples:

[0461] To add a new task "Create design document," the user inputs the task name, deadline, and person in charge into the terminal.

[0462] The terminal sends this information to a server which stores it in a database where it can be accessed by other parties.

[0463] Scheduling and Reminders

[0464] The system manages the scheduling of tasks and events through the following process:

[0465] 1. The user sets the task details and due date using the device interface, which may involve a calendar widget or time picker.

[0466] 2. The device sends this information to the server.

[0467] 3. The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the scheduled date and time approaches.

[0468] 4. The device displays the reminder notification to the user.

[0469] Examples:

[0470] The user inputs the "weekly meeting" schedule into the terminal and sets the date and time of the meeting.

[0471] The server stores this information in a database and sends a reminder notification to the device one hour before the meeting.

[0472] The user checks the reminder notification on their device.

[0473] Problem solving and risk management

[0474] The system provides problem solving and risk management through the following processes:

[0475] 1. The user enters details of the problems and risks that have arisen in the project through the terminal interface.

[0476] 2. The device sends this information to the server.

[0477] 3. The server stores the received information in a database.

[0478] 4. The server sends the saved problem and risk information to the generative AI model and requests a solution. The generative AI model uses a conversational language model, such as OpenAI's GPT series.

[0479] 5. The generative AI model generates a solution based on the received information and sends it to the server.

[0480] 6. The server sends the generated solution to the terminal and presents it to the user.

[0481] Examples:

[0482] The user enters the problem "out of resources" into the terminal.

[0483] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[0484] The server sends the solution to the terminal, and the user confirms the solution.

[0485] Communication Support

[0486] The system supports communication between stakeholders through the following processes:

[0487] 1. The user enters a question or message about the project using the terminal interface.

[0488] 2. The terminal sends this message to the server.

[0489] 3. The server stores the received message in a database and requests an answer from the generative AI model.

[0490] 4. The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0491] 5. The server sends the generated answer to the terminal and displays it to the user.

[0492] Examples:

[0493] The user inputs a "question about the progress of the project" into the terminal.

[0494] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[0495] The server sends the answer to the terminal, and the user confirms the answer.

[0496] Use of emotion engine

[0497] The system manages projects taking into account the user's emotional state through the following process.

[0498] 1. The emotion engine evaluates the emotional state based on the user's input and actions.

[0499] 2. The device sends the output of the emotion engine to the server.

[0500] 3. The server integrates information from the emotion engine and the generative AI model, which then assigns and adjusts tasks according to the user's emotional state.

[0501] 4. The server uses the generative AI model to provide encouragement and advice based on the user's emotional state.

[0502] 5. The device displays the generated encouragement or advice to the user.

[0503] Examples:

[0504] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[0505] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[0506] The device displays this suggestion to the user, helping the user to reduce stress.

[0507] Example prompt sentence:

[0508] "Please tell me more about the current project progress."

[0509] "Propose solutions to resource shortages."

[0510] "How can I reassign tasks for stressed users?"

[0511] In this way, the project management support system of the present invention can improve the efficiency and success rate of project management by effectively utilizing the generative AI model and emotion engine.

[0512] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0513] Managing and sharing project information

[0514] Step 1:

[0515] Users enter details such as project status, progress, task assignments, and resource reservations through the device interface, using text boxes and drop-down menus, and the data is then sorted into appropriate fields.

[0516] Input: Project information such as task name, due date, and assignee

[0517] Output: Project information stored in a data structure in the terminal.

[0518] Step 2:

[0519] The device receives the information entered by the user, converts it into an appropriate format (e.g., JSON) and sends it to the server. This conversion process ensures that the data is in a consistent format.

[0520] Input: Various project information entered by the user

[0521] Output: JSON formatted data

[0522] Step 3:

[0523] The server parses the JSON data received from the device, performs the necessary database operations, and stores the information in a common database management system such as PostgreSQL or MySQL.

[0524] Input: Project information in JSON format

[0525] Output: Project information stored in a database

[0526] Scheduling and Reminders

[0527] Step 1:

[0528] Users use the device interface to set task details and deadlines, using a calendar widget and time picker.

[0529] Input: Task name, due date, reminder settings

[0530] Output: Schedule information stored in a data structure in the terminal

[0531] Step 2:

[0532] The terminal then formats and transmits this information to the server.

[0533] Input: User-entered schedule information

[0534] Output: Data sent to the server in JSON format

[0535] Step 3:

[0536] The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the time approaches.

[0537] Input: JSON formatted schedule information sent from the device

[0538] Output: Schedule information and reminder notification schedules stored in the database

[0539] Step 4:

[0540] The terminal receives and displays the reminder notification for display to the user.

[0541] Input: Reminder notification received from the server

[0542] Output: Reminder notification displayed in the user interface

[0543] Problem solving and risk management

[0544] Step 1:

[0545] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[0546] Input: Issue or risk details

[0547] Output: Problem information stored in a data structure in the terminal.

[0548] Step 2:

[0549] The terminal then formats and transmits this information to the server.

[0550] Input: User-entered issue or risk information

[0551] Output: Data sent to the server in JSON format

[0552] Step 3:

[0553] The server stores the received information in a database and then sends it to the generative AI model to request a solution.

[0554] Input: JSON formatted problem information sent from the terminal

[0555] Output: Solution request to the generative AI model

[0556] Step 4:

[0557] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0558] Input: Problem information sent from the server

[0559] Output: The solution generated by the generative AI model

[0560] Step 5:

[0561] The server transmits the generated solution to the terminal and presents it to the user.

[0562] Input: The solution received from the generative AI model

[0563] Output: Solution sent to terminal

[0564] Step 6:

[0565] The terminal displays the solution to the user.

[0566] Input: Solution received from the server

[0567] Output: The solution displayed in the user interface

[0568] Communication Support

[0569] Step 1:

[0570] Users enter questions or messages about the project using the terminal interface.

[0571] Input: The text of your question or message

[0572] Output: Messages stored in a data structure in the terminal

[0573] Step 2:

[0574] The terminal changes the format of this message for transmission to the server and sends it.

[0575] Input: The message entered by the user

[0576] Output: Data sent to the server in JSON format

[0577] Step 3:

[0578] The server stores the received message in a database and requests an answer from the generative AI model.

[0579] Input: JSON formatted message sent from the terminal

[0580] Output: A request to the generative AI model

[0581] Step 4:

[0582] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0583] Input: The message sent from the server

[0584] Output: The answer generated by the generative AI model

[0585] Step 5:

[0586] The server sends the generated answer to the terminal and displays it to the user.

[0587] Input: The answer received from the generative AI model

[0588] Output: Answer sent to terminal

[0589] Step 6:

[0590] The terminal displays the answer to the user.

[0591] Input: The answer received from the server

[0592] Output: The answer displayed in the user interface

[0593] Use of emotion engine

[0594] Step 1:

[0595] The emotion engine assesses the user's emotional state based on their input and actions.

[0596] Input: User input and behavioral data

[0597] Output: Evaluated emotional state data

[0598] Step 2:

[0599] The terminal transmits the output of the emotion engine to the server.

[0600] Input: Emotional state data from the emotion engine

[0601] Output: Emotional state data sent to the server

[0602] Step 3:

[0603] The server integrates information from the emotion engine and the generative AI model to assign and adjust tasks according to the emotional state.

[0604] Input: Emotional state data from the emotion engine and information from the generative AI model

[0605] Output: Adjusted task assignment data

[0606] Step 4:

[0607] The server uses a generative AI model to generate encouragement and advice based on the user's emotional state.

[0608] Input: Emotional state data from the emotion engine

[0609] Output: Generated encouragement and advice

[0610] Step 5:

[0611] The terminal displays the generated encouragement or advice to the user.

[0612] Input: Encouragement or advice received from the server

[0613] Output: Encouragement or advice displayed in the user interface

[0614] Prompt for the example:

[0615] "Please tell me more about the current project progress."

[0616] "Propose solutions to resource shortages."

[0617] "How can I reassign tasks for stressed users?"

[0618] (Application example 2)

[0619] 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."

[0620] While conventional project management systems provide basic functions such as project information management, schedule management, problem solving, and communication support, they do not optimize responses or task allocations that take into account the user's emotions and stress. This increases the burden and stress on users as the project progresses, resulting in issues such as reduced project efficiency and a higher risk of failure. Furthermore, factory automation requires collaborative work between humans and robots, necessitating efficient, human-centered project management that breaks away from mechanistic management.

[0621] 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.

[0622] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating user emotions using an emotion engine and optimizing task allocation based on the emotions, means for combining the generative AI model and the emotion engine to provide encouragement and advice to the user, and means for monitoring the user's stress level and reallocating tasks and readjusting resources. This enables project management that takes user emotions and stress into consideration, improving factory productivity and efficiency and increasing the project success rate.

[0623] A "generative AI model" is a type of artificial intelligence that generates new information and solutions based on data.

[0624] An "emotion engine" is a system for assessing a user's emotional state and providing responses or suggestions based on that.

[0625] "Project information" includes information such as project status, progress, task assignments, and resource reservations.

[0626] "Task assignment" is the process of assigning specific tasks or jobs to each member of a project.

[0627] "Reminder notification" is a mechanism that sends notifications to users before set deadlines or important events, helping them to remember to respond.

[0628] "Risk management" is the process of identifying risks that may arise during the project and taking measures to address them.

[0629] A "solution" is a method or means for providing an appropriate response or treatment to a particular problem or risk.

[0630] "Message management" is a system for organizing and storing messages sent and received in order to facilitate efficient communication between project members.

[0631] "Encouragement" refers to encouraging words or actions that are used to motivate users or reduce stress.

[0632] The "stress level" is an index that indicates the user's mental and physical tension, and if it is high, it may interfere with work.

[0633] "Task reassignment" is the process of reviewing existing tasks and reassigning them to more appropriate members.

[0634] "Resource readjustment" refers to the reallocation of resources such as personnel, equipment, and time based on the progress of the project and the results of the emotion engine.

[0635] This invention is a project management support system that uses a generative AI model and an emotion engine, and aims to optimize the operation of robots in factories. This system consists of four main elements: a user, a terminal, a server, and an emotion engine. Specifically, the invention is implemented in the following form.

[0636] Managing and sharing project information

[0637] A user inputs information such as project status, progress, task assignment, and resource reservation through the terminal interface. The terminal converts the input information into an appropriate format and sends it to the server, which stores the information in a database for centralized management. For example, when a user adds a new task "Realigning the Production Line," the user inputs the task name, deadline, and person in charge into the terminal. The terminal sends this information to the server, which stores it in a database so that other parties can access the information.

[0638] Scheduling and Reminders

[0639] The user enters task details and deadlines through the device. The device sends this information to the server, which saves the task and deadline information in a database and schedules reminders. When the scheduled date and time approaches, the server prepares a reminder notification and sends it to the device. The device then displays the received reminder notification to the user. For example, a user enters an appointment for a "weekly meeting" into the device and sets the date and time of the meeting. The server saves the information in a database and sends a reminder notification to the device one hour before the meeting, allowing the user to check the reminder.

[0640] Problem solving and risk management

[0641] The user enters details of problems and risks that have arisen in the project through the device interface. The device sends this information to the server, which stores the received details of the problems and risks in a database. The server then sends the problem and risk information stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device, which then displays the solution to the user. For example, if a user enters the problem "lack of resources" into the device, the server sends this problem to the generative AI model and receives the solution "reallocate resources" from the AI ​​model. The server then sends the solution to the device, allowing the user to check the solution.

[0642] Communication Support

[0643] The user uses the device interface to input a question or message about the project. The device sends the input message to the server, which stores the received message in a database and requests an answer from the generative AI model. The generative AI model analyzes the message, generates an appropriate answer, and sends it to the server. The server sends the generated answer to the device, which then displays the answer to the user. For example, if a user inputs a "question about the progress of the project" into the device, the server sends the question to the generative AI model, which receives information about the "latest progress report" from the AI ​​model. The server sends the answer to the device, allowing the user to check the answer.

[0644] Utilizing the Emotion Engine

[0645] The emotion engine can recognize the user's emotions based on their input and actions. The device sends the emotion engine's output to a server, which then integrates the generative AI model and the emotion engine's information to assign and adjust tasks according to the user's emotional state. The emotion engine continuously evaluates the user's emotions as the project progresses, and the server combines the emotion engine's data with the generative AI model to generate appropriate encouragement and advice, which the device displays to the user. The emotion engine also monitors the user's stress level through their input and actions, and the server uses this data to rebalance tasks and readjust resources. For example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses the generative AI model to generate a suggestion to "review task priorities and perform less demanding tasks first." The device displays these suggestions to the user, helping them reduce stress.

[0646] Examples of concrete examples and prompts

[0647] For example, an operator can use a factory robot to add a new task, "Realign the Line," and set a deadline. The robot will then display a reminder before the deadline and proactively detect potential issues. If the operator feels stressed, the robot will use its emotion engine to determine their stress level and optimize task allocation.

[0648] Example prompt sentence:

[0649] User task: "Realign line". Deadline: 10 October 2023, 15:00.

[0650] In this way, the project management support system of the present invention dramatically improves the efficiency of project management in factories and increases the success rate of projects.

[0651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0652] Step 1:

[0653] The user inputs project information through the terminal interface.

[0654] Specifically, to add a new task, "Re-adjusting the production line," the user inputs detailed information such as the task name, deadline, and person in charge. The device converts this input information into an appropriate format and sends it to the server. This formats the input data, making it easier for the server to process.

[0655] Input: Task name, due date, assignee

[0656] Output: Formatted task information

[0657] Step 2:

[0658] The server receives the task information sent from the terminal.

[0659] The server stores the received task information in a database for centralized management. When storing the information in the database, the project progress and resource allocation information are also updated simultaneously, allowing other stakeholders to check the latest project status in real time.

[0660] Input: Formatted task information

[0661] Output: Task information stored in the database

[0662] Step 3:

[0663] The user sets task details and deadlines through the terminal.

[0664] Specifically, a user enters a "weekly meeting" into their device and sets the date and time of the meeting. The device then sends this information to the server, which stores the task and deadline information in a database and schedules reminders.

[0665] Input: Task details, due date

[0666] Output: Reminder schedule information

[0667] Step 4:

[0668] The server prepares the reminder notification and sends it to the device when the set date and time approaches.

[0669] The server periodically checks the saved reminder schedule and issues reminders at the appropriate time, so that users are notified just before the deadline.

[0670] Input: Reminder schedule information

[0671] Output: Reminder notification to user device

[0672] Step 5:

[0673] The user enters details of the problems and risks that have arisen in the project from the terminal.

[0674] Specifically, a user inputs the problem "lack of resources" into the terminal, and the terminal sends the problem information to the server, which then stores the information in a database.

[0675] Input: Issue or risk details

[0676] Output: Problem information stored in the database

[0677] Step 6:

[0678] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0679] The generative AI model generates a solution based on the received information and sends it to the server, which adds the solution from the generative AI model to the database.

[0680] Input: Problem information

[0681] Output: A solution from the generative AI model

[0682] Step 7:

[0683] The server sends the generated solution to the terminal, which displays the solution to the user.

[0684] The user can then use the displayed solutions to try to solve the problem.

[0685] As a specific example, the solution "Reallocate resources" may be displayed.

[0686] Input: Solution from a generative AI model

[0687] Output: Solution displayed on user's terminal

[0688] Step 8:

[0689] The user uses the device interface to input questions or messages about the project, and the device sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[0690] Input: Questions and messages from users

[0691] Output: The question information sent to the generative AI model

[0692] Step 9:

[0693] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server, which then sends the generated response to the device, which then displays the response to the user.

[0694] As a concrete example, when a user inputs a "question about the progress of a project," the generative AI model can generate and display an "up-to-date progress report."

[0695] Input: Questions and messages from users

[0696] Output: Answer from the generative AI model

[0697] Step 10:

[0698] The emotion engine recognizes emotions based on the user's input and actions, and sends the state to the server.

[0699] The device monitors the user's stress level based on the output from the emotion engine. Specifically, if the emotion engine determines that the user's stress level is "high," it sends that information to the server, which then integrates the generative AI model and the emotion engine's information to suggest appropriate measures.

[0700] Input: User input and actions

[0701] Output: Emotional state information from the emotion engine

[0702] Step 11:

[0703] The server generates proposals for task reallocation and resource readjustment based on information from the generative AI model and emotion engine, and sends them to the device. The device displays the proposals to the user, helping to reduce the user's stress.

[0704] As a specific example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses a generative AI model to generate a suggestion to "review task priorities and perform less burdensome tasks first."

[0705] Input: Emotional state information from the emotion engine

[0706] Output: Task reassignment and resource realignment suggestions

[0707] 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.

[0708] 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.

[0709] 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.

[0710] [Second embodiment]

[0711] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0712] 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.

[0713] 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).

[0714] 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.

[0715] 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.

[0716] 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).

[0717] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0718] 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.

[0719] 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.

[0720] 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.

[0721] 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.

[0722] 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."

[0723] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[0724] Managing and sharing project information

[0725] 1. Enter and manage project information

[0726] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[0727] The terminal converts the input information into an appropriate format and transmits it to the server.

[0728] The server stores the received information in a database and manages it centrally.

[0729] Examples:

[0730] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[0731] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[0732] Scheduling and Reminders

[0733] 1. Enter tasks and deadlines and set reminders

[0734] The user inputs task details and deadlines through the terminal interface.

[0735] The terminal transmits the input information to the server.

[0736] The server stores task and deadline information in a database and schedules reminders.

[0737] 2. Sending reminders

[0738] The server prepares a reminder notification when the set date and time approaches.

[0739] The server sends a reminder notification to the terminal.

[0740] The terminal displays the reminder to the user.

[0741] Examples:

[0742] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[0743] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[0744] The user can check the reminder on the device.

[0745] Problem solving and risk management

[0746] 1. Enter and manage issues and risks

[0747] Users use the terminal interface to enter details of problems and risks that arise in the project.

[0748] The terminal transmits the input information to the server.

[0749] The server stores the details of the received issues and risks in a database.

[0750] 2. Solution generation and delivery

[0751] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0752] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0753] The server transmits the generated solution to the terminal.

[0754] The terminal displays the solution to the user.

[0755] Examples:

[0756] The user enters the problem "out of resources" into the terminal.

[0757] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[0758] The server sends the solution to the terminal, and the user confirms the solution.

[0759] Communication Support

[0760] 1. Sending and Managing Messages

[0761] Users use the terminal interface to enter questions and messages about the project.

[0762] The terminal transmits the input message to the server.

[0763] The server stores the received message in a database and requests an answer from the generative AI model.

[0764] 2. Generating and Providing Answers

[0765] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0766] The server sends the generated response to the terminal.

[0767] The terminal displays the answer to the user.

[0768] Examples:

[0769] The user inputs a "question about the progress of the project" into the terminal.

[0770] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[0771] The server sends the answer to the terminal, and the user confirms the answer.

[0772] The above is an embodiment of the present invention. This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving the efficiency and success rate of projects.

[0773] The processing flow will be explained below.

[0774] Managing and sharing project information

[0775] Step 1:

[0776] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[0777] Step 2:

[0778] The terminal converts the information entered by the user into an appropriate format and transmits it to the server.

[0779] Step 3:

[0780] The server stores the received information in a database and manages it centrally. The stored information can be viewed by related parties.

[0781] Scheduling and Reminders

[0782] Step 1:

[0783] The user inputs task details and deadlines through the terminal interface.

[0784] Step 2:

[0785] The terminal transmits the task information input by the user to the server.

[0786] Step 3:

[0787] The server stores the received task and deadline information in a database and schedules reminders.

[0788] Step 4:

[0789] The server prepares a reminder notification when the set date and time approaches.

[0790] Step 5:

[0791] The server sends a reminder notification to the terminal.

[0792] Step 6:

[0793] The terminal displays the received reminder notification to the user.

[0794] Problem solving and risk management

[0795] Step 1:

[0796] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[0797] Step 2:

[0798] The terminal transmits the problem and risk information entered by the user to the server.

[0799] Step 3:

[0800] The server stores the details of the received issues and risks in a database.

[0801] Step 4:

[0802] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[0803] Step 5:

[0804] The generative AI model generates a solution based on the information it receives and sends it to the server.

[0805] Step 6:

[0806] The server transmits the generated solution to the terminal.

[0807] Step 7:

[0808] The terminal displays the solution to the user.

[0809] Communication Support

[0810] Step 1:

[0811] Users use the terminal interface to enter questions and messages about the project.

[0812] Step 2:

[0813] The terminal transmits the message entered by the user to the server.

[0814] Step 3:

[0815] The server stores the received message in a database and requests an answer from the generative AI model.

[0816] Step 4:

[0817] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[0818] Step 5:

[0819] The server sends the generated response to the terminal.

[0820] Step 6:

[0821] The terminal displays the answer to the user.

[0822] Specific examples

[0823] Examples of adding tasks and reminders

[0824] Step 1:

[0825] The user inputs the task "Create meeting materials" into the interface of the terminal.

[0826] Step 2:

[0827] The terminal sends detailed information about the task (task name, deadline, etc.) to the server.

[0828] Step 3:

[0829] The server stores the received information in a database and sets a reminder to "create meeting materials" for 10:00 AM next Friday.

[0830] Step 4:

[0831] The server prepares a reminder notification when the set date and time (Friday 10:00 AM) approaches.

[0832] Step 5:

[0833] The server sends a reminder notification to the terminal.

[0834] Step 6:

[0835] The terminal displays a reminder for the task "Create meeting materials" to the user.

[0836] Example 1

[0837] 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."

[0838] In project management, a lot of information is scattered, making it difficult to centrally manage information such as status, progress, task assignment, and resource reservations. There are also challenges such as missing task deadlines and finding quick and appropriate solutions when problems or risks arise. Furthermore, there is a need to improve the efficiency of communication between stakeholders.

[0839] 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.

[0840] In this invention, the server includes means for centrally managing project information using a generative AI model, means for transmitting information entered by a user, such as project status, progress, task assignment, and resource reservation, to the server via a terminal and storing the information in a database, means for inputting project tasks and deadlines and sending reminder notifications, means for generating reminder notifications when a set date and time approaches and sending them to the user's terminal, means for transmitting information on problems and risks entered by the user to the generative AI model and providing solutions, and means for managing messages between parties and providing appropriate answers using the generative AI model. This enables centralized management of project information, prevention of missed deadlines, faster problem resolution, and efficient communication between parties.

[0841] A "generative AI model" is a model generated using artificial intelligence technology, and is a system that analyzes and infers based on input data to provide optimal solutions and answers.

[0842] A "server" is a centralized computer that receives, stores, processes, and transmits information from multiple terminals over a network.

[0843] A "user" is a person or group who uses this system to input, manage, and view project information.

[0844] A "terminal" is a device used by a user to input information, and includes a PC, tablet, smartphone, etc.

[0845] "Project status" is information that indicates the current state and progress of a project.

[0846] "Progress" is an indicator of how far tasks and activities within a project have progressed.

[0847] "Task assignment" refers to assigning a specific task to a specific user.

[0848] "Resource reservation" refers to reserving resources (people, equipment, materials, etc.) required for a project in advance for use during a specific period of time.

[0849] A "database" is a system for organizing large amounts of data and making it easy to search, update, and manage.

[0850] "Reminder notification" is a function that notifies the user of important events or tasks at a set date and time.

[0851] "Issues and risks" refer to obstacles or uncertainties that could impede the progress of a project.

[0852] A "solution" refers to a specific means or method for resolving a specific problem or risk.

[0853] "Messages" are a means of communication for users to exchange project-related information and questions.

[0854] "Answer" refers to the appropriate information or explanation provided by the generative AI model in response to a received message.

[0855] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[0856] Managing and sharing project information

[0857] 1. Enter and manage project information

[0858] Through the terminal interface, users input details such as project status, progress, task assignments, and resource reservations. The terminal converts the input information into an appropriate format and sends it to the server, which stores the received information in a database for centralized management.

[0859] For example, a user can add a new task, "Create a design document," by entering the task name, deadline, and assignee into a terminal. This information is converted to JSON format on the terminal and sent to the server. The server stores this information in a database, making it accessible to other parties.

[0860] Scheduling and Reminders

[0861] 2. Enter tasks and deadlines and set reminders

[0862] The user enters task details and deadlines through the device interface, and the device sends the information to the server, which stores the task and deadline information in a database and schedules reminders.

[0863] 3. Send reminders

[0864] When the set date and time approaches, the server prepares a reminder notification and sends it to the user's device, which then displays the reminder to the user.

[0865] For example, a user can schedule a "weekly meeting" on their device and set the date and time of the meeting. The server stores the information in a database and sends a reminder notification to the device one hour before the meeting. The user can then check the reminder on their device.

[0866] Problem solving and risk management

[0867] 4. Enter and manage issues and risks

[0868] The user enters details of problems and risks that have arisen in the project using the terminal interface. The terminal sends the entered information to the server, which stores the received details of problems and risks in a database.

[0869] 5. Solution generation and delivery

[0870] The server sends information about problems and risks stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device and displays it to the user.

[0871] For example, a user inputs a problem called "lack of resources" into a device. The server sends this problem to a generative AI model and receives a solution to "resource reallocation" from the AI ​​model. The server then sends the solution to the device, where the user confirms it.

[0872] Communication Support

[0873] 6. Sending and Managing Messages

[0874] Users use the device interface to input questions or messages about the project. The device then sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[0875] 7. Generating and Providing Answers

[0876] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The server then sends the generated response to the device, where the user can confirm the response.

[0877] For example, a user enters a question about the progress of a project into a device. The server sends the question to a generative AI model and receives the latest progress report from the AI ​​model. The server then sends the answer to the device, and the user confirms the answer.

[0878] This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving project efficiency and success rates.

[0879] Prompt Sentence Examples

[0880] Creating Design Documentation

[0881] Weekly Meeting

[0882] "Lack of resources"

[0883] "Please tell me about the progress of the project."

[0884] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0885] Managing and sharing project information

[0886] Entering and managing project information

[0887] Step 1:

[0888] The user uses the terminal interface to input project status, progress, task assignments, and resource reservations. The input data includes project information. For example, the user might input "Task name: Create design document," "Deadline: 2023-10-15," and "Responsible person: Tanaka."

[0889] Step 2:

[0890] The terminal receives the input information and converts it into an appropriate format (for example, JSON format). The input data is the project information entered by the user, and the output data is the formatted project information. Specifically, it converts it into {"task_name": "Create design document", "deadline": "2023-10-15", "assignee": "Tanaka"}.

[0891] Step 3:

[0892] The terminal sends the formatted information to the server, where the input data is the project information formatted by the terminal and the output data is the project information sent to the server.

[0893] Step 4:

[0894] The server validates the received information and stores it in a database. The input data is the project information sent to the server, and the output data is the project information stored in the database. A database system such as MySQL is used to store the data.

[0895] Scheduling and Reminders

[0896] Enter tasks and deadlines and set reminders

[0897] Step 1:

[0898] The user enters task details and deadlines through the device interface. Specifically, the user enters the task name, deadline, reminder settings, etc. For example, the input data might be "Task name: Weekly meeting" and "Date and time: 2023-10-20 10:00."

[0899] Step 2:

[0900] The terminal transmits the input information to the server. The input data is the task information input by the user, and the output data is the task information transmitted to the server.

[0901] Step 3:

[0902] The server stores task and deadline information in a database and sets reminder schedules. The input data is the task information sent to the server, and the output data is the task information stored in the database and the set reminder schedule.

[0903] Sending reminders

[0904] Step 4:

[0905] The server prepares the reminder notification when the set date and time approaches. The input data is the reminder schedule stored in the database, and the output data is the prepared reminder notification.

[0906] Step 5:

[0907] The server sends the reminder notification to the terminal, where the input data is the prepared reminder notification and the output data is the reminder notification sent to the terminal.

[0908] Step 6:

[0909] The terminal displays the reminder to the user. The input data is the reminder notification sent to the terminal, and the output data is the reminder displayed to the user.

[0910] Problem solving and risk management

[0911] Enter and manage issues and risks

[0912] Step 1:

[0913] The user inputs details of problems and risks that have occurred in the project using the terminal interface. The input data includes information about the problems and risks. For example, the user inputs "Problem: Lack of resources."

[0914] Step 2:

[0915] The terminal sends the input information to the server. The input data is the problem or risk information entered by the user, and the output data is the problem or risk information sent to the server.

[0916] Step 3:

[0917] The server stores the received problem and risk information in a database. The input data is the problem and risk information sent to the server, and the output data is the problem and risk information stored in the database.

[0918] Solution generation and delivery

[0919] Step 4:

[0920] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution. The input data is the problem and risk information stored in the database, and the output data is the request sent to the generative AI model.

[0921] Step 5:

[0922] The generative AI model generates solutions based on the received information and sends them to the server. The input data is the problem and risk information sent to the generative AI model, and the output data is the generated solution.

[0923] Step 6:

[0924] The server sends the generated solution to the terminal. The input data is the solution received from the generative AI model, and the output data is the solution sent to the terminal.

[0925] Step 7:

[0926] The terminal displays the solution to the user. The input data is the solution sent to the terminal, and the output data is the solution displayed to the user.

[0927] Communication Support

[0928] Sending and Managing Messages

[0929] Step 1:

[0930] The user inputs a question or message about the project using the terminal interface. The input data includes the message content. For example, the user inputs "Please tell me about the progress of the project."

[0931] Step 2:

[0932] The terminal sends the input message to the server. The input data is the message input by the user, and the output data is the message sent to the server.

[0933] Step 3:

[0934] The server stores the received message in a database and requests a response from the generative AI model. The input data is the message sent to the server, and the output data is the request sent to the generative AI model.

[0935] Generate and provide answers

[0936] Step 4:

[0937] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The input data is the message information sent to the generative AI model, and the output data is the generated response.

[0938] Step 5:

[0939] The server sends the generated answer to the terminal. The input data is the answer received from the generative AI model, and the output data is the answer sent to the terminal.

[0940] Step 6:

[0941] The terminal displays the answer to the user. The input data is the answer sent to the terminal, and the output data is the answer displayed to the user.

[0942] (Application example 1)

[0943] 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."

[0944] Conventional project management systems make information management cumbersome, making it difficult to manage task progress, resources, and problem-solving, especially in large-scale projects. Similarly, in factory production processes, robots often fail to properly manage production and detect anomalies, resulting in problems that reduce production efficiency and safety. The purpose of this invention is to solve these problems and efficiently manage projects and factory robot production.

[0945] 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.

[0946] In this invention, the server includes means for centrally managing information about the project using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for inputting production information and transmitting it to factory robots, means for sending information about abnormalities detected by the robots to the server and providing solutions from the generative AI model, and means for managing robot maintenance and cleaning schedules and sending reminders. This enables efficient centralized management of information and task progress management, as well as rapid management of the production process and problem resolution using robots.

[0947] A "generative AI model" is an artificial intelligence algorithm that automatically generates solutions to problems or appropriate answers based on information entered by the user.

[0948] "Project Information" means all detailed information related to a Project, including but not limited to Project status, progress, task assignments, and resource reservations.

[0949] A "task" refers to a specific task or activity required to move a project forward.

[0950] A "reminder notification" is a notification that notifies the user of a deadline for an event or task when the set date and time approaches.

[0951] "Issues and risks" refer to challenges and uncertainties that may arise in a project or production process.

[0952] "Solution" refers to the specific response to an encountered problem or risk provided by the generative AI model.

[0953] "Messages" refer to information and questions exchanged between project participants.

[0954] "Production information" refers to detailed information related to the production tasks performed by factory robots.

[0955] "Factory robot" refers to an automated machine used to carry out production processes within a factory.

[0956] "Anomaly information" refers to details of errors or abnormalities detected by factory robots during the production process.

[0957] "Resources" refers to the people, equipment, materials, etc. needed to carry out a project or production process.

[0958] This invention is a project management support tool that uses a generative AI model to support information management, task management, problem solving, and communication. It can also be applied to production management of factory robots, realizing an efficient production process.

[0959] System Overview

[0960] The system consists of three main components: the user, the device, and the server. It centralizes information about projects and production tasks managed by the user and provides solutions using generative AI models.

[0961] Hardware and Software

[0962] Hardware:

[0963] Administrator device (tablet, smartphone)

[0964] Factory robot (control terminal)

[0965] Server (central management system)

[0966] software:

[0967] Database management system (MySQL)

[0968] AI model (GPT-4)

[0969] Interface tool (React Native)

[0970] Data processing and calculation

[0971] Manage and share project information:

[0972] Users input information such as project status, progress, task assignments, and resource reservations from their tablets or smartphones. The information is converted into an appropriate format by the interface tool and sent to the server, which stores the information in a database and makes it accessible to stakeholders.

[0973] Tasks and Reminders:

[0974] Users input tasks and deadlines on their tablets or smartphones, which are then converted by the interface tool and sent to the server, which schedules reminders based on the information entered and sends notifications to the user's device when the specified date and time approaches.

[0975] Problem solving and risk management:

[0976] When a user or robot detects and inputs information about a problem or risk, the information is sent to the server via the interface tool. The server then sends the information to the generative AI model, which then generates a solution. The generated solution is then provided to the user's device or robot via the server.

[0977] Communication support:

[0978] The user enters a question about the project on a tablet or smartphone. The question is sent to the server via the interface tool, and the server sends the question to the generative AI model. The AI ​​model generates an appropriate answer, which is then provided to the user's device via the server.

[0979] Specific examples

[0980] A user adds a "Create design documentation" task by entering the task name, due date, and assignee on a tablet. The interface tool converts the information into the appropriate format and sends it to the server. The server stores it in a database and sets a reminder notification.

[0981] A factory robot detects a "delay in material supply" and sends that information to a server. The server sends a prompt message to the generative AI model informing it of the "delay in material supply" and receives a "suggestion for an alternative supply route" from the AI ​​model. The server sends the suggestion to the robot, which displays it on a display.

[0982] Example prompt sentence:

[0983] There has been a delay in the supply of materials. Please suggest an alternative supply route.

[0984] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0985] Step 1:

[0986] A user inputs project information (e.g., task name, deadline, and assignee) from a tablet or smartphone. The input information is sent to the server through an interface tool (e.g., React Native). This operation allows the user to enter detailed project information into the device, and the interface tool converts the data into an appropriate format and sends it to a database management system (e.g., MySQL).

[0987] Step 2:

[0988] The server stores the received project information in a database management system. This process centralizes project information and makes it accessible to stakeholders. The server creates a new entry in the database and registers the input information, allowing other users and robots to act on this information.

[0989] Step 3:

[0990] The user inputs information to set reminders for tasks and deadlines from their device and sends it to the server. The server receives this information and creates a reminder schedule. The server prepares reminder notifications when the date and time are approaching and sends the notifications to the user's device.

[0991] Step 4:

[0992] A factory robot detects a delay in material supply during the production process and sends the abnormality information to a server. The robot's built-in sensors and control system detect the abnormality and generate detailed error information. The data is then sent to the server via the robot's control terminal.

[0993] Step 5:

[0994] Based on the received anomaly information, the server sends a prompt to the generative AI model (e.g., GPT-4) requesting a solution. The prompt includes details of the anomaly and the required measures. Example: "There has been a delay in the supply of materials. Please propose an alternative supply route."

[0995] Step 6:

[0996] The generative AI model analyzes the prompt and generates an appropriate solution. The generated solution is sent back to the server. The AI ​​model analyzes past data and knowledge bases to propose a rational response.

[0997] Step 7:

[0998] The server sends the solutions received from the generative AI model to the factory robot, which displays the solutions on a display and takes optimal action to avoid unnecessary downtime.

[0999] Step 8:

[1000] A user types a question about a project into a tablet or smartphone and sends it to the server, which then forwards it to the generative AI model and requests an appropriate answer.

[1001] Step 9:

[1002] The generative AI model analyzes the user's question and generates an appropriate answer. The generated answer is sent to the user's device via the server. The device displays the answer to the user, supporting timely information provision.

[1003] Through the above processing steps, project management and factory robot production management are carried out efficiently, and unified information management, problem solving, and communication support are achieved.

[1004] 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.

[1005] This invention is a project management support tool that uses a generative AI model and an emotion engine, and enables project information management, schedule management, problem solving and risk management, communication support, as well as task allocation and advice provision that takes user emotions into consideration. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[1006] Managing and sharing project information

[1007] 1. Enter and manage project information

[1008] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[1009] The terminal converts the input information into an appropriate format and transmits it to the server.

[1010] The server stores the received information in a database and manages it centrally.

[1011] Examples:

[1012] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[1013] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[1014] Scheduling and Reminders

[1015] 1. Enter tasks and deadlines and set reminders

[1016] The user inputs task details and deadlines through the terminal interface.

[1017] The terminal transmits the input information to the server.

[1018] The server stores task and deadline information in a database and schedules reminders.

[1019] 2. Sending reminders

[1020] The server prepares a reminder notification when the set date and time approaches.

[1021] The server sends a reminder notification to the terminal.

[1022] The terminal displays the received reminder notification to the user.

[1023] Examples:

[1024] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[1025] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[1026] The user can check the reminder on the device.

[1027] Problem solving and risk management

[1028] 1. Enter and manage issues and risks

[1029] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[1030] The terminal transmits the input information to the server.

[1031] The server stores the details of the received issues and risks in a database.

[1032] 2. Solution generation and delivery

[1033] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[1034] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1035] The server transmits the generated solution to the terminal.

[1036] The terminal displays the solution to the user.

[1037] Examples:

[1038] The user enters the problem "out of resources" into the terminal.

[1039] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[1040] The server sends the solution to the terminal, and the user confirms the solution.

[1041] Communication Support

[1042] 1. Sending and Managing Messages

[1043] Users use the terminal interface to enter questions and messages about the project.

[1044] The terminal transmits the input message to the server.

[1045] The server stores the received message in a database and requests an answer from the generative AI model.

[1046] 2. Generating and Providing Answers

[1047] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1048] The server sends the generated response to the terminal.

[1049] The terminal displays the answer to the user.

[1050] Examples:

[1051] The user inputs a "question about the progress of the project" into the terminal.

[1052] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[1053] The server sends the answer to the terminal, and the user confirms the answer.

[1054] Utilizing the Emotion Engine

[1055] 1. Considering emotions when assigning tasks

[1056] Based on the user's input and actions, the emotion engine recognizes the user's emotions.

[1057] The terminal transmits the output of the emotion engine to the server.

[1058] The server integrates information from the generative AI model and the emotion engine to assign and adjust tasks according to the user's emotional state.

[1059] 2. Providing encouragement and advice based on progress

[1060] The emotion engine continuously assesses the user's emotions as the project progresses.

[1061] The server combines data from the emotion engine with generative AI models to generate appropriate encouragement and advice.

[1062] The terminal displays the generated encouragement or advice to the user.

[1063] 3. Monitoring user stress levels

[1064] The emotion engine monitors stress levels through user input and behavior.

[1065] The server uses this data to rebalance tasks and re-allocate resources as needed.

[1066] Examples:

[1067] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[1068] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[1069] The device displays this suggestion to the user, helping the user to reduce stress.

[1070] In this way, by combining a generative AI model and an emotion engine, the project management support tool of the present invention can realize information management, schedule management, problem solving, communication support, and task management that takes into account the user's emotions, thereby further improving the efficiency and success rate of projects.

[1071] The processing flow will be explained below.

[1072] Utilizing the Emotion Engine

[1073] Considering emotions when assigning tasks

[1074] Step 1:

[1075] The user inputs data related to his / her emotional state (for example, diary entries, questionnaire responses, etc.) through the terminal interface.

[1076] Step 2:

[1077] The terminal transmits the data input by the user to the emotion engine.

[1078] Step 3:

[1079] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1080] Step 4:

[1081] The server integrates the analysis results received from the emotion engine with the generative AI model.

[1082] Step 5:

[1083] The server uses a generative AI model to generate optimal task allocations based on the user's emotional state and sends the results to the device.

[1084] Step 6:

[1085] The terminal displays the task assignments received from the server to the user.

[1086] Providing encouragement and advice based on progress

[1087] Step 1:

[1088] The emotion engine continuously monitors the user's emotional state as the project progresses.

[1089] Step 2:

[1090] Users report project progress and emotional state to the terminal.

[1091] Step 3:

[1092] The terminal sends the reported data to the emotion engine.

[1093] Step 4:

[1094] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1095] Step 5:

[1096] The server uses the output data of the emotion engine and the generative AI model to generate encouragement and advice for the user.

[1097] Step 6:

[1098] The server transmits the generated encouragement and advice to the terminal.

[1099] Step 7:

[1100] The terminal displays encouragement and advice to the user.

[1101] Monitoring user stress levels

[1102] Step 1:

[1103] The emotion engine monitors stress levels through user input and behavior.

[1104] Step 2:

[1105] The user inputs the task progress and emotional state into the terminal.

[1106] Step 3:

[1107] The terminal transmits the input data to the emotion engine.

[1108] Step 4:

[1109] The emotion engine analyzes the user's stress level and sends the results to the server.

[1110] Step 5:

[1111] The server uses the data from the emotion engine to rebalance tasks and reallocate resources as needed.

[1112] Step 6:

[1113] The server sends the proposed task rebalancing and resource allocation to the terminal.

[1114] Step 7:

[1115] The terminal displays the rebalancing proposal received from the server to the user.

[1116] Examples:

[1117] Step 1: The user enters "relaxed" as the emotional state on a day when they are not feeling particularly stressed.

[1118] Step 2: The device sends this information to the emotion engine.

[1119] Step 3: The emotion engine analyzes the emotion based on the user's input data and sends the result to the server.

[1120] Step 4: The server integrates the results from the emotion engine with the generative AI model and selects a suitable task for the user.

[1121] Step 5: The server sends the generated task assignment to the terminal.

[1122] Step 6: The terminal displays the task assignment to the user.

[1123] Step 7: The user confirms the new task and completes it.

[1124] In this way, the project management support tool of the present invention combines a generative AI model and an emotion engine to realize information management, schedule management, problem solving, communication support, as well as task management and advice that take the user's emotions into consideration, thereby further improving the efficiency and success rate of projects.

[1125] Example 2

[1126] 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."

[1127] Project management requires efficient centralized information management, task scheduling, problem solving, risk management, and communication support, but conventional systems lack consideration for the user's emotional state and stress level, which can result in a decline in project efficiency and success rate.

[1128] 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.

[1129] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating a user's emotional state using an emotion engine and assigning and adjusting tasks, and means for providing encouragement and advice using a generative AI model according to the user's emotional state. This enables project management that takes into account the user's emotional state and stress level, thereby improving the efficiency and success rate of projects.

[1130] A "generative AI model" is an artificial intelligence system that uses artificial intelligence technology to automatically generate solutions and suggestions based on input data.

[1131] "Project information" refers to all data necessary for managing and operating a project, such as project status, progress, task assignments, resource reservations, details of issues and risks, and communications.

[1132] "Centralized management" is a method of consolidating all project-related information in one place and managing it consistently.

[1133] "Tasks and deadlines" refer to the specific tasks that need to be completed within a project and the dates and times by which they should be completed.

[1134] "Reminder notification" is a notification function that notifies users of approaching task deadlines or important events.

[1135] "Issues and risks" refer to factors including obstacles and uncertainties that could impede the progress of a project.

[1136] "Providing a solution" means presenting appropriate ways and means to deal with problems or risks that have arisen.

[1137] "Messages between participants" refers to the content of communications and correspondence exchanged between members involved in a project.

[1138] An "emotion engine" is a system that analyzes the emotional state of a user based on their input and behavior and identifies those emotions.

[1139] "Emotional state" refers to a user's psychological state or mood, and includes measures such as stress level and satisfaction.

[1140] "Task allocation and coordination" refers to distributing work within a project to the appropriate people and changing the schedule and content as necessary.

[1141] "Providing encouragement and advice" refers to sending advice and support messages to increase motivation and improve work efficiency according to the user's emotional state.

[1142] This invention is a project management support system that uses a generative AI model and an emotion engine. This system is mainly composed of four main components: a server, a terminal, a user, and an emotion engine.

[1143] Managing and sharing project information

[1144] The system centralizes project information through the following processes:

[1145] 1. The user provides details about the project status, progress, task assignments, resource reservations, etc. through the device interface, which includes input using text boxes and drop-down menus.

[1146] 2. The terminal receives the information entered by the user, converts it into an appropriate format (for example, JSON format), and sends it to the server.

[1147] 3. The server centralizes the information by storing the received data in a database, which can use a common database management system such as PostgreSQL or MySQL.

[1148] Examples:

[1149] To add a new task "Create design document," the user inputs the task name, deadline, and person in charge into the terminal.

[1150] The terminal sends this information to a server which stores it in a database where it can be accessed by other parties.

[1151] Scheduling and Reminders

[1152] The system manages the scheduling of tasks and events through the following process:

[1153] 1. The user sets the task details and due date using the device interface, which may involve a calendar widget or time picker.

[1154] 2. The device sends this information to the server.

[1155] 3. The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the scheduled date and time approaches.

[1156] 4. The device displays the reminder notification to the user.

[1157] Examples:

[1158] The user inputs the "weekly meeting" schedule into the terminal and sets the date and time of the meeting.

[1159] The server stores this information in a database and sends a reminder notification to the device one hour before the meeting.

[1160] The user checks the reminder notification on their device.

[1161] Problem solving and risk management

[1162] The system provides problem solving and risk management through the following processes:

[1163] 1. The user enters details of the problems and risks that have arisen in the project through the terminal interface.

[1164] 2. The device sends this information to the server.

[1165] 3. The server stores the received information in a database.

[1166] 4. The server sends the stored problem and risk information to the generative AI model and requests a solution. The generative AI model uses a conversational language model, such as OpenAI's GPT series.

[1167] 5. The generative AI model generates a solution based on the received information and sends it to the server.

[1168] 6. The server sends the generated solution to the terminal and presents it to the user.

[1169] Examples:

[1170] The user enters the problem "out of resources" into the terminal.

[1171] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[1172] The server sends the solution to the terminal, and the user confirms the solution.

[1173] Communication Support

[1174] The system supports communication between stakeholders through the following processes:

[1175] 1. The user enters a question or message about the project using the terminal interface.

[1176] 2. The terminal sends this message to the server.

[1177] 3. The server stores the received message in a database and requests an answer from the generative AI model.

[1178] 4. The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1179] 5. The server sends the generated answer to the terminal and displays it to the user.

[1180] Examples:

[1181] The user inputs a "question about the progress of the project" into the terminal.

[1182] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[1183] The server sends the answer to the terminal, and the user confirms the answer.

[1184] Use of emotion engine

[1185] The system manages projects taking into account the user's emotional state through the following process.

[1186] 1. The emotion engine evaluates the emotional state based on the user's input and actions.

[1187] 2. The device sends the output of the emotion engine to the server.

[1188] 3. The server integrates information from the emotion engine and the generative AI model, which then assigns and adjusts tasks according to the user's emotional state.

[1189] 4. The server uses the generative AI model to provide encouragement and advice based on the user's emotional state.

[1190] 5. The device displays the generated encouragement or advice to the user.

[1191] Examples:

[1192] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[1193] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[1194] The device displays this suggestion to the user, helping the user to reduce stress.

[1195] Example prompt sentence:

[1196] "Please tell me more about the current project progress."

[1197] "Propose solutions to resource shortages."

[1198] "How can I reassign tasks for stressed users?"

[1199] In this way, the project management support system of the present invention can improve the efficiency and success rate of project management by effectively utilizing the generative AI model and emotion engine.

[1200] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1201] Managing and sharing project information

[1202] Step 1:

[1203] Users enter details such as project status, progress, task assignments, and resource reservations through the device interface, using text boxes and drop-down menus, and the data is then sorted into appropriate fields.

[1204] Input: Project information such as task name, due date, and assignee

[1205] Output: Project information stored in a data structure in the terminal.

[1206] Step 2:

[1207] The device receives the information entered by the user, converts it into an appropriate format (e.g., JSON) and sends it to the server. This conversion process ensures that the data is in a consistent format.

[1208] Input: Various project information entered by the user

[1209] Output: JSON formatted data

[1210] Step 3:

[1211] The server parses the JSON data received from the device, performs the necessary database operations, and stores the information in a common database management system such as PostgreSQL or MySQL.

[1212] Input: Project information in JSON format

[1213] Output: Project information stored in a database

[1214] Scheduling and Reminders

[1215] Step 1:

[1216] Users use the device interface to set task details and deadlines, using a calendar widget and time picker.

[1217] Input: Task name, due date, reminder settings

[1218] Output: Schedule information stored in a data structure in the terminal

[1219] Step 2:

[1220] The terminal then formats and transmits this information to the server.

[1221] Input: User-entered schedule information

[1222] Output: Data sent to the server in JSON format

[1223] Step 3:

[1224] The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the time approaches.

[1225] Input: JSON formatted schedule information sent from the device

[1226] Output: Schedule information and reminder notification schedules stored in the database

[1227] Step 4:

[1228] The terminal receives and displays the reminder notification for display to the user.

[1229] Input: Reminder notification received from the server

[1230] Output: Reminder notification displayed in the user interface

[1231] Problem solving and risk management

[1232] Step 1:

[1233] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[1234] Input: Issue or risk details

[1235] Output: Problem information stored in a data structure in the terminal.

[1236] Step 2:

[1237] The terminal then formats and transmits this information to the server.

[1238] Input: User-entered issue or risk information

[1239] Output: Data sent to the server in JSON format

[1240] Step 3:

[1241] The server stores the received information in a database and then sends it to the generative AI model to request a solution.

[1242] Input: JSON formatted problem information sent from the terminal

[1243] Output: Solution request to the generative AI model

[1244] Step 4:

[1245] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1246] Input: Problem information sent from the server

[1247] Output: The solution generated by the generative AI model

[1248] Step 5:

[1249] The server transmits the generated solution to the terminal and presents it to the user.

[1250] Input: The solution received from the generative AI model

[1251] Output: Solution sent to terminal

[1252] Step 6:

[1253] The terminal displays the solution to the user.

[1254] Input: Solution received from the server

[1255] Output: The solution displayed in the user interface

[1256] Communication Support

[1257] Step 1:

[1258] Users enter questions or messages about the project using the terminal interface.

[1259] Input: The text of your question or message

[1260] Output: Messages stored in a data structure in the terminal

[1261] Step 2:

[1262] The terminal changes the format of this message for transmission to the server and sends it.

[1263] Input: The message entered by the user

[1264] Output: Data sent to the server in JSON format

[1265] Step 3:

[1266] The server stores the received message in a database and requests an answer from the generative AI model.

[1267] Input: JSON formatted message sent from the terminal

[1268] Output: A request to the generative AI model

[1269] Step 4:

[1270] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1271] Input: The message sent from the server

[1272] Output: The answer generated by the generative AI model

[1273] Step 5:

[1274] The server sends the generated answer to the terminal and displays it to the user.

[1275] Input: The answer received from the generative AI model

[1276] Output: Answer sent to terminal

[1277] Step 6:

[1278] The terminal displays the answer to the user.

[1279] Input: The answer received from the server

[1280] Output: The answer displayed in the user interface

[1281] Use of emotion engine

[1282] Step 1:

[1283] The emotion engine assesses the user's emotional state based on their input and actions.

[1284] Input: User input and behavioral data

[1285] Output: Evaluated emotional state data

[1286] Step 2:

[1287] The terminal transmits the output of the emotion engine to the server.

[1288] Input: Emotional state data from the emotion engine

[1289] Output: Emotional state data sent to the server

[1290] Step 3:

[1291] The server integrates information from the emotion engine and the generative AI model to assign and adjust tasks according to the emotional state.

[1292] Input: Emotional state data from the emotion engine and information from the generative AI model

[1293] Output: Adjusted task assignment data

[1294] Step 4:

[1295] The server uses a generative AI model to generate encouragement and advice based on the user's emotional state.

[1296] Input: Emotional state data from the emotion engine

[1297] Output: Generated encouragement and advice

[1298] Step 5:

[1299] The terminal displays the generated encouragement or advice to the user.

[1300] Input: Encouragement or advice received from the server

[1301] Output: Encouragement or advice displayed in the user interface

[1302] Prompt for the example:

[1303] "Please tell me more about the current project progress."

[1304] "Propose solutions to resource shortages."

[1305] "How can I reassign tasks for stressed users?"

[1306] (Application example 2)

[1307] 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."

[1308] While conventional project management systems provide basic functions such as project information management, schedule management, problem solving, and communication support, they do not optimize responses or task allocations that take into account the user's emotions and stress. This increases the burden and stress on users as the project progresses, resulting in issues such as reduced project efficiency and a higher risk of failure. Furthermore, factory automation requires collaborative work between humans and robots, necessitating efficient, human-centered project management that breaks away from mechanistic management.

[1309] 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.

[1310] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating user emotions using an emotion engine and optimizing task allocation based on the emotions, means for combining the generative AI model and the emotion engine to provide encouragement and advice to the user, and means for monitoring the user's stress level and reallocating tasks and readjusting resources. This enables project management that takes user emotions and stress into consideration, improving factory productivity and efficiency and increasing the project success rate.

[1311] A "generative AI model" is a type of artificial intelligence that generates new information and solutions based on data.

[1312] An "emotion engine" is a system for assessing a user's emotional state and providing responses or suggestions based on that.

[1313] "Project information" includes information such as project status, progress, task assignments, and resource reservations.

[1314] "Task assignment" is the process of assigning specific tasks or jobs to each member of a project.

[1315] "Reminder notification" is a mechanism that sends notifications to users before set deadlines or important events, helping them to remember to respond.

[1316] "Risk management" is the process of identifying risks that may arise during the project and taking measures to address them.

[1317] A "solution" is a method or means for providing an appropriate response or treatment to a particular problem or risk.

[1318] "Message management" is a system for organizing and storing messages sent and received in order to facilitate efficient communication between project members.

[1319] "Encouragement" refers to encouraging words or actions that are used to motivate users or reduce stress.

[1320] The "stress level" is an index that indicates the user's mental and physical tension, and if it is high, it may interfere with work.

[1321] "Task reassignment" is the process of reviewing existing tasks and reassigning them to more appropriate members.

[1322] "Resource readjustment" refers to the reallocation of resources such as personnel, equipment, and time based on the progress of the project and the results of the emotion engine.

[1323] This invention is a project management support system that uses a generative AI model and an emotion engine, and aims to optimize the operation of robots in factories. This system consists of four main elements: a user, a terminal, a server, and an emotion engine. Specifically, the invention is implemented in the following form.

[1324] Managing and sharing project information

[1325] A user inputs information such as project status, progress, task assignment, and resource reservation through the terminal interface. The terminal converts the input information into an appropriate format and sends it to the server, which stores the information in a database for centralized management. For example, when a user adds a new task "Realigning the Production Line," the user inputs the task name, deadline, and person in charge into the terminal. The terminal sends this information to the server, which stores it in a database so that other parties can access the information.

[1326] Scheduling and Reminders

[1327] The user enters task details and deadlines through the device. The device sends this information to the server, which saves the task and deadline information in a database and schedules reminders. When the scheduled date and time approaches, the server prepares a reminder notification and sends it to the device. The device then displays the received reminder notification to the user. For example, a user enters an appointment for a "weekly meeting" into the device and sets the date and time of the meeting. The server saves the information in a database and sends a reminder notification to the device one hour before the meeting, allowing the user to check the reminder.

[1328] Problem solving and risk management

[1329] The user enters details of problems and risks that have arisen in the project through the device interface. The device sends this information to the server, which stores the received details of the problems and risks in a database. The server then sends the problem and risk information stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device, which then displays the solution to the user. For example, if a user enters the problem "lack of resources" into the device, the server sends this problem to the generative AI model and receives the solution "reallocate resources" from the AI ​​model. The server then sends the solution to the device, allowing the user to check the solution.

[1330] Communication Support

[1331] The user uses the device interface to input a question or message about the project. The device sends the input message to the server, which stores the received message in a database and requests an answer from the generative AI model. The generative AI model analyzes the message, generates an appropriate answer, and sends it to the server. The server sends the generated answer to the device, which then displays the answer to the user. For example, if a user inputs a "question about the progress of the project" into the device, the server sends the question to the generative AI model, which receives information about the "latest progress report" from the AI ​​model. The server sends the answer to the device, allowing the user to check the answer.

[1332] Utilizing the Emotion Engine

[1333] The emotion engine can recognize the user's emotions based on their input and actions. The device sends the emotion engine's output to a server, which then integrates the generative AI model and the emotion engine's information to assign and adjust tasks according to the user's emotional state. The emotion engine continuously evaluates the user's emotions as the project progresses, and the server combines the emotion engine's data with the generative AI model to generate appropriate encouragement and advice, which the device displays to the user. The emotion engine also monitors the user's stress level through their input and actions, and the server uses this data to rebalance tasks and readjust resources. For example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses the generative AI model to generate a suggestion to "review task priorities and perform less demanding tasks first." The device displays these suggestions to the user, helping them reduce stress.

[1334] Examples of concrete examples and prompts

[1335] For example, an operator can use a factory robot to add a new task, "Realign the Line," and set a deadline. The robot will then display a reminder before the deadline and proactively detect potential issues. If the operator feels stressed, the robot will use its emotion engine to determine their stress level and optimize task allocation.

[1336] Example prompt sentence:

[1337] User task: "Realign line". Deadline: 10 October 2023, 15:00.

[1338] In this way, the project management support system of the present invention dramatically improves the efficiency of project management in factories and increases the success rate of projects.

[1339] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1340] Step 1:

[1341] The user inputs project information through the terminal interface.

[1342] Specifically, to add a new task, "Re-adjusting the production line," the user inputs detailed information such as the task name, deadline, and person in charge. The device converts this input information into an appropriate format and sends it to the server. This formats the input data, making it easier for the server to process.

[1343] Input: Task name, due date, assignee

[1344] Output: Formatted task information

[1345] Step 2:

[1346] The server receives the task information sent from the terminal.

[1347] The server stores the received task information in a database for centralized management. When storing the information in the database, the project progress and resource allocation information are also updated simultaneously, allowing other stakeholders to check the latest project status in real time.

[1348] Input: Formatted task information

[1349] Output: Task information stored in the database

[1350] Step 3:

[1351] The user sets task details and deadlines through the terminal.

[1352] Specifically, a user enters a "weekly meeting" into their device and sets the date and time of the meeting. The device then sends this information to the server, which stores the task and deadline information in a database and schedules reminders.

[1353] Input: Task details, due date

[1354] Output: Reminder schedule information

[1355] Step 4:

[1356] The server prepares the reminder notification and sends it to the device when the set date and time approaches.

[1357] The server periodically checks the saved reminder schedule and issues reminders at the appropriate time, so that users are notified just before the deadline.

[1358] Input: Reminder schedule information

[1359] Output: Reminder notification to user device

[1360] Step 5:

[1361] The user enters details of the problems and risks that have arisen in the project from the terminal.

[1362] Specifically, a user inputs the problem "lack of resources" into the terminal, and the terminal sends the problem information to the server, which then stores the information in a database.

[1363] Input: Issue or risk details

[1364] Output: Problem information stored in the database

[1365] Step 6:

[1366] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[1367] The generative AI model generates a solution based on the received information and sends it to the server, which adds the solution from the generative AI model to the database.

[1368] Input: Problem information

[1369] Output: A solution from the generative AI model

[1370] Step 7:

[1371] The server sends the generated solution to the terminal, which displays the solution to the user.

[1372] The user can then use the displayed solutions to try to solve the problem.

[1373] As a specific example, the solution "Reallocate resources" may be displayed.

[1374] Input: Solution from a generative AI model

[1375] Output: Solution displayed on user's terminal

[1376] Step 8:

[1377] The user uses the device interface to input questions or messages about the project, and the device sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[1378] Input: Questions and messages from users

[1379] Output: The question information sent to the generative AI model

[1380] Step 9:

[1381] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server, which then sends the generated response to the device, which then displays the response to the user.

[1382] As a concrete example, when a user inputs a "question about the progress of a project," the generative AI model can generate and display an "up-to-date progress report."

[1383] Input: Questions and messages from users

[1384] Output: Answer from the generative AI model

[1385] Step 10:

[1386] The emotion engine recognizes emotions based on the user's input and actions, and sends the state to the server.

[1387] The device monitors the user's stress level based on the output from the emotion engine. Specifically, if the emotion engine determines that the user's stress level is "high," it sends that information to the server, which then integrates the generative AI model and the emotion engine's information to suggest appropriate measures.

[1388] Input: User input and actions

[1389] Output: Emotional state information from the emotion engine

[1390] Step 11:

[1391] The server generates proposals for task reallocation and resource readjustment based on information from the generative AI model and emotion engine, and sends them to the device. The device displays the proposals to the user, helping to reduce the user's stress.

[1392] As a specific example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses a generative AI model to generate a suggestion to "review task priorities and perform less burdensome tasks first."

[1393] Input: Emotional state information from the emotion engine

[1394] Output: Task reassignment and resource realignment suggestions

[1395] 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.

[1396] 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.

[1397] 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.

[1398] [Third embodiment]

[1399] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1400] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1401] 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).

[1402] 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.

[1403] 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.

[1404] 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).

[1405] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1406] 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.

[1407] 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.

[1408] 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.

[1409] 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.

[1410] 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."

[1411] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[1412] Managing and sharing project information

[1413] 1. Enter and manage project information

[1414] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[1415] The terminal converts the input information into an appropriate format and transmits it to the server.

[1416] The server stores the received information in a database and manages it centrally.

[1417] Examples:

[1418] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[1419] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[1420] Scheduling and Reminders

[1421] 1. Enter tasks and deadlines and set reminders

[1422] The user inputs task details and deadlines through the terminal interface.

[1423] The terminal transmits the input information to the server.

[1424] The server stores task and deadline information in a database and schedules reminders.

[1425] 2. Sending reminders

[1426] The server prepares a reminder notification when the set date and time approaches.

[1427] The server sends a reminder notification to the terminal.

[1428] The terminal displays the reminder to the user.

[1429] Examples:

[1430] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[1431] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[1432] The user can check the reminder on the device.

[1433] Problem solving and risk management

[1434] 1. Enter and manage issues and risks

[1435] Users use the terminal interface to enter details of problems and risks that arise in the project.

[1436] The terminal transmits the input information to the server.

[1437] The server stores the details of the received issues and risks in a database.

[1438] 2. Solution generation and delivery

[1439] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[1440] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1441] The server transmits the generated solution to the terminal.

[1442] The terminal displays the solution to the user.

[1443] Examples:

[1444] The user enters the problem "out of resources" into the terminal.

[1445] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[1446] The server sends the solution to the terminal, and the user confirms the solution.

[1447] Communication Support

[1448] 1. Sending and Managing Messages

[1449] Users use the terminal interface to enter questions and messages about the project.

[1450] The terminal transmits the input message to the server.

[1451] The server stores the received message in a database and requests an answer from the generative AI model.

[1452] 2. Generating and Providing Answers

[1453] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1454] The server sends the generated response to the terminal.

[1455] The terminal displays the answer to the user.

[1456] Examples:

[1457] The user inputs a "question about the progress of the project" into the terminal.

[1458] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[1459] The server sends the answer to the terminal, and the user confirms the answer.

[1460] The above is an embodiment of the present invention. This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving the efficiency and success rate of projects.

[1461] The processing flow will be explained below.

[1462] Managing and sharing project information

[1463] Step 1:

[1464] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[1465] Step 2:

[1466] The terminal converts the information entered by the user into an appropriate format and transmits it to the server.

[1467] Step 3:

[1468] The server stores the received information in a database and manages it centrally. The stored information can be viewed by related parties.

[1469] Scheduling and Reminders

[1470] Step 1:

[1471] The user inputs task details and deadlines through the terminal interface.

[1472] Step 2:

[1473] The terminal transmits the task information input by the user to the server.

[1474] Step 3:

[1475] The server stores the received task and deadline information in a database and schedules reminders.

[1476] Step 4:

[1477] The server prepares a reminder notification when the set date and time approaches.

[1478] Step 5:

[1479] The server sends a reminder notification to the terminal.

[1480] Step 6:

[1481] The terminal displays the received reminder notification to the user.

[1482] Problem solving and risk management

[1483] Step 1:

[1484] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[1485] Step 2:

[1486] The terminal transmits the problem and risk information entered by the user to the server.

[1487] Step 3:

[1488] The server stores the details of the received issues and risks in a database.

[1489] Step 4:

[1490] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[1491] Step 5:

[1492] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1493] Step 6:

[1494] The server transmits the generated solution to the terminal.

[1495] Step 7:

[1496] The terminal displays the solution to the user.

[1497] Communication Support

[1498] Step 1:

[1499] Users use the terminal interface to enter questions and messages about the project.

[1500] Step 2:

[1501] The terminal transmits the message entered by the user to the server.

[1502] Step 3:

[1503] The server stores the received message in a database and requests an answer from the generative AI model.

[1504] Step 4:

[1505] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1506] Step 5:

[1507] The server sends the generated response to the terminal.

[1508] Step 6:

[1509] The terminal displays the answer to the user.

[1510] Specific examples

[1511] Examples of adding tasks and reminders

[1512] Step 1:

[1513] The user inputs the task "Create meeting materials" into the interface of the terminal.

[1514] Step 2:

[1515] The terminal sends detailed information about the task (task name, deadline, etc.) to the server.

[1516] Step 3:

[1517] The server stores the received information in a database and sets a reminder to "create meeting materials" for 10:00 AM next Friday.

[1518] Step 4:

[1519] The server prepares a reminder notification when the set date and time (Friday 10:00 AM) approaches.

[1520] Step 5:

[1521] The server sends a reminder notification to the terminal.

[1522] Step 6:

[1523] The terminal displays a reminder for the task "Create meeting materials" to the user.

[1524] Example 1

[1525] 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."

[1526] In project management, a lot of information is scattered, making it difficult to centrally manage information such as status, progress, task assignment, and resource reservations. There are also challenges such as missing task deadlines and finding quick and appropriate solutions when problems or risks arise. Furthermore, there is a need to improve the efficiency of communication between stakeholders.

[1527] 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.

[1528] In this invention, the server includes means for centrally managing project information using a generative AI model, means for transmitting information entered by a user, such as project status, progress, task assignment, and resource reservation, to the server via a terminal and storing the information in a database, means for inputting project tasks and deadlines and sending reminder notifications, means for generating reminder notifications when a set date and time approaches and sending them to the user's terminal, means for transmitting information on problems and risks entered by the user to the generative AI model and providing solutions, and means for managing messages between parties and providing appropriate answers using the generative AI model. This enables centralized management of project information, prevention of missed deadlines, faster problem resolution, and efficient communication between parties.

[1529] A "generative AI model" is a model generated using artificial intelligence technology, and is a system that analyzes and infers based on input data to provide optimal solutions and answers.

[1530] A "server" is a centralized computer that receives, stores, processes, and transmits information from multiple terminals over a network.

[1531] A "user" is a person or group who uses this system to input, manage, and view project information.

[1532] A "terminal" is a device used by a user to input information, and includes a PC, tablet, smartphone, etc.

[1533] "Project status" is information that indicates the current state and progress of a project.

[1534] "Progress" is an indicator of how far tasks and activities within a project have progressed.

[1535] "Task assignment" refers to assigning a specific task to a specific user.

[1536] "Resource reservation" refers to reserving resources (people, equipment, materials, etc.) required for a project in advance for use during a specific period of time.

[1537] A "database" is a system for organizing large amounts of data and making it easy to search, update, and manage.

[1538] "Reminder notification" is a function that notifies the user of important events or tasks at a set date and time.

[1539] "Issues and risks" refer to obstacles or uncertainties that could impede the progress of a project.

[1540] A "solution" refers to a specific means or method for resolving a specific problem or risk.

[1541] "Messages" are a means of communication for users to exchange project-related information and questions.

[1542] "Answer" refers to the appropriate information or explanation provided by the generative AI model in response to a received message.

[1543] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[1544] Managing and sharing project information

[1545] 1. Enter and manage project information

[1546] Through the terminal interface, users input details such as project status, progress, task assignments, and resource reservations. The terminal converts the input information into an appropriate format and sends it to the server, which stores the received information in a database for centralized management.

[1547] For example, a user can add a new task, "Create a design document," by entering the task name, deadline, and assignee into a terminal. This information is converted to JSON format on the terminal and sent to the server. The server stores this information in a database, making it accessible to other parties.

[1548] Scheduling and Reminders

[1549] 2. Enter tasks and deadlines and set reminders

[1550] The user enters task details and deadlines through the device interface, and the device sends the information to the server, which stores the task and deadline information in a database and schedules reminders.

[1551] 3. Send reminders

[1552] When the set date and time approaches, the server prepares a reminder notification and sends it to the user's device, which then displays the reminder to the user.

[1553] For example, a user can schedule a "weekly meeting" on their device and set the date and time of the meeting. The server stores the information in a database and sends a reminder notification to the device one hour before the meeting. The user can then check the reminder on their device.

[1554] Problem solving and risk management

[1555] 4. Enter and manage issues and risks

[1556] The user enters details of problems and risks that have arisen in the project using the terminal interface. The terminal sends the entered information to the server, which stores the received details of problems and risks in a database.

[1557] 5. Solution generation and delivery

[1558] The server sends information about problems and risks stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device and displays it to the user.

[1559] For example, a user inputs a problem called "lack of resources" into a device. The server sends this problem to a generative AI model and receives a solution to "resource reallocation" from the AI ​​model. The server then sends the solution to the device, where the user confirms it.

[1560] Communication Support

[1561] 6. Sending and Managing Messages

[1562] Users use the device interface to input questions or messages about the project. The device then sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[1563] 7. Generating and Providing Answers

[1564] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The server then sends the generated response to the device, where the user can confirm the response.

[1565] For example, a user enters a question about the progress of a project into a device. The server sends the question to a generative AI model and receives the latest progress report from the AI ​​model. The server then sends the answer to the device, and the user confirms the answer.

[1566] This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving project efficiency and success rates.

[1567] Prompt Sentence Examples

[1568] Creating Design Documentation

[1569] Weekly Meeting

[1570] "Lack of resources"

[1571] "Please tell me about the progress of the project."

[1572] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1573] Managing and sharing project information

[1574] Entering and managing project information

[1575] Step 1:

[1576] The user uses the terminal interface to input project status, progress, task assignments, and resource reservations. The input data includes project information. For example, the user might input "Task name: Create design document," "Deadline: 2023-10-15," and "Responsible person: Tanaka."

[1577] Step 2:

[1578] The terminal receives the input information and converts it into an appropriate format (for example, JSON format). The input data is the project information entered by the user, and the output data is the formatted project information. Specifically, it converts it into {"task_name": "Create design document", "deadline": "2023-10-15", "assignee": "Tanaka"}.

[1579] Step 3:

[1580] The terminal sends the formatted information to the server, where the input data is the project information formatted by the terminal and the output data is the project information sent to the server.

[1581] Step 4:

[1582] The server validates the received information and stores it in a database. The input data is the project information sent to the server, and the output data is the project information stored in the database. A database system such as MySQL is used to store the data.

[1583] Scheduling and Reminders

[1584] Enter tasks and deadlines and set reminders

[1585] Step 1:

[1586] The user enters task details and deadlines through the device interface. Specifically, the user enters the task name, deadline, reminder settings, etc. For example, the input data might be "Task name: Weekly meeting" and "Date and time: 2023-10-20 10:00."

[1587] Step 2:

[1588] The terminal transmits the input information to the server. The input data is the task information input by the user, and the output data is the task information transmitted to the server.

[1589] Step 3:

[1590] The server stores task and deadline information in a database and sets reminder schedules. The input data is the task information sent to the server, and the output data is the task information stored in the database and the set reminder schedule.

[1591] Sending reminders

[1592] Step 4:

[1593] The server prepares the reminder notification when the set date and time approaches. The input data is the reminder schedule stored in the database, and the output data is the prepared reminder notification.

[1594] Step 5:

[1595] The server sends the reminder notification to the terminal, where the input data is the prepared reminder notification and the output data is the reminder notification sent to the terminal.

[1596] Step 6:

[1597] The terminal displays the reminder to the user. The input data is the reminder notification sent to the terminal, and the output data is the reminder displayed to the user.

[1598] Problem solving and risk management

[1599] Enter and manage issues and risks

[1600] Step 1:

[1601] The user inputs details of problems and risks that have occurred in the project using the terminal interface. The input data includes information about the problems and risks. For example, the user inputs "Problem: Lack of resources."

[1602] Step 2:

[1603] The terminal sends the input information to the server. The input data is the problem or risk information entered by the user, and the output data is the problem or risk information sent to the server.

[1604] Step 3:

[1605] The server stores the received problem and risk information in a database. The input data is the problem and risk information sent to the server, and the output data is the problem and risk information stored in the database.

[1606] Solution generation and delivery

[1607] Step 4:

[1608] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution. The input data is the problem and risk information stored in the database, and the output data is the request sent to the generative AI model.

[1609] Step 5:

[1610] The generative AI model generates solutions based on the received information and sends them to the server. The input data is the problem and risk information sent to the generative AI model, and the output data is the generated solution.

[1611] Step 6:

[1612] The server sends the generated solution to the terminal. The input data is the solution received from the generative AI model, and the output data is the solution sent to the terminal.

[1613] Step 7:

[1614] The terminal displays the solution to the user. The input data is the solution sent to the terminal, and the output data is the solution displayed to the user.

[1615] Communication Support

[1616] Sending and Managing Messages

[1617] Step 1:

[1618] The user inputs a question or message about the project using the terminal interface. The input data includes the message content. For example, the user inputs "Please tell me about the progress of the project."

[1619] Step 2:

[1620] The terminal sends the input message to the server. The input data is the message input by the user, and the output data is the message sent to the server.

[1621] Step 3:

[1622] The server stores the received message in a database and requests a response from the generative AI model. The input data is the message sent to the server, and the output data is the request sent to the generative AI model.

[1623] Generate and provide answers

[1624] Step 4:

[1625] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The input data is the message information sent to the generative AI model, and the output data is the generated response.

[1626] Step 5:

[1627] The server sends the generated answer to the terminal. The input data is the answer received from the generative AI model, and the output data is the answer sent to the terminal.

[1628] Step 6:

[1629] The terminal displays the answer to the user. The input data is the answer sent to the terminal, and the output data is the answer displayed to the user.

[1630] (Application example 1)

[1631] 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."

[1632] Conventional project management systems make information management cumbersome, making it difficult to manage task progress, resources, and problem-solving, especially in large-scale projects. Similarly, in factory production processes, robots often fail to properly manage production and detect anomalies, resulting in problems that reduce production efficiency and safety. The purpose of this invention is to solve these problems and efficiently manage projects and factory robot production.

[1633] 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.

[1634] In this invention, the server includes means for centrally managing information about the project using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for inputting production information and transmitting it to factory robots, means for sending information about abnormalities detected by the robots to the server and providing solutions from the generative AI model, and means for managing robot maintenance and cleaning schedules and sending reminders. This enables efficient centralized management of information and task progress management, as well as rapid management of the production process and problem resolution using robots.

[1635] A "generative AI model" is an artificial intelligence algorithm that automatically generates solutions to problems or appropriate answers based on information entered by the user.

[1636] "Project Information" means all detailed information related to a Project, including but not limited to Project status, progress, task assignments, and resource reservations.

[1637] A "task" refers to a specific task or activity required to move a project forward.

[1638] A "reminder notification" is a notification that notifies the user of a deadline for an event or task when the set date and time approaches.

[1639] "Issues and risks" refer to challenges and uncertainties that may arise in a project or production process.

[1640] "Solution" refers to the specific response to an encountered problem or risk provided by the generative AI model.

[1641] "Messages" refer to information and questions exchanged between project participants.

[1642] "Production information" refers to detailed information related to the production tasks performed by factory robots.

[1643] "Factory robot" refers to an automated machine used to carry out production processes within a factory.

[1644] "Anomaly information" refers to details of errors or abnormalities detected by factory robots during the production process.

[1645] "Resources" refers to the people, equipment, materials, etc. needed to carry out a project or production process.

[1646] This invention is a project management support tool that uses a generative AI model to support information management, task management, problem solving, and communication. It can also be applied to production management of factory robots, realizing an efficient production process.

[1647] System Overview

[1648] The system consists of three main components: the user, the device, and the server. It centralizes information about projects and production tasks managed by the user and provides solutions using generative AI models.

[1649] Hardware and Software

[1650] Hardware:

[1651] Administrator device (tablet, smartphone)

[1652] Factory robot (control terminal)

[1653] Server (central management system)

[1654] software:

[1655] Database management system (MySQL)

[1656] AI model (GPT-4)

[1657] Interface tool (React Native)

[1658] Data processing and calculation

[1659] Manage and share project information:

[1660] Users input information such as project status, progress, task assignments, and resource reservations from their tablets or smartphones. The information is converted into an appropriate format by the interface tool and sent to the server, which stores the information in a database and makes it accessible to stakeholders.

[1661] Tasks and Reminders:

[1662] Users input tasks and deadlines on their tablets or smartphones, which are then converted by the interface tool and sent to the server, which schedules reminders based on the information entered and sends notifications to the user's device when the specified date and time approaches.

[1663] Problem solving and risk management:

[1664] When a user or robot detects and inputs information about a problem or risk, the information is sent to the server via the interface tool. The server then sends the information to the generative AI model, which then generates a solution. The generated solution is then provided to the user's device or robot via the server.

[1665] Communication support:

[1666] The user enters a question about the project on a tablet or smartphone. The question is sent to the server via the interface tool, and the server sends the question to the generative AI model. The AI ​​model generates an appropriate answer, which is then provided to the user's device via the server.

[1667] Specific examples

[1668] A user adds a "Create design documentation" task by entering the task name, due date, and assignee on a tablet. The interface tool converts the information into the appropriate format and sends it to the server. The server stores it in a database and sets a reminder notification.

[1669] A factory robot detects a "delay in material supply" and sends that information to a server. The server sends a prompt message to the generative AI model informing it of the "delay in material supply" and receives a "suggestion for an alternative supply route" from the AI ​​model. The server sends the suggestion to the robot, which displays it on a display.

[1670] Example prompt sentence:

[1671] There has been a delay in the supply of materials. Please suggest an alternative supply route.

[1672] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1673] Step 1:

[1674] A user inputs project information (e.g., task name, deadline, and assignee) from a tablet or smartphone. The input information is sent to the server through an interface tool (e.g., React Native). This operation allows the user to enter detailed project information into the device, and the interface tool converts the data into an appropriate format and sends it to a database management system (e.g., MySQL).

[1675] Step 2:

[1676] The server stores the received project information in a database management system. This process centralizes project information and makes it accessible to stakeholders. The server creates a new entry in the database and registers the input information, allowing other users and robots to act on this information.

[1677] Step 3:

[1678] The user inputs information to set reminders for tasks and deadlines from their device and sends it to the server. The server receives this information and creates a reminder schedule. The server prepares reminder notifications when the date and time are approaching and sends the notifications to the user's device.

[1679] Step 4:

[1680] A factory robot detects a delay in material supply during the production process and sends the abnormality information to a server. The robot's built-in sensors and control system detect the abnormality and generate detailed error information. The data is then sent to the server via the robot's control terminal.

[1681] Step 5:

[1682] Based on the received anomaly information, the server sends a prompt to the generative AI model (e.g., GPT-4) requesting a solution. The prompt includes details of the anomaly and the required measures. Example: "There has been a delay in the supply of materials. Please propose an alternative supply route."

[1683] Step 6:

[1684] The generative AI model analyzes the prompt and generates an appropriate solution. The generated solution is sent back to the server. The AI ​​model analyzes past data and knowledge bases to propose a rational response.

[1685] Step 7:

[1686] The server sends the solutions received from the generative AI model to the factory robot, which displays the solutions on a display and takes optimal action to avoid unnecessary downtime.

[1687] Step 8:

[1688] A user types a question about a project into a tablet or smartphone and sends it to the server, which then forwards it to the generative AI model and requests an appropriate answer.

[1689] Step 9:

[1690] The generative AI model analyzes the user's question and generates an appropriate answer. The generated answer is sent to the user's device via the server. The device displays the answer to the user, supporting timely information provision.

[1691] Through the above processing steps, project management and factory robot production management are carried out efficiently, and unified information management, problem solving, and communication support are achieved.

[1692] 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.

[1693] This invention is a project management support tool that uses a generative AI model and an emotion engine, and enables project information management, schedule management, problem solving and risk management, communication support, as well as task allocation and advice provision that takes user emotions into consideration. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[1694] Managing and sharing project information

[1695] 1. Enter and manage project information

[1696] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[1697] The terminal converts the input information into an appropriate format and transmits it to the server.

[1698] The server stores the received information in a database and manages it centrally.

[1699] Examples:

[1700] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[1701] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[1702] Scheduling and Reminders

[1703] 1. Enter tasks and deadlines and set reminders

[1704] The user inputs task details and deadlines through the terminal interface.

[1705] The terminal transmits the input information to the server.

[1706] The server stores task and deadline information in a database and schedules reminders.

[1707] 2. Sending reminders

[1708] The server prepares a reminder notification when the set date and time approaches.

[1709] The server sends a reminder notification to the terminal.

[1710] The terminal displays the received reminder notification to the user.

[1711] Examples:

[1712] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[1713] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[1714] The user can check the reminder on the device.

[1715] Problem solving and risk management

[1716] 1. Enter and manage issues and risks

[1717] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[1718] The terminal transmits the input information to the server.

[1719] The server stores the details of the received issues and risks in a database.

[1720] 2. Solution generation and delivery

[1721] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[1722] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1723] The server transmits the generated solution to the terminal.

[1724] The terminal displays the solution to the user.

[1725] Examples:

[1726] The user enters the problem "out of resources" into the terminal.

[1727] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[1728] The server sends the solution to the terminal, and the user confirms the solution.

[1729] Communication Support

[1730] 1. Sending and Managing Messages

[1731] Users use the terminal interface to enter questions and messages about the project.

[1732] The terminal transmits the input message to the server.

[1733] The server stores the received message in a database and requests an answer from the generative AI model.

[1734] 2. Generating and Providing Answers

[1735] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1736] The server sends the generated response to the terminal.

[1737] The terminal displays the answer to the user.

[1738] Examples:

[1739] The user inputs a "question about the progress of the project" into the terminal.

[1740] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[1741] The server sends the answer to the terminal, and the user confirms the answer.

[1742] Utilizing the Emotion Engine

[1743] 1. Considering emotions when assigning tasks

[1744] Based on the user's input and actions, the emotion engine recognizes the user's emotions.

[1745] The terminal transmits the output of the emotion engine to the server.

[1746] The server integrates information from the generative AI model and the emotion engine to assign and adjust tasks according to the user's emotional state.

[1747] 2. Providing encouragement and advice based on progress

[1748] The emotion engine continuously assesses the user's emotions as the project progresses.

[1749] The server combines data from the emotion engine with generative AI models to generate appropriate encouragement and advice.

[1750] The terminal displays the generated encouragement or advice to the user.

[1751] 3. Monitoring user stress levels

[1752] The emotion engine monitors stress levels through user input and behavior.

[1753] The server uses this data to rebalance tasks and re-allocate resources as needed.

[1754] Examples:

[1755] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[1756] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[1757] The device displays this suggestion to the user, helping the user to reduce stress.

[1758] In this way, by combining a generative AI model and an emotion engine, the project management support tool of the present invention can realize information management, schedule management, problem solving, communication support, and task management that takes into account the user's emotions, thereby further improving the efficiency and success rate of projects.

[1759] The processing flow will be explained below.

[1760] Utilizing the Emotion Engine

[1761] Considering emotions when assigning tasks

[1762] Step 1:

[1763] The user inputs data related to his / her emotional state (for example, diary entries, questionnaire responses, etc.) through the terminal interface.

[1764] Step 2:

[1765] The terminal transmits the data input by the user to the emotion engine.

[1766] Step 3:

[1767] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1768] Step 4:

[1769] The server integrates the analysis results received from the emotion engine with the generative AI model.

[1770] Step 5:

[1771] The server uses a generative AI model to generate optimal task allocations based on the user's emotional state and sends the results to the device.

[1772] Step 6:

[1773] The terminal displays the task assignments received from the server to the user.

[1774] Providing encouragement and advice based on progress

[1775] Step 1:

[1776] The emotion engine continuously monitors the user's emotional state as the project progresses.

[1777] Step 2:

[1778] Users report project progress and emotional state to the terminal.

[1779] Step 3:

[1780] The terminal sends the reported data to the emotion engine.

[1781] Step 4:

[1782] The emotion engine analyzes the user's emotional state and sends the results to the server.

[1783] Step 5:

[1784] The server uses the output data of the emotion engine and the generative AI model to generate encouragement and advice for the user.

[1785] Step 6:

[1786] The server transmits the generated encouragement and advice to the terminal.

[1787] Step 7:

[1788] The terminal displays encouragement and advice to the user.

[1789] Monitoring user stress levels

[1790] Step 1:

[1791] The emotion engine monitors stress levels through user input and behavior.

[1792] Step 2:

[1793] The user inputs the task progress and emotional state into the terminal.

[1794] Step 3:

[1795] The terminal transmits the input data to the emotion engine.

[1796] Step 4:

[1797] The emotion engine analyzes the user's stress level and sends the results to the server.

[1798] Step 5:

[1799] The server uses the data from the emotion engine to rebalance tasks and reallocate resources as needed.

[1800] Step 6:

[1801] The server sends the proposed task rebalancing and resource allocation to the terminal.

[1802] Step 7:

[1803] The terminal displays the rebalancing proposal received from the server to the user.

[1804] Examples:

[1805] Step 1: The user enters "relaxed" as the emotional state on a day when they are not feeling particularly stressed.

[1806] Step 2: The device sends this information to the emotion engine.

[1807] Step 3: The emotion engine analyzes the emotion based on the user's input data and sends the result to the server.

[1808] Step 4: The server integrates the results from the emotion engine with the generative AI model and selects a suitable task for the user.

[1809] Step 5: The server sends the generated task assignment to the terminal.

[1810] Step 6: The terminal displays the task assignment to the user.

[1811] Step 7: The user confirms the new task and completes it.

[1812] In this way, the project management support tool of the present invention combines a generative AI model and an emotion engine to realize information management, schedule management, problem solving, communication support, as well as task management and advice that take the user's emotions into consideration, thereby further improving the efficiency and success rate of projects.

[1813] Example 2

[1814] 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."

[1815] Project management requires efficient centralized information management, task scheduling, problem solving, risk management, and communication support, but conventional systems lack consideration for the user's emotional state and stress level, which can result in a decline in project efficiency and success rate.

[1816] 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.

[1817] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating a user's emotional state using an emotion engine and assigning and adjusting tasks, and means for providing encouragement and advice using a generative AI model according to the user's emotional state. This enables project management that takes into account the user's emotional state and stress level, thereby improving the efficiency and success rate of projects.

[1818] A "generative AI model" is an artificial intelligence system that uses artificial intelligence technology to automatically generate solutions and suggestions based on input data.

[1819] "Project information" refers to all data necessary for managing and operating a project, such as project status, progress, task assignments, resource reservations, details of issues and risks, and communications.

[1820] "Centralized management" is a method of consolidating all project-related information in one place and managing it consistently.

[1821] "Tasks and deadlines" refer to the specific tasks that need to be completed within a project and the dates and times by which they should be completed.

[1822] "Reminder notification" is a notification function that notifies users of approaching task deadlines or important events.

[1823] "Issues and risks" refer to factors including obstacles and uncertainties that could impede the progress of a project.

[1824] "Providing a solution" means presenting appropriate ways and means to deal with problems or risks that have arisen.

[1825] "Messages between participants" refers to the content of communications and correspondence exchanged between members involved in a project.

[1826] An "emotion engine" is a system that analyzes the emotional state of a user based on their input and behavior and identifies those emotions.

[1827] "Emotional state" refers to a user's psychological state or mood, and includes measures such as stress level and satisfaction.

[1828] "Task allocation and coordination" refers to distributing work within a project to the appropriate people and changing the schedule and content as necessary.

[1829] "Providing encouragement and advice" refers to sending advice and support messages to increase motivation and improve work efficiency according to the user's emotional state.

[1830] This invention is a project management support system that uses a generative AI model and an emotion engine. This system is mainly composed of four main components: a server, a terminal, a user, and an emotion engine.

[1831] Managing and sharing project information

[1832] The system centralizes project information through the following processes:

[1833] 1. The user provides details about the project status, progress, task assignments, resource reservations, etc. through the device interface, which includes input using text boxes and drop-down menus.

[1834] 2. The terminal receives the information entered by the user, converts it into an appropriate format (for example, JSON format), and sends it to the server.

[1835] 3. The server centralizes the information by storing the received data in a database, which can use a common database management system such as PostgreSQL or MySQL.

[1836] Examples:

[1837] To add a new task "Create design document," the user inputs the task name, deadline, and person in charge into the terminal.

[1838] The terminal sends this information to a server which stores it in a database where it can be accessed by other parties.

[1839] Scheduling and Reminders

[1840] The system manages the scheduling of tasks and events through the following process:

[1841] 1. The user sets the task details and due date using the device interface, which may involve a calendar widget or time picker.

[1842] 2. The device sends this information to the server.

[1843] 3. The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the scheduled date and time approaches.

[1844] 4. The device displays the reminder notification to the user.

[1845] Examples:

[1846] The user inputs the "weekly meeting" schedule into the terminal and sets the date and time of the meeting.

[1847] The server stores this information in a database and sends a reminder notification to the device one hour before the meeting.

[1848] The user checks the reminder notification on their device.

[1849] Problem solving and risk management

[1850] The system provides problem solving and risk management through the following processes:

[1851] 1. The user enters details of the problems and risks that have arisen in the project through the terminal interface.

[1852] 2. The device sends this information to the server.

[1853] 3. The server stores the received information in a database.

[1854] 4. The server sends the stored problem and risk information to the generative AI model and requests a solution. The generative AI model uses a conversational language model, such as OpenAI's GPT series.

[1855] 5. The generative AI model generates a solution based on the received information and sends it to the server.

[1856] 6. The server sends the generated solution to the terminal and presents it to the user.

[1857] Examples:

[1858] The user enters the problem "out of resources" into the terminal.

[1859] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[1860] The server sends the solution to the terminal, and the user confirms the solution.

[1861] Communication Support

[1862] The system supports communication between stakeholders through the following processes:

[1863] 1. The user enters a question or message about the project using the terminal interface.

[1864] 2. The terminal sends this message to the server.

[1865] 3. The server stores the received message in a database and requests an answer from the generative AI model.

[1866] 4. The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1867] 5. The server sends the generated answer to the terminal and displays it to the user.

[1868] Examples:

[1869] The user inputs a "question about the progress of the project" into the terminal.

[1870] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[1871] The server sends the answer to the terminal, and the user confirms the answer.

[1872] Use of emotion engine

[1873] The system manages projects taking into account the user's emotional state through the following process.

[1874] 1. The emotion engine evaluates the emotional state based on the user's input and actions.

[1875] 2. The device sends the output of the emotion engine to the server.

[1876] 3. The server integrates information from the emotion engine and the generative AI model, which then assigns and adjusts tasks according to the user's emotional state.

[1877] 4. The server uses the generative AI model to provide encouragement and advice based on the user's emotional state.

[1878] 5. The device displays the generated encouragement or advice to the user.

[1879] Examples:

[1880] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[1881] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[1882] The device displays this suggestion to the user, helping the user to reduce stress.

[1883] Example prompt sentence:

[1884] "Please tell me more about the current project progress."

[1885] "Propose solutions to resource shortages."

[1886] "How can I reassign tasks for stressed users?"

[1887] In this way, the project management support system of the present invention can improve the efficiency and success rate of project management by effectively utilizing the generative AI model and emotion engine.

[1888] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1889] Managing and sharing project information

[1890] Step 1:

[1891] Users enter details such as project status, progress, task assignments, and resource reservations through the device interface, using text boxes and drop-down menus, and the data is then sorted into appropriate fields.

[1892] Input: Project information such as task name, due date, and assignee

[1893] Output: Project information stored in a data structure in the terminal.

[1894] Step 2:

[1895] The device receives the information entered by the user, converts it into an appropriate format (e.g., JSON) and sends it to the server. This conversion process ensures that the data is in a consistent format.

[1896] Input: Various project information entered by the user

[1897] Output: JSON formatted data

[1898] Step 3:

[1899] The server parses the JSON data received from the device, performs the necessary database operations, and stores the information in a common database management system such as PostgreSQL or MySQL.

[1900] Input: Project information in JSON format

[1901] Output: Project information stored in a database

[1902] Scheduling and Reminders

[1903] Step 1:

[1904] Users use the device interface to set task details and deadlines, using a calendar widget and time picker.

[1905] Input: Task name, due date, reminder settings

[1906] Output: Schedule information stored in a data structure in the terminal

[1907] Step 2:

[1908] The terminal then formats and transmits this information to the server.

[1909] Input: User-entered schedule information

[1910] Output: Data sent to the server in JSON format

[1911] Step 3:

[1912] The server stores the received information in a database, schedules reminders, and creates and sends reminder notifications to the device when the time approaches.

[1913] Input: JSON formatted schedule information sent from the device

[1914] Output: Schedule information and reminder notification schedules stored in the database

[1915] Step 4:

[1916] The terminal receives and displays the reminder notification for display to the user.

[1917] Input: Reminder notification received from the server

[1918] Output: Reminder notification displayed in the user interface

[1919] Problem solving and risk management

[1920] Step 1:

[1921] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[1922] Input: Issue or risk details

[1923] Output: Problem information stored in a data structure in the terminal.

[1924] Step 2:

[1925] The terminal then formats and transmits this information to the server.

[1926] Input: User-entered issue or risk information

[1927] Output: Data sent to the server in JSON format

[1928] Step 3:

[1929] The server stores the received information in a database and then sends it to the generative AI model to request a solution.

[1930] Input: JSON formatted problem information sent from the terminal

[1931] Output: Solution request to the generative AI model

[1932] Step 4:

[1933] The generative AI model generates a solution based on the information it receives and sends it to the server.

[1934] Input: Problem information sent from the server

[1935] Output: The solution generated by the generative AI model

[1936] Step 5:

[1937] The server transmits the generated solution to the terminal and presents it to the user.

[1938] Input: The solution received from the generative AI model

[1939] Output: Solution sent to terminal

[1940] Step 6:

[1941] The terminal displays the solution to the user.

[1942] Input: Solution received from the server

[1943] Output: The solution displayed in the user interface

[1944] Communication Support

[1945] Step 1:

[1946] Users enter questions or messages about the project using the terminal interface.

[1947] Input: The text of your question or message

[1948] Output: Messages stored in a data structure in the terminal

[1949] Step 2:

[1950] The terminal changes the format of this message for transmission to the server and sends it.

[1951] Input: The message entered by the user

[1952] Output: Data sent to the server in JSON format

[1953] Step 3:

[1954] The server stores the received message in a database and requests an answer from the generative AI model.

[1955] Input: JSON formatted message sent from the terminal

[1956] Output: A request to the generative AI model

[1957] Step 4:

[1958] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[1959] Input: The message sent from the server

[1960] Output: The answer generated by the generative AI model

[1961] Step 5:

[1962] The server sends the generated answer to the terminal and displays it to the user.

[1963] Input: The answer received from the generative AI model

[1964] Output: Answer sent to terminal

[1965] Step 6:

[1966] The terminal displays the answer to the user.

[1967] Input: The answer received from the server

[1968] Output: The answer displayed in the user interface

[1969] Use of emotion engine

[1970] Step 1:

[1971] The emotion engine assesses the user's emotional state based on their input and actions.

[1972] Input: User input and behavioral data

[1973] Output: Evaluated emotional state data

[1974] Step 2:

[1975] The terminal transmits the output of the emotion engine to the server.

[1976] Input: Emotional state data from the emotion engine

[1977] Output: Emotional state data sent to the server

[1978] Step 3:

[1979] The server integrates information from the emotion engine and the generative AI model to assign and adjust tasks according to the emotional state.

[1980] Input: Emotional state data from the emotion engine and information from the generative AI model

[1981] Output: Adjusted task assignment data

[1982] Step 4:

[1983] The server uses a generative AI model to generate encouragement and advice based on the user's emotional state.

[1984] Input: Emotional state data from the emotion engine

[1985] Output: Generated encouragement and advice

[1986] Step 5:

[1987] The terminal displays the generated encouragement or advice to the user.

[1988] Input: Encouragement or advice received from the server

[1989] Output: Encouragement or advice displayed in the user interface

[1990] Prompt for the example:

[1991] "Please tell me more about the current project progress."

[1992] "Propose solutions to resource shortages."

[1993] "How can I reassign tasks for stressed users?"

[1994] (Application example 2)

[1995] 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."

[1996] While conventional project management systems provide basic functions such as project information management, schedule management, problem solving, and communication support, they do not optimize responses or task allocations that take into account the user's emotions and stress. This increases the burden and stress on users as the project progresses, resulting in issues such as reduced project efficiency and a higher risk of failure. Furthermore, factory automation requires collaborative work between humans and robots, necessitating efficient, human-centered project management that breaks away from mechanistic management.

[1997] 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.

[1998] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating user emotions using an emotion engine and optimizing task allocation based on the emotions, means for combining the generative AI model and the emotion engine to provide encouragement and advice to the user, and means for monitoring the user's stress level and reallocating tasks and readjusting resources. This enables project management that takes user emotions and stress into consideration, improving factory productivity and efficiency and increasing the project success rate.

[1999] A "generative AI model" is a type of artificial intelligence that generates new information and solutions based on data.

[2000] An "emotion engine" is a system for assessing a user's emotional state and providing responses or suggestions based on that.

[2001] "Project information" includes information such as project status, progress, task assignments, and resource reservations.

[2002] "Task assignment" is the process of assigning specific tasks or jobs to each member of a project.

[2003] "Reminder notification" is a mechanism that sends notifications to users before set deadlines or important events, helping them to remember to respond.

[2004] "Risk management" is the process of identifying risks that may arise during the project and taking measures to address them.

[2005] A "solution" is a method or means for providing an appropriate response or treatment to a particular problem or risk.

[2006] "Message management" is a system for organizing and storing messages sent and received in order to facilitate efficient communication between project members.

[2007] "Encouragement" refers to encouraging words or actions that are used to motivate users or reduce stress.

[2008] The "stress level" is an index that indicates the user's mental and physical tension, and if it is high, it may interfere with work.

[2009] "Task reassignment" is the process of reviewing existing tasks and reassigning them to more appropriate members.

[2010] "Resource readjustment" refers to the reallocation of resources such as personnel, equipment, and time based on the progress of the project and the results of the emotion engine.

[2011] This invention is a project management support system that uses a generative AI model and an emotion engine, and aims to optimize the operation of robots in factories. This system consists of four main elements: a user, a terminal, a server, and an emotion engine. Specifically, the invention is implemented in the following form.

[2012] Managing and sharing project information

[2013] A user inputs information such as project status, progress, task assignment, and resource reservation through the terminal interface. The terminal converts the input information into an appropriate format and sends it to the server, which stores the information in a database for centralized management. For example, when a user adds a new task "Realigning the Production Line," the user inputs the task name, deadline, and person in charge into the terminal. The terminal sends this information to the server, which stores it in a database so that other parties can access the information.

[2014] Scheduling and Reminders

[2015] The user enters task details and deadlines through the device. The device sends this information to the server, which saves the task and deadline information in a database and schedules reminders. When the scheduled date and time approaches, the server prepares a reminder notification and sends it to the device. The device then displays the received reminder notification to the user. For example, a user enters an appointment for a "weekly meeting" into the device and sets the date and time of the meeting. The server saves the information in a database and sends a reminder notification to the device one hour before the meeting, allowing the user to check the reminder.

[2016] Problem solving and risk management

[2017] The user enters details of problems and risks that have arisen in the project through the device interface. The device sends this information to the server, which stores the received details of the problems and risks in a database. The server then sends the problem and risk information stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device, which then displays the solution to the user. For example, if a user enters the problem "lack of resources" into the device, the server sends this problem to the generative AI model and receives the solution "reallocate resources" from the AI ​​model. The server then sends the solution to the device, allowing the user to check the solution.

[2018] Communication Support

[2019] The user uses the device interface to input a question or message about the project. The device sends the input message to the server, which stores the received message in a database and requests an answer from the generative AI model. The generative AI model analyzes the message, generates an appropriate answer, and sends it to the server. The server sends the generated answer to the device, which then displays the answer to the user. For example, if a user inputs a "question about the progress of the project" into the device, the server sends the question to the generative AI model, which receives information about the "latest progress report" from the AI ​​model. The server sends the answer to the device, allowing the user to check the answer.

[2020] Utilizing the Emotion Engine

[2021] The emotion engine can recognize the user's emotions based on their input and actions. The device sends the emotion engine's output to a server, which then integrates the generative AI model and the emotion engine's information to assign and adjust tasks according to the user's emotional state. The emotion engine continuously evaluates the user's emotions as the project progresses, and the server combines the emotion engine's data with the generative AI model to generate appropriate encouragement and advice, which the device displays to the user. The emotion engine also monitors the user's stress level through their input and actions, and the server uses this data to rebalance tasks and readjust resources. For example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses the generative AI model to generate a suggestion to "review task priorities and perform less demanding tasks first." The device displays these suggestions to the user, helping them reduce stress.

[2022] Examples of concrete examples and prompts

[2023] For example, an operator can use a factory robot to add a new task, "Realign the Line," and set a deadline. The robot will then display a reminder before the deadline and proactively detect potential issues. If the operator feels stressed, the robot will use its emotion engine to determine their stress level and optimize task allocation.

[2024] Example prompt sentence:

[2025] User task: "Realign line". Deadline: 10 October 2023, 15:00.

[2026] In this way, the project management support system of the present invention dramatically improves the efficiency of project management in factories and increases the success rate of projects.

[2027] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2028] Step 1:

[2029] The user inputs project information through the terminal interface.

[2030] Specifically, to add a new task, "Re-adjusting the production line," the user inputs detailed information such as the task name, deadline, and person in charge. The device converts this input information into an appropriate format and sends it to the server. This formats the input data, making it easier for the server to process.

[2031] Input: Task name, due date, assignee

[2032] Output: Formatted task information

[2033] Step 2:

[2034] The server receives the task information sent from the terminal.

[2035] The server stores the received task information in a database for centralized management. When storing the information in the database, the project progress and resource allocation information are also updated simultaneously, allowing other stakeholders to check the latest project status in real time.

[2036] Input: Formatted task information

[2037] Output: Task information stored in the database

[2038] Step 3:

[2039] The user sets task details and deadlines through the terminal.

[2040] Specifically, a user enters a "weekly meeting" into their device and sets the date and time of the meeting. The device then sends this information to the server, which stores the task and deadline information in a database and schedules reminders.

[2041] Input: Task details, due date

[2042] Output: Reminder schedule information

[2043] Step 4:

[2044] The server prepares the reminder notification and sends it to the device when the set date and time approaches.

[2045] The server periodically checks the saved reminder schedule and issues reminders at the appropriate time, so that users are notified just before the deadline.

[2046] Input: Reminder schedule information

[2047] Output: Reminder notification to user device

[2048] Step 5:

[2049] The user enters details of the problems and risks that have arisen in the project from the terminal.

[2050] Specifically, a user inputs the problem "lack of resources" into the terminal, and the terminal sends the problem information to the server, which then stores the information in a database.

[2051] Input: Issue or risk details

[2052] Output: Problem information stored in the database

[2053] Step 6:

[2054] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[2055] The generative AI model generates a solution based on the received information and sends it to the server, which adds the solution from the generative AI model to the database.

[2056] Input: Problem information

[2057] Output: A solution from the generative AI model

[2058] Step 7:

[2059] The server sends the generated solution to the terminal, which displays the solution to the user.

[2060] The user can then use the displayed solutions to try to solve the problem.

[2061] As a specific example, the solution "Reallocate resources" may be displayed.

[2062] Input: Solution from a generative AI model

[2063] Output: Solution displayed on user's terminal

[2064] Step 8:

[2065] The user uses the device interface to input questions or messages about the project, and the device sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[2066] Input: Questions and messages from users

[2067] Output: The question information sent to the generative AI model

[2068] Step 9:

[2069] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server, which then sends the generated response to the device, which then displays the response to the user.

[2070] As a concrete example, when a user inputs a "question about the progress of a project," the generative AI model can generate and display an "up-to-date progress report."

[2071] Input: Questions and messages from users

[2072] Output: Answer from the generative AI model

[2073] Step 10:

[2074] The emotion engine recognizes emotions based on the user's input and actions, and sends the state to the server.

[2075] The device monitors the user's stress level based on the output from the emotion engine. Specifically, if the emotion engine determines that the user's stress level is "high," it sends that information to the server, which then integrates the generative AI model and the emotion engine's information to suggest appropriate measures.

[2076] Input: User input and actions

[2077] Output: Emotional state information from the emotion engine

[2078] Step 11:

[2079] The server generates proposals for task reallocation and resource readjustment based on information from the generative AI model and emotion engine, and sends them to the device. The device displays the proposals to the user, helping to reduce the user's stress.

[2080] As a specific example, if a user is feeling stressed about multiple tasks, the emotion engine detects this and notifies the server, which then uses a generative AI model to generate a suggestion to "review task priorities and perform less burdensome tasks first."

[2081] Input: Emotional state information from the emotion engine

[2082] Output: Task reassignment and resource realignment suggestions

[2083] 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.

[2084] 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.

[2085] 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.

[2086] [Fourth embodiment]

[2087] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[2088] 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.

[2089] 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).

[2090] 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.

[2091] 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.

[2092] 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).

[2093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[2094] 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.

[2095] 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.

[2096] 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.

[2097] 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.

[2098] 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.

[2099] 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."

[2100] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[2101] Managing and sharing project information

[2102] 1. Enter and manage project information

[2103] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[2104] The terminal converts the input information into an appropriate format and transmits it to the server.

[2105] The server stores the received information in a database and manages it centrally.

[2106] Examples:

[2107] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[2108] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[2109] Scheduling and Reminders

[2110] 1. Enter tasks and deadlines and set reminders

[2111] The user inputs task details and deadlines through the terminal interface.

[2112] The terminal transmits the input information to the server.

[2113] The server stores task and deadline information in a database and schedules reminders.

[2114] 2. Sending reminders

[2115] The server prepares a reminder notification when the set date and time approaches.

[2116] The server sends a reminder notification to the terminal.

[2117] The terminal displays the reminder to the user.

[2118] Examples:

[2119] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[2120] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[2121] The user can check the reminder on the device.

[2122] Problem solving and risk management

[2123] 1. Enter and manage issues and risks

[2124] Users use the terminal interface to enter details of problems and risks that arise in the project.

[2125] The terminal transmits the input information to the server.

[2126] The server stores the details of the received issues and risks in a database.

[2127] 2. Solution generation and delivery

[2128] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[2129] The generative AI model generates a solution based on the information it receives and sends it to the server.

[2130] The server transmits the generated solution to the terminal.

[2131] The terminal displays the solution to the user.

[2132] Examples:

[2133] The user enters the problem "out of resources" into the terminal.

[2134] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[2135] The server sends the solution to the terminal, and the user confirms the solution.

[2136] Communication Support

[2137] 1. Sending and Managing Messages

[2138] Users use the terminal interface to enter questions and messages about the project.

[2139] The terminal transmits the input message to the server.

[2140] The server stores the received message in a database and requests an answer from the generative AI model.

[2141] 2. Generating and Providing Answers

[2142] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[2143] The server sends the generated response to the terminal.

[2144] The terminal displays the answer to the user.

[2145] Examples:

[2146] The user inputs a "question about the progress of the project" into the terminal.

[2147] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[2148] The server sends the answer to the terminal, and the user confirms the answer.

[2149] The above is an embodiment of the present invention. This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving the efficiency and success rate of projects.

[2150] The processing flow will be explained below.

[2151] Managing and sharing project information

[2152] Step 1:

[2153] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[2154] Step 2:

[2155] The terminal converts the information entered by the user into an appropriate format and transmits it to the server.

[2156] Step 3:

[2157] The server stores the received information in a database and manages it centrally. The stored information can be viewed by related parties.

[2158] Scheduling and Reminders

[2159] Step 1:

[2160] The user inputs task details and deadlines through the terminal interface.

[2161] Step 2:

[2162] The terminal transmits the task information input by the user to the server.

[2163] Step 3:

[2164] The server stores the received task and deadline information in a database and schedules reminders.

[2165] Step 4:

[2166] The server prepares a reminder notification when the set date and time approaches.

[2167] Step 5:

[2168] The server sends a reminder notification to the terminal.

[2169] Step 6:

[2170] The terminal displays the received reminder notification to the user.

[2171] Problem solving and risk management

[2172] Step 1:

[2173] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[2174] Step 2:

[2175] The terminal transmits the problem and risk information entered by the user to the server.

[2176] Step 3:

[2177] The server stores the details of the received issues and risks in a database.

[2178] Step 4:

[2179] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[2180] Step 5:

[2181] The generative AI model generates a solution based on the information it receives and sends it to the server.

[2182] Step 6:

[2183] The server transmits the generated solution to the terminal.

[2184] Step 7:

[2185] The terminal displays the solution to the user.

[2186] Communication Support

[2187] Step 1:

[2188] Users use the terminal interface to enter questions and messages about the project.

[2189] Step 2:

[2190] The terminal transmits the message entered by the user to the server.

[2191] Step 3:

[2192] The server stores the received message in a database and requests an answer from the generative AI model.

[2193] Step 4:

[2194] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[2195] Step 5:

[2196] The server sends the generated response to the terminal.

[2197] Step 6:

[2198] The terminal displays the answer to the user.

[2199] Specific examples

[2200] Examples of adding tasks and reminders

[2201] Step 1:

[2202] The user inputs the task "Create meeting materials" into the interface of the terminal.

[2203] Step 2:

[2204] The terminal sends detailed information about the task (task name, deadline, etc.) to the server.

[2205] Step 3:

[2206] The server stores the received information in a database and sets a reminder to "create meeting materials" for 10:00 AM next Friday.

[2207] Step 4:

[2208] The server prepares a reminder notification when the set date and time (Friday 10:00 AM) approaches.

[2209] Step 5:

[2210] The server sends a reminder notification to the terminal.

[2211] Step 6:

[2212] The terminal displays a reminder for the task "Create meeting materials" to the user.

[2213] Example 1

[2214] 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."

[2215] In project management, a lot of information is scattered, making it difficult to centrally manage information such as status, progress, task assignment, and resource reservations. There are also challenges such as missing task deadlines and finding quick and appropriate solutions when problems or risks arise. Furthermore, there is a need to improve the efficiency of communication between stakeholders.

[2216] 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.

[2217] In this invention, the server includes means for centrally managing project information using a generative AI model, means for transmitting information entered by a user, such as project status, progress, task assignment, and resource reservation, to the server via a terminal and storing the information in a database, means for inputting project tasks and deadlines and sending reminder notifications, means for generating reminder notifications when a set date and time approaches and sending them to the user's terminal, means for transmitting information on problems and risks entered by the user to the generative AI model and providing solutions, and means for managing messages between parties and providing appropriate answers using the generative AI model. This enables centralized management of project information, prevention of missed deadlines, faster problem resolution, and efficient communication between parties.

[2218] A "generative AI model" is a model generated using artificial intelligence technology, and is a system that analyzes and infers based on input data to provide optimal solutions and answers.

[2219] A "server" is a centralized computer that receives, stores, processes, and transmits information from multiple terminals over a network.

[2220] A "user" is a person or group who uses this system to input, manage, and view project information.

[2221] A "terminal" is a device used by a user to input information, and includes a PC, tablet, smartphone, etc.

[2222] "Project status" is information that indicates the current state and progress of a project.

[2223] "Progress" is an indicator of how far tasks and activities within a project have progressed.

[2224] "Task assignment" refers to assigning a specific task to a specific user.

[2225] "Resource reservation" refers to reserving resources (people, equipment, materials, etc.) required for a project in advance for use during a specific period of time.

[2226] A "database" is a system for organizing large amounts of data and making it easy to search, update, and manage.

[2227] "Reminder notification" is a function that notifies the user of important events or tasks at a set date and time.

[2228] "Issues and risks" refer to obstacles or uncertainties that could impede the progress of a project.

[2229] A "solution" refers to a specific means or method for resolving a specific problem or risk.

[2230] "Messages" are a means of communication for users to exchange project-related information and questions.

[2231] "Answer" refers to the appropriate information or explanation provided by the generative AI model in response to a received message.

[2232] This invention is a project management support tool that uses a generative AI model to effectively manage project information, schedule management, problem solving and risk management, and communication support. This system consists of three main elements: users, terminals, and servers.

[2233] Managing and sharing project information

[2234] 1. Enter and manage project information

[2235] Through the terminal interface, users input details such as project status, progress, task assignments, and resource reservations. The terminal converts the input information into an appropriate format and sends it to the server, which stores the received information in a database for centralized management.

[2236] For example, a user can add a new task, "Create a design document," by entering the task name, deadline, and assignee into a terminal. This information is converted to JSON format on the terminal and sent to the server. The server stores this information in a database, making it accessible to other parties.

[2237] Scheduling and Reminders

[2238] 2. Enter tasks and deadlines and set reminders

[2239] The user enters task details and deadlines through the device interface, and the device sends the information to the server, which stores the task and deadline information in a database and schedules reminders.

[2240] 3. Send reminders

[2241] When the set date and time approaches, the server prepares a reminder notification and sends it to the user's device, which then displays the reminder to the user.

[2242] For example, a user can schedule a "weekly meeting" on their device and set the date and time of the meeting. The server stores the information in a database and sends a reminder notification to the device one hour before the meeting. The user can then check the reminder on their device.

[2243] Problem solving and risk management

[2244] 4. Enter and manage issues and risks

[2245] The user enters details of problems and risks that have arisen in the project using the terminal interface. The terminal sends the entered information to the server, which stores the received details of problems and risks in a database.

[2246] 5. Solution generation and delivery

[2247] The server sends information about problems and risks stored in the database to the generative AI model and requests a solution. The generative AI model generates a solution based on the received information and sends it to the server. The server then sends the generated solution to the device and displays it to the user.

[2248] For example, a user inputs a problem called "lack of resources" into a device. The server sends this problem to a generative AI model and receives a solution to "resource reallocation" from the AI ​​model. The server then sends the solution to the device, where the user confirms it.

[2249] Communication Support

[2250] 6. Sending and Managing Messages

[2251] Users use the device interface to input questions or messages about the project. The device then sends the input messages to the server, which stores the received messages in a database and requests answers from the generative AI model.

[2252] 7. Generating and Providing Answers

[2253] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The server then sends the generated response to the device, where the user can confirm the response.

[2254] For example, a user enters a question about the progress of a project into a device. The server sends the question to a generative AI model and receives the latest progress report from the AI ​​model. The server then sends the answer to the device, and the user confirms the answer.

[2255] This system enables effective project information management, schedule management, problem solving and risk management, and communication support, thereby improving project efficiency and success rates.

[2256] Prompt Sentence Examples

[2257] Creating Design Documentation

[2258] Weekly Meeting

[2259] "Lack of resources"

[2260] "Please tell me about the progress of the project."

[2261] The flow of the identification process in the first embodiment will be described with reference to FIG.

[2262] Managing and sharing project information

[2263] Entering and managing project information

[2264] Step 1:

[2265] The user uses the terminal interface to input project status, progress, task assignments, and resource reservations. The input data includes project information. For example, the user might input "Task name: Create design document," "Deadline: 2023-10-15," and "Responsible person: Tanaka."

[2266] Step 2:

[2267] The terminal receives the input information and converts it into an appropriate format (for example, JSON format). The input data is the project information entered by the user, and the output data is the formatted project information. Specifically, it converts it into {"task_name": "Create design document", "deadline": "2023-10-15", "assignee": "Tanaka"}.

[2268] Step 3:

[2269] The terminal sends the formatted information to the server, where the input data is the project information formatted by the terminal and the output data is the project information sent to the server.

[2270] Step 4:

[2271] The server validates the received information and stores it in a database. The input data is the project information sent to the server, and the output data is the project information stored in the database. A database system such as MySQL is used to store the data.

[2272] Scheduling and Reminders

[2273] Enter tasks and deadlines and set reminders

[2274] Step 1:

[2275] The user enters task details and deadlines through the device interface. Specifically, the user enters the task name, deadline, reminder settings, etc. For example, the input data might be "Task name: Weekly meeting" and "Date and time: 2023-10-20 10:00."

[2276] Step 2:

[2277] The terminal transmits the input information to the server. The input data is the task information input by the user, and the output data is the task information transmitted to the server.

[2278] Step 3:

[2279] The server stores task and deadline information in a database and sets reminder schedules. The input data is the task information sent to the server, and the output data is the task information stored in the database and the set reminder schedule.

[2280] Sending reminders

[2281] Step 4:

[2282] The server prepares the reminder notification when the set date and time approaches. The input data is the reminder schedule stored in the database, and the output data is the prepared reminder notification.

[2283] Step 5:

[2284] The server sends the reminder notification to the terminal, where the input data is the prepared reminder notification and the output data is the reminder notification sent to the terminal.

[2285] Step 6:

[2286] The terminal displays the reminder to the user. The input data is the reminder notification sent to the terminal, and the output data is the reminder displayed to the user.

[2287] Problem solving and risk management

[2288] Enter and manage issues and risks

[2289] Step 1:

[2290] The user inputs details of problems and risks that have occurred in the project using the terminal interface. The input data includes information about the problems and risks. For example, the user inputs "Problem: Lack of resources."

[2291] Step 2:

[2292] The terminal sends the input information to the server. The input data is the problem or risk information entered by the user, and the output data is the problem or risk information sent to the server.

[2293] Step 3:

[2294] The server stores the received problem and risk information in a database. The input data is the problem and risk information sent to the server, and the output data is the problem and risk information stored in the database.

[2295] Solution generation and delivery

[2296] Step 4:

[2297] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution. The input data is the problem and risk information stored in the database, and the output data is the request sent to the generative AI model.

[2298] Step 5:

[2299] The generative AI model generates solutions based on the received information and sends them to the server. The input data is the problem and risk information sent to the generative AI model, and the output data is the generated solution.

[2300] Step 6:

[2301] The server sends the generated solution to the terminal. The input data is the solution received from the generative AI model, and the output data is the solution sent to the terminal.

[2302] Step 7:

[2303] The terminal displays the solution to the user. The input data is the solution sent to the terminal, and the output data is the solution displayed to the user.

[2304] Communication Support

[2305] Sending and Managing Messages

[2306] Step 1:

[2307] The user inputs a question or message about the project using the terminal interface. The input data includes the message content. For example, the user inputs "Please tell me about the progress of the project."

[2308] Step 2:

[2309] The terminal sends the input message to the server. The input data is the message input by the user, and the output data is the message sent to the server.

[2310] Step 3:

[2311] The server stores the received message in a database and requests a response from the generative AI model. The input data is the message sent to the server, and the output data is the request sent to the generative AI model.

[2312] Generate and provide answers

[2313] Step 4:

[2314] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server. The input data is the message information sent to the generative AI model, and the output data is the generated response.

[2315] Step 5:

[2316] The server sends the generated answer to the terminal. The input data is the answer received from the generative AI model, and the output data is the answer sent to the terminal.

[2317] Step 6:

[2318] The terminal displays the answer to the user. The input data is the answer sent to the terminal, and the output data is the answer displayed to the user.

[2319] (Application example 1)

[2320] 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."

[2321] Conventional project management systems make information management cumbersome, making it difficult to manage task progress, resources, and problem-solving, especially in large-scale projects. Similarly, in factory production processes, robots often fail to properly manage production and detect anomalies, resulting in problems that reduce production efficiency and safety. The purpose of this invention is to solve these problems and efficiently manage projects and factory robot production.

[2322] 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.

[2323] In this invention, the server includes means for centrally managing information about the project using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for inputting production information and transmitting it to factory robots, means for sending information about abnormalities detected by the robots to the server and providing solutions from the generative AI model, and means for managing robot maintenance and cleaning schedules and sending reminders. This enables efficient centralized management of information and task progress management, as well as rapid management of the production process and problem resolution using robots.

[2324] A "generative AI model" is an artificial intelligence algorithm that automatically generates solutions to problems or appropriate answers based on information entered by the user.

[2325] "Project Information" means all detailed information related to a Project, including but not limited to Project status, progress, task assignments, and resource reservations.

[2326] A "task" refers to a specific task or activity required to move a project forward.

[2327] A "reminder notification" is a notification that notifies the user of a deadline for an event or task when the set date and time approaches.

[2328] "Issues and risks" refer to challenges and uncertainties that may arise in a project or production process.

[2329] "Solution" refers to the specific response to an encountered problem or risk provided by the generative AI model.

[2330] "Messages" refer to information and questions exchanged between project participants.

[2331] "Production information" refers to detailed information related to the production tasks performed by factory robots.

[2332] "Factory robot" refers to an automated machine used to carry out production processes within a factory.

[2333] "Anomaly information" refers to details of errors or abnormalities detected by factory robots during the production process.

[2334] "Resources" refers to the people, equipment, materials, etc. needed to carry out a project or production process.

[2335] This invention is a project management support tool that uses a generative AI model to support information management, task management, problem solving, and communication. It can also be applied to production management of factory robots, realizing an efficient production process.

[2336] System Overview

[2337] The system consists of three main components: the user, the device, and the server. It centralizes information about projects and production tasks managed by the user and provides solutions using generative AI models.

[2338] Hardware and Software

[2339] Hardware:

[2340] Administrator device (tablet, smartphone)

[2341] Factory robot (control terminal)

[2342] Server (central management system)

[2343] software:

[2344] Database management system (MySQL)

[2345] AI model (GPT-4)

[2346] Interface tool (React Native)

[2347] Data processing and calculation

[2348] Manage and share project information:

[2349] Users input information such as project status, progress, task assignments, and resource reservations from their tablets or smartphones. The information is converted into an appropriate format by the interface tool and sent to the server, which stores the information in a database and makes it accessible to stakeholders.

[2350] Tasks and Reminders:

[2351] Users input tasks and deadlines on their tablets or smartphones, which are then converted by the interface tool and sent to the server, which schedules reminders based on the information entered and sends notifications to the user's device when the specified date and time approaches.

[2352] Problem solving and risk management:

[2353] When a user or robot detects and inputs information about a problem or risk, the information is sent to the server via the interface tool. The server then sends the information to the generative AI model, which then generates a solution. The generated solution is then provided to the user's device or robot via the server.

[2354] Communication support:

[2355] The user enters a question about the project on a tablet or smartphone. The question is sent to the server via the interface tool, and the server sends the question to the generative AI model. The AI ​​model generates an appropriate answer, which is then provided to the user's device via the server.

[2356] Specific examples

[2357] A user adds a "Create design documentation" task by entering the task name, due date, and assignee on a tablet. The interface tool converts the information into the appropriate format and sends it to the server. The server stores it in a database and sets a reminder notification.

[2358] A factory robot detects a "delay in material supply" and sends that information to a server. The server sends a prompt message to the generative AI model informing it of the "delay in material supply" and receives a "suggestion for an alternative supply route" from the AI ​​model. The server sends the suggestion to the robot, which displays it on a display.

[2359] Example prompt sentence:

[2360] There has been a delay in the supply of materials. Please suggest an alternative supply route.

[2361] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2362] Step 1:

[2363] A user inputs project information (e.g., task name, deadline, and assignee) from a tablet or smartphone. The input information is sent to the server through an interface tool (e.g., React Native). This operation allows the user to enter detailed project information into the device, and the interface tool converts the data into an appropriate format and sends it to a database management system (e.g., MySQL).

[2364] Step 2:

[2365] The server stores the received project information in a database management system. This process centralizes project information and makes it accessible to stakeholders. The server creates a new entry in the database and registers the input information, allowing other users and robots to act on this information.

[2366] Step 3:

[2367] The user inputs information to set reminders for tasks and deadlines from their device and sends it to the server. The server receives this information and creates a reminder schedule. The server prepares reminder notifications when the date and time are approaching and sends the notifications to the user's device.

[2368] Step 4:

[2369] A factory robot detects a delay in material supply during the production process and sends the abnormality information to a server. The robot's built-in sensors and control system detect the abnormality and generate detailed error information. The data is then sent to the server via the robot's control terminal.

[2370] Step 5:

[2371] Based on the received anomaly information, the server sends a prompt to the generative AI model (e.g., GPT-4) requesting a solution. The prompt includes details of the anomaly and the required measures. Example: "There has been a delay in the supply of materials. Please propose an alternative supply route."

[2372] Step 6:

[2373] The generative AI model analyzes the prompt and generates an appropriate solution. The generated solution is sent back to the server. The AI ​​model analyzes past data and knowledge bases to propose a rational response.

[2374] Step 7:

[2375] The server sends the solutions received from the generative AI model to the factory robot, which displays the solutions on a display and takes optimal action to avoid unnecessary downtime.

[2376] Step 8:

[2377] A user types a question about a project into a tablet or smartphone and sends it to the server, which then forwards it to the generative AI model and requests an appropriate answer.

[2378] Step 9:

[2379] The generative AI model analyzes the user's question and generates an appropriate answer. The generated answer is sent to the user's device via the server. The device displays the answer to the user, supporting timely information provision.

[2380] Through the above processing steps, project management and factory robot production management are carried out efficiently, and unified information management, problem solving, and communication support are achieved.

[2381] 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.

[2382] This invention is a project management support tool that uses a generative AI model and an emotion engine, and enables project information management, schedule management, problem solving and risk management, communication support, as well as task allocation and advice provision that takes user emotions into consideration. This system consists of four main elements: the user, the terminal, the server, and the emotion engine.

[2383] Managing and sharing project information

[2384] 1. Enter and manage project information

[2385] Through the terminal interface, users enter details such as project status, progress, task assignments, and resource reservations.

[2386] The terminal converts the input information into an appropriate format and transmits it to the server.

[2387] The server stores the received information in a database and manages it centrally.

[2388] Examples:

[2389] To add a new task "creating a design document," the user inputs the task name, deadline, and person in charge into the terminal.

[2390] The device sends this information to a server, which stores it in a database, making it accessible to other parties.

[2391] Scheduling and Reminders

[2392] 1. Enter tasks and deadlines and set reminders

[2393] The user inputs task details and deadlines through the terminal interface.

[2394] The terminal transmits the input information to the server.

[2395] The server stores task and deadline information in a database and schedules reminders.

[2396] 2. Sending reminders

[2397] The server prepares a reminder notification when the set date and time approaches.

[2398] The server sends a reminder notification to the terminal.

[2399] The terminal displays the received reminder notification to the user.

[2400] Examples:

[2401] The user inputs the schedule for the "weekly meeting" through the terminal and sets the date and time of the meeting.

[2402] The server stores the information in a database and sends a reminder to the device one hour before the meeting.

[2403] The user can check the reminder on the device.

[2404] Problem solving and risk management

[2405] 1. Enter and manage issues and risks

[2406] Users enter details of problems and risks that have arisen in the project through a terminal interface.

[2407] The terminal transmits the input information to the server.

[2408] The server stores the details of the received issues and risks in a database.

[2409] 2. Solution generation and delivery

[2410] The server sends the problem and risk information stored in the database to the generative AI model and requests a solution.

[2411] The generative AI model generates a solution based on the information it receives and sends it to the server.

[2412] The server transmits the generated solution to the terminal.

[2413] The terminal displays the solution to the user.

[2414] Examples:

[2415] The user enters the problem "out of resources" into the terminal.

[2416] The server sends this problem to a generative AI model and receives a "resource reallocation" solution from the AI ​​model.

[2417] The server sends the solution to the terminal, and the user confirms the solution.

[2418] Communication Support

[2419] 1. Sending and Managing Messages

[2420] Users use the terminal interface to enter questions and messages about the project.

[2421] The terminal transmits the input message to the server.

[2422] The server stores the received message in a database and requests an answer from the generative AI model.

[2423] 2. Generating and Providing Answers

[2424] The generative AI model analyzes the message, generates an appropriate response, and sends it to the server.

[2425] The server sends the generated response to the terminal.

[2426] The terminal displays the answer to the user.

[2427] Examples:

[2428] The user inputs a "question about the progress of the project" into the terminal.

[2429] The server sends this question to the generative AI model and receives "latest progress report" information from the AI ​​model.

[2430] The server sends the answer to the terminal, and the user confirms the answer.

[2431] Utilizing the Emotion Engine

[2432] 1. Considering emotions when assigning tasks

[2433] Based on the user's input and actions, the emotion engine recognizes the user's emotions.

[2434] The terminal transmits the output of the emotion engine to the server.

[2435] The server integrates information from the generative AI model and the emotion engine to assign and adjust tasks according to the user's emotional state.

[2436] 2. Providing encouragement and advice based on progress

[2437] The emotion engine continuously assesses the user's emotions as the project progresses.

[2438] The server combines data from the emotion engine with generative AI models to generate appropriate encouragement and advice.

[2439] The terminal displays the generated encouragement or advice to the user.

[2440] 3. Monitoring user stress levels

[2441] The emotion engine monitors stress levels through user input and behavior.

[2442] The server uses this data to rebalance tasks and re-allocate resources as needed.

[2443] Examples:

[2444] If the user is feeling stressed about multiple tasks, the emotion engine will detect this and notify the server.

[2445] The server uses a generative AI model to generate suggestions such as "review task priorities and do less demanding tasks first."

[2446] The device displays this suggestion to the user, helping the user to reduce stress.

[2447] In this way, by combining a generative AI model and an emotion engine, the project management support tool of the present invention can realize information management, schedule management, problem solving, communication support, and task management that takes into account the user's emotions, thereby further improving the efficiency and success rate of projects.

[2448] The processing flow will be explained below.

[2449] Utilizing the Emotion Engine

[2450] Considering emotions when assigning tasks

[2451] Step 1:

[2452] The user inputs data related to his / her emotional state (for example, diary entries, questionnaire responses, etc.) through the terminal interface.

[2453] Step 2:

[2454] The terminal transmits the data input by the user to the emotion engine.

[2455] Step 3:

[2456] The emotion engine analyzes the user's emotional state and sends the results to the server.

[2457] Step 4:

[2458] The server integrates the analysis results received from the emotion engine with the generative AI model.

[2459] Step 5:

[2460] The server uses a generative AI model to generate optimal task allocations based on the user's emotional state and sends the results to the device.

[2461] Step 6:

[2462] The terminal displays the task assignments received from the server to the user.

[2463] Providing encouragement and advice based on progress

[2464] Step 1:

[2465] The emotion engine continuously monitors the user's emotional state as the project progresses.

[2466] Step 2:

[2467] Users report project progress and emotional state to the terminal.

[2468] Step 3:

[2469] The terminal sends the reported data to the emotion engine.

[2470] Step 4:

[2471] The emotion engine analyzes the user's emotional state and sends the results to the server.

[2472] Step 5:

[2473] The server uses the output data of the emotion engine and the generative AI model to generate encouragement and advice for the user.

[2474] Step 6:

[2475] The server transmits the generated encouragement and advice to the terminal.

[2476] Step 7:

[2477] The terminal displays encouragement and advice to the user.

[2478] Monitoring user stress levels

[2479] Step 1:

[2480] The emotion engine monitors stress levels through user input and behavior.

[2481] Step 2:

[2482] The user inputs the task progress and emotional state into the terminal.

[2483] Step 3:

[2484] The terminal transmits the input data to the emotion engine.

[2485] Step 4:

[2486] The emotion engine analyzes the user's stress level and sends the results to the server.

[2487] Step 5:

[2488] The server uses the data from the emotion engine to rebalance tasks and reallocate resources as needed.

[2489] Step 6:

[2490] The server sends the proposed task rebalancing and resource allocation to the terminal.

[2491] Step 7:

[2492] The terminal displays the rebalancing proposal received from the server to the user.

[2493] Examples:

[2494] Step 1: The user enters "relaxed" as the emotional state on a day when they are not feeling particularly stressed.

[2495] Step 2: The device sends this information to the emotion engine.

[2496] Step 3: The emotion engine analyzes the emotion based on the user's input data and sends the result to the server.

[2497] Step 4: The server integrates the results from the emotion engine with the generative AI model and selects a suitable task for the user.

[2498] Step 5: The server sends the generated task assignment to the terminal.

[2499] Step 6: The terminal displays the task assignment to the user.

[2500] Step 7: The user confirms the new task and completes it.

[2501] In this way, the project management support tool of the present invention combines a generative AI model and an emotion engine to realize information management, schedule management, problem solving, communication support, as well as task management and advice that take the user's emotions into consideration, thereby further improving the efficiency and success rate of projects.

[2502] Example 2

[2503] 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."

[2504] Project management requires efficient centralized information management, task scheduling, problem solving, risk management, and communication support, but conventional systems lack consideration for the user's emotional state and stress level, which can result in a decline in project efficiency and success rate.

[2505] 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.

[2506] In this invention, the server includes means for centrally managing project information using a generative AI model, means for inputting project tasks and deadlines and sending reminder notifications, means for inputting details of problems and risks and providing solutions using a generative AI model, means for managing messages between stakeholders and providing appropriate answers using a generative AI model, means for evaluating a user's emotional state using an emotion engine and assigning and adjusting tasks, and means for providing encouragement and advice using a generative AI model according to the user's emotional state. This enables project management that takes into account the user's emotional state and stress level, thereby improving the efficiency and success rate of projects.

[2507] A "generative AI model" is an artificial intelligence system that uses artificial intelligence technology to automatically generate solutions and suggestions based on input data.

[2508] "Project information" refers to all data necessary for managing and operating a project, such as project status, progress, task assignments, resource reservations, details of issues and risks, and communications.

[2509] "Centralized management" is a method of consolidating all project-related information in one place and managing it consistently.

[2510] "Tasks and deadlines" refer to the specific tasks that need to be completed within a project and the dates and times by which they should be completed.

[2511] "Reminder notification" is a notification function that notifies users of approaching task deadlines or important events.

[2512] "Issues and risks" refer to factors including obstacles and uncertainties tha...

Claims

1. A means to centralize project information using generative AI models, and A way to enter project tasks and deadlines and send reminders, A way to input details of a problem or risk and have a generative AI model provide a solution; A system that includes a means to manage messages between parties and provide appropriate responses using generative AI models.

2. The system of claim 1, wherein the means for centrally managing project information stores information entered by users, such as project status, progress, task assignment, and resource reservation, in a database and makes it accessible to those involved.

3. 2. The system according to claim 1, wherein the reminder notification sending means prepares a reminder notification and sends it to the user's terminal when the set date and time approaches.

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