system

Abstract

The system according to this embodiment aims to reduce the workload of managers and free up time for member development and management. [Solution] The system according to the embodiment comprises a task creation unit, a role assignment unit, a progress monitoring unit, and a feedback unit. The task creation unit identifies tasks. The role assignment unit assigns tasks to members based on the tasks identified by the task creation unit. The progress monitoring unit aggregates the activities and progress of each person in charge for the tasks assigned by the role assignment unit. The feedback unit provides feedback based on the activities and progress aggregated by the progress monitoring unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the workload of management positions becomes excessive and sufficient time for member training and management cannot be ensured.

[0005] The system according to the embodiment aims to reduce the workload of management positions and ensure time for member training and management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a task creation unit, a role assignment unit, a progress monitoring unit, and a feedback unit. The task creation unit identifies tasks. The role assignment unit assigns tasks to members based on the tasks identified by the task creation unit. The progress monitoring unit aggregates the activities and progress of each person in charge for the tasks assigned by the role assignment unit. The feedback unit provides feedback based on the activities and progress aggregated by the progress monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the workload of managers and free up time for training and managing team members. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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. Also, the database 24 and the communication I / F 26 are 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).

[0019] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that reduces the workload of managers and improves employee satisfaction and managerial aspirations. This AI agent system works by having multiple AI agents cooperate to support managers in order to alleviate "collaboration fatigue" that arises when managers participate in various projects and working groups. First, a task creation AI agent identifies the tasks necessary for the project or working group that has been convened. At this time, specific tasks are identified based on the purpose and content of the project. For example, in the case of a new product development project, tasks such as planning, prototyping, testing, and marketing are identified. Next, a role assignment AI agent assigns responsibilities to each task, taking into account the activity history, career history, work experience, and communication history of the members under its supervision. For example, a member with extensive prototyping experience is assigned to the prototyping task, and a member with marketing experience is assigned to the marketing task. Furthermore, a progress monitoring AI agent aggregates the activities and progress of each person in charge for their task. The progress of each member is grasped in real time, and delays and problems in tasks are detected early. For example, if the prototyping task is behind schedule, the cause is identified and appropriate measures are taken. Furthermore, the AI ​​feedback agent provides feedback on the entire process, including assignment suitability and tasks requiring supervisor support, which are then communicated to managers. This allows managers to properly educate and support their team members. For example, if a prototype task is behind schedule and it is determined that the cause is a lack of skills on the part of the team member, the manager can provide appropriate guidance to that member. This system enables both managers and team members to work more effectively, leading to increased employee satisfaction and aspirations for management. With the support of the AI ​​agent, managers can focus on developing and managing their team members, and team members can maximize their skills by being assigned appropriate tasks. In this way, the AI ​​agent system reduces the workload of managers and improves employee satisfaction and aspirations for management.

[0029] The AI ​​agent system according to this embodiment comprises a task creation unit, a role assignment unit, a progress monitoring unit, and a feedback unit. The task creation unit identifies tasks. For example, the task creation unit identifies specific tasks based on the project's objectives and content. For example, in a new product development project, the task creation unit can identify tasks such as planning, prototyping, testing, and marketing. The task creation unit can also dynamically change the level of detail of tasks according to the project's progress. For example, in the initial stages of the project, the task creation unit sets the level of detail of tasks high and provides specific instructions. In the middle of the project, it sets the level of detail of tasks to a moderate level, respecting the autonomy of the members. In the final stages of the project, it sets the level of detail of tasks low to encourage the members' creativity and ingenuity. The role assignment unit assigns tasks to members based on the tasks identified by the task creation unit. For example, the role assignment unit assigns responsibilities to each task by considering the members' activity history, career history, work experience, and communication history using communication tools. For example, the role assignment department can assign members with extensive prototyping experience to prototyping tasks and members with marketing experience to marketing tasks. The role assignment department can also analyze members' skill sets in detail and dynamically assign the most suitable roles. The progress monitoring department aggregates the activities and progress of each person in charge of the tasks assigned by the role assignment department. For example, the progress monitoring department can grasp each member's progress in real time and detect delays and problems early. For example, if a prototyping task is behind schedule, the progress monitoring department can identify the cause and take appropriate measures. The progress monitoring department can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring department improves the accuracy of progress predictions based on past project data. The feedback department provides feedback based on the activities and progress aggregated by the progress monitoring department. For example, the feedback department provides feedback to managers on tasks that require assignment suitability or supervisor support.For example, if the progress of a prototype task is behind schedule, the feedback unit will inform the manager that the cause is a lack of skills on the part of the team members. The feedback unit can also provide information that enables managers to educate and support team members based on the feedback. For example, the feedback unit can provide specific guidance methods for educating and supporting team members. In this way, the AI ​​agent system according to the embodiment can reduce the workload of managers and improve employee satisfaction and management aspirations.

[0030] The task creation department identifies tasks. For example, it identifies specific tasks based on the project's objectives and content. Specifically, in the initial stages of a project, it grasps the overall picture of the project and lists the necessary tasks. For example, in a new product development project, it can identify tasks such as planning, prototyping, testing, and marketing. The task creation department can also dynamically change the level of detail of tasks according to the project's progress. For example, in the initial stages of a project, the level of detail of tasks is set high, providing specific instructions. This allows members to proceed with their work according to clear instructions. In the middle stages of the project, the level of detail of tasks is set to a medium level, respecting the autonomy of the members. This allows members to proceed with their work based on their own judgment and exercise their creativity. In the final stages of the project, the level of detail of tasks is set low, encouraging creativity and ingenuity from the members. This allows members to proceed with their work utilizing their own ideas, maximizing the project's results. The task creation department can use AI to monitor the project's progress in real time and adjust the level of detail of tasks as needed. For example, AI analyzes the project's progress and the members' work status, automatically adjusting the level of detail in tasks. This allows the task creation unit to respond flexibly to the project's progress, thereby improving the project's success rate.

[0031] The role allocation department assigns tasks to members based on the tasks identified by the task creation department. The role allocation department considers, for example, each member's activity history, background, work experience, and communication history when assigning tasks. Specifically, the role allocation department analyzes each member's skill set and past work experience in detail to assign the most suitable task. For example, a member with extensive prototyping experience can be assigned to a prototyping task, and a member with marketing experience can be assigned to a marketing task. Furthermore, the role allocation department can dynamically assign the most suitable role by analyzing members' skill sets in detail. For example, the role allocation department analyzes members' skill sets and assigns the most suitable role. In addition, the role allocation department can utilize AI to analyze members' skill sets and work history in real time and assign the most suitable task. For example, AI analyzes members' skill sets and work history and automatically assigns the most suitable task. This allows the role allocation department to achieve optimal task assignment based on members' skill sets and work history, improving project efficiency. Furthermore, the role assignment department can assign tasks based on members' skill sets and work history, taking into account their growth and career paths. This allows members to maximize their skills, grow professionally, and contribute to the success of the project.

[0032] The progress monitoring department aggregates the activities and progress of each person in charge of tasks assigned by the role allocation department. For example, the progress monitoring department grasps the progress of each member in real time and detects task delays and problems early. Specifically, the progress monitoring department monitors the progress of each member in real time and visualizes the progress of tasks. For example, if a prototype task is behind schedule, the progress monitoring department identifies the cause and takes appropriate measures. The progress monitoring department can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring department improves the accuracy of progress predictions based on past project data. Furthermore, the progress monitoring department can use AI to analyze progress in real time and detect task delays and problems early. For example, AI analyzes the progress of each member and automatically identifies task delays and problems. This allows the progress monitoring department to detect task delays and problems early and take appropriate measures. Furthermore, the progress monitoring team makes it easier to grasp the overall progress of the project by visualizing the progress of each member. This allows the progress monitoring team to understand the project's progress in real time, identify task delays and problems early, and take appropriate measures.

[0033] The Feedback Department provides feedback based on activities and progress compiled by the Progress Monitoring Department. For example, the Feedback Department provides feedback to managers regarding assignment suitability and tasks requiring supervisor support. Specifically, the Feedback Department analyzes each member's progress and activities and provides appropriate feedback. For instance, if a prototype task is behind schedule, it will provide feedback to managers indicating that the cause is a lack of skills on the part of the member. The Feedback Department can also provide information to managers to educate and support members based on the feedback. For example, it can provide specific guidance methods for educating and supporting members. Furthermore, the Feedback Department can utilize AI to analyze each member's progress and activities in real time and provide appropriate feedback. For example, AI can analyze each member's progress and activities and automatically provide appropriate feedback. This allows the Feedback Department to provide appropriate feedback based on each member's progress and activities, improving project efficiency. The Feedback Department can also provide feedback that considers each member's growth and career path based on their progress and activities. This allows members to grow while maximizing their skills and contribute to the project's success.

[0034] The task creation unit can identify specific tasks based on the project's objectives and content. For example, in a new product development project, the task creation unit can identify tasks such as planning, prototyping, testing, and marketing. For example, the task creation unit can identify tasks considering the project's goals, scope, and requirements. This clarifies the tasks by identifying specific tasks based on the project's objectives and content. Some or all of the above-described processes in the task creation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the task creation unit inputs the project's goals and requirements into the generative AI, and the generative AI identifies the tasks.

[0035] The role assignment department can assign tasks to members by considering their activity history, background, work experience, and communication history using communication tools. For example, the role assignment department can assign tasks by considering members' past project experience and work history. For instance, it might assign members with extensive prototyping experience to prototyping tasks and members with marketing experience to marketing tasks. The role assignment department can also analyze members' communication history using communication tools to assign appropriate tasks. For example, it can determine a member's suitability for a task based on their communication history. This allows for task assignments tailored to each member's aptitude. Some or all of the above processes in the role assignment department may be performed using, for example, a generative AI, or not. For example, the role assignment department could input members' activity history and background into a generative AI, which would then assign tasks.

[0036] The progress monitoring unit can grasp the progress of each member in real time and detect task delays and problems early. For example, the progress monitoring unit monitors the progress of each member in real time and identifies task delays and problems. For example, the progress monitoring unit uses project management tools to grasp the progress in real time. The progress monitoring unit can also analyze progress data to detect task delays and problems early. For example, the progress monitoring unit identifies the cause of delays based on progress data. This allows for the early detection of task delays and problems, enabling appropriate countermeasures to be taken. Some or all of the above processes in the progress monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress monitoring unit inputs progress data into a generative AI, and the generative AI identifies delays and problems.

[0037] The feedback department can provide managers with feedback on assignment suitability and tasks requiring supervisor support. For example, the feedback department can analyze the progress and activities of each member's tasks and evaluate their assignment suitability. For example, the feedback department can determine assignment suitability based on the member's skills and experience. The feedback department can also identify tasks requiring supervisor support and notify managers. For example, if a task is behind schedule, the feedback department can identify the cause and determine whether supervisor support is needed. This allows managers to appropriately educate and support members. Some or all of the above processes in the feedback department may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback department inputs member activity data into a generative AI, which then evaluates assignment suitability and the need for support.

[0038] The feedback department can provide information that enables managers to educate and follow up with members based on feedback. For example, the feedback department can analyze the progress and activities of each member's tasks and provide the information necessary for education and follow-up. For example, the feedback department can identify the causes of a member's lack of skills or delays in task progress and notify managers. The feedback department can also provide specific guidance methods regarding the education and follow-up of members. For example, the feedback department can propose training programs and follow-up methods to improve members' skills. This allows managers to support the growth of members by providing information that enables them to educate and follow up with them. Some or all of the above processes in the feedback department may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback department inputs member activity data into a generative AI, and the generative AI provides the information necessary for education and follow-up.

[0039] The task creation unit can dynamically change the level of detail of tasks according to the progress of the project. For example, in the early stages of the project, the task creation unit can set the level of detail of tasks to be high and provide specific instructions. For example, in the middle of the project, the task creation unit can set the level of detail of tasks to be medium and respect the autonomy of the members. Furthermore, in the final stages of the project, the task creation unit can set the level of detail of tasks to be low and encourage the creativity and ingenuity of the members. For example, by dynamically changing the level of detail of tasks according to the progress of the project, flexible task management becomes possible. Some or all of the above processes in the task creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the task creation unit inputs project progress data into the generation AI, and the generation AI dynamically changes the level of detail of tasks.

[0040] The task creation unit can automatically generate tasks for similar projects by referring to past project data. For example, the task creation unit can extract similar tasks from past project data and apply them to a new project. For example, the task creation unit can analyze past project data and reflect patterns of successful tasks in a new project. The task creation unit can also incorporate measures to avoid failed tasks into a new project based on past project data. For example, the task creation unit can efficiently generate tasks for similar projects by referring to past project data. This allows for the efficient generation of tasks for similar projects by referring to past project data. Some or all of the above processes in the task creation unit may be performed using a generation AI, or not. For example, the task creation unit inputs past project data into a generation AI, and the generation AI automatically generates tasks for similar projects.

[0041] The task creation unit can optimize tasks by considering the geographical factors of the project when creating them. For example, the task creation unit can prioritize assigning tasks that require on-site surveys, taking into account the geographical factors of the project. For example, the task creation unit can prioritize assigning tasks that can be performed remotely, taking into account the geographical factors. The task creation unit can also assign tasks that require on-site work to appropriate members, taking into account the geographical factors. For example, the task creation unit optimizes tasks by considering the geographical factors of the project. In this way, task optimization is achieved by considering the geographical factors of the project. Some or all of the above processing in the task creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task creation unit inputs geographical factor data of the project into a generative AI, and the generative AI optimizes the tasks.

[0042] The task creation unit can improve the accuracy of tasks by referring to project-related literature during task creation. For example, the task creation unit can refer to project-related literature and specifically define the details of the task. For example, the task creation unit can optimize the task implementation method based on the related literature. The task creation unit can also refer to related literature to identify task risks in advance and take countermeasures. For example, the task creation unit improves the accuracy of tasks by referring to project-related literature. Thus, the accuracy of tasks is improved by referring to project-related literature. Some or all of the above processing in the task creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the task creation unit inputs project-related literature data into a generation AI, and the generation AI improves the accuracy of the task.

[0043] The role allocation unit can analyze members' skill sets in detail and dynamically assign the most suitable roles. For example, the role allocation unit can analyze members' skill sets and assign the most suitable roles. For example, the role allocation unit can dynamically adjust roles based on members' skill sets. The role allocation unit can also optimize roles by considering members' skill sets. For example, the role allocation unit can analyze members' skill sets in detail and assign the most suitable roles. This allows for the assignment of the most suitable roles by analyzing members' skill sets in detail. Some or all of the above processes in the role allocation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the role allocation unit inputs members' skill set data into a generative AI, and the generative AI dynamically assigns the most suitable roles.

[0044] The role assignment unit can improve the accuracy of role assignment by referring to members' past performance data. For example, the role assignment unit assigns the optimal role based on members' past performance data. For example, the role assignment unit analyzes past performance data to improve the accuracy of role assignment. The role assignment unit can also optimize roles by referring to members' past performance data. For example, the role assignment unit improves the accuracy of role assignment by referring to members' past performance data. This improves the accuracy of role assignment by referring to members' past performance data. Some or all of the above processing in the role assignment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the role assignment unit inputs members' past performance data into a generative AI, and the generative AI improves the accuracy of role assignment.

[0045] The role allocation department can assign optimal roles to members, taking into account their geographical factors. For example, the role allocation department can assign roles requiring on-site work to appropriate members, taking into account their geographical factors. For example, the role allocation department can assign roles that can be performed remotely to appropriate members, taking into account their geographical factors. The role allocation department can also assign roles requiring on-site surveys to appropriate members, taking into account their geographical factors. For example, the role allocation department can assign optimal roles to members, taking into account their geographical factors when assigning roles. This makes optimal role allocation possible by considering the geographical factors of the members. Some or all of the above processing in the role allocation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the role allocation department inputs the geographical factor data of the members into a generative AI, and the generative AI assigns the optimal roles.

[0046] The role assignment unit can improve the accuracy of role assignment by referring to relevant literature of its members during the role assignment process. For example, the role assignment unit can improve the accuracy of role assignment by referring to relevant literature of its members. For example, the role assignment unit can optimize the method of role assignment based on relevant literature. The role assignment unit can also refer to relevant literature to identify risks in role assignment in advance and take countermeasures. For example, the role assignment unit improves the accuracy of role assignment by referring to relevant literature of its members during the role assignment process. This improves the accuracy of role assignment by referring to relevant literature of its members. Some or all of the above processing in the role assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the role assignment unit inputs the relevant literature data of its members into a generative AI, and the generative AI improves the accuracy of role assignment.

[0047] The progress monitoring unit can analyze the progress of each task in detail and identify problems early. For example, the progress monitoring unit can analyze the progress of each task in real time and identify problems early. For example, the progress monitoring unit can analyze the progress of tasks in detail and identify the cause of delays. The progress monitoring unit can also analyze the progress of each task, discover problems early, and take countermeasures. For example, the progress monitoring unit can analyze the progress of each task in detail and identify problems early. This allows for the early identification of problems and the implementation of appropriate countermeasures by analyzing the progress of each task in detail. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the progress monitoring unit inputs task progress data into a generation AI, and the generation AI identifies problems early.

[0048] The progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions based on past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions by analyzing past project data. The progress monitoring unit can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. As a result, the accuracy of progress predictions is improved by referring to past project data. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit inputs past project data into a generation AI, and the generation AI improves the accuracy of progress predictions.

[0049] The progress monitoring unit can optimize the project's progress by considering its geographical factors during progress monitoring. For example, the progress monitoring unit can optimize the progress of tasks that require on-site surveys by considering the project's geographical factors. For example, the progress monitoring unit can optimize the progress of tasks that can be performed remotely by considering geographical factors. Furthermore, the progress monitoring unit can also optimize the progress of tasks that require on-site work by considering geographical factors. For example, the progress monitoring unit optimizes the project's progress by considering its geographical factors during progress monitoring. This optimizes the project's progress by considering its geographical factors. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit inputs the project's geographical factor data into a generative AI, and the generative AI optimizes the progress.

[0050] The progress monitoring unit can improve the accuracy of the progress status by referring to relevant project literature during progress monitoring. For example, the progress monitoring unit can improve the accuracy of the progress status by referring to relevant project literature. For example, the progress monitoring unit can improve the accuracy of the progress status based on relevant literature. The progress monitoring unit can also refer to relevant literature to identify risks in the progress status in advance and take countermeasures. For example, the progress monitoring unit improves the accuracy of the progress status by referring to relevant project literature during progress monitoring. As a result, the accuracy of the progress status is improved by referring to relevant project literature. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit inputs project-related literature data into a generation AI, and the generation AI improves the accuracy of the progress status.

[0051] The feedback unit can dynamically change the level of detail of the feedback to provide appropriate guidance. For example, the feedback unit can set the level of detail of the feedback to high to provide specific guidance. For example, the feedback unit can set the level of detail of the feedback to medium to respect the autonomy of the members. Alternatively, the feedback unit can set the level of detail of the feedback to low to encourage members' creativity and ingenuity. For example, the feedback unit can dynamically change the level of detail of the feedback to provide appropriate guidance. By dynamically changing the level of detail of the feedback, appropriate guidance can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the level of detail of the feedback into a generative AI, and the generative AI dynamically changes the level of detail.

[0052] The feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit improves the accuracy of feedback based on past feedback data. For example, the feedback unit analyzes past feedback data to improve the accuracy of feedback. The feedback unit can also improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit improves the accuracy of feedback by referring to past feedback data. As a result, the accuracy of feedback is improved by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using a generation AI, for example, or without a generation AI. For example, the feedback unit inputs past feedback data into a generation AI, and the generation AI improves the accuracy of feedback.

[0053] The feedback unit can optimize feedback by considering the geographical factors of the project. For example, the feedback unit can optimize feedback for tasks that require on-site work by considering the geographical factors of the project. For example, the feedback unit can optimize feedback for tasks that can be performed remotely by considering geographical factors. The feedback unit can also optimize feedback for tasks that require on-site surveys by considering geographical factors. For example, the feedback unit optimizes feedback by considering the geographical factors of the project when providing feedback. This optimizes feedback by considering the geographical factors of the project. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs geographical factor data of the project into a generative AI, and the generative AI optimizes the feedback.

[0054] The feedback unit can improve the accuracy of its feedback by referring to relevant project literature during the feedback process. For example, the feedback unit can improve the accuracy of its feedback by referring to relevant project literature. For example, the feedback unit can optimize the feedback method based on the relevant literature. The feedback unit can also refer to relevant literature to identify feedback risks in advance and take countermeasures. For example, the feedback unit improves the accuracy of its feedback by referring to relevant project literature during the feedback process. As a result, the accuracy of the feedback is improved by referring to relevant project literature. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs project-related literature data into a generative AI, and the generative AI improves the accuracy of the feedback.

[0055] The feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. For example, the feedback unit can provide feedback at an appropriate time based on the user's calendar. For example, the feedback unit can provide feedback before important meetings or events based on calendar information. The feedback unit can also refer to calendar information and provide feedback according to the user's schedule. For example, the feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. This ensures that feedback is provided at an appropriate time by referring to the user's calendar information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the user's calendar information into a generative AI, and the generative AI provides feedback at an appropriate time.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The task creation unit can dynamically change the level of detail of tasks according to the progress of the project. For example, in the early stages of the project, the level of detail of tasks can be set high to provide specific instructions. In the middle stages of the project, the level of detail can be set to medium to respect the autonomy of the members. Furthermore, in the final stages of the project, the level of detail can be set low to encourage the creativity and ingenuity of the members. This allows for flexible task management by dynamically changing the level of detail of tasks according to the progress of the project. Some or all of the above processing in the task creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the task creation unit inputs project progress data into the generation AI, and the generation AI dynamically changes the level of detail of the tasks.

[0058] The role assignment unit can analyze members' skill sets in detail and dynamically assign the most suitable roles. For example, it can analyze members' skill sets and assign the most suitable roles. Based on members' skill sets, it can dynamically adjust roles. It can also optimize roles by considering members' skill sets. This allows for the assignment of the most suitable roles by analyzing members' skill sets in detail. Some or all of the above processes in the role assignment unit may be performed using a generative AI, or not. For example, the role assignment unit inputs members' skill set data into a generative AI, and the generative AI dynamically assigns the most suitable roles.

[0059] The progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. For example, it can improve the accuracy of progress predictions based on past project data. It can improve the accuracy of progress predictions by analyzing past project data. It can also improve the accuracy of progress predictions by referring to past project data. In this way, the accuracy of progress predictions is improved by referring to past project data. Some or all of the above processing in the progress monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the progress monitoring unit inputs past project data into a generation AI, and the generation AI improves the accuracy of progress predictions.

[0060] The feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, it can improve the accuracy of feedback based on past feedback data. It can improve the accuracy of feedback by analyzing past feedback data. It can also improve the accuracy of feedback by referring to past feedback data. In this way, the accuracy of feedback is improved by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback unit inputs past feedback data into a generation AI, and the generation AI improves the accuracy of the feedback.

[0061] The task creation unit can automatically generate tasks for similar projects by referring to past project data. For example, it can extract similar tasks from past project data and apply them to a new project. It can also analyze past project data and reflect patterns of successful tasks in the new project. Furthermore, it can incorporate measures to avoid failed tasks into the new project based on past project data. In this way, tasks for similar projects can be efficiently generated by referring to past project data. Some or all of the above processes in the task creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the task creation unit can input past project data into a generation AI, and the generation AI can automatically generate tasks for similar projects.

[0062] The feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. For example, it can provide feedback at an appropriate time based on the user's calendar. Based on the calendar information, it can provide feedback before important meetings or events. It can also refer to the calendar information and provide feedback according to the user's schedule. In this way, by referring to the user's calendar information, feedback is provided at an appropriate time. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit inputs the user's calendar information into a generative AI, and the generative AI provides feedback at an appropriate time.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The task creation team identifies tasks. The task creation team identifies specific tasks based on the project's objectives and content. For example, in a new product development project, tasks such as planning, prototyping, testing, and marketing are identified. The task creation team also dynamically changes the level of detail of tasks according to the project's progress. In the early stages of the project, tasks are set to a high level of detail, and specific instructions are provided. In the middle stages of the project, tasks are set to a medium level of detail, respecting the autonomy of the members. In the final stages of the project, tasks are set to a low level of detail, encouraging the members' creativity and ingenuity. Step 2: The Role Assignment Department assigns tasks to members based on the tasks identified by the Task Creation Department. The Role Assignment Department assigns responsibilities to each task considering each member's activity history, background, work experience, and communication history using communication tools. For example, prototyping tasks are assigned to members with extensive prototyping experience, and marketing tasks are assigned to members with marketing experience. The Role Assignment Department also conducts a detailed analysis of each member's skill set and dynamically assigns the most suitable roles. Step 3: The progress monitoring team consolidates the activities and progress of each person in charge of the tasks assigned by the role allocation team. The progress monitoring team grasps the progress of each member in real time and detects task delays and problems early. For example, if a prototype task is behind schedule, they identify the cause and take appropriate measures. The progress monitoring team also improves the accuracy of progress predictions by referring to past project data. Step 4: The Feedback Department provides feedback based on the activities and progress compiled by the Progress Monitoring Department. The Feedback Department provides feedback to managers on tasks that require support from a supervisor or require assignment suitability. For example, if the progress of a prototype task is behind schedule, the Feedback Department will provide feedback to the manager that the cause is a lack of skills on the part of the team member. The Feedback Department also provides information that managers can use to educate and support team members based on the feedback. This reduces the workload of managers and improves employee satisfaction and management aspirations.

[0065] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that reduces the workload of managers and improves employee satisfaction and managerial aspirations. This AI agent system works by having multiple AI agents cooperate to support managers in order to alleviate "collaboration fatigue" that arises when managers participate in various projects and working groups. First, a task creation AI agent identifies the tasks necessary for the project or working group that has been convened. At this time, specific tasks are identified based on the purpose and content of the project. For example, in the case of a new product development project, tasks such as planning, prototyping, testing, and marketing are identified. Next, a role assignment AI agent assigns responsibilities to each task, taking into account the activity history, career history, work experience, and communication history of the members under its supervision. For example, a member with extensive prototyping experience is assigned to the prototyping task, and a member with marketing experience is assigned to the marketing task. Furthermore, a progress monitoring AI agent aggregates the activities and progress of each person in charge for their task. The progress of each member is grasped in real time, and delays and problems in tasks are detected early. For example, if the prototyping task is behind schedule, the cause is identified and appropriate measures are taken. Furthermore, the AI ​​feedback agent provides feedback on the entire process, including assignment suitability and tasks requiring supervisor support, which are then communicated to managers. This allows managers to properly educate and support their team members. For example, if a prototype task is behind schedule and it is determined that the cause is a lack of skills on the part of the team member, the manager can provide appropriate guidance to that member. This system enables both managers and team members to work more effectively, leading to increased employee satisfaction and aspirations for management. With the support of the AI ​​agent, managers can focus on developing and managing their team members, and team members can maximize their skills by being assigned appropriate tasks. In this way, the AI ​​agent system reduces the workload of managers and improves employee satisfaction and aspirations for management.

[0066] The AI ​​agent system according to this embodiment comprises a task creation unit, a role assignment unit, a progress monitoring unit, and a feedback unit. The task creation unit identifies tasks. For example, the task creation unit identifies specific tasks based on the project's objectives and content. For example, in a new product development project, the task creation unit can identify tasks such as planning, prototyping, testing, and marketing. The task creation unit can also dynamically change the level of detail of tasks according to the project's progress. For example, in the initial stages of the project, the task creation unit sets the level of detail of tasks high and provides specific instructions. In the middle of the project, it sets the level of detail of tasks to a moderate level, respecting the autonomy of the members. In the final stages of the project, it sets the level of detail of tasks low to encourage the members' creativity and ingenuity. The role assignment unit assigns tasks to members based on the tasks identified by the task creation unit. For example, the role assignment unit assigns responsibilities to each task by considering the members' activity history, career history, work experience, and communication history using communication tools. For example, the role assignment department can assign members with extensive prototyping experience to prototyping tasks and members with marketing experience to marketing tasks. The role assignment department can also analyze members' skill sets in detail and dynamically assign the most suitable roles. The progress monitoring department aggregates the activities and progress of each person in charge of the tasks assigned by the role assignment department. For example, the progress monitoring department can grasp each member's progress in real time and detect delays and problems early. For example, if a prototyping task is behind schedule, the progress monitoring department can identify the cause and take appropriate measures. The progress monitoring department can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring department improves the accuracy of progress predictions based on past project data. The feedback department provides feedback based on the activities and progress aggregated by the progress monitoring department. For example, the feedback department provides feedback to managers on tasks that require assignment suitability or supervisor support.For example, if the progress of a prototype task is behind schedule, the feedback unit will inform the manager that the cause is a lack of skills on the part of the team members. The feedback unit can also provide information that enables managers to educate and support team members based on the feedback. For example, the feedback unit can provide specific guidance methods for educating and supporting team members. In this way, the AI ​​agent system according to the embodiment can reduce the workload of managers and improve employee satisfaction and management aspirations.

[0067] The task creation department identifies tasks. For example, it identifies specific tasks based on the project's objectives and content. Specifically, in the initial stages of a project, it grasps the overall picture of the project and lists the necessary tasks. For example, in a new product development project, it can identify tasks such as planning, prototyping, testing, and marketing. The task creation department can also dynamically change the level of detail of tasks according to the project's progress. For example, in the initial stages of a project, the level of detail of tasks is set high, providing specific instructions. This allows members to proceed with their work according to clear instructions. In the middle stages of the project, the level of detail of tasks is set to a medium level, respecting the autonomy of the members. This allows members to proceed with their work based on their own judgment and exercise their creativity. In the final stages of the project, the level of detail of tasks is set low, encouraging creativity and ingenuity from the members. This allows members to proceed with their work utilizing their own ideas, maximizing the project's results. The task creation department can use AI to monitor the project's progress in real time and adjust the level of detail of tasks as needed. For example, AI analyzes the project's progress and the members' work status, automatically adjusting the level of detail in tasks. This allows the task creation unit to respond flexibly to the project's progress, thereby improving the project's success rate.

[0068] The role allocation department assigns tasks to members based on the tasks identified by the task creation department. The role allocation department considers, for example, each member's activity history, background, work experience, and communication history when assigning tasks. Specifically, the role allocation department analyzes each member's skill set and past work experience in detail to assign the most suitable task. For example, a member with extensive prototyping experience can be assigned to a prototyping task, and a member with marketing experience can be assigned to a marketing task. Furthermore, the role allocation department can dynamically assign the most suitable role by analyzing members' skill sets in detail. For example, the role allocation department analyzes members' skill sets and assigns the most suitable role. In addition, the role allocation department can utilize AI to analyze members' skill sets and work history in real time and assign the most suitable task. For example, AI analyzes members' skill sets and work history and automatically assigns the most suitable task. This allows the role allocation department to achieve optimal task assignment based on members' skill sets and work history, improving project efficiency. Furthermore, the role assignment department can assign tasks based on members' skill sets and work history, taking into account their growth and career paths. This allows members to maximize their skills, grow professionally, and contribute to the success of the project.

[0069] The progress monitoring department aggregates the activities and progress of each person in charge of tasks assigned by the role allocation department. For example, the progress monitoring department grasps the progress of each member in real time and detects task delays and problems early. Specifically, the progress monitoring department monitors the progress of each member in real time and visualizes the progress of tasks. For example, if a prototype task is behind schedule, the progress monitoring department identifies the cause and takes appropriate measures. The progress monitoring department can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring department improves the accuracy of progress predictions based on past project data. Furthermore, the progress monitoring department can use AI to analyze progress in real time and detect task delays and problems early. For example, AI analyzes the progress of each member and automatically identifies task delays and problems. This allows the progress monitoring department to detect task delays and problems early and take appropriate measures. Furthermore, the progress monitoring team makes it easier to grasp the overall progress of the project by visualizing the progress of each member. This allows the progress monitoring team to understand the project's progress in real time, identify task delays and problems early, and take appropriate measures.

[0070] The Feedback Department provides feedback based on activities and progress compiled by the Progress Monitoring Department. For example, the Feedback Department provides feedback to managers regarding assignment suitability and tasks requiring supervisor support. Specifically, the Feedback Department analyzes each member's progress and activities and provides appropriate feedback. For instance, if a prototype task is behind schedule, it will provide feedback to managers indicating that the cause is a lack of skills on the part of the member. The Feedback Department can also provide information to managers to educate and support members based on the feedback. For example, it can provide specific guidance methods for educating and supporting members. Furthermore, the Feedback Department can utilize AI to analyze each member's progress and activities in real time and provide appropriate feedback. For example, AI can analyze each member's progress and activities and automatically provide appropriate feedback. This allows the Feedback Department to provide appropriate feedback based on each member's progress and activities, improving project efficiency. The Feedback Department can also provide feedback that considers each member's growth and career path based on their progress and activities. This allows members to grow while maximizing their skills and contribute to the project's success.

[0071] The task creation unit can identify specific tasks based on the project's objectives and content. For example, in a new product development project, the task creation unit can identify tasks such as planning, prototyping, testing, and marketing. For example, the task creation unit can identify tasks considering the project's goals, scope, and requirements. This clarifies the tasks by identifying specific tasks based on the project's objectives and content. Some or all of the above-described processes in the task creation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the task creation unit inputs the project's goals and requirements into the generative AI, and the generative AI identifies the tasks.

[0072] The role assignment department can assign tasks to members by considering their activity history, background, work experience, and communication history using communication tools. For example, the role assignment department can assign tasks by considering members' past project experience and work history. For instance, it might assign members with extensive prototyping experience to prototyping tasks and members with marketing experience to marketing tasks. The role assignment department can also analyze members' communication history using communication tools to assign appropriate tasks. For example, it can determine a member's suitability for a task based on their communication history. This allows for task assignments tailored to each member's aptitude. Some or all of the above processes in the role assignment department may be performed using, for example, a generative AI, or not. For example, the role assignment department could input members' activity history and background into a generative AI, which would then assign tasks.

[0073] The progress monitoring unit can grasp the progress of each member in real time and detect task delays and problems early. For example, the progress monitoring unit monitors the progress of each member in real time and identifies task delays and problems. For example, the progress monitoring unit uses project management tools to grasp the progress in real time. The progress monitoring unit can also analyze progress data to detect task delays and problems early. For example, the progress monitoring unit identifies the cause of delays based on progress data. This allows for the early detection of task delays and problems, enabling appropriate countermeasures to be taken. Some or all of the above processes in the progress monitoring unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress monitoring unit inputs progress data into a generative AI, and the generative AI identifies delays and problems.

[0074] The feedback department can provide managers with feedback on assignment suitability and tasks requiring supervisor support. For example, the feedback department can analyze the progress and activities of each member's tasks and evaluate their assignment suitability. For example, the feedback department can determine assignment suitability based on the member's skills and experience. The feedback department can also identify tasks requiring supervisor support and notify managers. For example, if a task is behind schedule, the feedback department can identify the cause and determine whether supervisor support is needed. This allows managers to appropriately educate and support members. Some or all of the above processes in the feedback department may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback department inputs member activity data into a generative AI, which then evaluates assignment suitability and the need for support.

[0075] The feedback department can provide information that enables managers to educate and follow up with members based on feedback. For example, the feedback department can analyze the progress and activities of each member's tasks and provide the information necessary for education and follow-up. For example, the feedback department can identify the causes of a member's lack of skills or delays in task progress and notify managers. The feedback department can also provide specific guidance methods regarding the education and follow-up of members. For example, the feedback department can propose training programs and follow-up methods to improve members' skills. This allows managers to support the growth of members by providing information that enables them to educate and follow up with them. Some or all of the above processes in the feedback department may be performed using, for example, a generative AI, or not using a generative AI. For example, the feedback department inputs member activity data into a generative AI, and the generative AI provides the information necessary for education and follow-up.

[0076] The task creation unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the task creation unit will postpone less important tasks and prioritize more important ones. For example, if the user is relaxed, the task creation unit will prioritize assigning more difficult tasks. It can also prioritize assigning easier tasks if the user is tired. For example, the task creation unit can monitor the user's emotions in real time and adjust task priorities according to changes in emotions. This allows for efficient task management by adjusting task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the task creation unit may be performed using or without a generative AI. For example, the task creation unit inputs user emotion data into a generative AI, which then adjusts task priorities.

[0077] The task creation unit can dynamically change the level of detail of tasks according to the progress of the project. For example, in the early stages of the project, the task creation unit can set the level of detail of tasks to be high and provide specific instructions. For example, in the middle of the project, the task creation unit can set the level of detail of tasks to be medium and respect the autonomy of the members. Furthermore, in the final stages of the project, the task creation unit can set the level of detail of tasks to be low and encourage the creativity and ingenuity of the members. For example, by dynamically changing the level of detail of tasks according to the progress of the project, flexible task management becomes possible. Some or all of the above processes in the task creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the task creation unit inputs project progress data into the generation AI, and the generation AI dynamically changes the level of detail of tasks.

[0078] The task creation unit can automatically generate tasks for similar projects by referring to past project data. For example, the task creation unit can extract similar tasks from past project data and apply them to a new project. For example, the task creation unit can analyze past project data and reflect patterns of successful tasks in a new project. The task creation unit can also incorporate measures to avoid failed tasks into a new project based on past project data. For example, the task creation unit can efficiently generate tasks for similar projects by referring to past project data. This allows for the efficient generation of tasks for similar projects by referring to past project data. Some or all of the above processes in the task creation unit may be performed using a generation AI, or not. For example, the task creation unit inputs past project data into a generation AI, and the generation AI automatically generates tasks for similar projects.

[0079] The task creation unit can estimate the user's emotions and adjust the difficulty of tasks based on the estimated emotions. For example, if the user is stressed, the task creation unit will prioritize assigning easier tasks. For example, if the user is relaxed, the task creation unit will prioritize assigning more difficult tasks. Furthermore, if the user is tired, the task creation unit can also prioritize assigning easier tasks. For example, the task creation unit can monitor the user's emotions in real time and adjust the task difficulty according to changes in emotions. This enables efficient task management by adjusting the task difficulty according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the task creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task creation unit inputs user emotion data into a generative AI, and the generative AI adjusts the task difficulty.

[0080] The task creation unit can optimize tasks by considering the geographical factors of the project when creating them. For example, the task creation unit can prioritize assigning tasks that require on-site surveys, taking into account the geographical factors of the project. For example, the task creation unit can prioritize assigning tasks that can be performed remotely, taking into account the geographical factors. The task creation unit can also assign tasks that require on-site work to appropriate members, taking into account the geographical factors. For example, the task creation unit optimizes tasks by considering the geographical factors of the project. In this way, task optimization is achieved by considering the geographical factors of the project. Some or all of the above processing in the task creation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the task creation unit inputs geographical factor data of the project into a generative AI, and the generative AI optimizes the tasks.

[0081] The task creation unit can improve the accuracy of tasks by referring to project-related literature during task creation. For example, the task creation unit can refer to project-related literature and specifically define the details of the task. For example, the task creation unit can optimize the task implementation method based on the related literature. The task creation unit can also refer to related literature to identify task risks in advance and take countermeasures. For example, the task creation unit improves the accuracy of tasks by referring to project-related literature. Thus, the accuracy of tasks is improved by referring to project-related literature. Some or all of the above processing in the task creation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the task creation unit inputs project-related literature data into a generation AI, and the generation AI improves the accuracy of the task.

[0082] The role allocation unit can estimate the user's emotions and adjust the role allocation method based on the estimated user emotions. For example, if the user is stressed, the role allocation unit assigns a less burdensome role. For example, if the user is relaxed, the role allocation unit assigns an important role. Also, if the user is tired, the role allocation unit can assign a simpler role. For example, the role allocation unit monitors the user's emotions in real time and adjusts the role allocation method according to changes in emotions. This allows for efficient role allocation by adjusting the role allocation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the role allocation unit may be performed using a generative AI, or not using a generative AI. For example, the role allocation unit inputs user emotion data into a generative AI, and the generative AI adjusts the role allocation method.

[0083] The role allocation unit can analyze members' skill sets in detail and dynamically assign the most suitable roles. For example, the role allocation unit can analyze members' skill sets and assign the most suitable roles. For example, the role allocation unit can dynamically adjust roles based on members' skill sets. The role allocation unit can also optimize roles by considering members' skill sets. For example, the role allocation unit can analyze members' skill sets in detail and assign the most suitable roles. This allows for the assignment of the most suitable roles by analyzing members' skill sets in detail. Some or all of the above processes in the role allocation unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the role allocation unit inputs members' skill set data into a generative AI, and the generative AI dynamically assigns the most suitable roles.

[0084] The role assignment unit can improve the accuracy of role assignment by referring to members' past performance data. For example, the role assignment unit assigns the optimal role based on members' past performance data. For example, the role assignment unit analyzes past performance data to improve the accuracy of role assignment. The role assignment unit can also optimize roles by referring to members' past performance data. For example, the role assignment unit improves the accuracy of role assignment by referring to members' past performance data. This improves the accuracy of role assignment by referring to members' past performance data. Some or all of the above processing in the role assignment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the role assignment unit inputs members' past performance data into a generative AI, and the generative AI improves the accuracy of role assignment.

[0085] The role allocation unit can estimate the user's emotions and determine the priority of role allocation based on the estimated emotions. For example, if the user is stressed, the role allocation unit will prioritize assigning less burdensome roles. For example, if the user is relaxed, the role allocation unit will prioritize assigning important roles. Also, if the user is tired, the role allocation unit can prioritize assigning simpler roles. For example, the role allocation unit can monitor the user's emotions in real time and determine the priority of role allocation in response to changes in emotions. This enables efficient role allocation by determining the priority of role allocation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the role allocation unit may be performed using a generative AI, or not using a generative AI. For example, the role allocation unit inputs user emotion data into a generative AI, and the generative AI determines the priority of role allocation.

[0086] The role allocation department can assign optimal roles to members, taking into account their geographical factors. For example, the role allocation department can assign roles requiring on-site work to appropriate members, taking into account their geographical factors. For example, the role allocation department can assign roles that can be performed remotely to appropriate members, taking into account their geographical factors. The role allocation department can also assign roles requiring on-site surveys to appropriate members, taking into account their geographical factors. For example, the role allocation department can assign optimal roles to members, taking into account their geographical factors when assigning roles. This makes optimal role allocation possible by considering the geographical factors of the members. Some or all of the above processing in the role allocation department may be performed using, for example, a generative AI, or not using a generative AI. For example, the role allocation department inputs the geographical factor data of the members into a generative AI, and the generative AI assigns the optimal roles.

[0087] The role assignment unit can improve the accuracy of role assignment by referring to relevant literature of its members during the role assignment process. For example, the role assignment unit can improve the accuracy of role assignment by referring to relevant literature of its members. For example, the role assignment unit can optimize the method of role assignment based on relevant literature. The role assignment unit can also refer to relevant literature to identify risks in role assignment in advance and take countermeasures. For example, the role assignment unit improves the accuracy of role assignment by referring to relevant literature of its members during the role assignment process. This improves the accuracy of role assignment by referring to relevant literature of its members. Some or all of the above processing in the role assignment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the role assignment unit inputs the relevant literature data of its members into a generative AI, and the generative AI improves the accuracy of role assignment.

[0088] The progress monitoring unit can estimate the user's emotions and adjust the display method of the progress status based on the estimated user emotions. For example, if the user is feeling stressed, the progress monitoring unit provides a simple and highly visible display method. For example, if the user is relaxed, the progress monitoring unit provides a display method that includes detailed information. Furthermore, if the user is in a hurry, the progress monitoring unit can also provide a concise display method. For example, the progress monitoring unit monitors the user's emotions in real time and adjusts the display method of the progress status according to changes in emotions. This enables efficient progress management by adjusting the display method of the progress status according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using a generative AI, or not using a generative AI. For example, the progress monitoring unit inputs the user's emotion data into the generative AI, and the generative AI adjusts the display method of the progress status.

[0089] The progress monitoring unit can analyze the progress of each task in detail and identify problems early. For example, the progress monitoring unit can analyze the progress of each task in real time and identify problems early. For example, the progress monitoring unit can analyze the progress of tasks in detail and identify the cause of delays. The progress monitoring unit can also analyze the progress of each task, discover problems early, and take countermeasures. For example, the progress monitoring unit can analyze the progress of each task in detail and identify problems early. This allows for the early identification of problems and the implementation of appropriate countermeasures by analyzing the progress of each task in detail. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the progress monitoring unit inputs task progress data into a generation AI, and the generation AI identifies problems early.

[0090] The progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions based on past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions by analyzing past project data. The progress monitoring unit can also improve the accuracy of progress predictions by referring to past project data. For example, the progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. As a result, the accuracy of progress predictions is improved by referring to past project data. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit inputs past project data into a generation AI, and the generation AI improves the accuracy of progress predictions.

[0091] The progress monitoring unit can estimate the user's emotions and determine the priority of the progress based on the estimated emotions. For example, if the user is stressed, the progress monitoring unit will postpone less important tasks and prioritize more important tasks. For example, if the user is relaxed, the progress monitoring unit will prioritize assigning more difficult tasks. Also, if the user is tired, the progress monitoring unit can prioritize assigning easier tasks. For example, the progress monitoring unit monitors the user's emotions in real time and determines the priority of the progress according to changes in emotions. This enables efficient progress management by determining the priority of the progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using, for example, generative AI, or without generative AI. For example, the progress monitoring unit inputs user emotion data into a generating AI, which then determines the priority of the progress status.

[0092] The progress monitoring unit can optimize the project's progress by considering its geographical factors during progress monitoring. For example, the progress monitoring unit can optimize the progress of tasks that require on-site surveys by considering the project's geographical factors. For example, the progress monitoring unit can optimize the progress of tasks that can be performed remotely by considering geographical factors. Furthermore, the progress monitoring unit can also optimize the progress of tasks that require on-site work by considering geographical factors. For example, the progress monitoring unit optimizes the project's progress by considering its geographical factors during progress monitoring. This optimizes the project's progress by considering its geographical factors. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generative AI, or without a generative AI. For example, the progress monitoring unit inputs the project's geographical factor data into a generative AI, and the generative AI optimizes the progress.

[0093] The progress monitoring unit can improve the accuracy of the progress status by referring to relevant project literature during progress monitoring. For example, the progress monitoring unit can improve the accuracy of the progress status by referring to relevant project literature. For example, the progress monitoring unit can improve the accuracy of the progress status based on relevant literature. The progress monitoring unit can also refer to relevant literature to identify risks in the progress status in advance and take countermeasures. For example, the progress monitoring unit improves the accuracy of the progress status by referring to relevant project literature during progress monitoring. As a result, the accuracy of the progress status is improved by referring to relevant project literature. Some or all of the above processing in the progress monitoring unit may be performed using, for example, a generation AI, or without a generation AI. For example, the progress monitoring unit inputs project-related literature data into a generation AI, and the generation AI improves the accuracy of the progress status.

[0094] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing positive feedback. For example, if the user is relaxed, the feedback unit will provide detailed feedback. The feedback unit can also provide concise feedback if the user is tired. For example, the feedback unit can monitor the user's emotions in real time and adjust the content of the feedback according to changes in emotions. This ensures that appropriate feedback is provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit inputs the user's emotion data into a generative AI, and the generative AI adjusts the content of the feedback.

[0095] The feedback unit can dynamically change the level of detail of the feedback to provide appropriate guidance. For example, the feedback unit can set the level of detail of the feedback to high to provide specific guidance. For example, the feedback unit can set the level of detail of the feedback to medium to respect the autonomy of the members. Alternatively, the feedback unit can set the level of detail of the feedback to low to encourage members' creativity and ingenuity. For example, the feedback unit can dynamically change the level of detail of the feedback to provide appropriate guidance. By dynamically changing the level of detail of the feedback, appropriate guidance can be provided. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the level of detail of the feedback into a generative AI, and the generative AI dynamically changes the level of detail.

[0096] The feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit improves the accuracy of feedback based on past feedback data. For example, the feedback unit analyzes past feedback data to improve the accuracy of feedback. The feedback unit can also improve the accuracy of feedback by referring to past feedback data. For example, the feedback unit improves the accuracy of feedback by referring to past feedback data. As a result, the accuracy of feedback is improved by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using a generation AI, for example, or without a generation AI. For example, the feedback unit inputs past feedback data into a generation AI, and the generation AI improves the accuracy of feedback.

[0097] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated emotions. For example, if the user is stressed, the feedback unit will prioritize providing positive feedback. For example, if the user is relaxed, the feedback unit will prioritize providing detailed feedback. The feedback unit can also prioritize providing concise feedback if the user is tired. For example, the feedback unit can monitor the user's emotions in real time and determine the priority of feedback according to changes in emotions. This ensures efficient feedback by prioritizing feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using a generative AI, or not using a generative AI. For example, the feedback unit inputs user emotion data into a generative AI, and the generative AI determines the priority of feedback.

[0098] The feedback unit can optimize feedback by considering the geographical factors of the project. For example, the feedback unit can optimize feedback for tasks that require on-site work by considering the geographical factors of the project. For example, the feedback unit can optimize feedback for tasks that can be performed remotely by considering geographical factors. The feedback unit can also optimize feedback for tasks that require on-site surveys by considering geographical factors. For example, the feedback unit optimizes feedback by considering the geographical factors of the project when providing feedback. This optimizes feedback by considering the geographical factors of the project. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs geographical factor data of the project into a generative AI, and the generative AI optimizes the feedback.

[0099] The feedback unit can improve the accuracy of its feedback by referring to relevant project literature during the feedback process. For example, the feedback unit can improve the accuracy of its feedback by referring to relevant project literature. For example, the feedback unit can optimize the feedback method based on the relevant literature. The feedback unit can also refer to relevant literature to identify feedback risks in advance and take countermeasures. For example, the feedback unit improves the accuracy of its feedback by referring to relevant project literature during the feedback process. As a result, the accuracy of the feedback is improved by referring to relevant project literature. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs project-related literature data into a generative AI, and the generative AI improves the accuracy of the feedback.

[0100] The feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. For example, the feedback unit can provide feedback at an appropriate time based on the user's calendar. For example, the feedback unit can provide feedback before important meetings or events based on calendar information. The feedback unit can also refer to calendar information and provide feedback according to the user's schedule. For example, the feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. This ensures that feedback is provided at an appropriate time by referring to the user's calendar information. Some or all of the above processing in the feedback unit may be performed using, for example, a generative AI, or without a generative AI. For example, the feedback unit inputs the user's calendar information into a generative AI, and the generative AI provides feedback at an appropriate time.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The task creation unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, it can postpone less important tasks and prioritize more important ones. If the user is relaxed, it can prioritize assigning more difficult tasks. Conversely, if the user is tired, it can prioritize assigning easier tasks. This allows for efficient task management by adjusting task priorities according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the task creation unit may be performed using generative AI or not. For example, the task creation unit inputs user emotion data into the generative AI, and the generative AI adjusts task priorities.

[0103] The role assignment unit can estimate the user's emotions and adjust the role assignment method based on the estimated emotions. For example, if the user is stressed, a less burdensome role may be assigned. If the user is relaxed, an important role may be assigned. If the user is tired, a simple role may be assigned. This allows for efficient role assignment by adjusting the role assignment method according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the role assignment unit may be performed using generative AI or not. For example, the role assignment unit inputs user emotion data into the generative AI, and the generative AI adjusts the role assignment method.

[0104] The progress monitoring unit can estimate the user's emotions and adjust the display method of the progress status based on the estimated user emotions. For example, if the user is stressed, it can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. If the user is in a hurry, it can also provide a display method that gets straight to the point. This allows for efficient progress management by adjusting the display method of the progress status according to the user's emotions. Emotion estimation is achieved using an emotion engine or a generative AI. The generative AI is, but is not limited to, text-generating AI or multimodal-generating AI. Some or all of the above processing in the progress monitoring unit may be performed using a generative AI or not. For example, the progress monitoring unit inputs user emotion data into a generative AI, and the generative AI adjusts the display method of the progress status.

[0105] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is stressed, positive feedback will be prioritized. If the user is relaxed, detailed feedback will be provided. If the user is tired, concise feedback may also be provided. In this way, appropriate feedback is provided by adjusting the content of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using generative AI or not. For example, the feedback unit inputs user emotion data into the generative AI, and the generative AI adjusts the content of the feedback.

[0106] The task creation unit can dynamically change the level of detail of tasks according to the progress of the project. For example, in the early stages of the project, the level of detail of tasks can be set high to provide specific instructions. In the middle stages of the project, the level of detail can be set to medium to respect the autonomy of the members. Furthermore, in the final stages of the project, the level of detail can be set low to encourage the creativity and ingenuity of the members. This allows for flexible task management by dynamically changing the level of detail of tasks according to the progress of the project. Some or all of the above processing in the task creation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the task creation unit inputs project progress data into the generation AI, and the generation AI dynamically changes the level of detail of the tasks.

[0107] The role assignment unit can analyze members' skill sets in detail and dynamically assign the most suitable roles. For example, it can analyze members' skill sets and assign the most suitable roles. Based on members' skill sets, it can dynamically adjust roles. It can also optimize roles by considering members' skill sets. This allows for the assignment of the most suitable roles by analyzing members' skill sets in detail. Some or all of the above processes in the role assignment unit may be performed using a generative AI, or not. For example, the role assignment unit inputs members' skill set data into a generative AI, and the generative AI dynamically assigns the most suitable roles.

[0108] The progress monitoring unit can improve the accuracy of progress predictions by referring to past project data. For example, it can improve the accuracy of progress predictions based on past project data. It can improve the accuracy of progress predictions by analyzing past project data. It can also improve the accuracy of progress predictions by referring to past project data. In this way, the accuracy of progress predictions is improved by referring to past project data. Some or all of the above processing in the progress monitoring unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the progress monitoring unit inputs past project data into a generation AI, and the generation AI improves the accuracy of progress predictions.

[0109] The feedback unit can improve the accuracy of feedback by referring to past feedback data. For example, it can improve the accuracy of feedback based on past feedback data. It can improve the accuracy of feedback by analyzing past feedback data. It can also improve the accuracy of feedback by referring to past feedback data. In this way, the accuracy of feedback is improved by referring to past feedback data. Some or all of the above processing in the feedback unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the feedback unit inputs past feedback data into a generation AI, and the generation AI improves the accuracy of the feedback.

[0110] The task creation unit can automatically generate tasks for similar projects by referring to past project data. For example, it can extract similar tasks from past project data and apply them to a new project. It can also analyze past project data and reflect patterns of successful tasks in the new project. Furthermore, it can incorporate measures to avoid failed tasks into the new project based on past project data. In this way, tasks for similar projects can be efficiently generated by referring to past project data. Some or all of the above processes in the task creation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the task creation unit can input past project data into a generation AI, and the generation AI can automatically generate tasks for similar projects.

[0111] The feedback unit can provide feedback at an appropriate time by referring to the user's calendar information. For example, it can provide feedback at an appropriate time based on the user's calendar. Based on the calendar information, it can provide feedback before important meetings or events. It can also refer to the calendar information and provide feedback according to the user's schedule. In this way, by referring to the user's calendar information, feedback is provided at an appropriate time. Some or all of the above processing in the feedback unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the feedback unit inputs the user's calendar information into a generative AI, and the generative AI provides feedback at an appropriate time.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The task creation team identifies tasks. The task creation team identifies specific tasks based on the project's objectives and content. For example, in a new product development project, tasks such as planning, prototyping, testing, and marketing are identified. The task creation team also dynamically changes the level of detail of tasks according to the project's progress. In the early stages of the project, tasks are set to a high level of detail, and specific instructions are provided. In the middle stages of the project, tasks are set to a medium level of detail, respecting the autonomy of the members. In the final stages of the project, tasks are set to a low level of detail, encouraging the members' creativity and ingenuity. Step 2: The Role Assignment Department assigns tasks to members based on the tasks identified by the Task Creation Department. The Role Assignment Department assigns responsibilities to each task considering each member's activity history, background, work experience, and communication history using communication tools. For example, prototyping tasks are assigned to members with extensive prototyping experience, and marketing tasks are assigned to members with marketing experience. The Role Assignment Department also conducts a detailed analysis of each member's skill set and dynamically assigns the most suitable roles. Step 3: The progress monitoring team consolidates the activities and progress of each person in charge of the tasks assigned by the role allocation team. The progress monitoring team grasps the progress of each member in real time and detects task delays and problems early. For example, if a prototype task is behind schedule, they identify the cause and take appropriate measures. The progress monitoring team also improves the accuracy of progress predictions by referring to past project data. Step 4: The Feedback Department provides feedback based on the activities and progress compiled by the Progress Monitoring Department. The Feedback Department provides feedback to managers on tasks that require support from a supervisor or require assignment suitability. For example, if the progress of a prototype task is behind schedule, the Feedback Department will provide feedback to the manager that the cause is a lack of skills on the part of the team member. The Feedback Department also provides information that managers can use to educate and support team members based on the feedback. This reduces the workload of managers and improves employee satisfaction and management aspirations.

[0114] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0115] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0116] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0117] Each of the multiple elements described above, including the task creation unit, role assignment unit, progress monitoring unit, and feedback unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the task creation unit is implemented by the control unit 46A of the smart device 14 and identifies specific tasks based on the project's objectives and content. The role assignment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and assigns tasks based on the members' activity history and experience. The progress monitoring unit is implemented by, for example, the control unit 46A of the smart device 14 and monitors the progress of each person in charge in real time. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides feedback to managers based on progress. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0120] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0121] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0122] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0124] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0125] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0126] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0127] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0128] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0129] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0130] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0131] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0132] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the task creation unit, role assignment unit, progress monitoring unit, and feedback unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the task creation unit is implemented by the control unit 46A of the smart glasses 214 and identifies specific tasks based on the project's objectives and content. The role assignment unit is implemented by the identification processing unit 290 of the data processing unit 12 and assigns tasks based on members' activity history and experience. The progress monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of each person in charge in real time. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides feedback to managers based on progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the task creation unit, role assignment unit, progress monitoring unit, and feedback unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the task creation unit is implemented by the control unit 46A of the headset terminal 314 and identifies specific tasks based on the project's objectives and content. The role assignment unit is implemented by the identification processing unit 290 of the data processing unit 12 and assigns tasks based on members' activity history and experience. The progress monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of each person in charge in real time. The feedback unit is implemented by the identification processing unit 290 of the data processing unit 12 and provides feedback to managers based on progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] As shown in Figure 7, the 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.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0158] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0159] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0160] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0161] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0163] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0165] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0166] Each of the multiple elements described above, including the task creation unit, role assignment unit, progress monitoring unit, and feedback unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the task creation unit is implemented by the control unit 46A of the robot 414 and identifies specific tasks based on the project's objectives and content. The role assignment unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and assigns tasks based on the members' activity history and experience. The progress monitoring unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the progress of each person in charge in real time. The feedback unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and provides feedback to managers based on progress. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0167] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0168] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0169] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0170] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0171] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0172] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0173] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0174] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0175] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0176] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0177] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0178] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0179] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0180] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0181] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0182] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0183] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0184] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0185] (Note 1) The task creation department identifies the tasks, Based on the tasks identified by the task creation unit, the role assignment unit assigns tasks to members, The progress monitoring unit aggregates the activities and progress of each person in charge for the tasks assigned by the aforementioned role-sharing unit, The system includes a feedback unit that provides feedback based on the activities and progress aggregated by the aforementioned progress monitoring unit. A system characterized by the following features. (Note 2) The task creation unit described above, Identify specific tasks based on the project's objectives and content. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned division of roles is: We assign tasks to members based on their activity history, background, work experience, and communication history using various tools. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned progress monitoring unit is: We monitor each member's progress in real time and identify task delays and problems early. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned feedback unit is Provide feedback to managers regarding assignment suitability and tasks requiring supervisor support. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Provides information that managers can use to educate and support team members based on feedback. The system described in Appendix 1, characterized by the features described herein. (Note 7) The task creation unit described above, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The task creation unit described above, The level of detail of tasks is dynamically changed according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 9) The task creation unit described above, By referencing past project data, tasks for similar projects are automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 10) The task creation unit described above, It estimates the user's emotions and adjusts the task difficulty based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The task creation unit described above, When creating tasks, optimize them by considering the geographical factors of the project. The system described in Appendix 1, characterized by the features described herein. (Note 12) The task creation unit described above, When creating tasks, refer to relevant project literature to improve the accuracy of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned division of roles is: It estimates the user's emotions and adjusts the role assignment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned division of roles is: We analyze the skill sets of our team members in detail and dynamically assign them to the most suitable roles. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned division of roles is: Referencing members' past performance data improves the accuracy of role assignments. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned division of roles is: It estimates the user's emotions and determines the priority of role assignments based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned division of roles is: When assigning roles, consider the geographical factors of the team members and assign the most suitable roles. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned division of roles is: When assigning roles, members can improve the accuracy of their role assignments by referring to relevant literature. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned progress monitoring unit is: It estimates the user's emotions and adjusts how the progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned progress monitoring unit is: Analyze the progress of each task in detail and identify problems early on. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned progress monitoring unit is: By referring to past project data, we can improve the accuracy of progress predictions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned progress monitoring unit is: It estimates the user's emotions and determines the priority of the progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned progress monitoring unit is: When monitoring the project's progress, optimize the progress by considering the project's geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned progress monitoring unit is: When monitoring the project's progress, refer to relevant project literature to improve the accuracy of the progress report. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts the content of the feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is Dynamically adjust the level of detail in feedback to provide appropriate guidance. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is We improve the accuracy of feedback by referring to past feedback data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, optimize the feedback process by considering the project's geographical factors. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When providing feedback, refer to relevant project literature to improve the accuracy of the feedback. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned feedback unit is When providing feedback, we refer to the user's calendar information to provide feedback at an appropriate time. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The task creation department identifies the tasks, Based on the tasks identified by the task creation unit, the role assignment unit assigns tasks to members, The progress monitoring unit aggregates the activities and progress of each person in charge for the tasks assigned by the aforementioned role-sharing unit, The system includes a feedback unit that provides feedback based on the activities and progress aggregated by the aforementioned progress monitoring unit. A system characterized by the following features.

2. The task creation unit described above, Identify specific tasks based on the project's objectives and content. The system according to feature 1.

3. The aforementioned division of roles is: We assign tasks to members based on their activity history, background, work experience, and communication history using various tools. The system according to feature 1.

4. The aforementioned progress monitoring unit is: We monitor each member's progress in real time and identify task delays and problems early. The system according to feature 1.

5. The aforementioned feedback unit is Provide feedback to managers regarding assignment suitability and tasks requiring supervisor support. The system according to feature 1.

6. The aforementioned feedback unit is Provides information that managers can use to educate and support team members based on feedback. The system according to feature 1.

7. The task creation unit described above, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system according to feature 1.

8. The task creation unit described above, The level of detail of tasks is dynamically changed according to the project's progress. The system according to feature 1.

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