system

Abstract

The system according to this embodiment aims to efficiently perform automatic task assignment and progress management. [Solution] The system according to the embodiment comprises an assignment unit, a monitoring unit, and a prioritization unit. The assignment unit automatically assigns tasks. The monitoring unit monitors the progress of the tasks assigned by the assignment unit. The prioritization unit prioritizes the tasks based on the progress obtained by the 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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, task allocation and progress management are often performed manually, leaving room for improvement in efficiency and productivity.

[0005] The system according to the embodiment aims to efficiently perform automatic task allocation and progress management.

Means for Solving the Problems

[0006] The system according to the embodiment includes an allocation unit, a monitoring unit, and a prioritization unit. The allocation unit performs automatic task allocation. The monitoring unit monitors the progress of the tasks allocated by the allocation unit. The prioritization unit assigns priorities to the tasks based on the progress obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently perform automatic task assignment and progress management. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 task management system according to an embodiment of the present invention is a system that uses an AI agent to automatically assign tasks and manage their progress. This task management system analyzes past response history and automatically assigns tasks to members who are able to handle them. Next, it monitors the progress of tasks in real time and reallocates resources as needed. This mechanism can reduce the waiting time for those who have made requests and improve the overall efficiency and productivity of operations. For example, the AI ​​agent analyzes past response history. In this process, it identifies members who are able to handle the task by considering each member's skills, past performance, and current workload. For example, if there is a member who has shown high performance for a particular task in the past, the task can be assigned to that member. This prevents task overload and equalizes the burden on members. Next, the AI ​​agent monitors the progress of tasks in real time. For example, it can periodically check the progress of tasks and notify early if delays or problems occur. This prevents insufficient progress checks and allows for early detection of delays and problems. In addition, when changes occur in a task, it can automatically notify relevant parties and facilitate communication. Furthermore, the AI ​​agent prioritizes tasks. For example, it can analyze the urgency and importance of tasks and assign appropriate priorities. This allows for the automatic selection of the most suitable members for a project and the assignment of tasks, maximizing team efficiency. This system is expected to reduce the workload on team members, decrease overtime, and improve their health. It also improves overall efficiency and productivity, enhancing the company's competitiveness. The introduction of AI agents is not merely about automating processes; it contributes to the development of the entire business. For example, it can solve problems such as task overload, insufficient progress monitoring, and reliance on individual expertise in organizations with various work structures, from small and medium-sized enterprises to large corporations that manage projects. This enables efficient task assignment and progress management, providing an environment where team members can work more efficiently.This allows the task management system to reduce waiting times for those who submit requests and improve overall efficiency and productivity.

[0029] The task management system according to this embodiment comprises an assignment unit, a monitoring unit, and a prioritization unit. The assignment unit automatically assigns tasks. The assignment unit, for example, uses an AI agent to analyze past response history and assigns tasks to members who are able to handle them. For example, the assignment unit can assign tasks considering each member's skills, past performance, and current workload. For example, the assignment unit can assign tasks to members who have shown high performance for a particular task in the past. The assignment unit can also prevent task overload and equalize the burden on members. The monitoring unit monitors the progress of tasks assigned by the assignment unit. For example, the monitoring unit can periodically check the progress of tasks and notify early if delays or problems occur. For example, the monitoring unit can monitor the progress of tasks in real time and prevent insufficient progress checks. The monitoring unit can also automatically notify relevant parties when changes occur in a task and facilitate communication. The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. The prioritization unit can maximize team efficiency by, for example, automatically selecting the most suitable members for a project and assigning tasks to them. This enables the task management system according to the embodiment to automatically assign tasks, monitor progress, and prioritize tasks.

[0030] The assignment unit automatically assigns tasks. For example, it uses an AI agent to analyze past response history and assign tasks to members who are able to handle them. Specifically, the AI ​​agent analyzes each member's skill set, past task completion history, and current workload in detail. For example, the AI ​​agent uses natural language processing technology to analyze past task reports and feedback to understand each member's strengths and performance trends. The AI ​​agent also monitors each member's current workload in real time and adjusts task assignments to avoid overload. For example, if a particular member currently has multiple high-priority tasks, the AI ​​agent avoids assigning new tasks to that member and assigns them to other members. The AI ​​agent also selects the most suitable member based on the nature of the task and the required skills. For example, it prioritizes assigning tasks that use a particular programming language to members who are proficient in that language. This allows the assignment unit to achieve efficient task distribution and improve the overall productivity of the team. Furthermore, the assignment unit can accumulate task assignment history and use it as data to improve the accuracy of future task assignments. This allows the assignment unit to continuously learn and optimize task assignment.

[0031] The Monitoring Department monitors the progress of tasks assigned by the Assignment Department. For example, the Monitoring Department can periodically check the progress of tasks and provide early notification if delays or problems occur. Specifically, the Monitoring Department tracks the progress of each task in real time and provides a dashboard that visualizes the progress. The dashboard displays the progress of each task, the expected completion date, and the status of the person in charge, allowing project managers and team members to grasp the situation at a glance. The Monitoring Department also issues alerts if there are any abnormalities in the progress of a task, such as delays or if the task is not progressing as planned. These alerts are sent to relevant parties via email or chat tools to encourage prompt action. Furthermore, the Monitoring Department accumulates data on task progress and can perform analysis based on past progress data. This allows for quantitative evaluation of project progress and identification of areas for improvement. The Monitoring Department also facilitates communication by automatically notifying relevant parties when changes occur to tasks. For example, if a task deadline is changed or new requirements are added, notifications are sent to all relevant parties, enabling a quick response. This allows the monitoring unit to accurately grasp the progress of tasks and support the smooth running of the project.

[0032] The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks to determine appropriate priorities. Specifically, the prioritization unit comprehensively evaluates each task's deadline, dependencies, resource utilization, etc., to determine its priority. For example, tasks with approaching deadlines or those dependent on other tasks are given high priority. Furthermore, important tasks are given high priority based on the overall project goals and strategy. The prioritization unit uses AI to analyze these factors and automatically determine the optimal priority. For example, the AI ​​learns from past project data to understand patterns and trends in task prioritization. This allows the prioritization unit to flexibly adjust priorities in response to project progress and changes in circumstances. In addition, the prioritization unit periodically reviews task priorities and updates them based on the latest situation. For example, if new tasks are added or the status of existing tasks changes, the priority is re-evaluated and tasks are carried out in the optimal order. This allows the prioritization unit to support the efficient progress of the project and achieve optimal resource allocation.

[0033] The notification unit can notify of changes to a task. For example, the notification unit automatically sends notifications to stakeholders when changes occur to a task. For example, the notification unit can notify of task changes in real time to encourage prompt action. For example, the notification unit can provide detailed information about task changes so that stakeholders can take appropriate action. This enables prompt action by notifying of task changes. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can perform notifications using an AI model that detects task changes and sends notifications to stakeholders.

[0034] The reallocation unit can reallocate resources. The reallocation unit reallocates resources considering, for example, the progress of tasks and the workload of members. The reallocation unit can efficiently allocate resources according to the progress of tasks. The reallocation unit can reallocate resources to equalize the workload of members. This enables efficient task management by reallocating resources. Some or all of the above processing in the reallocation unit may be performed using, for example, AI, or not using AI. For example, the reallocation unit can reallocate resources using an AI model that takes the progress of tasks and the workload of members as input and outputs the reallocation of resources.

[0035] The assignment unit can assign tasks considering each member's skills, past performance, and current workload. For example, the assignment unit can analyze each member's skill set in detail and assign tasks specialized in specific skills. For example, the assignment unit can assign tasks according to a member's area of ​​expertise based on past performance data. For example, the assignment unit can monitor the current workload in real time and adjust tasks to prevent overload. This makes it possible to assign tasks optimally by considering each member's skills and workload. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not. For example, the assignment unit can assign tasks using an AI model that takes each member's skills, past performance, and current workload as input and outputs the optimal task assignment.

[0036] The monitoring unit can periodically check the progress of tasks and provide early notification if delays or problems occur. For example, the monitoring unit can periodically check the progress of tasks and issue an alert if progress is behind schedule. For example, the monitoring unit can monitor the progress of tasks in real time and provide immediate notification if problems occur. For example, the monitoring unit can visualize the progress of tasks and detect delays or problems early. This allows for early detection of delays and problems by periodically checking the progress of tasks. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can take task progress data as input and use an AI model to detect delays and problems to provide notifications.

[0037] The prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. For example, the prioritization unit can evaluate the urgency of tasks and process high-urgency tasks first. For example, the prioritization unit can evaluate the importance of tasks and process high-importance tasks first. For example, the prioritization unit can consider task dependencies and process tasks with dependencies first. This enables appropriate prioritization by analyzing the urgency and importance of tasks. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can prioritize tasks using an AI model that takes the urgency and importance of tasks as input and outputs priorities.

[0038] The assignment unit can perform a detailed analysis of each member's skill set and assign tasks specialized in specific skills. For example, the assignment unit can assign coding tasks to members with strong programming skills. For example, the assignment unit can assign client support tasks to members with strong communication skills. For example, the assignment unit can assign data analysis tasks to members with strong analytical skills. This makes it possible to assign tasks specialized in specific skills by analyzing each member's skill set in detail. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input each member's skill set data into a generating AI and have the generating AI perform task assignments specialized in specific skills.

[0039] The assignment unit can optimize task assignments by considering members' past task completion times. For example, the assignment unit can assign urgent tasks to members who have completed tasks quickly in the past. For example, the assignment unit can assign time-efficient tasks to members who have completed tasks that took a long time in the past. For example, the assignment unit can adjust task assignments based on past task completion times. This optimizes task assignments by considering members' past task completion times. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' past task completion time data into a generating AI and have the generating AI execute a method to optimize task assignments.

[0040] The assignment unit can assign the most suitable tasks by considering the geographical location of the members. For example, if a member is nearby, the assignment unit can assign tasks that require on-site attention. For example, if a member is far away, the assignment unit can assign tasks that can be handled remotely. For example, the assignment unit can assign the most suitable tasks based on the members' location information. This makes it possible to assign tasks optimally by considering the geographical location of the members. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the geographical location information of the members into a generating AI and have the generating AI perform the optimal task assignment.

[0041] The assignment unit can analyze members' social media activity and assign relevant tasks. For example, if a member is promoting a specific skill on social media, the assignment unit can assign tasks related to that skill. For example, if a member is showing interest in a specific project on social media, the assignment unit can assign tasks related to that project. For example, the assignment unit can assign the most suitable tasks based on a member's social media activity. This makes it possible to assign relevant tasks by analyzing a member's social media activity. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input members' social media activity data into a generating AI and have the generating AI perform the relevant task assignment.

[0042] The monitoring unit can visualize the progress of tasks in real time and predict delays in progress. For example, the monitoring unit can display the progress of tasks in a graph and predict delays. For example, the monitoring unit can display the progress of tasks in a chart and predict delays. For example, the monitoring unit can display the progress of tasks on a dashboard and predict delays. This makes it possible to predict delays in progress by visualizing the progress of tasks in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data into a generating AI and have the generating AI execute a method for predicting delays in progress.

[0043] The monitoring unit can automatically generate feedback for members according to the progress of the task. For example, if the task is progressing smoothly, the monitoring unit can automatically generate positive feedback for the member. For example, if the task is behind schedule, the monitoring unit can automatically generate feedback for the member pointing out areas for improvement. The monitoring unit can automatically generate feedback for members according to the progress of the task. This helps maintain member motivation by automatically generating feedback according to the progress of the task. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data into a generating AI and have the generating AI execute a method for automatically generating feedback.

[0044] The monitoring unit can analyze the progress of tasks from a geographical perspective and compare progress by region. For example, the monitoring unit can display the task progress by region on a map and compare progress. For example, the monitoring unit can display the task progress by region on a graph and compare progress. For example, the monitoring unit can display the task progress by region on a chart and compare progress. This allows for comparison of progress by region by analyzing the task progress from a geographical perspective. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data by region into a generating AI and have the generating AI perform a progress comparison.

[0045] The monitoring unit can improve the accuracy of progress by referring to external data related to the progress of tasks. For example, the monitoring unit can acquire data from an external project management tool to improve the accuracy of progress. For example, the monitoring unit can acquire data from an external time management tool to improve the accuracy of progress. For example, the monitoring unit can acquire data from an external resource management tool to improve the accuracy of progress. In this way, the accuracy of progress is improved by referring to external data related to the progress of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input external data into a generating AI and have the generating AI perform the improvement of progress accuracy.

[0046] The prioritization unit can dynamically change priorities by comprehensively evaluating the urgency and importance of tasks. For example, the prioritization unit can process tasks that are both highly urgent and highly important with the highest priority. For example, the prioritization unit can process tasks that are less urgent but highly important with the next highest priority. For example, the prioritization unit can process tasks that are highly urgent but less important with the next highest priority. In this way, priorities can be dynamically changed by comprehensively evaluating the urgency and importance of tasks. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task urgency and importance data into a generating AI and have the generating AI perform the dynamic change of priorities.

[0047] The prioritization unit can optimize priorities by considering task dependencies. For example, the prioritization unit may process tasks with dependencies first, and then process other dependent tasks. For example, the prioritization unit may prioritize tasks with complex dependencies to ensure smooth overall progress. The prioritization unit can optimize task priorities by considering dependencies. This allows for optimization of priorities by considering task dependencies. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task dependency data into a generating AI and have the generating AI perform priority optimization.

[0048] The prioritization unit can analyze task priorities from a geographical perspective and set priorities for each region. For example, the prioritization unit can display task priorities for each region on a map and set priorities. For example, the prioritization unit can display task priorities for each region on a graph and set priorities. For example, the prioritization unit can display task priorities for each region on a chart and set priorities. In this way, by analyzing task priorities from a geographical perspective, priorities for each region can be set. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task priority data for each region into a generating AI and have the generating AI perform the priority setting.

[0049] The prioritization unit can improve the accuracy of prioritization by referring to external data related to task prioritization. For example, the prioritization unit can obtain data from an external project management tool to improve the accuracy of prioritization. For example, the prioritization unit can obtain data from an external time management tool to improve the accuracy of prioritization. For example, the prioritization unit can obtain data from an external resource management tool to improve the accuracy of prioritization. In this way, the accuracy of prioritization is improved by referring to external data related to task prioritization. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input external data into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0050] The notification unit can send notifications about task changes in real time to prompt immediate action. For example, the notification unit sends a notification in real time when a task change occurs. The notification unit can send notifications about task changes immediately to prompt action. For example, the notification unit can send notifications to relevant parties in real time when a task change occurs. This enables immediate action by sending notifications about task changes in real time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task change data into a generating AI and have the generating AI execute real-time notifications.

[0051] The notification unit can analyze the notification history and learn the optimal notification timing. For example, the notification unit can analyze the notification history and learn the optimal notification timing. For example, the notification unit can propose the optimal notification timing based on the notification history. For example, the notification unit can analyze the notification history and set the optimal notification timing. In this way, the optimal notification timing can be learned by analyzing the notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input notification history data into a generating AI and have the generating AI perform learning of the optimal notification timing.

[0052] The notification unit can customize the content of notifications from a geographical perspective and optimize notifications for each region. For example, the notification unit can customize and optimize the content of notifications for each region. For example, the notification unit can display and optimize the content of notifications for each region on a map. For example, the notification unit can display and optimize the content of notifications for each region on a graph. In this way, notifications for each region can be optimized by customizing the content of notifications from a geographical perspective. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input region-specific notification data into a generating AI and have the generating AI perform the customization of the notification content.

[0053] The reallocation unit can dynamically reallocate resources according to the progress of the task. For example, if the task is progressing smoothly, the reallocation unit can reduce the reallocation of resources. For example, if the task is behind schedule, the reallocation unit can increase the reallocation of resources. The reallocation unit can dynamically reallocate resources according to the progress of the task. This enables efficient resource allocation by dynamically reallocating resources according to the progress of the task. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input task progress data into a generating AI and cause the generating AI to execute a method for dynamically reallocating resources.

[0054] The reallocation unit can monitor the load status of members in real time and reallocate resources to prevent overload. For example, the reallocation unit can monitor the load status of members in real time and reallocate resources to prevent overload. For example, if the load status of members is high, the reallocation unit can increase the reallocation of resources. For example, if the load status of members is low, the reallocation unit can decrease the reallocation of resources. In this way, by monitoring the load status of members in real time, resources can be reallocated to prevent overload. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without using AI. For example, the reallocation unit can input member load status data into a generating AI and cause the generating AI to perform resource reallocation to prevent overload.

[0055] The reallocation unit can analyze resource reallocation from a geographical perspective and optimize resource allocation for each region. For example, the reallocation unit can display and optimize resource allocation for each region on a map. For example, the reallocation unit can display and optimize resource allocation for each region on a graph. For example, the reallocation unit can display and optimize resource allocation for each region on a chart. In this way, by analyzing resource reallocation from a geographical perspective, resource allocation for each region can be optimized. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input regional resource allocation data into a generating AI and have the generating AI perform resource allocation optimization.

[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 assignment unit can assign tasks while considering the health status of the members. For example, if a member is fatigued, the assignment unit can assign a lighter task. For example, if a member is in good health, the assignment unit can assign an important task. For example, if a member is ill, the assignment unit can reassign the task to another member. This makes it possible to assign tasks optimally by considering the health status of the members.

[0058] The reallocation unit can assign training tasks to improve members' skills according to the progress of the task. For example, if the task is progressing smoothly, the reallocation unit can assign training tasks to members to acquire new skills. For example, if the task is behind schedule, the reallocation unit can assign training tasks to members to strengthen existing skills. For example, once a task is completed, the reallocation unit can assign training tasks to members for the next project. This allows for the improvement of members' skills according to the progress of the task.

[0059] The assignment department can assign tasks while considering members' career goals. For example, if a member wants to acquire a specific skill, the assignment department can assign tasks related to that skill. For example, if a member is aiming for a specific position, the assignment department can assign tasks related to that position. For example, if a member is interested in a specific project, the assignment department can assign tasks related to that project. This makes it possible to assign tasks optimally by considering members' career goals.

[0060] The monitoring unit can automatically generate feedback for members based on the progress of the task. For example, if the task is progressing smoothly, the monitoring unit can automatically generate positive feedback for the member. For example, if the task is behind schedule, the monitoring unit can automatically generate feedback for the member pointing out areas for improvement. The monitoring unit can automatically generate feedback for members based on the progress of the task. This allows for the maintenance of member motivation by automatically generating feedback according to the progress of the task.

[0061] The assignment unit can assign the most suitable tasks by considering the geographical location of the members. For example, if a member is nearby, the assignment unit will assign tasks that require on-site attention. For example, if a member is far away, the assignment unit can assign tasks that can be handled remotely. For example, the assignment unit can assign the most suitable tasks based on the members' location information. This makes it possible to assign tasks optimally by considering the geographical location of the members.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The assignment unit automatically assigns tasks. For example, the assignment unit uses an AI agent to analyze past response history and assign tasks to members who are able to handle them. Tasks can be assigned considering each member's skills, past performance, and current workload. Tasks can be assigned to members who have shown high performance for specific tasks in the past, preventing task overload and ensuring that the burden is evenly distributed among members. Step 2: The monitoring unit monitors the progress of tasks assigned by the assignment unit. For example, the monitoring unit can periodically check the progress of tasks and provide early notification if delays or problems occur. It can monitor task progress in real time, preventing insufficient progress checks. In addition, it can automatically notify stakeholders of any changes to the task, facilitating communication. Step 3: The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. By automatically selecting the most suitable members for the project and assigning tasks, the team's efficiency can be maximized.

[0064] (Example of form 2) The task management system according to an embodiment of the present invention is a system that uses an AI agent to automatically assign tasks and manage their progress. This task management system analyzes past response history and automatically assigns tasks to members who are able to handle them. Next, it monitors the progress of tasks in real time and reallocates resources as needed. This mechanism can reduce the waiting time for those who have made requests and improve the overall efficiency and productivity of operations. For example, the AI ​​agent analyzes past response history. In this process, it identifies members who are able to handle the task by considering each member's skills, past performance, and current workload. For example, if there is a member who has shown high performance for a particular task in the past, the task can be assigned to that member. This prevents task overload and equalizes the burden on members. Next, the AI ​​agent monitors the progress of tasks in real time. For example, it can periodically check the progress of tasks and notify early if delays or problems occur. This prevents insufficient progress checks and allows for early detection of delays and problems. In addition, when changes occur in a task, it can automatically notify relevant parties and facilitate communication. Furthermore, the AI ​​agent prioritizes tasks. For example, it can analyze the urgency and importance of tasks and assign appropriate priorities. This allows for the automatic selection of the most suitable members for a project and the assignment of tasks, maximizing team efficiency. This system is expected to reduce the workload on team members, decrease overtime, and improve their health. It also improves overall efficiency and productivity, enhancing the company's competitiveness. The introduction of AI agents is not merely about automating processes; it contributes to the development of the entire business. For example, it can solve problems such as task overload, insufficient progress monitoring, and reliance on individual expertise in organizations with various work structures, from small and medium-sized enterprises to large corporations that manage projects. This enables efficient task assignment and progress management, providing an environment where team members can work more efficiently.This allows the task management system to reduce waiting times for those who submit requests and improve overall efficiency and productivity.

[0065] The task management system according to this embodiment comprises an assignment unit, a monitoring unit, and a prioritization unit. The assignment unit automatically assigns tasks. The assignment unit, for example, uses an AI agent to analyze past response history and assigns tasks to members who are able to handle them. For example, the assignment unit can assign tasks considering each member's skills, past performance, and current workload. For example, the assignment unit can assign tasks to members who have shown high performance for a particular task in the past. The assignment unit can also prevent task overload and equalize the burden on members. The monitoring unit monitors the progress of tasks assigned by the assignment unit. For example, the monitoring unit can periodically check the progress of tasks and notify early if delays or problems occur. For example, the monitoring unit can monitor the progress of tasks in real time and prevent insufficient progress checks. The monitoring unit can also automatically notify relevant parties when changes occur in a task and facilitate communication. The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. The prioritization unit can maximize team efficiency by, for example, automatically selecting the most suitable members for a project and assigning tasks to them. This enables the task management system according to the embodiment to automatically assign tasks, monitor progress, and prioritize tasks.

[0066] The assignment unit automatically assigns tasks. For example, it uses an AI agent to analyze past response history and assign tasks to members who are able to handle them. Specifically, the AI ​​agent analyzes each member's skill set, past task completion history, and current workload in detail. For example, the AI ​​agent uses natural language processing technology to analyze past task reports and feedback to understand each member's strengths and performance trends. The AI ​​agent also monitors each member's current workload in real time and adjusts task assignments to avoid overload. For example, if a particular member currently has multiple high-priority tasks, the AI ​​agent avoids assigning new tasks to that member and assigns them to other members. The AI ​​agent also selects the most suitable member based on the nature of the task and the required skills. For example, it prioritizes assigning tasks that use a particular programming language to members who are proficient in that language. This allows the assignment unit to achieve efficient task distribution and improve the overall productivity of the team. Furthermore, the assignment unit can accumulate task assignment history and use it as data to improve the accuracy of future task assignments. This allows the assignment unit to continuously learn and optimize task assignment.

[0067] The Monitoring Department monitors the progress of tasks assigned by the Assignment Department. For example, the Monitoring Department can periodically check the progress of tasks and provide early notification if delays or problems occur. Specifically, the Monitoring Department tracks the progress of each task in real time and provides a dashboard that visualizes the progress. The dashboard displays the progress of each task, the expected completion date, and the status of the person in charge, allowing project managers and team members to grasp the situation at a glance. The Monitoring Department also issues alerts if there are any abnormalities in the progress of a task, such as delays or if the task is not progressing as planned. These alerts are sent to relevant parties via email or chat tools to encourage prompt action. Furthermore, the Monitoring Department accumulates data on task progress and can perform analysis based on past progress data. This allows for quantitative evaluation of project progress and identification of areas for improvement. The Monitoring Department also facilitates communication by automatically notifying relevant parties when changes occur to tasks. For example, if a task deadline is changed or new requirements are added, notifications are sent to all relevant parties, enabling a quick response. This allows the monitoring unit to accurately grasp the progress of tasks and support the smooth running of the project.

[0068] The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks to determine appropriate priorities. Specifically, the prioritization unit comprehensively evaluates each task's deadline, dependencies, resource utilization, etc., to determine its priority. For example, tasks with approaching deadlines or those dependent on other tasks are given high priority. Furthermore, important tasks are given high priority based on the overall project goals and strategy. The prioritization unit uses AI to analyze these factors and automatically determine the optimal priority. For example, the AI ​​learns from past project data to understand patterns and trends in task prioritization. This allows the prioritization unit to flexibly adjust priorities in response to project progress and changes in circumstances. In addition, the prioritization unit periodically reviews task priorities and updates them based on the latest situation. For example, if new tasks are added or the status of existing tasks changes, the priority is re-evaluated and tasks are carried out in the optimal order. This allows the prioritization unit to support the efficient progress of the project and achieve optimal resource allocation.

[0069] The notification unit can notify of changes to a task. For example, the notification unit automatically sends notifications to stakeholders when changes occur to a task. For example, the notification unit can notify of task changes in real time to encourage prompt action. For example, the notification unit can provide detailed information about task changes so that stakeholders can take appropriate action. This enables prompt action by notifying of task changes. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can perform notifications using an AI model that detects task changes and sends notifications to stakeholders.

[0070] The reallocation unit can reallocate resources. The reallocation unit reallocates resources considering, for example, the progress of tasks and the workload of members. The reallocation unit can efficiently allocate resources according to the progress of tasks. The reallocation unit can reallocate resources to equalize the workload of members. This enables efficient task management by reallocating resources. Some or all of the above processing in the reallocation unit may be performed using, for example, AI, or not using AI. For example, the reallocation unit can reallocate resources using an AI model that takes the progress of tasks and the workload of members as input and outputs the reallocation of resources.

[0071] The assignment unit can assign tasks considering each member's skills, past performance, and current workload. For example, the assignment unit can analyze each member's skill set in detail and assign tasks specialized in specific skills. For example, the assignment unit can assign tasks according to a member's area of ​​expertise based on past performance data. For example, the assignment unit can monitor the current workload in real time and adjust tasks to prevent overload. This makes it possible to assign tasks optimally by considering each member's skills and workload. Some or all of the above processes in the assignment unit may be performed using AI, for example, or not. For example, the assignment unit can assign tasks using an AI model that takes each member's skills, past performance, and current workload as input and outputs the optimal task assignment.

[0072] The monitoring unit can periodically check the progress of tasks and provide early notification if delays or problems occur. For example, the monitoring unit can periodically check the progress of tasks and issue an alert if progress is behind schedule. For example, the monitoring unit can monitor the progress of tasks in real time and provide immediate notification if problems occur. For example, the monitoring unit can visualize the progress of tasks and detect delays or problems early. This allows for early detection of delays and problems by periodically checking the progress of tasks. Some or all of the above processes in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can take task progress data as input and use an AI model to detect delays and problems to provide notifications.

[0073] The prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. For example, the prioritization unit can evaluate the urgency of tasks and process high-urgency tasks first. For example, the prioritization unit can evaluate the importance of tasks and process high-importance tasks first. For example, the prioritization unit can consider task dependencies and process tasks with dependencies first. This enables appropriate prioritization by analyzing the urgency and importance of tasks. Some or all of the above processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can prioritize tasks using an AI model that takes the urgency and importance of tasks as input and outputs priorities.

[0074] The assignment unit can estimate the user's emotions and adjust the task assignment method based on the estimated emotions. For example, if the user is stressed, the assignment unit may prioritize assigning easy tasks. For example, if the user is relaxed, the assignment unit may assign complex tasks. For example, if the user is in a hurry, the assignment unit may assign tasks that can be completed quickly. This allows for more appropriate task assignment by adjusting the task assignment method 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 processing in the assignment unit may be performed using AI or not. For example, the assignment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the task assignment method based on emotions.

[0075] The assignment unit can perform a detailed analysis of each member's skill set and assign tasks specialized in specific skills. For example, the assignment unit can assign coding tasks to members with strong programming skills. For example, the assignment unit can assign client support tasks to members with strong communication skills. For example, the assignment unit can assign data analysis tasks to members with strong analytical skills. This makes it possible to assign tasks specialized in specific skills by analyzing each member's skill set in detail. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input each member's skill set data into a generating AI and have the generating AI perform task assignments specialized in specific skills.

[0076] The assignment unit can optimize task assignments by considering members' past task completion times. For example, the assignment unit can assign urgent tasks to members who have completed tasks quickly in the past. For example, the assignment unit can assign time-efficient tasks to members who have completed tasks that took a long time in the past. For example, the assignment unit can adjust task assignments based on past task completion times. This optimizes task assignments by considering members' past task completion times. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input members' past task completion time data into a generating AI and have the generating AI execute a method to optimize task assignments.

[0077] The assignment unit can estimate the user's emotions and determine the task assignment order based on the estimated emotions. For example, if the user is stressed, the assignment unit can assign easy tasks first. If the user is relaxed, the assignment unit can assign complex tasks first. If the user is in a hurry, the assignment unit can assign tasks that can be completed quickly first. This allows for more appropriate task assignment by determining the task assignment order 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 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 assignment unit may be performed using AI or not. For example, the assignment unit can input user emotion data into a generative AI and have the generative AI perform the determination of the task assignment order based on emotions.

[0078] The assignment unit can assign the most suitable tasks by considering the geographical location of the members. For example, if a member is nearby, the assignment unit can assign tasks that require on-site attention. For example, if a member is far away, the assignment unit can assign tasks that can be handled remotely. For example, the assignment unit can assign the most suitable tasks based on the members' location information. This makes it possible to assign tasks optimally by considering the geographical location of the members. Some or all of the above processing in the assignment unit may be performed using AI, for example, or without AI. For example, the assignment unit can input the geographical location information of the members into a generating AI and have the generating AI perform the optimal task assignment.

[0079] The assignment unit can analyze members' social media activity and assign relevant tasks. For example, if a member is promoting a specific skill on social media, the assignment unit can assign tasks related to that skill. For example, if a member is showing interest in a specific project on social media, the assignment unit can assign tasks related to that project. For example, the assignment unit can assign the most suitable tasks based on a member's social media activity. This makes it possible to assign relevant tasks by analyzing a member's social media activity. Some or all of the above processing in the assignment unit may be performed using AI, for example, or not using AI. For example, the assignment unit can input members' social media activity data into a generating AI and have the generating AI perform the relevant task assignment.

[0080] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of progress notifications. For example, if the user is relaxed, the monitoring unit can increase the frequency of progress notifications. For example, if the user is in a hurry, the monitoring unit can provide progress notifications in real time. This allows for more appropriate progress management by adjusting the progress monitoring 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into the generative AI and have the generative AI adjust the progress monitoring method based on emotions.

[0081] The monitoring unit can visualize the progress of tasks in real time and predict delays in progress. For example, the monitoring unit can display the progress of tasks in a graph and predict delays. For example, the monitoring unit can display the progress of tasks in a chart and predict delays. For example, the monitoring unit can display the progress of tasks on a dashboard and predict delays. This makes it possible to predict delays in progress by visualizing the progress of tasks in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data into a generating AI and have the generating AI execute a method for predicting delays in progress.

[0082] The monitoring unit can automatically generate feedback for members according to the progress of the task. For example, if the task is progressing smoothly, the monitoring unit can automatically generate positive feedback for the member. For example, if the task is behind schedule, the monitoring unit can automatically generate feedback for the member pointing out areas for improvement. The monitoring unit can automatically generate feedback for members according to the progress of the task. This helps maintain member motivation by automatically generating feedback according to the progress of the task. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data into a generating AI and have the generating AI execute a method for automatically generating feedback.

[0083] The monitoring unit can estimate the user's emotions and adjust the frequency of progress notifications based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of progress notifications. For example, if the user is relaxed, the monitoring unit can increase the frequency of progress notifications. For example, if the user is in a hurry, the monitoring unit can provide real-time progress notifications. This allows for more appropriate progress management by adjusting the frequency of progress notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of progress notifications based on emotions.

[0084] The monitoring unit can analyze the progress of tasks from a geographical perspective and compare progress by region. For example, the monitoring unit can display the task progress by region on a map and compare progress. For example, the monitoring unit can display the task progress by region on a graph and compare progress. For example, the monitoring unit can display the task progress by region on a chart and compare progress. This allows for comparison of progress by region by analyzing the task progress from a geographical perspective. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task progress data by region into a generating AI and have the generating AI perform a progress comparison.

[0085] The monitoring unit can improve the accuracy of progress by referring to external data related to the progress of tasks. For example, the monitoring unit can acquire data from an external project management tool to improve the accuracy of progress. For example, the monitoring unit can acquire data from an external time management tool to improve the accuracy of progress. For example, the monitoring unit can acquire data from an external resource management tool to improve the accuracy of progress. In this way, the accuracy of progress is improved by referring to external data related to the progress of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input external data into a generating AI and have the generating AI perform the improvement of progress accuracy.

[0086] The prioritization unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the prioritization unit can prioritize simple tasks. For example, if the user is relaxed, the prioritization unit can prioritize complex tasks. For example, if the user is in a hurry, the prioritization unit can prioritize tasks that can be completed quickly. This allows for more appropriate 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 prioritization unit may be performed using AI or not. For example, the prioritization unit can input user emotion data into the generative AI and have the generative AI adjust task priorities based on emotions.

[0087] The prioritization unit can dynamically change priorities by comprehensively evaluating the urgency and importance of tasks. For example, the prioritization unit can process tasks that are both highly urgent and highly important with the highest priority. For example, the prioritization unit can process tasks that are less urgent but highly important with the next highest priority. For example, the prioritization unit can process tasks that are highly urgent but less important with the next highest priority. In this way, priorities can be dynamically changed by comprehensively evaluating the urgency and importance of tasks. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task urgency and importance data into a generating AI and have the generating AI perform the dynamic change of priorities.

[0088] The prioritization unit can optimize priorities by considering task dependencies. For example, the prioritization unit may process tasks with dependencies first, and then process other dependent tasks. For example, the prioritization unit may prioritize tasks with complex dependencies to ensure smooth overall progress. The prioritization unit can optimize task priorities by considering dependencies. This allows for optimization of priorities by considering task dependencies. Some or all of the above-described processes in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task dependency data into a generating AI and have the generating AI perform priority optimization.

[0089] The prioritization unit can estimate the user's emotions and adjust the display method of task priorities based on the estimated user emotions. For example, if the user is stressed, the prioritization unit can provide a simple display method. For example, if the user is relaxed, the prioritization unit can provide a detailed display method. For example, if the user is in a hurry, the prioritization unit can provide a concise display method. This allows for more appropriate task management by adjusting the display method of task priorities 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 prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input user emotion data into the generative AI and have the generative AI adjust the display method of task priorities based on emotions.

[0090] The prioritization unit can analyze task priorities from a geographical perspective and set priorities for each region. For example, the prioritization unit can display task priorities for each region on a map and set priorities. For example, the prioritization unit can display task priorities for each region on a graph and set priorities. For example, the prioritization unit can display task priorities for each region on a chart and set priorities. In this way, by analyzing task priorities from a geographical perspective, priorities for each region can be set. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input task priority data for each region into a generating AI and have the generating AI perform the priority setting.

[0091] The prioritization unit can improve the accuracy of prioritization by referring to external data related to task prioritization. For example, the prioritization unit can obtain data from an external project management tool to improve the accuracy of prioritization. For example, the prioritization unit can obtain data from an external time management tool to improve the accuracy of prioritization. For example, the prioritization unit can obtain data from an external resource management tool to improve the accuracy of prioritization. In this way, the accuracy of prioritization is improved by referring to external data related to task prioritization. Some or all of the above processing in the prioritization unit may be performed using AI, for example, or without AI. For example, the prioritization unit can input external data into a generating AI and have the generating AI perform the improvement of priority accuracy.

[0092] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a concise notification. For example, if the user is relaxed, the notification unit can provide a detailed notification. For example, if the user is in a hurry, the notification unit can provide a notification that allows for a quick response. By adjusting the content of the notification according to the user's emotions, more appropriate notifications can be provided. 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 notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI perform emotion-based adjustment of the notification content.

[0093] The notification unit can send notifications about task changes in real time to prompt immediate action. For example, the notification unit sends a notification in real time when a task change occurs. The notification unit can send notifications about task changes immediately to prompt action. For example, the notification unit can send notifications to relevant parties in real time when a task change occurs. This enables immediate action by sending notifications about task changes in real time. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input task change data into a generating AI and have the generating AI execute real-time notifications.

[0094] The notification unit can analyze the notification history and learn the optimal notification timing. For example, the notification unit can analyze the notification history and learn the optimal notification timing. For example, the notification unit can propose the optimal notification timing based on the notification history. For example, the notification unit can analyze the notification history and set the optimal notification timing. In this way, the optimal notification timing can be learned by analyzing the notification history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without using AI. For example, the notification unit can input notification history data into a generating AI and have the generating AI perform learning of the optimal notification timing.

[0095] The notification unit can estimate the user's emotions and adjust the notification frequency based on the estimated emotions. For example, if the user is stressed, the notification unit can reduce the notification frequency. For example, if the user is relaxed, the notification unit can increase the notification frequency. For example, if the user is in a hurry, the notification unit can adjust the notification frequency in real time. This allows for more appropriate notifications by adjusting the notification frequency 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into the generative AI and have the generative AI perform emotion-based notification frequency adjustments.

[0096] The notification unit can customize the content of notifications from a geographical perspective and optimize notifications for each region. For example, the notification unit can customize and optimize the content of notifications for each region. For example, the notification unit can display and optimize the content of notifications for each region on a map. For example, the notification unit can display and optimize the content of notifications for each region on a graph. In this way, notifications for each region can be optimized by customizing the content of notifications from a geographical perspective. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input region-specific notification data into a generating AI and have the generating AI perform the customization of the notification content.

[0097] The reallocation unit can estimate the user's emotions and adjust the resource reallocation method based on the estimated user emotions. For example, if the user is stressed, the reallocation unit can reduce resource reallocation. For example, if the user is relaxed, the reallocation unit can increase resource reallocation. For example, if the user is in a hurry, the reallocation unit can reallocate resources quickly. This allows for more appropriate resource allocation by adjusting the resource reallocation 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 reallocation unit may be performed using AI or not using AI. For example, the reallocation unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the resource reallocation method based on emotions.

[0098] The reallocation unit can dynamically reallocate resources according to the progress of the task. For example, if the task is progressing smoothly, the reallocation unit can reduce the reallocation of resources. For example, if the task is behind schedule, the reallocation unit can increase the reallocation of resources. The reallocation unit can dynamically reallocate resources according to the progress of the task. This enables efficient resource allocation by dynamically reallocating resources according to the progress of the task. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input task progress data into a generating AI and cause the generating AI to execute a method for dynamically reallocating resources.

[0099] The reallocation unit can monitor the load status of members in real time and reallocate resources to prevent overload. For example, the reallocation unit can monitor the load status of members in real time and reallocate resources to prevent overload. For example, if the load status of members is high, the reallocation unit can increase the reallocation of resources. For example, if the load status of members is low, the reallocation unit can decrease the reallocation of resources. In this way, by monitoring the load status of members in real time, resources can be reallocated to prevent overload. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without using AI. For example, the reallocation unit can input member load status data into a generating AI and cause the generating AI to perform resource reallocation to prevent overload.

[0100] The reallocation unit can estimate the user's emotions and determine the priority of resource reallocation based on the estimated user emotions. For example, if the user is stressed, the reallocation unit may lower the priority of resource reallocation. For example, if the user is relaxed, the reallocation unit may raise the priority of resource reallocation. For example, if the user is in a hurry, the reallocation unit may quickly determine the priority of resource reallocation. This allows for more appropriate resource allocation by determining the priority of resource reallocation 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 reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input user emotion data into a generative AI and have the generative AI perform the determination of priority of resource reallocation based on emotions.

[0101] The reallocation unit can analyze resource reallocation from a geographical perspective and optimize resource allocation for each region. For example, the reallocation unit can display and optimize resource allocation for each region on a map. For example, the reallocation unit can display and optimize resource allocation for each region on a graph. For example, the reallocation unit can display and optimize resource allocation for each region on a chart. In this way, by analyzing resource reallocation from a geographical perspective, resource allocation for each region can be optimized. Some or all of the above processing in the reallocation unit may be performed using AI, for example, or without AI. For example, the reallocation unit can input regional resource allocation data into a generating AI and have the generating AI perform resource allocation optimization.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The assignment unit can assign tasks while considering the health status of the members. For example, if a member is fatigued, the assignment unit can assign a lighter task. For example, if a member is in good health, the assignment unit can assign an important task. For example, if a member is ill, the assignment unit can reassign the task to another member. This makes it possible to assign tasks optimally by considering the health status of the members.

[0104] The notification system can send messages to members to boost their motivation based on the progress of the task. For example, if the task is progressing smoothly, the notification system can send a message of praise to the member. For example, if the task is behind schedule, the notification system can send a message of encouragement to the member. For example, if the task is completed, the notification system can send a message of gratitude to the member. This helps maintain member motivation according to the progress of the task.

[0105] The reallocation unit can assign training tasks to improve members' skills according to the progress of the task. For example, if the task is progressing smoothly, the reallocation unit can assign training tasks to members to acquire new skills. For example, if the task is behind schedule, the reallocation unit can assign training tasks to members to strengthen existing skills. For example, once a task is completed, the reallocation unit can assign training tasks to members for the next project. This allows for the improvement of members' skills according to the progress of the task.

[0106] The assignment department can assign tasks while considering members' career goals. For example, if a member wants to acquire a specific skill, the assignment department can assign tasks related to that skill. For example, if a member is aiming for a specific position, the assignment department can assign tasks related to that position. For example, if a member is interested in a specific project, the assignment department can assign tasks related to that project. This makes it possible to assign tasks optimally by considering members' career goals.

[0107] The monitoring unit can automatically generate feedback for members based on the progress of the task. For example, if the task is progressing smoothly, the monitoring unit can automatically generate positive feedback for the member. For example, if the task is behind schedule, the monitoring unit can automatically generate feedback for the member pointing out areas for improvement. The monitoring unit can automatically generate feedback for members based on the progress of the task. This allows for the maintenance of member motivation by automatically generating feedback according to the progress of the task.

[0108] The prioritization unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the prioritization unit will prioritize simple tasks. If the user is relaxed, the prioritization unit can prioritize complex tasks. If the user is in a hurry, the prioritization unit can prioritize tasks that can be completed quickly. This allows for more effective task management by adjusting task priorities according to the user's emotions.

[0109] The assignment unit can assign the most suitable tasks by considering the geographical location of the members. For example, if a member is nearby, the assignment unit will assign tasks that require on-site attention. For example, if a member is far away, the assignment unit can assign tasks that can be handled remotely. For example, the assignment unit can assign the most suitable tasks based on the members' location information. This makes it possible to assign tasks optimally by considering the geographical location of the members.

[0110] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of progress notifications. For example, if the user is relaxed, the monitoring unit can increase the frequency of progress notifications. For example, if the user is in a hurry, the monitoring unit can provide progress notifications in real time. This allows for more appropriate progress management by adjusting the progress monitoring method according to the user's emotions.

[0111] The notification unit can estimate the user's emotions and adjust the content of the notification based on those emotions. For example, if the user is stressed, the notification unit can provide a concise notification. If the user is relaxed, for example, the notification unit can provide a detailed notification. If the user is in a hurry, for example, the notification unit can provide a notification that allows for a quick response. By adjusting the content of notifications according to the user's emotions, more appropriate notifications become possible.

[0112] The resource reallocation unit can estimate the user's emotions and adjust the resource reallocation method based on the estimated emotions. For example, if the user is stressed, the reallocation unit can reduce resource reallocation. For example, if the user is relaxed, the reallocation unit can increase resource reallocation. For example, if the user is in a hurry, the reallocation unit can quickly reallocate resources. This allows for more appropriate resource allocation by adjusting the resource reallocation method according to the user's emotions.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The assignment unit automatically assigns tasks. For example, the assignment unit uses an AI agent to analyze past response history and assign tasks to members who are able to handle them. Tasks can be assigned considering each member's skills, past performance, and current workload. Tasks can be assigned to members who have shown high performance for specific tasks in the past, preventing task overload and ensuring that the burden is evenly distributed among members. Step 2: The monitoring unit monitors the progress of tasks assigned by the assignment unit. For example, the monitoring unit can periodically check the progress of tasks and provide early notification if delays or problems occur. It can monitor task progress in real time, preventing insufficient progress checks. In addition, it can automatically notify stakeholders of any changes to the task, facilitating communication. Step 3: The prioritization unit prioritizes tasks based on the progress obtained by the monitoring unit. For example, the prioritization unit can analyze the urgency and importance of tasks and assign appropriate priorities. By automatically selecting the most suitable members for the project and assigning tasks, the team's efficiency can be maximized.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the allocation unit, monitoring unit, prioritization unit, notification unit, and redistribution unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the allocation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The prioritization unit is implemented by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The redistribution unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the allocation unit, monitoring unit, prioritization unit, notification unit, and redistribution unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the allocation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The prioritization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The redistribution unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the allocation unit, monitoring unit, prioritization unit, notification unit, and redistribution unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the allocation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The prioritization unit is implemented by the specific processing unit 290 of the data processing device 12. The notification unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The redistribution unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] Each of the multiple elements described above, including the allocation unit, monitoring unit, prioritization unit, notification unit, and redistribution unit, is implemented, for example, in at least one of the robot 414 and the data processing device 12. For example, the allocation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The prioritization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12. The notification unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The redistribution unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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."

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] (Note 1) The assignment unit performs automatic task assignment, A monitoring unit monitors the progress of tasks assigned by the aforementioned assignment unit, The system includes a prioritization unit that prioritizes tasks based on the progress obtained by the monitoring unit. A system characterized by the following features. (Note 2) It includes a notification unit that notifies users of changes to tasks. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a reallocation unit that reallocates resources. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned allocation unit is, Tasks are assigned considering each member's skills, past performance, and current workload. The system described in Appendix 1, characterized by the features described herein. (Note 5) The monitoring unit, Regularly check the progress of the task and notify us promptly if any delays or problems occur. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned prioritization unit, Analyze the urgency and importance of tasks and set appropriate priorities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned allocation unit is, It estimates the user's emotions and adjusts how tasks are assigned based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned allocation unit is, Conduct a detailed analysis of each member's skill set and assign tasks that specialize in specific skills. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned allocation unit is, Optimize task assignments by considering members' past task completion times. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned allocation unit is, It estimates the user's emotions and determines the task assignment order based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned allocation unit is, Assign the most suitable tasks, taking into account the geographical location of each member. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned allocation unit is, Analyze members' social media activity and assign relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 13) The monitoring unit, We estimate user sentiment and adjust progress monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The monitoring unit, Visualize task progress in real time and predict delays. The system described in Appendix 1, characterized by the features described herein. (Note 15) The monitoring unit, Automatically generate feedback for team members based on the progress of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The monitoring unit, It estimates the user's emotions and adjusts the frequency of progress notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The monitoring unit, Analyze the progress of tasks from a geographical perspective and compare progress by region. The system described in Appendix 1, characterized by the features described herein. (Note 18) The monitoring unit, Referencing external data related to task progress improves the accuracy of progress. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned prioritization unit, 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 20) The aforementioned prioritization unit, The system dynamically adjusts priorities by comprehensively evaluating the urgency and importance of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned prioritization unit, Prioritize tasks by considering their dependencies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned prioritization unit, It estimates the user's emotions and adjusts how task priorities are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned prioritization unit, Analyze task priorities from a geographical perspective and set priorities for each region. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned prioritization unit, Referencing external data related to task prioritization improves the accuracy of prioritization. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned notification unit, Send real-time notifications about task changes to encourage immediate action. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned notification unit, It analyzes the notification history and learns the optimal timing for notifications. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the frequency of notifications based on those emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned notification unit, Customize notification content from a geographical perspective and optimize notifications for each region. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned redistribution unit is It estimates the user's emotions and adjusts how resources are reallocated based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 31) The aforementioned redistribution unit is Dynamically reallocate resources based on the progress of tasks. The system described in Appendix 3, characterized by the features described herein. (Note 32) The aforementioned redistribution unit is Monitor member workload in real time and reallocate resources to prevent overload. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned redistribution unit is It estimates user sentiment and determines the priority of resource reallocation based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 34) The aforementioned redistribution unit is Analyze resource reallocation from a geographical perspective and optimize resource allocation for each region. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0187] 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 assignment unit performs automatic task assignment, A monitoring unit monitors the progress of tasks assigned by the aforementioned assignment unit, The system includes a prioritization unit that prioritizes tasks based on the progress obtained by the monitoring unit. A system characterized by the following features.

2. It includes a notification unit that notifies users of changes to tasks. The system according to feature 1.

3. It includes a reallocation unit that reallocates resources. The system according to feature 1.

4. The aforementioned allocation unit is, Tasks are assigned considering each member's skills, past performance, and current workload. The system according to feature 1.

5. The monitoring unit, Regularly check the progress of the task and notify us promptly if any delays or problems occur. The system according to feature 1.

6. The aforementioned prioritization unit, Analyze the urgency and importance of tasks and set appropriate priorities. The system according to feature 1.

7. The aforementioned allocation unit is, It estimates the user's emotions and adjusts how tasks are assigned based on those estimated emotions. The system according to feature 1.

8. The aforementioned allocation unit is, Conduct a detailed analysis of each member's skill set and assign tasks that specialize in specific skills. The system according to feature 1.

9. The aforementioned allocation unit is, Optimize task assignments by considering members' past task completion times. The system according to feature 1.

10. The aforementioned allocation unit is, It estimates the user's emotions and determines the task assignment order based on the estimated user emotions. The system according to feature 1.

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