system

Abstract

The system according to this embodiment aims to track the progress of project tasks in real time and automatically propose solutions when delays occur. [Solution] The system according to the embodiment comprises a tracking unit, a visualization unit, a proposal unit, and a notification unit. The tracking unit tracks the task progress of project members in real time. The visualization unit visualizes the task progress tracked by the tracking unit. The proposal unit automatically proposes solutions when delays occur based on the task progress visualized by the visualization unit. The notification unit notifies of resource reallocation or changes in priority based on the solutions proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, there is a problem that it is difficult to track the task progress of a project in real time and quickly respond when a delay occurs.

[0005] The system according to the embodiment aims to track the task progress of a project in real time and automatically propose a solution when a delay occurs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, a visualization unit, a proposal unit, and a notification unit. The tracking unit tracks the task progress of project members in real time. The visualization unit visualizes the task progress tracked by the tracking unit. The proposal unit automatically proposes solutions when delays occur based on the task progress visualized by the visualization unit. The notification unit notifies of resource reallocation or changes in priority based on the solutions proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can track the progress of project tasks in real time and automatically suggest solutions if delays occur. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that streamlines project management using an AI agent. This project management system tracks the task progress of project members in real time and visualizes the status of each task. If delays occur, the AI ​​automatically proposes solutions and notifies the user of the reallocation of necessary resources and changes in priorities. Next, it centrally manages data for each project, performs progress and risk analysis, and provides an easy-to-understand dashboard for leaders. Furthermore, it has team members report their progress regularly, automatically updates the progress status, and automatically sends reminders to members who are behind schedule. When progress is delayed or risks increase, it sets customizable alerts and immediately notifies the project manager. Finally, it tracks the project progress history in detail, automatically generates periodic reports, and improves future project plans based on past data. Based on project progress data, it provides analysis results and recommended actions to support important decision-making. In this way, the project management system can efficiently track, visualize, propose, and notify about the task progress of project members.

[0029] The project management system according to this embodiment comprises a tracking unit, a visualization unit, a proposal unit, and a notification unit. The tracking unit tracks the task progress of project members in real time. The tracking unit obtains task progress data from each member's device connected to the project management system, for example. The tracking unit can analyze the task progress using AI and update it in real time. For example, the tracking unit collects data such as the task start time, end time, and progress rate, and visualizes the progress. The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays the task progress using graphs and charts, for example. The visualization unit can display the task progress in an easy-to-understand visual way using AI. For example, the visualization unit displays the task progress using different colors and highlights tasks that are behind schedule. The proposal unit automatically proposes solutions when delays occur based on the task progress visualized by the visualization unit. The proposal unit analyzes the cause of the delay using AI and proposes appropriate solutions. The proposal unit can propose solutions such as reallocating resources or changing task priorities. For example, the proposal unit suggests allocating additional resources to tasks that are experiencing delays. The notification unit notifies the team of resource reallocation or priority changes based on the solutions proposed by the proposal unit. The notification unit sends notifications to each member's device connected to the project management system, for example. The notification unit can use AI to send notifications at the appropriate time. For example, the notification unit notifies the team of resource reallocation or priority changes based on the progress of a task. This allows the project management system to efficiently track, visualize, propose, and notify about the task progress of project members.

[0030] The tracking unit tracks the task progress of project members in real time. For example, it obtains task progress data from each member's device connected to the project management system. Specifically, a dedicated application is installed on each member's device, and data such as task start time, end time, progress rate, and work content are automatically collected through this application. This data is sent to a cloud server and stored in a central database. The tracking unit can use AI to analyze task progress and update it in real time. The AI ​​analyzes the collected data and uses algorithms to evaluate task progress. For example, the AI ​​calculates the task progress rate and detects delays and deviations in progress by comparing planned progress with actual progress. The AI ​​can also predict each member's work patterns and task completion based on past data. This allows the tracking unit to accurately understand the project's progress and respond quickly as needed. Furthermore, the tracking unit regularly updates task progress data and monitors project progress in real time. This allows project managers to always understand the project's progress and make appropriate decisions.

[0031] The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays task progress using, for example, graphs and charts. Specifically, it uses visual tools such as Gantt charts, bar graphs, and pie charts to clearly display task progress. Gantt charts visually show the start and end dates of tasks, allowing for a quick overview of each task's progress. Bar graphs show the progress rate of each task, highlighting tasks that are behind schedule. Pie charts show the overall project progress, visually displaying the progress percentage of each task. The visualization unit can also use AI to visually display task progress in an easy-to-understand way. The AI ​​analyzes the collected data and displays the progress using color coding. For example, tasks progressing smoothly are displayed in green, while tasks experiencing delays are displayed in red. This allows project managers to quickly grasp the progress and respond to delayed tasks. Furthermore, the visualization unit updates task progress in real time, providing the latest information. This allows project managers to stay informed about the project's progress and make appropriate decisions.

[0032] The proposal department automatically proposes solutions when delays occur based on task progress visualized by the visualization department. For example, the proposal department uses AI to analyze the cause of delays and propose appropriate solutions. Specifically, the AI ​​analyzes task progress data and historical data to identify the cause of delays. For example, the AI ​​analyzes data such as task progress rate, working time, and resource usage to identify the cause of the delay. Once the cause of the delay is identified, the AI ​​proposes appropriate solutions. For example, it can propose solutions such as reallocating resources or changing task priorities. Specifically, it may propose allocating additional resources to tasks experiencing delays. It may also propose changing task priorities to prioritize important tasks. Furthermore, the proposal department can propose optimal solutions based on historical data. For example, it may propose the optimal solution by referring to solutions used when similar delays occurred in the past. This allows the proposal department to propose quick and appropriate solutions when delays occur, ensuring smooth project progress.

[0033] The notification unit notifies members of resource reallocation and priority changes based on solutions proposed by the proposal unit. For example, the notification unit sends notifications to each member's device connected to the project management system. Specifically, it sends notifications regarding resource reallocation and priority changes through applications installed on each member's device. Notifications are sent in real time, allowing each member to receive them immediately. The notification unit can use AI to send notifications at the appropriate time. The AI ​​analyzes task progress and member work status to send notifications at the optimal time. For example, it sends an immediate notification if a task is behind schedule or if resource reallocation is necessary. Furthermore, the notification unit can customize notification content. For example, it can customize notification content according to each member's role and assigned tasks, accurately conveying necessary information. In addition, the notification unit manages notification history and allows referencing past notification content. This allows project managers to review notification history and make appropriate decisions based on past notification content. As a result, the notification unit can send the right notifications at the right time to ensure smooth project progress and improve the work efficiency of project members.

[0034] The management department can centrally manage data for each project and perform progress and risk analysis. For example, the management department stores project data in a database connected to the project management system. The management department can use AI to analyze project progress and risks. For example, the management department can analyze task progress data and evaluate progress. The management department can perform risk analysis, identify risks, evaluate them, and propose countermeasures. For example, the management department can analyze project progress and identify high-risk tasks. The management department evaluates risks, assessing their impact and probability of occurrence. The management department proposes countermeasures for risks to mitigate or avoid them. As a result, the project management system enables centralized management of project data and progress and risk analysis.

[0035] The service provider can provide leaders with an easy-to-understand dashboard. For example, the service provider can display the dashboard on a display connected to the project management system. The service provider can use AI to visually display project progress and risks in an easy-to-understand way. For example, the service provider can display project progress using graphs and charts. The service provider can customize the dashboard layout and display content. For example, the service provider can change the type of information displayed and the layout according to the leader's needs. As a result, the project management system makes it easier for leaders to grasp project progress by providing an easy-to-understand dashboard.

[0036] The update unit can have team members report their progress regularly and automatically update the progress status. For example, the update unit receives progress reports from each member's device connected to the project management system. The update unit can use AI to automatically update the progress status. For example, the update unit analyzes the progress report data and updates the progress status in real time. The update unit can customize the frequency and content of progress reports. For example, the update unit changes the frequency and content of progress reports according to the project's needs. This allows the project management system to automatically update the progress status of team members and stay informed of the latest progress.

[0037] The sending unit can automatically send reminders to members who are falling behind. For example, it can send reminders to each member's device connected to the project management system. The sending unit can use AI to send reminders based on progress. For example, it can send reminders to members who are falling behind. The sending unit can customize the content and timing of reminders. For example, it can change the content and timing of reminders according to the project's needs. This allows the project management system to prevent delays by sending reminders to members who are behind.

[0038] The alerting unit can set customizable alerts to immediately notify project managers when progress is behind schedule or risks increase. For example, the alerting unit sends alerts to project managers' devices connected to the project management system. The alerting unit can use AI to send alerts based on progress and risk. For example, it sends alerts to project managers when progress is behind schedule or risks increase. The alerting unit allows for customization of alert content and timing. For example, it changes the alert content and timing according to project needs. This enables the project management system to respond quickly by providing immediate notification when progress is behind schedule or risks increase.

[0039] The generation unit can track the project's progress history in detail and automatically generate periodic reports. For example, the generation unit stores the project's progress history in a database connected to the project management system. Using AI, the generation unit can analyze the project's progress history and automatically generate reports. For example, it generates progress and risk reports based on the project's progress history. The generation unit can customize the content and frequency of reports. For example, it changes the report content and frequency according to the project's needs. This allows the project management system to leverage historical data by tracking the project's progress history in detail and automatically generating reports.

[0040] The improvement department can improve future project plans based on past data. For example, the improvement department analyzes past data stored in a database connected to the project management system. The improvement department can use AI to improve project plans based on past data. For example, the improvement department analyzes the progress and risks of past projects to improve future project plans. The improvement department can customize the content and methods of improvements. For example, the improvement department changes the content and methods of improvements according to the needs of the project. As a result, the project management system improves the accuracy of its plans by improving future project plans based on past data.

[0041] The support department can provide analytical results and recommended actions to assist in important decision-making based on project progress data. For example, the support department analyzes project progress data stored in a database connected to the project management system. The support department can use AI to analyze project progress data and provide results and actions to support decision-making. For example, the support department analyzes project progress data and proposes appropriate actions to leaders. The support department can customize the content of the analytical results and recommended actions. For example, the support department changes the type and content of information provided according to the project's needs. As a result, the project management system supports decision-making by providing analytical results and recommended actions based on project progress data.

[0042] The tracking unit can select the optimal tracking method according to each member's work style. For example, the tracking unit analyzes each member's work history and survey results connected to the project management system. Using AI, the tracking unit can identify each member's work style and select the optimal tracking method. For example, the tracking unit can select an online tool-based tracking method for remote workers. It can also select a tracking method that emphasizes direct communication for office workers. Furthermore, the tracking unit can set a flexible tracking schedule for members on a flexible work schedule. As a result, the project management system can efficiently manage progress by selecting a tracking method that suits each member's work style.

[0043] The tracking unit can adjust the level of detail in tracking based on the importance of the task. For example, the tracking unit analyzes task importance data stored in a database connected to the project management system. The tracking unit can use AI to evaluate task importance and adjust the level of detail in tracking. For example, the tracking unit can request detailed progress reports for high-importance tasks, simplified progress reports for low-importance tasks, and progress reports with a moderate level of detail for tasks of moderate importance. This allows the project management system to efficiently manage progress by adjusting the level of detail in tracking according to the importance of the task.

[0044] The tracking unit can prioritize tracking highly relevant tasks by considering the geographical location information of team members when tracking task progress. For example, the tracking unit obtains the geographical location information of team members using GPS data or location services connected to the project management system. The tracking unit can use AI to analyze the geographical location information of team members and identify highly relevant tasks. For example, if a team member is in a specific region, the tracking unit will prioritize tracking tasks related to that region. Furthermore, if a team member is on a business trip, the tracking unit can prioritize tracking tasks at their business trip destination. In addition, if a team member is working remotely, the tracking unit can prioritize tracking tasks at their home. As a result, the project management system can efficiently track highly relevant tasks by considering the geographical location information of team members.

[0045] The tracking unit can analyze members' social media activity and track related tasks when tracking task progress. For example, the tracking unit analyzes social media data connected to the project management system. Using AI, the tracking unit can analyze members' social media activity and identify related tasks. For instance, the tracking unit tracks related tasks based on information members share on social media. Furthermore, the tracking unit can infer task progress from members' social media activity. In addition, the tracking unit can analyze members' social media activity and adjust task priorities. This allows the project management system to efficiently track related tasks by analyzing members' social media activity.

[0046] The visualization unit can adjust the level of detail of the visualization based on the importance of the task. For example, the visualization unit analyzes task importance data stored in a database connected to the project management system. The visualization unit can use AI to evaluate the importance of tasks and adjust the level of detail of the visualization. For example, the visualization unit can display detailed graphs for high-importance tasks. It can also display simplified graphs for low-importance tasks. Furthermore, it can display graphs with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently grasp information by adjusting the level of detail of the visualization according to the importance of the task.

[0047] The visualization unit can apply different visualization algorithms depending on the task category. For example, the visualization unit analyzes task category data stored in a database connected to the project management system. Using AI, the visualization unit can identify task categories and apply appropriate visualization algorithms. For example, the visualization unit can apply progress bar visualization to development tasks. It can also apply Kanban board visualization to design tasks. Furthermore, it can apply chart visualization to marketing tasks. This allows the project management system to efficiently grasp information by applying visualization algorithms appropriate to the task category.

[0048] The visualization unit can determine visualization priorities based on task submission dates. For example, the visualization unit analyzes task submission date data stored in a database connected to the project management system. The visualization unit can use AI to evaluate task submission dates and determine visualization priorities. For example, the visualization unit prioritizes the visualization of tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the visualization unit can appropriately visualize tasks with medium-term deadlines. This allows the project management system to efficiently grasp information by determining visualization priorities based on task submission dates.

[0049] The visualization unit can adjust the order of visualization based on the relevance of tasks. For example, the visualization unit analyzes task relevance data stored in a database connected to the project management system. The visualization unit can use AI to evaluate the relevance of tasks and adjust the order of visualization. For example, the visualization unit prioritizes the visualization of highly relevant tasks. It can also postpone the visualization of less relevant tasks. Furthermore, it can appropriately visualize tasks of moderate relevance. This allows the project management system to efficiently grasp information by adjusting the order of visualization based on the relevance of tasks.

[0050] The proposal function can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal function analyzes task importance data stored in a database connected to the project management system. The proposal function can use AI to evaluate task importance and adjust the level of detail of the proposal. For example, the proposal function can provide detailed proposals for high-importance tasks, simplified proposals for low-importance tasks, and proposals with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently generate proposals by adjusting the level of detail according to the importance of the task.

[0051] The proposal department can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal department analyzes task category data stored in a database connected to the project management system. Using AI, the proposal department can identify the task category and apply the appropriate proposal algorithm. For example, the proposal department can make technical proposals for development tasks, creative proposals for design tasks, and strategic proposals for marketing tasks. This allows the project management system to make efficient proposals by applying proposal algorithms appropriate to the task category.

[0052] The proposal department can prioritize proposals based on task submission deadlines. For example, the proposal department analyzes task submission deadline data stored in a database connected to the project management system. The proposal department can use AI to evaluate task submission deadlines and determine proposal priorities. For example, the proposal department will prioritize proposing tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, it can appropriately propose tasks with medium-term deadlines. This allows the project management system to prioritize proposals based on task submission deadlines, enabling more efficient proposals.

[0053] The proposal department can adjust the order of proposals based on the relevance of tasks during the proposal process. For example, the proposal department analyzes task relevance data stored in a database connected to the project management system. The proposal department can use AI to evaluate task relevance and adjust the order of proposals. For example, the proposal department can prioritize proposing tasks with high relevance. It can also postpone tasks with low relevance. Furthermore, it can appropriately propose tasks with moderate relevance. This allows the project management system to make efficient proposals by adjusting the order of proposals based on task relevance.

[0054] The notification unit can adjust the level of detail of notifications based on the importance of the task. For example, the notification unit analyzes task importance data stored in a database connected to the project management system. The notification unit can use AI to evaluate task importance and adjust the level of detail of notifications. For example, the notification unit can provide detailed notifications for high-importance tasks, simplified notifications for low-importance tasks, and notifications with an appropriate level of detail for tasks of medium importance. This enables the project management system to provide efficient notifications by adjusting the level of detail of notifications according to the importance of the task.

[0055] The notification unit can apply different notification algorithms depending on the task category when sending notifications. For example, the notification unit analyzes task category data stored in a database connected to the project management system. Using AI, the notification unit can identify task categories and apply appropriate notification algorithms. For example, the notification unit can send technical notifications for development tasks, creative notifications for design tasks, and strategic notifications for marketing tasks. This enables the project management system to send notifications efficiently by applying notification algorithms tailored to task categories.

[0056] The notification unit can determine the priority of notifications based on the task submission timing when sending notifications. For example, the notification unit analyzes task submission timing data stored in a database connected to the project management system. The notification unit can use AI to evaluate task submission timing and determine the priority of notifications. For example, the notification unit will prioritize notifications for tasks with approaching deadlines. It can also postpone notifications for tasks with distant deadlines. Furthermore, it can send notifications appropriately for tasks with medium-term deadlines. This enables the project management system to send notifications efficiently by prioritizing notifications based on task submission timing.

[0057] The notification unit can adjust the order of notifications based on the relevance of tasks. For example, the notification unit analyzes task relevance data stored in a database connected to the project management system. The notification unit can use AI to evaluate task relevance and adjust the order of notifications. For example, the notification unit can prioritize notifications for highly relevant tasks. It can also postpone notifications for less relevant tasks. Furthermore, it can appropriately notify users of tasks of moderate relevance. This enables the project management system to provide efficient notifications by adjusting the order of notifications based on task relevance.

[0058] The management department can adjust the level of detail in data management based on the importance of each project. For example, the management department can analyze project importance data stored in a database connected to the project management system. The management department can use AI to evaluate project importance and adjust the level of detail in management. For example, the management department can perform detailed data management for high-importance projects, simplified data management for low-importance projects, and appropriate level of detail for projects of moderate importance. This allows the project management system to perform efficient data management by adjusting the level of detail in management according to the importance of each project.

[0059] The management department can prioritize data management based on project submission deadlines. For example, the management department can analyze project submission deadline data stored in a database connected to the project management system. The management department can use AI to evaluate project submission deadlines and determine management priorities. For example, the management department can prioritize data management for projects with approaching deadlines. Conversely, the management department can postpone data management for projects with distant deadlines. Furthermore, the management department can perform data management for projects with medium-term deadlines at a moderate pace. This enables efficient data management by allowing the project management system to prioritize management based on project submission deadlines.

[0060] The system can adjust the level of detail displayed on the dashboard based on the project's importance. For example, the system can analyze project importance data stored in a database connected to the project management system. The system can use AI to evaluate project importance and adjust the level of detail. For example, the system can display a detailed dashboard for high-importance projects, a simplified dashboard for low-importance projects, and a dashboard with appropriate detail for projects of medium importance. This allows the project management system to efficiently grasp information by adjusting the level of detail displayed on the dashboard according to the project's importance.

[0061] The system can determine the display priority of projects based on their submission dates when displaying them on the dashboard. For example, the system can analyze project submission date data stored in a database connected to the project management system. The system can use AI to evaluate project submission dates and determine the display priority. For example, the system can prioritize displaying information on projects with approaching deadlines. It can also postpone displaying information on projects with distant deadlines. Furthermore, it can display information on projects with medium-term deadlines appropriately. This allows the project management system to efficiently grasp information by determining the display priority of the dashboard based on project submission dates.

[0062] The update unit can adjust the level of detail of progress updates based on the importance of the task. For example, the update unit analyzes task importance data stored in a database connected to the project management system. The update unit can use AI to evaluate task importance and adjust the level of detail of the update. For example, the update unit can provide detailed progress updates for high-importance tasks. It can also provide simplified progress updates for low-importance tasks. Furthermore, it can provide progress updates with an appropriate level of detail for tasks of medium importance. This enables efficient progress management by allowing the project management system to adjust the level of detail of progress updates according to the importance of the task.

[0063] The update unit can determine the priority of updates based on the task submission date when updating progress status. For example, the update unit analyzes task submission date data stored in a database connected to the project management system. The update unit can use AI to evaluate task submission dates and determine the priority of updates. For example, the update unit can prioritize updating the progress of tasks with approaching deadlines. It can also postpone updating the progress of tasks with distant deadlines. Furthermore, it can update the progress of tasks with medium-term deadlines at a moderate pace. As a result, the project management system can efficiently manage progress by determining the priority of progress updates based on task submission dates.

[0064] The sending unit can adjust the level of detail in reminders based on the importance of the task. For example, the sending unit analyzes task importance data stored in a database connected to the project management system. The sending unit can use AI to evaluate task importance and adjust the level of detail in reminders. For example, the sending unit can send detailed reminders for high-importance tasks, simplified reminders for low-importance tasks, and moderately detailed reminders for tasks of medium importance. This allows the project management system to efficiently send reminders by adjusting the level of detail in reminders according to the importance of the task.

[0065] The sending unit can determine the priority of sending reminders based on the task submission date. For example, the sending unit analyzes task submission date data stored in a database connected to the project management system. The sending unit can use AI to evaluate task submission dates and determine the priority of sending reminders. For example, the sending unit can prioritize sending reminders for tasks with approaching deadlines. It can also postpone sending reminders for tasks with distant deadlines. Furthermore, it can send reminders for tasks with medium-length deadlines at an appropriate rate. This allows the project management system to efficiently send reminders by prioritizing them based on task submission dates.

[0066] The alerting unit can adjust the level of detail displayed based on the importance of the task when displaying an alert. For example, the alerting unit analyzes task importance data stored in a database connected to the project management system. The alerting unit can use AI to evaluate the importance of tasks and adjust the level of detail displayed. For example, the alerting unit can display detailed alerts for high-importance tasks. It can also display simplified alerts for low-importance tasks. Furthermore, it can display alerts with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently display alerts by adjusting the level of detail displayed according to the importance of the task.

[0067] The alert unit can determine the display priority based on the task submission date when displaying alerts. For example, the alert unit analyzes task submission date data stored in a database connected to the project management system. The alert unit can use AI to evaluate task submission dates and determine the display priority. For example, the alert unit will prioritize displaying alerts for tasks with approaching deadlines. It can also postpone displaying alerts for tasks with distant deadlines. Furthermore, it can display alerts for tasks with medium-term deadlines appropriately. This allows the project management system to efficiently display alerts by prioritizing alert display based on task submission dates.

[0068] The generation unit can adjust the level of detail generated based on the importance of the project during report generation. For example, the generation unit analyzes project importance data stored in a database connected to the project management system. The generation unit can use AI to evaluate project importance and adjust the level of detail generated. For example, the generation unit can generate detailed reports for high-importance projects. It can also generate simplified reports for low-importance projects. Furthermore, it can generate reports with an appropriate level of detail for projects of medium importance. This allows the project management system to efficiently generate reports by adjusting the level of detail generated according to the importance of the project.

[0069] The generation unit can determine the priority of report generation based on project submission deadlines. For example, the generation unit analyzes project submission deadline data stored in a database connected to the project management system. The generation unit can use AI to evaluate project submission deadlines and determine the priority of report generation. For example, the generation unit can prioritize generating reports for projects with approaching deadlines. It can also postpone generating reports for projects with distant deadlines. Furthermore, it can generate reports for projects with medium-term deadlines at an appropriate rate. This enables the project management system to efficiently generate reports by prioritizing report generation based on project submission deadlines.

[0070] The improvement department can adjust the level of detail of project plan improvements based on historical data. For example, the improvement department analyzes historical data stored in a database connected to the project management system. The improvement department can use AI to adjust the level of detail of project plan improvements based on historical data. For example, the improvement department can propose detailed improvement methods based on past success stories. It can also propose simplified improvement methods based on past failure stories. Furthermore, the improvement department can analyze historical data and propose improvement methods with an appropriate level of detail. As a result, the project management system can efficiently improve project plans by adjusting the level of detail of improvements based on historical data.

[0071] The improvement department can determine the priority of improvements based on project submission deadlines when improving project plans. For example, the improvement department can analyze project submission deadline data stored in a database connected to the project management system. The improvement department can use AI to evaluate project submission deadlines and determine the priority of improvements. For example, the improvement department can prioritize improvements for projects with approaching deadlines. It can also postpone improvements for projects with distant deadlines. Furthermore, the improvement department can moderately improve projects with medium-term deadlines. This allows the project management system to efficiently improve projects by determining the priority of improvements based on project submission deadlines.

[0072] The support department can adjust the level of detail of support provided based on the importance of the project during decision-making support. For example, the support department analyzes project importance data stored in a database connected to the project management system. The support department can use AI to evaluate project importance and adjust the level of detail of support. For example, the support department can provide detailed decision-making support for high-importance projects. Conversely, it can provide simplified decision-making support for low-importance projects. Furthermore, it can provide decision-making support with an appropriate level of detail for projects of medium importance. This enables the project management system to provide efficient decision-making support by adjusting the level of detail of decision-making support according to the importance of the project.

[0073] The support department can prioritize support based on project submission deadlines when providing decision-making assistance. For example, the support department analyzes project submission deadline data stored in a database connected to the project management system. The support department can use AI to evaluate project submission deadlines and determine support priorities. For example, the support department can prioritize decision-making support for projects with approaching deadlines. Conversely, the support department can postpone decision-making support for projects with distant deadlines. Furthermore, the support department can provide appropriate decision-making support for projects with medium-term deadlines. This enables efficient decision-making support by allowing the project management system to prioritize decision-making support based on project submission deadlines.

[0074] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0075] The management department can analyze project progress data and adjust the frequency of reports to leaders according to the progress status. For example, the management department can analyze progress data stored in a database connected to the project management system. The management department can use AI to evaluate progress and adjust the reporting frequency. For example, the management department can reduce the reporting frequency when progress is on track. Conversely, it can increase the reporting frequency when progress is behind schedule. Furthermore, the management department can immediately report if progress changes rapidly. As a result, the project management system can efficiently manage progress by adjusting the reporting frequency according to the progress status.

[0076] The service provider can analyze project progress data and adjust the frequency of dashboard display according to the progress status. For example, the service provider can analyze progress data stored in a database connected to the project management system. The service provider can use AI to evaluate the progress status and adjust the display frequency. For example, the service provider can reduce the display frequency when progress is on track. Conversely, the service provider can increase the display frequency when progress is behind schedule. Furthermore, the service provider can immediately display information if the progress changes rapidly. As a result, the project management system can efficiently grasp information by adjusting the display frequency according to the progress status.

[0077] The update unit can analyze project progress data and adjust the update frequency according to the progress status. For example, the update unit analyzes progress data stored in a database connected to the project management system. The update unit can use AI to evaluate the progress status and adjust the update frequency. For example, if progress is on track, the update unit can reduce the update frequency. Conversely, if progress is behind schedule, the update unit can increase the update frequency. Furthermore, if progress changes rapidly, the update unit can perform updates immediately. As a result, the project management system can efficiently manage progress by adjusting the update frequency according to the progress status.

[0078] The sending unit can analyze project progress data and adjust the frequency of reminder transmissions according to the progress status. For example, the sending unit analyzes progress data stored in a database connected to the project management system. The sending unit can use AI to evaluate the progress status and adjust the transmission frequency. For example, if progress is on track, the sending unit can reduce the transmission frequency. Conversely, if progress is behind schedule, the sending unit can increase the transmission frequency. Furthermore, if progress changes rapidly, the sending unit can immediately send a reminder. This enables efficient progress management by allowing the project management system to adjust the reminder transmission frequency according to the progress status.

[0079] The alert unit can analyze project progress data and adjust the frequency of alerts sent according to the progress status. For example, the alert unit analyzes progress data stored in a database connected to the project management system. The alert unit can use AI to evaluate the progress status and adjust the frequency of alerts. For example, the alert unit can reduce the frequency of alerts sent when progress is on track. Conversely, it can increase the frequency of alerts sent when progress is behind schedule. Furthermore, the alert unit can send an alert immediately if the progress changes rapidly. As a result, the project management system can efficiently manage progress by adjusting the frequency of alerts sent according to the progress status.

[0080] The following briefly describes the processing flow for example form 1.

[0081] Step 1: The tracking unit tracks the task progress of project members in real time. The tracking unit acquires task progress data from each member's device connected to the project management system, analyzes the task progress using AI, and updates the data in real time. For example, it collects data such as task start time, end time, and progress rate. Step 2: The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays the task progress using graphs and charts, and uses AI to make it visually easy to understand. For example, it displays the task progress using different colors and highlights tasks that are behind schedule. Step 3: The proposal unit automatically suggests solutions when delays occur based on the task progress visualized by the visualization unit. The proposal unit uses AI to analyze the cause of the delay and suggests appropriate solutions such as reallocating resources or changing task priorities. For example, it might suggest allocating additional resources to the task experiencing the delay. Step 4: The notification unit notifies the team of resource reallocation and priority changes based on the solutions proposed by the proposal unit. The notification unit sends notifications to each member's device connected to the project management system and uses AI to send notifications at the appropriate time. For example, it notifies the team of resource reallocation and priority changes based on the progress of tasks.

[0082] (Example of form 2) The project management system according to an embodiment of the present invention is a system that streamlines project management using an AI agent. This project management system tracks the task progress of project members in real time and visualizes the status of each task. If delays occur, the AI ​​automatically proposes solutions and notifies the user of the reallocation of necessary resources and changes in priorities. Next, it centrally manages data for each project, performs progress and risk analysis, and provides an easy-to-understand dashboard for leaders. Furthermore, it has team members report their progress regularly, automatically updates the progress status, and automatically sends reminders to members who are behind schedule. When progress is delayed or risks increase, it sets customizable alerts and immediately notifies the project manager. Finally, it tracks the project progress history in detail, automatically generates periodic reports, and improves future project plans based on past data. Based on project progress data, it provides analysis results and recommended actions to support important decision-making. In this way, the project management system can efficiently track, visualize, propose, and notify about the task progress of project members.

[0083] The project management system according to this embodiment comprises a tracking unit, a visualization unit, a proposal unit, and a notification unit. The tracking unit tracks the task progress of project members in real time. The tracking unit obtains task progress data from each member's device connected to the project management system, for example. The tracking unit can analyze the task progress using AI and update it in real time. For example, the tracking unit collects data such as the task start time, end time, and progress rate, and visualizes the progress. The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays the task progress using graphs and charts, for example. The visualization unit can display the task progress in an easy-to-understand visual way using AI. For example, the visualization unit displays the task progress using different colors and highlights tasks that are behind schedule. The proposal unit automatically proposes solutions when delays occur based on the task progress visualized by the visualization unit. The proposal unit analyzes the cause of the delay using AI and proposes appropriate solutions. The proposal unit can propose solutions such as reallocating resources or changing task priorities. For example, the proposal unit suggests allocating additional resources to tasks that are experiencing delays. The notification unit notifies the team of resource reallocation or priority changes based on the solutions proposed by the proposal unit. The notification unit sends notifications to each member's device connected to the project management system, for example. The notification unit can use AI to send notifications at the appropriate time. For example, the notification unit notifies the team of resource reallocation or priority changes based on the progress of a task. This allows the project management system to efficiently track, visualize, propose, and notify about the task progress of project members.

[0084] The tracking unit tracks the task progress of project members in real time. For example, it obtains task progress data from each member's device connected to the project management system. Specifically, a dedicated application is installed on each member's device, and data such as task start time, end time, progress rate, and work content are automatically collected through this application. This data is sent to a cloud server and stored in a central database. The tracking unit can use AI to analyze task progress and update it in real time. The AI ​​analyzes the collected data and uses algorithms to evaluate task progress. For example, the AI ​​calculates the task progress rate and detects delays and deviations in progress by comparing planned progress with actual progress. The AI ​​can also predict each member's work patterns and task completion based on past data. This allows the tracking unit to accurately understand the project's progress and respond quickly as needed. Furthermore, the tracking unit regularly updates task progress data and monitors project progress in real time. This allows project managers to always understand the project's progress and make appropriate decisions.

[0085] The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays task progress using, for example, graphs and charts. Specifically, it uses visual tools such as Gantt charts, bar graphs, and pie charts to clearly display task progress. Gantt charts visually show the start and end dates of tasks, allowing for a quick overview of each task's progress. Bar graphs show the progress rate of each task, highlighting tasks that are behind schedule. Pie charts show the overall project progress, visually displaying the progress percentage of each task. The visualization unit can also use AI to visually display task progress in an easy-to-understand way. The AI ​​analyzes the collected data and displays the progress using color coding. For example, tasks progressing smoothly are displayed in green, while tasks experiencing delays are displayed in red. This allows project managers to quickly grasp the progress and respond to delayed tasks. Furthermore, the visualization unit updates task progress in real time, providing the latest information. This allows project managers to stay informed about the project's progress and make appropriate decisions.

[0086] The proposal department automatically proposes solutions when delays occur based on task progress visualized by the visualization department. For example, the proposal department uses AI to analyze the cause of delays and propose appropriate solutions. Specifically, the AI ​​analyzes task progress data and historical data to identify the cause of delays. For example, the AI ​​analyzes data such as task progress rate, working time, and resource usage to identify the cause of the delay. Once the cause of the delay is identified, the AI ​​proposes appropriate solutions. For example, it can propose solutions such as reallocating resources or changing task priorities. Specifically, it may propose allocating additional resources to tasks experiencing delays. It may also propose changing task priorities to prioritize important tasks. Furthermore, the proposal department can propose optimal solutions based on historical data. For example, it may propose the optimal solution by referring to solutions used when similar delays occurred in the past. This allows the proposal department to propose quick and appropriate solutions when delays occur, ensuring smooth project progress.

[0087] The notification unit notifies members of resource reallocation and priority changes based on solutions proposed by the proposal unit. For example, the notification unit sends notifications to each member's device connected to the project management system. Specifically, it sends notifications regarding resource reallocation and priority changes through applications installed on each member's device. Notifications are sent in real time, allowing each member to receive them immediately. The notification unit can use AI to send notifications at the appropriate time. The AI ​​analyzes task progress and member work status to send notifications at the optimal time. For example, it sends an immediate notification if a task is behind schedule or if resource reallocation is necessary. Furthermore, the notification unit can customize notification content. For example, it can customize notification content according to each member's role and assigned tasks, accurately conveying necessary information. In addition, the notification unit manages notification history and allows referencing past notification content. This allows project managers to review notification history and make appropriate decisions based on past notification content. As a result, the notification unit can send the right notifications at the right time to ensure smooth project progress and improve the work efficiency of project members.

[0088] The management department can centrally manage data for each project and perform progress and risk analysis. For example, the management department stores project data in a database connected to the project management system. The management department can use AI to analyze project progress and risks. For example, the management department can analyze task progress data and evaluate progress. The management department can perform risk analysis, identify risks, evaluate them, and propose countermeasures. For example, the management department can analyze project progress and identify high-risk tasks. The management department evaluates risks, assessing their impact and probability of occurrence. The management department proposes countermeasures for risks to mitigate or avoid them. As a result, the project management system enables centralized management of project data and progress and risk analysis.

[0089] The service provider can provide leaders with an easy-to-understand dashboard. For example, the service provider can display the dashboard on a display connected to the project management system. The service provider can use AI to visually display project progress and risks in an easy-to-understand way. For example, the service provider can display project progress using graphs and charts. The service provider can customize the dashboard layout and display content. For example, the service provider can change the type of information displayed and the layout according to the leader's needs. As a result, the project management system makes it easier for leaders to grasp project progress by providing an easy-to-understand dashboard.

[0090] The update unit can have team members report their progress regularly and automatically update the progress status. For example, the update unit receives progress reports from each member's device connected to the project management system. The update unit can use AI to automatically update the progress status. For example, the update unit analyzes the progress report data and updates the progress status in real time. The update unit can customize the frequency and content of progress reports. For example, the update unit changes the frequency and content of progress reports according to the project's needs. This allows the project management system to automatically update the progress status of team members and stay informed of the latest progress.

[0091] The sending unit can automatically send reminders to members who are falling behind. For example, it can send reminders to each member's device connected to the project management system. The sending unit can use AI to send reminders based on progress. For example, it can send reminders to members who are falling behind. The sending unit can customize the content and timing of reminders. For example, it can change the content and timing of reminders according to the project's needs. This allows the project management system to prevent delays by sending reminders to members who are behind.

[0092] The alerting unit can set customizable alerts to immediately notify project managers when progress is behind schedule or risks increase. For example, the alerting unit sends alerts to project managers' devices connected to the project management system. The alerting unit can use AI to send alerts based on progress and risk. For example, it sends alerts to project managers when progress is behind schedule or risks increase. The alerting unit allows for customization of alert content and timing. For example, it changes the alert content and timing according to project needs. This enables the project management system to respond quickly by providing immediate notification when progress is behind schedule or risks increase.

[0093] The generation unit can track the project's progress history in detail and automatically generate periodic reports. For example, the generation unit stores the project's progress history in a database connected to the project management system. Using AI, the generation unit can analyze the project's progress history and automatically generate reports. For example, it generates progress and risk reports based on the project's progress history. The generation unit can customize the content and frequency of reports. For example, it changes the report content and frequency according to the project's needs. This allows the project management system to leverage historical data by tracking the project's progress history in detail and automatically generating reports.

[0094] The improvement department can improve future project plans based on past data. For example, the improvement department analyzes past data stored in a database connected to the project management system. The improvement department can use AI to improve project plans based on past data. For example, the improvement department analyzes the progress and risks of past projects to improve future project plans. The improvement department can customize the content and methods of improvements. For example, the improvement department changes the content and methods of improvements according to the needs of the project. As a result, the project management system improves the accuracy of its plans by improving future project plans based on past data.

[0095] The support department can provide analytical results and recommended actions to assist in important decision-making based on project progress data. For example, the support department analyzes project progress data stored in a database connected to the project management system. The support department can use AI to analyze project progress data and provide results and actions to support decision-making. For example, the support department analyzes project progress data and proposes appropriate actions to leaders. The support department can customize the content of the analytical results and recommended actions. For example, the support department changes the type and content of information provided according to the project's needs. As a result, the project management system supports decision-making by providing analytical results and recommended actions based on project progress data.

[0096] The tracking unit can estimate the user's emotions and adjust the frequency of task progress tracking based on the estimated emotions. For example, the tracking unit estimates the user's emotions using sensors or cameras connected to the project management system. The tracking unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the tracking unit analyzes the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the tracking unit can adjust the frequency of task progress tracking. For example, if the user is stressed, the tracking unit reduces the tracking frequency to alleviate the burden. Conversely, if the user is relaxed, the tracking unit increases the tracking frequency to enable more detailed progress management. Furthermore, if the user is in a hurry, the tracking unit increases the tracking frequency to encourage a quicker response. This allows the project management system to reduce the burden and enable efficient progress management by adjusting the tracking frequency according to the user's emotions.

[0097] The tracking unit can select the optimal tracking method according to each member's work style. For example, the tracking unit analyzes each member's work history and survey results connected to the project management system. Using AI, the tracking unit can identify each member's work style and select the optimal tracking method. For example, the tracking unit can select an online tool-based tracking method for remote workers. It can also select a tracking method that emphasizes direct communication for office workers. Furthermore, the tracking unit can set a flexible tracking schedule for members on a flexible work schedule. As a result, the project management system can efficiently manage progress by selecting a tracking method that suits each member's work style.

[0098] The tracking unit can adjust the level of detail in tracking based on the importance of the task. For example, the tracking unit analyzes task importance data stored in a database connected to the project management system. The tracking unit can use AI to evaluate task importance and adjust the level of detail in tracking. For example, the tracking unit can request detailed progress reports for high-importance tasks, simplified progress reports for low-importance tasks, and progress reports with a moderate level of detail for tasks of moderate importance. This allows the project management system to efficiently manage progress by adjusting the level of detail in tracking according to the importance of the task.

[0099] The tracking unit can estimate the user's emotions and determine the priority of tasks to track based on the estimated emotions. For example, the tracking unit estimates the user's emotions using sensors or cameras connected to the project management system. The tracking unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the tracking unit analyzes the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the tracking unit can determine the priority of tasks to track. For example, if the user is stressed, the tracking unit will postpone lower-priority tasks. Conversely, if the user is relaxed, the tracking unit can track higher-priority tasks first. Furthermore, if the user is in a hurry, the tracking unit can prioritize important tasks. This allows the project management system to efficiently manage progress by prioritizing tasks according to the user's emotions.

[0100] The tracking unit can prioritize tracking highly relevant tasks by considering the geographical location information of team members when tracking task progress. For example, the tracking unit obtains the geographical location information of team members using GPS data or location services connected to the project management system. The tracking unit can use AI to analyze the geographical location information of team members and identify highly relevant tasks. For example, if a team member is in a specific region, the tracking unit will prioritize tracking tasks related to that region. Furthermore, if a team member is on a business trip, the tracking unit can prioritize tracking tasks at their business trip destination. In addition, if a team member is working remotely, the tracking unit can prioritize tracking tasks at their home. As a result, the project management system can efficiently track highly relevant tasks by considering the geographical location information of team members.

[0101] The tracking unit can analyze members' social media activity and track related tasks when tracking task progress. For example, the tracking unit analyzes social media data connected to the project management system. Using AI, the tracking unit can analyze members' social media activity and identify related tasks. For instance, the tracking unit tracks related tasks based on information members share on social media. Furthermore, the tracking unit can infer task progress from members' social media activity. In addition, the tracking unit can analyze members' social media activity and adjust task priorities. This allows the project management system to efficiently track related tasks by analyzing members' social media activity.

[0102] The visualization unit can estimate the user's emotions and adjust the visualization's presentation based on the estimated emotions. For example, the visualization unit estimates the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using an emotion estimation function, such as an emotion engine or generative AI. For example, the visualization unit analyzes the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the visualization unit can adjust the visualization's presentation. For example, if the user is stressed, the visualization unit displays a simple graph. If the user is relaxed, it can display a detailed graph. Furthermore, if the user is in a hurry, it can display a concise graph. This allows the project management system to reduce visual burden and enable efficient information gathering by adjusting the visualization's presentation according to the user's emotions.

[0103] The visualization unit can adjust the level of detail of the visualization based on the importance of the task. For example, the visualization unit analyzes task importance data stored in a database connected to the project management system. The visualization unit can use AI to evaluate the importance of tasks and adjust the level of detail of the visualization. For example, the visualization unit can display detailed graphs for high-importance tasks. It can also display simplified graphs for low-importance tasks. Furthermore, it can display graphs with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently grasp information by adjusting the level of detail of the visualization according to the importance of the task.

[0104] The visualization unit can apply different visualization algorithms depending on the task category. For example, the visualization unit analyzes task category data stored in a database connected to the project management system. Using AI, the visualization unit can identify task categories and apply appropriate visualization algorithms. For example, the visualization unit can apply progress bar visualization to development tasks. It can also apply Kanban board visualization to design tasks. Furthermore, it can apply chart visualization to marketing tasks. This allows the project management system to efficiently grasp information by applying visualization algorithms appropriate to the task category.

[0105] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, the visualization unit estimates the user's emotions using sensors or cameras connected to the project management system. The visualization unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the visualization unit analyzes the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the visualization unit can adjust the length of the visualization. For example, if the user is stressed, the visualization unit can provide a short visualization. If the user is relaxed, the visualization unit can provide a longer visualization. Furthermore, if the user is in a hurry, the visualization unit can provide a concise and to-the-point visualization. This allows the project management system to reduce visual burden and enable efficient information gathering by adjusting the visualization length according to the user's emotions.

[0106] The visualization unit can determine visualization priorities based on task submission dates. For example, the visualization unit analyzes task submission date data stored in a database connected to the project management system. The visualization unit can use AI to evaluate task submission dates and determine visualization priorities. For example, the visualization unit prioritizes the visualization of tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the visualization unit can appropriately visualize tasks with medium-term deadlines. This allows the project management system to efficiently grasp information by determining visualization priorities based on task submission dates.

[0107] The visualization unit can adjust the order of visualization based on the relevance of tasks. For example, the visualization unit analyzes task relevance data stored in a database connected to the project management system. The visualization unit can use AI to evaluate the relevance of tasks and adjust the order of visualization. For example, the visualization unit prioritizes the visualization of highly relevant tasks. It can also postpone the visualization of less relevant tasks. Furthermore, it can appropriately visualize tasks of moderate relevance. This allows the project management system to efficiently grasp information by adjusting the order of visualization based on the relevance of tasks.

[0108] The proposal unit can estimate the user's emotions and adjust the way it presents its proposals based on those emotions. For example, the proposal unit can estimate the user's emotions using sensors or cameras connected to the project management system. The proposal unit can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the proposal unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the proposal unit can adjust the way it presents its proposals. For example, if the user is stressed, the proposal unit can make simple proposals. If the user is relaxed, the proposal unit can make detailed proposals. Furthermore, if the user is in a hurry, the proposal unit can make concise proposals. As a result, the project management system can reduce the burden on the user and enable efficient proposals by adjusting the way it presents proposals according to the user's emotions.

[0109] The proposal function can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal function analyzes task importance data stored in a database connected to the project management system. The proposal function can use AI to evaluate task importance and adjust the level of detail of the proposal. For example, the proposal function can provide detailed proposals for high-importance tasks, simplified proposals for low-importance tasks, and proposals with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently generate proposals by adjusting the level of detail according to the importance of the task.

[0110] The proposal department can apply different proposal algorithms depending on the task category when making a proposal. For example, the proposal department analyzes task category data stored in a database connected to the project management system. Using AI, the proposal department can identify the task category and apply the appropriate proposal algorithm. For example, the proposal department can make technical proposals for development tasks, creative proposals for design tasks, and strategic proposals for marketing tasks. This allows the project management system to make efficient proposals by applying proposal algorithms appropriate to the task category.

[0111] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit can estimate the user's emotions using sensors or cameras connected to the project management system. The suggestion unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the suggestion unit can analyze the user's facial expressions and voice and calculate an emotion score. The suggestion unit can adjust the length of the suggestion based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can make a short suggestion. Conversely, if the user is relaxed, the suggestion unit can make a longer suggestion. Furthermore, if the user is in a hurry, the suggestion unit can make a concise suggestion that gets straight to the point. As a result, the project management system can reduce the burden on the user and enable efficient suggestions by adjusting the length of suggestions according to the user's emotions.

[0112] The proposal department can prioritize proposals based on task submission deadlines. For example, the proposal department analyzes task submission deadline data stored in a database connected to the project management system. The proposal department can use AI to evaluate task submission deadlines and determine proposal priorities. For example, the proposal department will prioritize proposing tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, it can appropriately propose tasks with medium-term deadlines. This allows the project management system to prioritize proposals based on task submission deadlines, enabling more efficient proposals.

[0113] The proposal department can adjust the order of proposals based on the relevance of tasks during the proposal process. For example, the proposal department analyzes task relevance data stored in a database connected to the project management system. The proposal department can use AI to evaluate task relevance and adjust the order of proposals. For example, the proposal department can prioritize proposing tasks with high relevance. It can also postpone tasks with low relevance. Furthermore, it can appropriately propose tasks with moderate relevance. This allows the project management system to make efficient proposals by adjusting the order of proposals based on task relevance.

[0114] The notification unit can estimate the user's emotions and adjust the way notifications are presented based on those emotions. For example, the notification unit can estimate the user's emotions using sensors or cameras connected to the project management system. The notification unit can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the notification unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the notification unit can adjust the way notifications are presented. For example, if the user is feeling stressed, the notification unit can provide a simple notification. If the user is relaxed, the notification unit can provide a detailed notification. Furthermore, if the user is in a hurry, the notification unit can provide a concise notification. This allows the project management system to reduce the burden on users and enable efficient notifications by adjusting the way notifications are presented according to the user's emotions.

[0115] The notification unit can adjust the level of detail of notifications based on the importance of the task. For example, the notification unit analyzes task importance data stored in a database connected to the project management system. The notification unit can use AI to evaluate task importance and adjust the level of detail of notifications. For example, the notification unit can provide detailed notifications for high-importance tasks, simplified notifications for low-importance tasks, and notifications with an appropriate level of detail for tasks of medium importance. This enables the project management system to provide efficient notifications by adjusting the level of detail of notifications according to the importance of the task.

[0116] The notification unit can apply different notification algorithms depending on the task category when sending notifications. For example, the notification unit analyzes task category data stored in a database connected to the project management system. Using AI, the notification unit can identify task categories and apply appropriate notification algorithms. For example, the notification unit can send technical notifications for development tasks, creative notifications for design tasks, and strategic notifications for marketing tasks. This enables the project management system to send notifications efficiently by applying notification algorithms tailored to task categories.

[0117] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated emotions. For example, the notification unit estimates the user's emotions using sensors or cameras connected to the project management system. The notification unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the notification unit analyzes the user's facial expressions and voice and calculates an emotion score. The notification unit can adjust the length of the notification based on the estimated emotions. For example, if the user is feeling stressed, the notification unit can send a short notification. Conversely, if the user is relaxed, the notification unit can send a longer notification. Furthermore, if the user is in a hurry, the notification unit can send a concise notification that gets straight to the point. As a result, the project management system can reduce the burden on users and enable efficient notifications by adjusting the length of notifications according to the user's emotions.

[0118] The notification unit can determine the priority of notifications based on the task submission timing when sending notifications. For example, the notification unit analyzes task submission timing data stored in a database connected to the project management system. The notification unit can use AI to evaluate task submission timing and determine the priority of notifications. For example, the notification unit will prioritize notifications for tasks with approaching deadlines. It can also postpone notifications for tasks with distant deadlines. Furthermore, it can send notifications appropriately for tasks with medium-term deadlines. This enables the project management system to send notifications efficiently by prioritizing notifications based on task submission timing.

[0119] The notification unit can adjust the order of notifications based on the relevance of tasks. For example, the notification unit analyzes task relevance data stored in a database connected to the project management system. The notification unit can use AI to evaluate task relevance and adjust the order of notifications. For example, the notification unit can prioritize notifications for highly relevant tasks. It can also postpone notifications for less relevant tasks. Furthermore, it can appropriately notify users of tasks of moderate relevance. This enables the project management system to provide efficient notifications by adjusting the order of notifications based on task relevance.

[0120] The management department can estimate the user's emotions and adjust data management methods based on those estimated emotions. For example, the management department can estimate the user's emotions using sensors or cameras connected to the project management system. The management department can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the management department can analyze the user's facial expressions and voice to calculate an emotion score. The management department can adjust data management methods based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple data management method. If the user is relaxed, the management department can provide a detailed data management method. Furthermore, if the user is in a hurry, the management department can provide a concise data management method. This allows the project management system to reduce the burden and enable efficient data management by adjusting data management methods according to the user's emotions.

[0121] The management department can adjust the level of detail in data management based on the importance of each project. For example, the management department can analyze project importance data stored in a database connected to the project management system. The management department can use AI to evaluate project importance and adjust the level of detail in management. For example, the management department can perform detailed data management for high-importance projects, simplified data management for low-importance projects, and appropriate level of detail for projects of moderate importance. This allows the project management system to perform efficient data management by adjusting the level of detail in management according to the importance of each project.

[0122] The management department can estimate the user's emotions and determine data management priorities based on those estimated emotions. For example, the management department can estimate user emotions using sensors or cameras connected to the project management system. It can also estimate user emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the management department can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the management department can determine data management priorities. For example, if the user is stressed, the management department will postpone low-priority data management. Conversely, if the user is relaxed, the management department can prioritize high-priority data management. Furthermore, if the user is in a hurry, the management department can prioritize important data management. This allows the project management system to efficiently manage data by prioritizing data management according to the user's emotions.

[0123] The management department can prioritize data management based on project submission deadlines. For example, the management department can analyze project submission deadline data stored in a database connected to the project management system. The management department can use AI to evaluate project submission deadlines and determine management priorities. For example, the management department can prioritize data management for projects with approaching deadlines. Conversely, the management department can postpone data management for projects with distant deadlines. Furthermore, the management department can perform data management for projects with medium-term deadlines at a moderate pace. This enables efficient data management by allowing the project management system to prioritize management based on project submission deadlines.

[0124] The system can estimate the user's emotions and adjust the dashboard display based on those emotions. For example, the system can estimate the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, it can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the system can adjust the dashboard display. For example, if the user is stressed, it can provide a simple dashboard. If the user is relaxed, it can provide a detailed dashboard. Furthermore, if the user is in a hurry, it can provide a concise dashboard. This allows the project management system to reduce the burden on the user and enable efficient information gathering by adjusting the dashboard display according to the user's emotions.

[0125] The system can adjust the level of detail displayed on the dashboard based on the project's importance. For example, the system can analyze project importance data stored in a database connected to the project management system. The system can use AI to evaluate project importance and adjust the level of detail. For example, the system can display a detailed dashboard for high-importance projects, a simplified dashboard for low-importance projects, and a dashboard with appropriate detail for projects of medium importance. This allows the project management system to efficiently grasp information by adjusting the level of detail displayed on the dashboard according to the project's importance.

[0126] The system can estimate the user's emotions and adjust the display order of the dashboard based on the estimated emotions. For example, the system can estimate the user's emotions using sensors or cameras connected to the project management system. The system can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the system can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the system can adjust the display order of the dashboard. For example, if the user is feeling stressed, the system can display important information first. If the user is relaxed, the system can display detailed information later. Furthermore, if the user is in a hurry, the system can display concise information first. As a result, the project management system can reduce the burden on the user and enable efficient information gathering by adjusting the display order of the dashboard according to the user's emotions.

[0127] The system can determine the display priority of projects based on their submission dates when displaying them on the dashboard. For example, the system can analyze project submission date data stored in a database connected to the project management system. The system can use AI to evaluate project submission dates and determine the display priority. For example, the system can prioritize displaying information on projects with approaching deadlines. It can also postpone displaying information on projects with distant deadlines. Furthermore, it can display information on projects with medium-term deadlines appropriately. This allows the project management system to efficiently grasp information by determining the display priority of the dashboard based on project submission dates.

[0128] The update unit can estimate the user's emotions and adjust the frequency of progress updates based on the estimated emotions. For example, the update unit estimates the user's emotions using sensors or cameras connected to the project management system. The update unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the update unit analyzes the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the update unit can adjust the frequency of progress updates. For example, if the user is stressed, the update unit reduces the update frequency to alleviate the burden. Conversely, if the user is relaxed, the update unit increases the update frequency to enable more detailed progress management. Furthermore, if the user is in a hurry, the update unit increases the update frequency to encourage quicker responses. This allows the project management system to reduce the burden and enable efficient progress management by adjusting the frequency of progress updates according to the user's emotions.

[0129] The update unit can adjust the level of detail of progress updates based on the importance of the task. For example, the update unit analyzes task importance data stored in a database connected to the project management system. The update unit can use AI to evaluate task importance and adjust the level of detail of the update. For example, the update unit can provide detailed progress updates for high-importance tasks. It can also provide simplified progress updates for low-importance tasks. Furthermore, it can provide progress updates with an appropriate level of detail for tasks of medium importance. This enables efficient progress management by allowing the project management system to adjust the level of detail of progress updates according to the importance of the task.

[0130] The update unit can estimate the user's emotions and adjust the order in which progress updates are displayed based on those estimated emotions. For example, the update unit can estimate the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the update unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the update unit can adjust the order in which progress updates are displayed. For example, if the user is stressed, the update unit will display important progress updates first. If the user is relaxed, the update unit can display detailed progress updates later. Furthermore, if the user is in a hurry, the update unit can display concise progress updates first. This allows the project management system to reduce the burden on the user and enable efficient progress management by adjusting the order of progress updates according to the user's emotions.

[0131] The update unit can determine the priority of updates based on the task submission date when updating progress status. For example, the update unit analyzes task submission date data stored in a database connected to the project management system. The update unit can use AI to evaluate task submission dates and determine the priority of updates. For example, the update unit can prioritize updating the progress of tasks with approaching deadlines. It can also postpone updating the progress of tasks with distant deadlines. Furthermore, it can update the progress of tasks with medium-term deadlines at a moderate pace. As a result, the project management system can efficiently manage progress by determining the priority of progress updates based on task submission dates.

[0132] The sending unit can estimate the user's emotions and adjust the reminder delivery method based on the estimated emotions. For example, the sending unit can estimate the user's emotions using sensors or cameras connected to the project management system. The sending unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the sending unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the sending unit can adjust the reminder delivery method. For example, if the user is stressed, the sending unit can send a simple reminder. If the user is relaxed, the sending unit can send a more detailed reminder. Furthermore, if the user is in a hurry, the sending unit can send a concise reminder. This allows the project management system to reduce the burden and enable efficient reminder delivery by adjusting the reminder delivery method according to the user's emotions.

[0133] The sending unit can adjust the level of detail in reminders based on the importance of the task. For example, the sending unit analyzes task importance data stored in a database connected to the project management system. The sending unit can use AI to evaluate task importance and adjust the level of detail in reminders. For example, the sending unit can send detailed reminders for high-importance tasks, simplified reminders for low-importance tasks, and moderately detailed reminders for tasks of medium importance. This allows the project management system to efficiently send reminders by adjusting the level of detail in reminders according to the importance of the task.

[0134] The sending unit can estimate the user's emotions and adjust the order in which reminders are sent based on the estimated emotions. For example, the sending unit can estimate the user's emotions using sensors or cameras connected to the project management system. The sending unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the sending unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the sending unit can adjust the order in which reminders are sent. For example, if the user is stressed, the sending unit can send important reminders first. If the user is relaxed, the sending unit can send detailed reminders later. Furthermore, if the user is in a hurry, the sending unit can send concise reminders first. This allows the project management system to reduce the burden on the user and enable efficient reminder delivery by adjusting the order of reminders according to the user's emotions.

[0135] The sending unit can determine the priority of sending reminders based on the task submission date. For example, the sending unit analyzes task submission date data stored in a database connected to the project management system. The sending unit can use AI to evaluate task submission dates and determine the priority of sending reminders. For example, the sending unit can prioritize sending reminders for tasks with approaching deadlines. It can also postpone sending reminders for tasks with distant deadlines. Furthermore, it can send reminders for tasks with medium-length deadlines at an appropriate rate. This allows the project management system to efficiently send reminders by prioritizing them based on task submission dates.

[0136] The alert unit can estimate the user's emotions and adjust how alerts are displayed based on those emotions. For example, the alert unit can estimate the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, the alert unit can analyze the user's facial expressions and voice to calculate an emotion score. Based on the estimated emotions, the alert unit can adjust how alerts are displayed. For example, if the user is stressed, it can display a simple alert. If the user is relaxed, it can display a more detailed alert. Furthermore, if the user is in a hurry, it can display a concise alert. This allows the project management system to reduce the burden on users and enable efficient alert display by adjusting how alerts are displayed according to their emotions.

[0137] The alerting unit can adjust the level of detail displayed based on the importance of the task when displaying an alert. For example, the alerting unit analyzes task importance data stored in a database connected to the project management system. The alerting unit can use AI to evaluate the importance of tasks and adjust the level of detail displayed. For example, the alerting unit can display detailed alerts for high-importance tasks. It can also display simplified alerts for low-importance tasks. Furthermore, it can display alerts with an appropriate level of detail for tasks of medium importance. This allows the project management system to efficiently display alerts by adjusting the level of detail displayed according to the importance of the task.

[0138] The alert unit can estimate the user's emotions and adjust the display order of alerts based on the estimated emotions. For example, the alert unit estimates the user's emotions using sensors or cameras connected to the project management system. The alert unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the alert unit analyzes the user's facial expressions and voice to calculate an emotion score. The alert unit can adjust the display order of alerts based on the estimated emotions. For example, if the user is feeling stressed, the alert unit will display important alerts first. Also, if the user is relaxed, the alert unit can display detailed alerts later. Furthermore, if the user is in a hurry, the alert unit can display concise alerts first. As a result, the project management system can reduce the burden on the user and enable efficient alert display by adjusting the display order of alerts according to the user's emotions.

[0139] The alert unit can determine the display priority based on the task submission date when displaying alerts. For example, the alert unit analyzes task submission date data stored in a database connected to the project management system. The alert unit can use AI to evaluate task submission dates and determine the display priority. For example, the alert unit will prioritize displaying alerts for tasks with approaching deadlines. It can also postpone displaying alerts for tasks with distant deadlines. Furthermore, it can display alerts for tasks with medium-term deadlines appropriately. This allows the project management system to efficiently display alerts by prioritizing alert display based on task submission dates.

[0140] The generation unit can estimate the user's emotions and adjust the report generation method based on the estimated emotions. For example, the generation unit estimates the user's emotions using sensors or cameras connected to the project management system. The generation unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generation AI. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can adjust the report generation method based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple report. If the user is relaxed, the generation unit can generate a detailed report. Furthermore, if the user is in a hurry, the generation unit can generate a concise report. As a result, the project management system can reduce its workload and generate reports efficiently by adjusting the report generation method according to the user's emotions.

[0141] The generation unit can adjust the level of detail generated based on the importance of the project during report generation. For example, the generation unit analyzes project importance data stored in a database connected to the project management system. The generation unit can use AI to evaluate project importance and adjust the level of detail generated. For example, the generation unit can generate detailed reports for high-importance projects. It can also generate simplified reports for low-importance projects. Furthermore, it can generate reports with an appropriate level of detail for projects of medium importance. This allows the project management system to efficiently generate reports by adjusting the level of detail generated according to the importance of the project.

[0142] The generation unit can estimate the user's emotions and adjust the report generation order based on the estimated emotions. For example, the generation unit estimates the user's emotions using sensors or cameras connected to the project management system. The generation unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generation AI. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotion score. The generation unit can adjust the report generation order based on the estimated emotions. For example, if the user is feeling stressed, the generation unit will generate important reports first. Also, if the user is relaxed, the generation unit can generate detailed reports later. Furthermore, if the user is in a hurry, the generation unit can prioritize generating concise reports for earlier tasks. As a result, the project management system can reduce the burden and enable efficient report generation by adjusting the report generation order according to the user's emotions.

[0143] The generation unit can determine the priority of report generation based on project submission deadlines. For example, the generation unit analyzes project submission deadline data stored in a database connected to the project management system. The generation unit can use AI to evaluate project submission deadlines and determine the priority of report generation. For example, the generation unit can prioritize generating reports for projects with approaching deadlines. It can also postpone generating reports for projects with distant deadlines. Furthermore, it can generate reports for projects with medium-term deadlines at an appropriate rate. This enables the project management system to efficiently generate reports by prioritizing report generation based on project submission deadlines.

[0144] The improvement unit can estimate the user's emotions and adjust the project plan improvement methods based on the estimated emotions. For example, the improvement unit estimates the user's emotions using sensors or cameras connected to the project management system. The improvement unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the improvement unit analyzes the user's facial expressions and voice and calculates an emotion score. Based on the estimated emotions, the improvement unit can adjust the project plan improvement methods. For example, if the user is feeling stressed, the improvement unit can suggest simple improvement methods. If the user is relaxed, the improvement unit can suggest detailed improvement methods. Furthermore, if the user is in a hurry, the improvement unit can suggest concise improvement methods. In this way, the project management system can reduce the burden and enable efficient improvement by adjusting the project plan improvement methods according to the user's emotions.

[0145] The improvement department can adjust the level of detail of project plan improvements based on historical data. For example, the improvement department analyzes historical data stored in a database connected to the project management system. The improvement department can use AI to adjust the level of detail of project plan improvements based on historical data. For example, the improvement department can propose detailed improvement methods based on past success stories. It can also propose simplified improvement methods based on past failure stories. Furthermore, the improvement department can analyze historical data and propose improvement methods with an appropriate level of detail. As a result, the project management system can efficiently improve project plans by adjusting the level of detail of improvements based on historical data.

[0146] The improvement unit can estimate the user's emotions and adjust the order of improvements in the project plan based on the estimated emotions. For example, the improvement unit estimates the user's emotions using sensors or cameras connected to the project management system. The improvement unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the improvement unit analyzes the user's facial expressions and voice and calculates an emotion score. Based on the estimated emotions, the improvement unit can adjust the order of improvements in the project plan. For example, if the user is feeling stressed, the improvement unit will prioritize important improvements. Also, if the user is relaxed, the improvement unit can prioritize detailed improvements. Furthermore, if the user is in a hurry, the improvement unit can prioritize concise improvements. In this way, the project management system can reduce the burden and enable efficient improvements by adjusting the order of improvements in the project plan according to the user's emotions.

[0147] The improvement department can determine the priority of improvements based on project submission deadlines when improving project plans. For example, the improvement department can analyze project submission deadline data stored in a database connected to the project management system. The improvement department can use AI to evaluate project submission deadlines and determine the priority of improvements. For example, the improvement department can prioritize improvements for projects with approaching deadlines. It can also postpone improvements for projects with distant deadlines. Furthermore, the improvement department can moderately improve projects with medium-term deadlines. This allows the project management system to efficiently improve projects by determining the priority of improvements based on project submission deadlines.

[0148] The support unit can estimate the user's emotions and adjust the decision support method based on the estimated emotions. For example, the support unit can estimate the user's emotions using sensors or cameras connected to the project management system. The support unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the support unit can analyze the user's facial expressions and voice and calculate an emotion score. The support unit can adjust the decision support method based on the estimated emotions. For example, if the user is feeling stressed, the support unit can provide simple decision support. If the user is relaxed, the support unit can provide detailed decision support. Furthermore, if the user is in a hurry, the support unit can provide concise decision support. As a result, the project management system can reduce the burden and enable efficient decision support by adjusting the decision support method according to the user's emotions.

[0149] The support department can adjust the level of detail of support provided based on the importance of the project during decision-making support. For example, the support department analyzes project importance data stored in a database connected to the project management system. The support department can use AI to evaluate project importance and adjust the level of detail of support. For example, the support department can provide detailed decision-making support for high-importance projects. Conversely, it can provide simplified decision-making support for low-importance projects. Furthermore, it can provide decision-making support with an appropriate level of detail for projects of medium importance. This enables the project management system to provide efficient decision-making support by adjusting the level of detail of decision-making support according to the importance of the project.

[0150] The support unit can estimate the user's emotions and adjust the order of decision support based on the estimated emotions. For example, the support unit estimates the user's emotions using sensors or cameras connected to the project management system. The support unit can estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, the support unit analyzes the user's facial expressions and voice and calculates an emotion score. Based on the estimated emotions, the support unit can adjust the order of decision support. For example, if the user is feeling stressed, the support unit will provide important decision support first. Also, if the user is relaxed, the support unit can provide detailed decision support later. Furthermore, if the user is in a hurry, the support unit can provide concise decision support first. In this way, the project management system can reduce the burden and enable efficient decision support by adjusting the order of decision support according to the user's emotions.

[0151] The support department can prioritize support based on project submission deadlines when providing decision-making assistance. For example, the support department analyzes project submission deadline data stored in a database connected to the project management system. The support department can use AI to evaluate project submission deadlines and determine support priorities. For example, the support department can prioritize decision-making support for projects with approaching deadlines. Conversely, the support department can postpone decision-making support for projects with distant deadlines. Furthermore, the support department can provide appropriate decision-making support for projects with medium-term deadlines. This enables efficient decision-making support by allowing the project management system to prioritize decision-making support based on project submission deadlines.

[0152] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0153] The suggestion unit can estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, the suggestion unit can estimate the user's emotions using sensors or cameras connected to the project management system. The suggestion unit can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, if the user is feeling stressed, the suggestion unit can make simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can make detailed and complex suggestions. Furthermore, if the user is in a hurry, the suggestion unit can make concise and to-the-point suggestions. As a result, the project management system can reduce the burden on the user and enable efficient suggestions by adjusting the content of suggestions according to the user's emotions.

[0154] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, the notification unit can estimate the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For instance, if the user is stressed, the notification unit will delay the notification. Conversely, if the user is relaxed, the notification unit can send an immediate notification. Furthermore, if the user is in a hurry, the notification unit can prioritize important notifications. This allows the project management system to reduce the burden on users and enable more efficient notifications by adjusting the timing of notifications according to their emotions.

[0155] The system can estimate the user's emotions and adjust the dashboard display based on those emotions. For example, the system can estimate the user's emotions using sensors or cameras connected to the project management system. The system can also estimate the user's emotions using emotion estimation functions such as an emotion engine or generative AI. For example, if the user is stressed, the system can display only important information. If the user is relaxed, the system can display detailed information. Furthermore, if the user is in a hurry, the system can display concise information. As a result, the project management system can reduce the burden on the user and enable efficient information gathering by adjusting the dashboard display content according to the user's emotions.

[0156] The update unit can estimate the user's emotions and adjust the progress update method based on the estimated emotions. For example, the update unit estimates the user's emotions using sensors or cameras connected to the project management system. The update unit can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For example, if the user is stressed, the update unit can provide a simplified update method. If the user is relaxed, it can provide a more detailed update method. Furthermore, if the user is in a hurry, it can provide a concise update method. This allows the project management system to reduce the burden and enable efficient progress management by adjusting the progress update method according to the user's emotions.

[0157] The transmitting unit can estimate the user's emotions and adjust the content of reminders based on those emotions. For example, the transmitting unit can estimate the user's emotions using sensors or cameras connected to the project management system. It can also estimate the user's emotions using emotion estimation functions, such as an emotion engine or generative AI. For instance, if the user is feeling stressed, the transmitting unit can send a simple reminder. If the user is relaxed, it can send a more detailed reminder. Furthermore, if the user is in a hurry, it can send a concise reminder. This allows the project management system to reduce the burden on users and enable efficient reminder delivery by adjusting the content of reminders according to the user's emotions.

[0158] The management department can analyze project progress data and adjust the frequency of reports to leaders according to the progress status. For example, the management department can analyze progress data stored in a database connected to the project management system. The management department can use AI to evaluate progress and adjust the reporting frequency. For example, the management department can reduce the reporting frequency when progress is on track. Conversely, it can increase the reporting frequency when progress is behind schedule. Furthermore, the management department can immediately report if progress changes rapidly. As a result, the project management system can efficiently manage progress by adjusting the reporting frequency according to the progress status.

[0159] The service provider can analyze project progress data and adjust the frequency of dashboard display according to the progress status. For example, the service provider can analyze progress data stored in a database connected to the project management system. The service provider can use AI to evaluate the progress status and adjust the display frequency. For example, the service provider can reduce the display frequency when progress is on track. Conversely, the service provider can increase the display frequency when progress is behind schedule. Furthermore, the service provider can immediately display information if the progress changes rapidly. As a result, the project management system can efficiently grasp information by adjusting the display frequency according to the progress status.

[0160] The update unit can analyze project progress data and adjust the update frequency according to the progress status. For example, the update unit analyzes progress data stored in a database connected to the project management system. The update unit can use AI to evaluate the progress status and adjust the update frequency. For example, if progress is on track, the update unit can reduce the update frequency. Conversely, if progress is behind schedule, the update unit can increase the update frequency. Furthermore, if progress changes rapidly, the update unit can perform updates immediately. As a result, the project management system can efficiently manage progress by adjusting the update frequency according to the progress status.

[0161] The sending unit can analyze project progress data and adjust the frequency of reminder transmissions according to the progress status. For example, the sending unit analyzes progress data stored in a database connected to the project management system. The sending unit can use AI to evaluate the progress status and adjust the transmission frequency. For example, if progress is on track, the sending unit can reduce the transmission frequency. Conversely, if progress is behind schedule, the sending unit can increase the transmission frequency. Furthermore, if progress changes rapidly, the sending unit can immediately send a reminder. This enables efficient progress management by allowing the project management system to adjust the reminder transmission frequency according to the progress status.

[0162] The alert unit can analyze project progress data and adjust the frequency of alerts sent according to the progress status. For example, the alert unit analyzes progress data stored in a database connected to the project management system. The alert unit can use AI to evaluate the progress status and adjust the frequency of alerts. For example, the alert unit can reduce the frequency of alerts sent when progress is on track. Conversely, it can increase the frequency of alerts sent when progress is behind schedule. Furthermore, the alert unit can send an alert immediately if the progress changes rapidly. As a result, the project management system can efficiently manage progress by adjusting the frequency of alerts sent according to the progress status.

[0163] The following briefly describes the processing flow for example form 2.

[0164] Step 1: The tracking unit tracks the task progress of project members in real time. The tracking unit acquires task progress data from each member's device connected to the project management system, analyzes the task progress using AI, and updates the data in real time. For example, it collects data such as task start time, end time, and progress rate. Step 2: The visualization unit visualizes the task progress tracked by the tracking unit. The visualization unit displays the task progress using graphs and charts, and uses AI to make it visually easy to understand. For example, it displays the task progress using different colors and highlights tasks that are behind schedule. Step 3: The proposal unit automatically suggests solutions when delays occur based on the task progress visualized by the visualization unit. The proposal unit uses AI to analyze the cause of the delay and suggests appropriate solutions such as reallocating resources or changing task priorities. For example, it might suggest allocating additional resources to the task experiencing the delay. Step 4: The notification unit notifies the team of resource reallocation and priority changes based on the solutions proposed by the proposal unit. The notification unit sends notifications to each member's device connected to the project management system and uses AI to send notifications at the appropriate time. For example, it notifies the team of resource reallocation and priority changes based on the progress of tasks.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the tracking unit, visualization unit, proposal unit, notification unit, management unit, provision unit, update unit, transmission unit, alert unit, generation unit, improvement unit, and support unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the tracking unit is implemented by the processor 46 of the smart device 14 and tracks the task progress of project members in real time. The visualization unit displays the progress of tasks using the display 40A of the smart device 14. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes solutions when delays occur. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies of resource redistribution and changes in priority. The management unit stores project data in the database 24 of the data processing unit 12 and performs progress and risk analysis. The provision unit displays a dashboard on the display 40A of the smart device 14. The update unit automatically updates the progress using the processor 46 of the smart device 14. The transmission unit sends reminders via the control unit 46A of the smart device 14. The alert unit sends alerts via the control unit 46A of the smart device 14. The generation unit automatically generates reports via the specific processing unit 290 of the data processing device 12. The improvement unit improves the project plan via the specific processing unit 290 of the data processing device 12. The support unit supports decision-making via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0169] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the tracking unit, visualization unit, proposal unit, notification unit, management unit, provision unit, update unit, transmission unit, alert unit, generation unit, improvement unit, and support unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the tracking unit is implemented by the processor 46 of the smart glasses 214 and tracks the task progress of project members in real time. The visualization unit displays the task progress using the display of the smart glasses 214. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes solutions when delays occur. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies of resource redistribution and changes in priority. The management unit stores project data in the database 24 of the data processing unit 12 and performs progress and risk analysis. The provision unit displays a dashboard on the display of the smart glasses 214. The update unit automatically updates the progress using the processor 46 of the smart glasses 214. The transmission unit sends reminders via the control unit 46A of the smart glasses 214. The alert unit sends alerts via the control unit 46A of the smart glasses 214. The generation unit automatically generates reports via the specific processing unit 290 of the data processing device 12. The improvement unit improves the project plan via the specific processing unit 290 of the data processing device 12. The support unit assists decision-making via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0185] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] Each of the multiple elements described above, including the tracking unit, visualization unit, proposal unit, notification unit, management unit, provision unit, update unit, transmission unit, alert unit, generation unit, improvement unit, and support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the tracking unit is implemented by the processor 46 of the headset terminal 314 and tracks the task progress of project members in real time. The visualization unit displays the task progress using the display of the headset terminal 314. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes solutions when delays occur. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies of resource redistribution and changes in priority. The management unit stores project data in the database 24 of the data processing unit 12 and performs progress status and risk analysis. The provision unit displays a dashboard on the display of the headset terminal 314. The update unit automatically updates the progress status using the processor 46 of the headset terminal 314. The transmission unit sends reminders via the control unit 46A of the headset terminal 314. The alert unit sends alerts via the control unit 46A of the headset terminal 314. The generation unit automatically generates reports via the specific processing unit 290 of the data processing device 12. The improvement unit improves the project plan via the specific processing unit 290 of the data processing device 12. The support unit supports decision-making via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0201] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] Each of the multiple elements described above, including the tracking unit, visualization unit, proposal unit, notification unit, management unit, provision unit, update unit, transmission unit, alert unit, generation unit, improvement unit, and support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the tracking unit is implemented by the processor 46 of the robot 414 and tracks the task progress of project members in real time. The visualization unit displays the task progress using the display of the robot 414. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes solutions when delays occur. The notification unit is implemented by the control unit 46A of the robot 414 and notifies of resource redistribution and changes in priority. The management unit stores project data in the database 24 of the data processing unit 12 and performs progress and risk analysis. The provision unit displays a dashboard on the display of the robot 414. The update unit automatically updates the progress by the processor 46 of the robot 414. The transmission unit sends reminders by the control unit 46A of the robot 414. The alert unit transmits alerts via the control unit 46A of the robot 414. The generation unit automatically generates reports via the specific processing unit 290 of the data processing device 12. The improvement unit improves the project plan via the specific processing unit 290 of the data processing device 12. The support unit assists decision-making via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the devices and control units is not limited to the example described above and can be modified in various ways.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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."

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] (Note 1) A tracking unit that tracks the task progress of project members in real time, A visualization unit that visualizes the task progress tracked by the aforementioned tracking unit, A proposal unit automatically suggests a solution when a delay occurs based on the task progress visualized by the visualization unit, The system includes a notification unit that notifies the user of resource redistribution and changes in priority based on the solution proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The project management department centrally manages data for each project and conducts progress and risk analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a service area that provides a user-friendly dashboard for leaders. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an update function that requires team members to report their progress regularly and automatically updates the progress status. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a sender that automatically sends reminders to members who are behind schedule. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes an alerting section that allows for customizable alerts to be set up and immediately notified to project managers when progress is delayed or risks increase. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a generation unit that tracks the project's progress history in detail and automatically generates periodic reports. The system described in Appendix 1, characterized by the features described herein. (Note 8) It includes an improvement department that improves future project plans based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 9) Based on project progress data, the department provides support by offering analytical results and recommended actions to assist in important decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned tracking unit is It estimates the user's emotions and adjusts the frequency of task progress tracking based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned tracking unit is Select the optimal tracking method according to each member's work style. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned tracking unit is Adjust the level of detail in tracking based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned tracking unit is Estimate user emotions and prioritize tracking tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned tracking unit is When tracking task progress, prioritize tracking highly relevant tasks by considering the geographical location of team members. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned tracking unit is When tracking task progress, analyze members' social media activity and track related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization's presentation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, Adjust the level of detail in the visualization based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, Apply different visualization algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, Prioritize visualizations based on task submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, Adjust the order of visualizations based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When a notification is sent, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, It estimates the user's emotions and adjusts the length of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When sending notifications, we prioritize them based on when the task was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, We estimate user sentiment and adjust data management methods based on the estimated user sentiment. The system according to appended claim 1, characterized in that... (Appended claim 35) The management unit adjusts the detail level of management based on the importance of the project during data management The system according to appended claim 1, characterized in that... (Appended claim 36) The management unit estimates the user's emotion and determines the priority of data management based on the estimated user's emotion The system according to appended claim 1, characterized in that... (Appended claim 37) The management unit determines the priority of management based on the submission time of the project during data management The system according to appended claim 1, characterized in that... (Appended claim 38) The providing unit estimates the user's emotion and adjusts the display method of the dashboard based on the estimated user's emotion The system according to appended claim 1, characterized in that... (Appended claim 39) The providing unit adjusts the detail level of display based on the importance of the project during dashboard display The system according to appended claim 1, characterized in that... (Appended claim 40) The providing unit estimates the user's emotion and adjusts the display order of the dashboard based on the estimated user's emotion The system according to appended claim 1, characterized in that... (Appended claim 41) The providing unit determines the priority of display based on the submission time of the project during dashboard display The system according to appended claim 1, characterized in that... (Appended claim 42) The updating unit estimates the user's emotion and adjusts the update frequency of the progress status based on the estimated user's emotion The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned update unit is When updating progress, adjust the level of detail of the update based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned update unit is It estimates the user's emotions and adjusts the order in which progress updates are updated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned update unit is When updating progress, prioritize updates based on the task submission date. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned transmitting unit It estimates the user's emotions and adjusts how reminders are sent based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned transmitting unit When sending reminders, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned transmitting unit It estimates the user's emotions and adjusts the order in which reminders are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 49) The aforementioned transmitting unit When sending reminders, the system prioritizes sending them based on when the task was due. The system described in Appendix 1, characterized by the features described herein. (Note 50) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system according to appended note 1, characterized in that... (Appended note 51) The alert unit adjusts the display detail level based on the importance level of the task when displaying an alert. The system according to appended note 1, characterized in that... (Appended note 52) The alert unit estimates the user's emotion and adjusts the display order of alerts based on the estimated user emotion. The system according to appended note 1, characterized in that... (Appended note 53) The alert unit determines the display priority based on the submission time of the task when displaying an alert. The system according to appended note 1, characterized in that... (Appended note 54) The generation unit estimates the user's emotion and adjusts the report generation method based on the estimated user emotion. The system according to appended note 1, characterized in that... (Appended note 55) The generation unit adjusts the generation detail level based on the importance level of the project when generating a report. The system according to appended note 1, characterized in that... (Appended note 56) The generation unit estimates the user's emotion and adjusts the report generation order based on the estimated user emotion. The system according to appended note 1, characterized in that... (Appended note 57) The generation unit determines the generation priority based on the submission time of the project when generating a report. The system according to appended note 1, characterized in that... (Appended note 58) The improvement unit estimates the user's emotion and adjusts the project plan improvement method based on the estimated user emotion. The system described in Appendix 1, characterized by the features described herein. (Note 59) The aforementioned improvement unit is, When improving a project plan, adjust the level of detail of the improvements based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 60) The aforementioned improvement unit is, Estimate user sentiment and adjust the order of improvements in the project plan based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 61) The aforementioned improvement unit is, When improving a project plan, prioritize improvements based on the project's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 62) The aforementioned support unit, It estimates the user's emotions and adjusts the decision support method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 63) The aforementioned support unit, When providing decision support, adjust the level of detail of support based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 64) The aforementioned support unit, It estimates the user's emotions and adjusts the order of decision support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 65) The aforementioned support unit, When providing decision support, prioritize support based on the project submission date. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0237] 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. A tracking unit that tracks the task progress of project members in real time, A visualization unit that visualizes the task progress tracked by the aforementioned tracking unit, A proposal unit automatically suggests a solution when a delay occurs based on the task progress visualized by the visualization unit, The system includes a notification unit that notifies the user of resource redistribution and changes in priority based on the solution proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The project management department centrally manages data for each project and conducts progress and risk analysis. The system according to feature 1.

3. Equipped with a service area that provides a user-friendly dashboard for leaders. The system according to feature 1.

4. It includes an update function that requires team members to report their progress regularly and automatically updates the progress status. The system according to feature 1.

5. It includes a sender that automatically sends reminders to members who are behind schedule. The system according to feature 1.

6. It includes an alerting section that allows for customizable alerts to be set up and immediately notified to project managers when progress is delayed or risks increase. The system according to feature 1.

7. It includes a generation unit that tracks the project's progress history in detail and automatically generates periodic reports. The system according to feature 1.

8. It includes an improvement department that improves future project plans based on past data. The system according to feature 1.

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