system

Abstract

The system according to this embodiment aims to support cross-departmental project leaders and streamline project progress management. [Solution] The system according to the embodiment comprises a progress monitoring unit, a delay risk notification unit, an adjustment proposal unit, and a deliverable generation unit. The progress monitoring unit monitors the progress of the project in real time. The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. The adjustment proposal unit analyzes the dependencies of tasks based on the delay risks notified by the delay risk notification unit and proposes changes in assignment and priority. The deliverable generation unit automatically generates draft reports and deliverables based on team data, based on the changes in task assignment and priority proposed by the adjustment proposal unit.

Description

Technical Field

[0006]

[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 chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the cross-departmental project leader has excessive responsibilities but insufficient authority, making it difficult to manage the progress of the project.

[0005] The system according to the embodiment aims to assist the cross-departmental project leader and improve the efficiency of project progress management. <,

Means for Solving the Problems

[0006] The system according to this embodiment comprises a progress monitoring unit, a delay risk notification unit, an adjustment proposal unit, and a deliverable generation unit. The progress monitoring unit monitors the progress of the project in real time. The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. The adjustment proposal unit analyzes the dependencies of tasks based on the delay risks notified by the delay risk notification unit and proposes changes in assignment and priority. The deliverable generation unit automatically generates draft reports and deliverables based on team data, based on the changes in task assignment and priority proposed by the adjustment proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can support cross-departmental project leaders and streamline project progress management. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the 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 applicable 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 <� 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 receiving 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 receiving 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 agent AI tool according to an embodiment of the present invention is a system that supports cross-departmental project leaders who bear excessive responsibility but lack sufficient authority. This agent AI tool autonomously monitors and analyzes project progress and identifies delay risks and issues. It reduces the burden on leaders through suggesting the next course of action, adjusting tasks, and automatically generating deliverables. Designed to address the "responsibility beyond coordination" that existing tools cannot handle, it resolves the problems of insufficient authority and concentrated responsibility, maximizing the efficiency and results of the entire team. It is a solution specifically tailored to address the challenges associated with the increase in remote work and cross-departmental tasks. For example, the agent AI tool monitors project progress in real time and tracks the task progress of each member. For example, if task progress is behind schedule, the agent AI tool immediately notifies the leader of the delay risk. This allows the leader to take countermeasures quickly. Next, the agent AI tool analyzes task dependencies and suggests changes in assignment or priority. For example, if one task depends on another, the agent AI tool analyzes the dependencies and suggests the optimal task assignment. This ensures smooth project progress. Furthermore, the agent AI tool automatically generates draft reports and deliverables based on team data. For example, it automatically generates reports summarizing project progress and deliverable content. This reduces the manual workload for leaders and allows projects to proceed more efficiently. This agent AI tool complements leaders' lack of authority and creates an environment where leaders can more easily give instructions to team members. For example, the agent AI tool's objective suggestions encourage team members' acceptance and reduce the leader's burden. It also increases the project's success rate by allowing leaders to focus on strategic tasks. In this way, the agent AI tool aims to support project leaders who are burdened with excessive responsibility but not given sufficient authority, and to maximize the efficiency and results of the entire team. As a result, the agent AI tool can reduce the burden on project leaders and maximize the efficiency and results of the entire team.

[0029] The agent AI tool according to this embodiment comprises a progress monitoring unit, a delay risk notification unit, an adjustment proposal unit, and a deliverable generation unit. The progress monitoring unit monitors the progress of the project in real time. For example, the progress monitoring unit tracks the task progress of each member in real time. For example, the progress monitoring unit can monitor the progress of tasks in seconds and immediately detect delay risks. The progress monitoring unit can also monitor the progress of the project in minutes and detect delays early. Furthermore, the progress monitoring unit can monitor progress within a specific delay tolerance range and notify the leader. The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. For example, the delay risk notification unit immediately notifies of delay risks when the progress of a task is behind schedule. For example, the delay risk notification unit can consider a delay of 10% or more as a delay risk and issue a notification. The delay risk notification unit can also issue a warning when the delay of progress is 20% or more. Furthermore, the delay risk notification unit can issue an emergency notification if the progress is delayed by 30% or more. Based on the delay risk notified by the delay risk notification unit, the adjustment proposal unit analyzes task dependencies and proposes changes in assignment and priority. For example, the adjustment proposal unit can analyze task dependencies and propose the optimal task assignment. For example, the adjustment proposal unit can analyze the dependencies between tasks and propose the efficient use of resources. The adjustment proposal unit can also propose changes in priority based on the importance and urgency of tasks. Furthermore, the adjustment proposal unit can propose methods for reallocating resources to ensure smooth project progress. Based on the task assignment changes and changes in priority proposed by the adjustment proposal unit, the deliverable generation unit automatically generates draft reports and deliverables based on team data. For example, the deliverable generation unit automatically generates reports summarizing the project progress and the contents of deliverables. For example, the deliverable generation unit can automatically generate report items and automatically classify the types of deliverables. The deliverable generation unit can also generate reports based on the project progress rate and task completion status.Furthermore, the deliverable generation unit can automatically generate draft deliverables based on the team's data, reducing the manual workload for the leader. This allows the agent AI tool, according to this embodiment, to reduce the burden on the project leader and maximize the overall efficiency and results of the team.

[0030] The progress monitoring unit monitors the project's progress in real time. For example, it tracks the task progress of each member in real time. Specifically, it integrates with the task management tools and project management software used by each member to automatically collect data such as task start time, end time, and progress rate. This allows the project leader to grasp the progress of each member at a glance. The progress monitoring unit can monitor task progress in seconds, for example, and immediately detect delay risks. This makes it possible to take countermeasures early even if the project is behind schedule. The progress monitoring unit can also monitor project progress in minutes, enabling early detection of delays. Furthermore, the progress monitoring unit can monitor progress within a specific delay tolerance range and notify the leader. For example, it has a function that allows setting a delay tolerance range and automatically issues an alert if that range is exceeded. This allows the project leader to grasp delays early and take appropriate action. The progress monitoring unit can also use AI to analyze progress data and predict task progress. For example, based on past data, it's possible to predict the time it will take to complete a specific task and detect delay risks in advance. This allows project leaders to take measures to prevent delays. Furthermore, the progress monitoring department can analyze the workload of each member and propose the optimal allocation of resources. This maximizes the efficiency of the entire team and contributes to the success of the project.

[0031] The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. For example, the delay risk notification unit immediately notifies if a task is behind schedule. Specifically, it analyzes the progress of each task based on data acquired from the progress monitoring unit and detects signs of delay. For example, the delay risk notification unit can consider a delay risk as occurring when the progress is 10% or more and issue a notification. This allows project leaders to grasp delays early and take appropriate action. The delay risk notification unit can also issue a warning when the progress is 20% or more. This allows project leaders to take measures before the delay becomes serious. Furthermore, the delay risk notification unit can issue an emergency notification when the progress is 30% or more. This allows project leaders to respond quickly before the delay has a significant impact. The delay risk notification unit can also use AI to analyze progress data and predict delay risks. For example, it can predict the possibility of a particular task being delayed based on past data and notify in advance. This allows project leaders to take measures to proactively prevent delay risks. Furthermore, the delay risk notification system can analyze each member's workload and propose the optimal allocation of resources. This maximizes the overall efficiency of the team and contributes to the success of the project.

[0032] The Adjustment Proposal Department analyzes task dependencies based on delay risks notified by the Delay Risk Notification Department and proposes changes to assignments and priorities. For example, the Adjustment Proposal Department can analyze task dependencies and propose optimal task assignments. Specifically, it can analyze the dependencies and relationships of each task and propose efficient resource utilization. For example, if a particular task is delayed, it can change the assignment of other tasks that depend on it, thereby smoothing the progress of the entire project. The Adjustment Proposal Department can also propose changes to priorities based on the importance and urgency of tasks. For example, prioritizing high-importance tasks can increase the probability of project success. Furthermore, the Adjustment Proposal Department can propose methods for reallocating resources to smooth project progress. For example, if a particular member is overloaded, distributing that member's tasks to other members can improve overall efficiency. The Adjustment Proposal Department can utilize AI to analyze task dependencies and resource utilization and propose optimal adjustment plans. This allows project leaders to minimize delay risks and develop optimal strategies for project success. Furthermore, the coordination and proposal department can also make proposals based on past data and referencing successful case studies from similar projects. This allows project leaders to receive reliable proposals based on proven results.

[0033] The Deliverable Generation Unit automatically generates draft reports and deliverables based on team data, following changes in task assignments and priorities proposed by the Coordination and Proposal Unit. For example, the Deliverable Generation Unit automatically generates reports summarizing project progress and deliverable content. Specifically, it can automatically generate detailed reports based on the progress and completion status of each task, as well as analysis results of delay risks. This eliminates the need for project leaders to manually create reports. The Deliverable Generation Unit can also automatically generate report items and categorize deliverable types, ensuring consistency and readability in reports. Furthermore, the Deliverable Generation Unit can generate reports based on project progress rates and task completion status, allowing for an accurate understanding of the project's current status. Additionally, the Deliverable Generation Unit can automatically generate draft deliverables based on team data, reducing the leader's manual workload. For example, it can automatically generate draft reports based on project progress and task completion status, requiring only final review and revisions from the leader. This significantly reduces the time spent on report creation, allowing project leaders to focus on other important tasks. The deliverable generation unit can use AI to analyze data and optimize the content of reports and deliverables. This improves the accuracy and quality of reports, contributing to project success. Furthermore, the deliverable generation unit can generate reports based on past data, referencing successful case studies from similar projects. This allows project leaders to receive reliable reports based on proven results.

[0034] The progress monitoring unit can track each member's task progress in real time. For example, the progress monitoring unit can track each member's task progress in seconds and grasp the progress status in real time. For example, the progress monitoring unit can also track each member's task progress in minutes and detect delays early. For example, the progress monitoring unit can track each member's task progress within a specific delay tolerance range and notify the leader. This allows for real-time monitoring of each member's task progress. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input each member's task progress data into a generating AI and have the generating AI perform an analysis of the progress status.

[0035] The delay risk notification unit can immediately notify of delay risks when task progress is behind schedule. For example, the delay risk notification unit immediately notifies of delay risks when task progress is 10% or more behind schedule. For example, the delay risk notification unit can also issue a warning when task progress is 20% or more behind schedule. For example, the delay risk notification unit can issue an emergency notification when task progress is 30% or more behind schedule. This allows for immediate identification of delay risks and prompt response. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input task progress data into a generating AI and have the generating AI perform delay risk analysis.

[0036] The task assignment and proposal unit can analyze task dependencies and propose the optimal task assignment. For example, the task assignment and proposal can analyze the dependencies between tasks and propose the optimal task assignment. The task assignment and proposal unit can also analyze the dependencies between tasks and propose the efficient use of resources. For example, the task assignment and proposal unit can propose changes in priority based on the importance and urgency of tasks. For example, the task assignment and proposal unit can propose methods for reallocating resources to ensure smooth project progress. By analyzing task dependencies and proposing the optimal assignment, project progress is ensured. Some or all of the above processes in the task assignment and proposal unit may be performed using AI, or not. For example, the task assignment and proposal unit can input task dependency data into a generating AI and have the generating AI perform the optimal task assignment.

[0037] The deliverable generation unit can automatically generate reports summarizing the project's progress and the contents of deliverables. For example, the deliverable generation unit can automatically generate report items and automatically classify the types of deliverables. The deliverable generation unit can also generate reports based on project progress rates and task completion status. For example, the deliverable generation unit can automatically generate draft deliverables based on team data, reducing the manual workload for leaders. This reduces the manual workload for leaders through the automatic generation of reports. Some or all of the above-described processes in the deliverable generation unit may be performed using AI, or not. For example, the deliverable generation unit can input project progress data into a generation AI and have the generation AI generate the report.

[0038] The progress monitoring unit can analyze each member's past task progress data to improve the accuracy of progress monitoring. The progress monitoring unit can, for example, analyze each member's past task completion time to improve the prediction accuracy of progress monitoring. The progress monitoring unit can also, for example, analyze each member's past task delay patterns to improve the warning accuracy of progress monitoring. The progress monitoring unit can also, for example, optimize the alert settings for progress monitoring based on each member's past task progress data. This improves the accuracy of progress monitoring by analyzing past data. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input past task progress data into a generating AI and have the generating AI perform the improvement of progress monitoring accuracy.

[0039] The progress monitoring unit can adjust the level of detail in progress monitoring according to the importance of the project. For example, for important projects, the progress monitoring unit can increase the level of detail in progress monitoring and provide detailed progress information. For example, for less important projects, the progress monitoring unit can decrease the level of detail in progress monitoring and provide simplified information. The progress monitoring unit can also adjust the frequency of progress monitoring reports according to the importance of the project. This allows for the provision of appropriate information by adjusting the level of detail in progress monitoring according to the importance of the project. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input project importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in progress monitoring.

[0040] The progress monitoring unit can determine the priority of progress monitoring by considering the geographical location information of each member. For example, the progress monitoring unit sets the priority of progress monitoring based on the geographical location information of each member. For example, the progress monitoring unit can prioritize monitoring the progress of geographically distant members to prevent communication delays. For example, the progress monitoring unit can postpone monitoring the progress of geographically closer members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without using AI. For example, the progress monitoring unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0041] The progress monitoring unit can analyze each member's social media activity to improve the accuracy of progress monitoring. For example, the progress monitoring unit can analyze each member's social media activity to improve the accuracy of task progress predictions. For example, the progress monitoring unit can also infer members' motivation from their social media activity and optimize progress monitoring alert settings. For example, the progress monitoring unit can predict the risk of delays in a member's task progress based on their social media activity. As a result, analyzing social media activity improves the accuracy of progress monitoring. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input each member's social media data into a generating AI and have the generating AI perform the task of improving the accuracy of progress monitoring.

[0042] The delay risk notification unit can analyze past delay data to improve the accuracy of delay risk notifications. For example, the delay risk notification unit can improve the prediction accuracy of delay risk notifications based on past delay data. For example, the delay risk notification unit can also analyze past delay patterns to improve the warning accuracy of delay risk notifications. For example, the delay risk notification unit can optimize the alert settings for delay risk notifications based on past delay data. This improves the accuracy of delay risk notifications by analyzing past data. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input past delay data into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0043] The delay risk notification unit can adjust the level of detail in delay risk notifications according to the importance of the task. For example, for important tasks, the delay risk notification unit can increase the level of detail in delay risk notifications and provide more detailed information. For example, for tasks of low importance, the delay risk notification unit can decrease the level of detail in delay risk notifications and provide simplified information. The delay risk notification unit can also adjust the reporting frequency of delay risk notifications according to the importance of the task. This allows for the provision of appropriate information by adjusting the level of detail in notifications according to the importance of the task. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input task importance data into a generating AI and have the generating AI perform the adjustment of notification detail.

[0044] The delay risk notification unit can determine the priority of delay risk notifications by considering the geographical location information of each member. For example, the delay risk notification unit sets the priority of delay risk notifications based on the geographical location information of each member. For example, the delay risk notification unit can prevent communication delays by prioritizing the notification of delay risks of geographically distant members. For example, the delay risk notification unit can also efficiently allocate resources by postponing the notification of delay risks of geographically close members. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0045] The delay risk notification unit can analyze each member's social media activity to improve the accuracy of delay risk notifications. For example, the delay risk notification unit can analyze each member's social media activity to improve the accuracy of delay risk predictions. The delay risk notification unit can also, for example, infer members' motivation from their social media activity and optimize the alert settings for delay risk notifications. The delay risk notification unit can also, for example, predict members' task delay risk based on their social media activity. As a result, analyzing social media activity improves the accuracy of delay risk notifications. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input each member's social media data into a generating AI and have the generating AI perform improvements to the notification accuracy.

[0046] The adjustment proposal unit can analyze past task dependency data to improve the accuracy of adjustment proposals. For example, the adjustment proposal unit can make optimal adjustment proposals based on past task dependency data. The adjustment proposal unit can also analyze past task dependency data to improve the prediction accuracy of adjustment proposals. For example, the adjustment proposal unit can optimize the alert settings for adjustment proposals based on past task dependency data. This improves the accuracy of adjustment proposals by analyzing past data. Some or all of the above processes in the adjustment proposal unit may be performed using AI, for example, or without AI. For example, the adjustment proposal unit can input past task dependency data into a generating AI and have the generating AI perform improvements to the proposal accuracy.

[0047] The adjustment proposal unit can adjust the level of detail in adjustment proposals according to the importance of the task. For example, for important tasks, the adjustment proposal unit can increase the level of detail in the adjustment proposal and provide more detailed information. For example, for tasks of low importance, the adjustment proposal unit can decrease the level of detail in the adjustment proposal and provide simplified information. The adjustment proposal unit can also adjust the frequency of reporting adjustment proposals according to the importance of the task. This allows for the provision of appropriate information by adjusting the level of detail of proposals according to the importance of the task. Some or all of the above processing in the adjustment proposal unit may be performed using AI, for example, or without AI. For example, the adjustment proposal unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0048] The coordination proposal unit can determine the priority of coordination proposals by considering the geographical location information of each member. For example, the coordination proposal unit sets the priority of coordination proposals based on the geographical location information of each member. For example, the coordination proposal unit can prioritize coordination proposals from geographically distant members to prevent delays in communication. For example, the coordination proposal unit can postpone coordination proposals from geographically close members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the coordination proposal unit may be performed using AI, for example, or not using AI. For example, the coordination proposal unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0049] The adjustment proposal unit can analyze each member's social media activity to improve the accuracy of adjustment proposals. For example, the adjustment proposal unit can analyze each member's social media activity and make adjustment proposals that optimize task dependencies. For example, the adjustment proposal unit can also infer members' motivation from their social media activity and optimize the alert settings for adjustment proposals. For example, the adjustment proposal unit can predict the risk of delays in a member's task progress based on their social media activity and make adjustment proposals. In this way, analyzing social media activity improves the accuracy of adjustment proposals. Some or all of the above processes in the adjustment proposal unit may be performed using AI, for example, or not using AI. For example, the adjustment proposal unit can input each member's social media data into a generating AI and have the generating AI perform improvements to the accuracy of the proposals.

[0050] The deliverable generation unit can analyze past project data to improve the accuracy of deliverable generation. For example, the deliverable generation unit can generate the optimal deliverable based on past project data. The deliverable generation unit can also analyze past project data to improve the prediction accuracy of deliverable generation. For example, the deliverable generation unit can optimize the alert settings for deliverable generation based on past project data. This improves the accuracy of deliverable generation by analyzing past data. Some or all of the above processes in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input past project data into a generation AI and have the generation AI perform improvements to the generation accuracy.

[0051] The deliverable generation unit can adjust the level of detail in deliverable generation according to the importance of the project. For example, for important projects, the deliverable generation unit can increase the level of detail in deliverable generation and provide detailed information. For example, for less important projects, the deliverable generation unit can decrease the level of detail in deliverable generation and provide simplified information. The deliverable generation unit can also adjust the reporting frequency of deliverable generation according to the importance of the project. This allows for the provision of appropriate information by adjusting the level of detail in generation according to the importance of the project. Some or all of the above processing in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in generation.

[0052] The deliverable generation unit can determine the priority of deliverable generation by considering the geographical location information of each member. For example, the deliverable generation unit sets the priority of deliverable generation based on the geographical location information of each member. For example, the deliverable generation unit can prioritize the generation of deliverables for geographically distant members to prevent communication delays. For example, the deliverable generation unit can postpone the generation of deliverables for geographically closer members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input the geographical location data of each member into a generation AI and have the generation AI perform the priority determination.

[0053] The deliverable generation unit can analyze each member's social media activity and improve the accuracy of deliverable generation. For example, the deliverable generation unit can analyze each member's social media activity and generate the optimal deliverable. For example, the deliverable generation unit can also infer a member's motivation from their social media activity and optimize the alert settings for deliverable generation. For example, the deliverable generation unit can predict the risk of delays in a member's task progress based on their social media activity and generate deliverables accordingly. This improves the accuracy of deliverable generation by analyzing social media activity. Some or all of the above processes in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input each member's social media data into a generation AI and have the generation AI perform improvements to the generation accuracy.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The agent AI tool not only monitors project progress but also monitors the health status of each member and adjusts task assignments based on their health. For example, if a member reports feeling unwell, the agent AI tool can reassign that member's tasks to other members to reduce their burden. If a member is healthy, the agent AI tool can assign them additional tasks to accelerate project progress. Furthermore, if a member's health is moderate, the agent AI tool can appropriately adjust task assignments to maintain a balanced pace. This allows for task assignment adjustments based on member health status, maximizing project efficiency and results.

[0056] Agent AI tools can not only monitor project progress but also evaluate each member's skill level and adjust task assignments based on that skill level. For example, if a member has a high skill level, the agent AI tool can assign them more challenging tasks to accelerate project progress. Conversely, if a member has a low skill level, the agent AI tool can assign them easier tasks to help them improve their skills. Furthermore, if a member has a moderate skill level, the agent AI tool can appropriately adjust task assignments to maintain a balanced progress. This allows for task assignment adjustments tailored to each member's skill level, maximizing project efficiency and results.

[0057] Agent AI tools can not only monitor project progress but also manage each member's working hours and adjust task assignments based on those hours. For example, if a member's working hours are long, the agent AI tool can reduce their workload to prevent overwork. Conversely, if a member's working hours are short, the agent AI tool can assign additional tasks to that member to accelerate project progress. Furthermore, if a member's working hours are moderate, the agent AI tool can appropriately adjust task assignments to maintain a balanced progress. This allows for task assignment adjustments based on members' working hours, maximizing project efficiency and results.

[0058] The agent AI tool not only monitors project progress but also analyzes each member's learning history and adjusts task assignments based on that history. For example, if a member is learning a specific skill, the agent AI tool will assign tasks related to that skill to enhance learning effectiveness. If a member has just learned a new skill, the agent AI tool can assign tasks that utilize that skill, allowing them to gain practical experience. Furthermore, once a member completes their learning, the agent AI tool can assign more advanced tasks that utilize that skill, accelerating project progress. This allows for task assignment adjustments based on members' learning history, maximizing project efficiency and results.

[0059] The agent AI tool not only monitors project progress but also analyzes each member's communication style and adjusts task assignments based on that style. For example, if a member prefers face-to-face communication, the agent AI tool will assign them tasks that require face-to-face meetings. If a member prefers remote communication, the agent AI tool can assign them tasks that can be completed remotely. Furthermore, if a member prefers a hybrid communication style, the agent AI tool can assign them tasks that require both face-to-face and remote interaction. This allows for task assignment adjustments tailored to each member's communication style, maximizing project efficiency and results.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The progress monitoring unit monitors the project's progress in real time. For example, it can track each member's task progress down to the second or minute, and immediately detect delay risks. It can also monitor progress within a specific delay tolerance range and notify the leader. Step 2: The delay risk notification unit analyzes the progress of the task monitored by the progress monitoring unit and notifies of the delay risk. For example, it can immediately notify of the delay risk if the task is behind schedule, and can notify if the delay is 10% or more. It can also issue a warning if the delay is 20% or more, and an emergency notification if it is 30% or more. Step 3: The Adjustment Proposal Unit analyzes task dependencies based on the delay risk notified by the Delay Risk Notification Unit and proposes changes to assignments and priorities. For example, it analyzes task dependencies and proposes optimal task assignments and efficient resource utilization. It can also propose changes to priorities and methods for reallocating resources based on the importance and urgency of tasks. Step 4: The deliverable generation unit automatically generates draft reports and deliverables based on team data, using the task assignment changes and priority changes proposed by the adjustment proposal unit. For example, it can automatically generate reports summarizing project progress and deliverable content, automatically generate report items, and automatically classify deliverable types. It can also generate reports based on project progress rates and task completion status, and automatically generate draft deliverables based on team data.

[0062] (Example of form 2) The agent AI tool according to an embodiment of the present invention is a system that supports cross-departmental project leaders who bear excessive responsibility but lack sufficient authority. This agent AI tool autonomously monitors and analyzes project progress and identifies delay risks and issues. It reduces the burden on leaders through suggesting the next course of action, adjusting tasks, and automatically generating deliverables. Designed to address the "responsibility beyond coordination" that existing tools cannot handle, it resolves the problems of insufficient authority and concentrated responsibility, maximizing the efficiency and results of the entire team. It is a solution specifically tailored to address the challenges associated with the increase in remote work and cross-departmental tasks. For example, the agent AI tool monitors project progress in real time and tracks the task progress of each member. For example, if task progress is behind schedule, the agent AI tool immediately notifies the leader of the delay risk. This allows the leader to take countermeasures quickly. Next, the agent AI tool analyzes task dependencies and suggests changes in assignment or priority. For example, if one task depends on another, the agent AI tool analyzes the dependencies and suggests the optimal task assignment. This ensures smooth project progress. Furthermore, the agent AI tool automatically generates draft reports and deliverables based on team data. For example, it automatically generates reports summarizing project progress and deliverable content. This reduces the manual workload for leaders and allows projects to proceed more efficiently. This agent AI tool complements leaders' lack of authority and creates an environment where leaders can more easily give instructions to team members. For example, the agent AI tool's objective suggestions encourage team members' acceptance and reduce the leader's burden. It also increases the project's success rate by allowing leaders to focus on strategic tasks. In this way, the agent AI tool aims to support project leaders who are burdened with excessive responsibility but not given sufficient authority, and to maximize the efficiency and results of the entire team. As a result, the agent AI tool can reduce the burden on project leaders and maximize the efficiency and results of the entire team.

[0063] The agent AI tool according to this embodiment comprises a progress monitoring unit, a delay risk notification unit, an adjustment proposal unit, and a deliverable generation unit. The progress monitoring unit monitors the progress of the project in real time. For example, the progress monitoring unit tracks the task progress of each member in real time. For example, the progress monitoring unit can monitor the progress of tasks in seconds and immediately detect delay risks. The progress monitoring unit can also monitor the progress of the project in minutes and detect delays early. Furthermore, the progress monitoring unit can monitor progress within a specific delay tolerance range and notify the leader. The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. For example, the delay risk notification unit immediately notifies of delay risks when the progress of a task is behind schedule. For example, the delay risk notification unit can consider a delay of 10% or more as a delay risk and issue a notification. The delay risk notification unit can also issue a warning when the delay of progress is 20% or more. Furthermore, the delay risk notification unit can issue an emergency notification if the progress is delayed by 30% or more. Based on the delay risk notified by the delay risk notification unit, the adjustment proposal unit analyzes task dependencies and proposes changes in assignment and priority. For example, the adjustment proposal unit can analyze task dependencies and propose the optimal task assignment. For example, the adjustment proposal unit can analyze the dependencies between tasks and propose the efficient use of resources. The adjustment proposal unit can also propose changes in priority based on the importance and urgency of tasks. Furthermore, the adjustment proposal unit can propose methods for reallocating resources to ensure smooth project progress. Based on the task assignment changes and changes in priority proposed by the adjustment proposal unit, the deliverable generation unit automatically generates draft reports and deliverables based on team data. For example, the deliverable generation unit automatically generates reports summarizing the project progress and the contents of deliverables. For example, the deliverable generation unit can automatically generate report items and automatically classify the types of deliverables. The deliverable generation unit can also generate reports based on the project progress rate and task completion status.Furthermore, the deliverable generation unit can automatically generate draft deliverables based on the team's data, reducing the manual workload for the leader. This allows the agent AI tool, according to this embodiment, to reduce the burden on the project leader and maximize the overall efficiency and results of the team.

[0064] The progress monitoring unit monitors the project's progress in real time. For example, it tracks the task progress of each member in real time. Specifically, it integrates with the task management tools and project management software used by each member to automatically collect data such as task start time, end time, and progress rate. This allows the project leader to grasp the progress of each member at a glance. The progress monitoring unit can monitor task progress in seconds, for example, and immediately detect delay risks. This makes it possible to take countermeasures early even if the project is behind schedule. The progress monitoring unit can also monitor project progress in minutes, enabling early detection of delays. Furthermore, the progress monitoring unit can monitor progress within a specific delay tolerance range and notify the leader. For example, it has a function that allows setting a delay tolerance range and automatically issues an alert if that range is exceeded. This allows the project leader to grasp delays early and take appropriate action. The progress monitoring unit can also use AI to analyze progress data and predict task progress. For example, based on past data, it's possible to predict the time it will take to complete a specific task and detect delay risks in advance. This allows project leaders to take measures to prevent delays. Furthermore, the progress monitoring department can analyze the workload of each member and propose the optimal allocation of resources. This maximizes the efficiency of the entire team and contributes to the success of the project.

[0065] The delay risk notification unit analyzes the progress of tasks monitored by the progress monitoring unit and notifies of delay risks. For example, the delay risk notification unit immediately notifies if a task is behind schedule. Specifically, it analyzes the progress of each task based on data acquired from the progress monitoring unit and detects signs of delay. For example, the delay risk notification unit can consider a delay risk as occurring when the progress is 10% or more and issue a notification. This allows project leaders to grasp delays early and take appropriate action. The delay risk notification unit can also issue a warning when the progress is 20% or more. This allows project leaders to take measures before the delay becomes serious. Furthermore, the delay risk notification unit can issue an emergency notification when the progress is 30% or more. This allows project leaders to respond quickly before the delay has a significant impact. The delay risk notification unit can also use AI to analyze progress data and predict delay risks. For example, it can predict the possibility of a particular task being delayed based on past data and notify in advance. This allows project leaders to take measures to proactively prevent delay risks. Furthermore, the delay risk notification system can analyze each member's workload and propose the optimal allocation of resources. This maximizes the overall efficiency of the team and contributes to the success of the project.

[0066] The Adjustment Proposal Department analyzes task dependencies based on delay risks notified by the Delay Risk Notification Department and proposes changes to assignments and priorities. For example, the Adjustment Proposal Department can analyze task dependencies and propose optimal task assignments. Specifically, it can analyze the dependencies and relationships of each task and propose efficient resource utilization. For example, if a particular task is delayed, it can change the assignment of other tasks that depend on it, thereby smoothing the progress of the entire project. The Adjustment Proposal Department can also propose changes to priorities based on the importance and urgency of tasks. For example, prioritizing high-importance tasks can increase the probability of project success. Furthermore, the Adjustment Proposal Department can propose methods for reallocating resources to smooth project progress. For example, if a particular member is overloaded, distributing that member's tasks to other members can improve overall efficiency. The Adjustment Proposal Department can utilize AI to analyze task dependencies and resource utilization and propose optimal adjustment plans. This allows project leaders to minimize delay risks and develop optimal strategies for project success. Furthermore, the coordination and proposal department can also make proposals based on past data and referencing successful case studies from similar projects. This allows project leaders to receive reliable proposals based on proven results.

[0067] The Deliverable Generation Unit automatically generates draft reports and deliverables based on team data, following changes in task assignments and priorities proposed by the Coordination and Proposal Unit. For example, the Deliverable Generation Unit automatically generates reports summarizing project progress and deliverable content. Specifically, it can automatically generate detailed reports based on the progress and completion status of each task, as well as analysis results of delay risks. This eliminates the need for project leaders to manually create reports. The Deliverable Generation Unit can also automatically generate report items and categorize deliverable types, ensuring consistency and readability in reports. Furthermore, the Deliverable Generation Unit can generate reports based on project progress rates and task completion status, allowing for an accurate understanding of the project's current status. Additionally, the Deliverable Generation Unit can automatically generate draft deliverables based on team data, reducing the leader's manual workload. For example, it can automatically generate draft reports based on project progress and task completion status, requiring only final review and revisions from the leader. This significantly reduces the time spent on report creation, allowing project leaders to focus on other important tasks. The deliverable generation unit can use AI to analyze data and optimize the content of reports and deliverables. This improves the accuracy and quality of reports, contributing to project success. Furthermore, the deliverable generation unit can generate reports based on past data, referencing successful case studies from similar projects. This allows project leaders to receive reliable reports based on proven results.

[0068] The progress monitoring unit can track each member's task progress in real time. For example, the progress monitoring unit can track each member's task progress in seconds and grasp the progress status in real time. For example, the progress monitoring unit can also track each member's task progress in minutes and detect delays early. For example, the progress monitoring unit can track each member's task progress within a specific delay tolerance range and notify the leader. This allows for real-time monitoring of each member's task progress. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input each member's task progress data into a generating AI and have the generating AI perform an analysis of the progress status.

[0069] The delay risk notification unit can immediately notify of delay risks when task progress is behind schedule. For example, the delay risk notification unit immediately notifies of delay risks when task progress is 10% or more behind schedule. For example, the delay risk notification unit can also issue a warning when task progress is 20% or more behind schedule. For example, the delay risk notification unit can issue an emergency notification when task progress is 30% or more behind schedule. This allows for immediate identification of delay risks and prompt response. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input task progress data into a generating AI and have the generating AI perform delay risk analysis.

[0070] The task assignment and proposal unit can analyze task dependencies and propose the optimal task assignment. For example, the task assignment and proposal can analyze the dependencies between tasks and propose the optimal task assignment. The task assignment and proposal unit can also analyze the dependencies between tasks and propose the efficient use of resources. For example, the task assignment and proposal unit can propose changes in priority based on the importance and urgency of tasks. For example, the task assignment and proposal unit can propose methods for reallocating resources to ensure smooth project progress. By analyzing task dependencies and proposing the optimal assignment, project progress is ensured. Some or all of the above processes in the task assignment and proposal unit may be performed using AI, or not. For example, the task assignment and proposal unit can input task dependency data into a generating AI and have the generating AI perform the optimal task assignment.

[0071] The deliverable generation unit can automatically generate reports summarizing the project's progress and the contents of deliverables. For example, the deliverable generation unit can automatically generate report items and automatically classify the types of deliverables. The deliverable generation unit can also generate reports based on project progress rates and task completion status. For example, the deliverable generation unit can automatically generate draft deliverables based on team data, reducing the manual workload for leaders. This reduces the manual workload for leaders through the automatic generation of reports. Some or all of the above-described processes in the deliverable generation unit may be performed using AI, or not. For example, the deliverable generation unit can input project progress data into a generation AI and have the generation AI generate the report.

[0072] The progress monitoring unit can estimate the user's emotions and adjust the frequency of progress monitoring based on the estimated emotions. For example, if the user is stressed, the progress monitoring unit can reduce the frequency of progress monitoring to alleviate the burden. For example, if the user is relaxed, the progress monitoring unit can increase the frequency of progress monitoring to provide more detailed information. For example, if the user is in a hurry, the progress monitoring unit can increase the frequency of progress monitoring to encourage a quick response. In this way, the burden can be reduced by adjusting the frequency of progress monitoring according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the progress monitoring frequency.

[0073] The progress monitoring unit can analyze each member's past task progress data to improve the accuracy of progress monitoring. The progress monitoring unit can, for example, analyze each member's past task completion time to improve the prediction accuracy of progress monitoring. The progress monitoring unit can also, for example, analyze each member's past task delay patterns to improve the warning accuracy of progress monitoring. The progress monitoring unit can also, for example, optimize the alert settings for progress monitoring based on each member's past task progress data. This improves the accuracy of progress monitoring by analyzing past data. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input past task progress data into a generating AI and have the generating AI perform the improvement of progress monitoring accuracy.

[0074] The progress monitoring unit can adjust the level of detail in progress monitoring according to the importance of the project. For example, for important projects, the progress monitoring unit can increase the level of detail in progress monitoring and provide detailed progress information. For example, for less important projects, the progress monitoring unit can decrease the level of detail in progress monitoring and provide simplified information. The progress monitoring unit can also adjust the frequency of progress monitoring reports according to the importance of the project. This allows for the provision of appropriate information by adjusting the level of detail in progress monitoring according to the importance of the project. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input project importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in progress monitoring.

[0075] The progress monitoring unit can estimate the user's emotions and adjust the display method of the progress monitoring based on the estimated user emotions. For example, if the user is tense, the progress monitoring unit can provide a simple and highly visible display method. For example, if the user is relaxed, the progress monitoring unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the progress monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0076] The progress monitoring unit can determine the priority of progress monitoring by considering the geographical location information of each member. For example, the progress monitoring unit sets the priority of progress monitoring based on the geographical location information of each member. For example, the progress monitoring unit can prioritize monitoring the progress of geographically distant members to prevent communication delays. For example, the progress monitoring unit can postpone monitoring the progress of geographically closer members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the progress monitoring unit may be performed using AI, for example, or without using AI. For example, the progress monitoring unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0077] The progress monitoring unit can analyze each member's social media activity to improve the accuracy of progress monitoring. For example, the progress monitoring unit can analyze each member's social media activity to improve the accuracy of task progress predictions. For example, the progress monitoring unit can also infer members' motivation from their social media activity and optimize progress monitoring alert settings. For example, the progress monitoring unit can predict the risk of delays in a member's task progress based on their social media activity. As a result, analyzing social media activity improves the accuracy of progress monitoring. Some or all of the above processes in the progress monitoring unit may be performed using AI, for example, or without AI. For example, the progress monitoring unit can input each member's social media data into a generating AI and have the generating AI perform the task of improving the accuracy of progress monitoring.

[0078] The delay risk notification unit can estimate the user's emotions and adjust the timing of delay risk notifications based on the estimated emotions. For example, if the user is stressed, the delay risk notification unit can delay the timing of the delay risk notification to reduce the burden. For example, if the user is relaxed, the delay risk notification unit can advance the timing of the delay risk notification to encourage a quick response. For example, if the user is in a hurry, the delay risk notification unit can make the delay risk notification immediately to encourage a quick response. In this way, the burden can be reduced by adjusting the timing of notifications according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the notification timing.

[0079] The delay risk notification unit can analyze past delay data to improve the accuracy of delay risk notifications. For example, the delay risk notification unit can improve the prediction accuracy of delay risk notifications based on past delay data. For example, the delay risk notification unit can also analyze past delay patterns to improve the warning accuracy of delay risk notifications. For example, the delay risk notification unit can optimize the alert settings for delay risk notifications based on past delay data. This improves the accuracy of delay risk notifications by analyzing past data. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input past delay data into a generating AI and have the generating AI perform the task of improving notification accuracy.

[0080] The delay risk notification unit can adjust the level of detail in delay risk notifications according to the importance of the task. For example, for important tasks, the delay risk notification unit can increase the level of detail in delay risk notifications and provide more detailed information. For example, for tasks of low importance, the delay risk notification unit can decrease the level of detail in delay risk notifications and provide simplified information. The delay risk notification unit can also adjust the reporting frequency of delay risk notifications according to the importance of the task. This allows for the provision of appropriate information by adjusting the level of detail in notifications according to the importance of the task. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input task importance data into a generating AI and have the generating AI perform the adjustment of notification detail.

[0081] The delay risk notification unit can estimate the user's emotions and adjust the display method of the delay risk notification based on the estimated user emotions. For example, if the user is nervous, the delay risk notification unit provides a simple and highly visible display method. For example, if the user is relaxed, the delay risk notification unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the delay risk notification unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0082] The delay risk notification unit can determine the priority of delay risk notifications by considering the geographical location information of each member. For example, the delay risk notification unit sets the priority of delay risk notifications based on the geographical location information of each member. For example, the delay risk notification unit can prevent communication delays by prioritizing the notification of delay risks of geographically distant members. For example, the delay risk notification unit can also efficiently allocate resources by postponing the notification of delay risks of geographically close members. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0083] The delay risk notification unit can analyze each member's social media activity to improve the accuracy of delay risk notifications. For example, the delay risk notification unit can analyze each member's social media activity to improve the accuracy of delay risk predictions. The delay risk notification unit can also, for example, infer members' motivation from their social media activity and optimize the alert settings for delay risk notifications. The delay risk notification unit can also, for example, predict members' task delay risk based on their social media activity. As a result, analyzing social media activity improves the accuracy of delay risk notifications. Some or all of the above processing in the delay risk notification unit may be performed using AI, for example, or without AI. For example, the delay risk notification unit can input each member's social media data into a generating AI and have the generating AI perform improvements to the notification accuracy.

[0084] The adjustment suggestion unit can estimate the user's emotions and adjust the content of the adjustment suggestions based on the estimated emotions. For example, if the user is stressed, the adjustment suggestion unit can provide simple and easy-to-implement adjustment suggestions. For example, if the user is relaxed, the adjustment suggestion unit can provide detailed adjustment suggestions and increase the options. For example, if the user is in a hurry, the adjustment suggestion unit can provide adjustment suggestions that can be implemented quickly. In this way, by adjusting the content of the adjustment suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the adjustment suggestion unit may be performed using AI, for example, or not using AI. For example, the adjustment suggestion unit can input the user's emotion data into the generative AI and have the generative AI perform the adjustment of the suggestion content.

[0085] The adjustment proposal unit can analyze past task dependency data to improve the accuracy of adjustment proposals. For example, the adjustment proposal unit can make optimal adjustment proposals based on past task dependency data. The adjustment proposal unit can also analyze past task dependency data to improve the prediction accuracy of adjustment proposals. For example, the adjustment proposal unit can optimize the alert settings for adjustment proposals based on past task dependency data. This improves the accuracy of adjustment proposals by analyzing past data. Some or all of the above processes in the adjustment proposal unit may be performed using AI, for example, or without AI. For example, the adjustment proposal unit can input past task dependency data into a generating AI and have the generating AI perform improvements to the proposal accuracy.

[0086] The adjustment proposal unit can adjust the level of detail in adjustment proposals according to the importance of the task. For example, for important tasks, the adjustment proposal unit can increase the level of detail in the adjustment proposal and provide more detailed information. For example, for tasks of low importance, the adjustment proposal unit can decrease the level of detail in the adjustment proposal and provide simplified information. The adjustment proposal unit can also adjust the frequency of reporting adjustment proposals according to the importance of the task. This allows for the provision of appropriate information by adjusting the level of detail of proposals according to the importance of the task. Some or all of the above processing in the adjustment proposal unit may be performed using AI, for example, or without AI. For example, the adjustment proposal unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the proposals.

[0087] The adjustment suggestion unit can estimate the user's emotions and adjust the display method of the adjustment suggestion based on the estimated user emotions. For example, if the user is nervous, the adjustment suggestion unit can provide a simple and highly visible display method. For example, if the user is relaxed, the adjustment suggestion unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the adjustment suggestion unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment suggestion unit may be performed using AI, for example, or without AI. For example, the adjustment suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0088] The coordination proposal unit can determine the priority of coordination proposals by considering the geographical location information of each member. For example, the coordination proposal unit sets the priority of coordination proposals based on the geographical location information of each member. For example, the coordination proposal unit can prioritize coordination proposals from geographically distant members to prevent delays in communication. For example, the coordination proposal unit can postpone coordination proposals from geographically close members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the coordination proposal unit may be performed using AI, for example, or not using AI. For example, the coordination proposal unit can input the geographical location data of each member into a generating AI and have the generating AI perform the priority determination.

[0089] The adjustment proposal unit can analyze each member's social media activity to improve the accuracy of adjustment proposals. For example, the adjustment proposal unit can analyze each member's social media activity and make adjustment proposals that optimize task dependencies. For example, the adjustment proposal unit can also infer members' motivation from their social media activity and optimize the alert settings for adjustment proposals. For example, the adjustment proposal unit can predict the risk of delays in a member's task progress based on their social media activity and make adjustment proposals. In this way, analyzing social media activity improves the accuracy of adjustment proposals. Some or all of the above processes in the adjustment proposal unit may be performed using AI, for example, or not using AI. For example, the adjustment proposal unit can input each member's social media data into a generating AI and have the generating AI perform improvements to the accuracy of the proposals.

[0090] The deliverable generation unit can estimate the user's emotions and adjust the content of the deliverables based on the estimated emotions. For example, if the user is stressed, the deliverable generation unit can generate a simple and concise deliverable. For example, if the user is relaxed, the deliverable generation unit can also generate a deliverable that includes detailed information. For example, if the user is in a hurry, the deliverable generation unit can provide a deliverable that can be generated quickly. By adjusting the content of the deliverables according to the user's emotions, more appropriate deliverables are generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the deliverable generation unit may be performed using AI, or not using AI. For example, the deliverable generation unit can input user emotion data into the generation AI and have the generation AI adjust the content of the generated deliverables.

[0091] The deliverable generation unit can analyze past project data to improve the accuracy of deliverable generation. For example, the deliverable generation unit can generate the optimal deliverable based on past project data. The deliverable generation unit can also analyze past project data to improve the prediction accuracy of deliverable generation. For example, the deliverable generation unit can optimize the alert settings for deliverable generation based on past project data. This improves the accuracy of deliverable generation by analyzing past data. Some or all of the above processes in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input past project data into a generation AI and have the generation AI perform improvements to the generation accuracy.

[0092] The deliverable generation unit can adjust the level of detail in deliverable generation according to the importance of the project. For example, for important projects, the deliverable generation unit can increase the level of detail in deliverable generation and provide detailed information. For example, for less important projects, the deliverable generation unit can decrease the level of detail in deliverable generation and provide simplified information. The deliverable generation unit can also adjust the reporting frequency of deliverable generation according to the importance of the project. This allows for the provision of appropriate information by adjusting the level of detail in generation according to the importance of the project. Some or all of the above processing in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input project importance data into the generation AI and have the generation AI perform the adjustment of the level of detail in generation.

[0093] The deliverable generation unit can estimate the user's emotions and adjust the display method of the deliverable generation based on the estimated user emotions. For example, if the user is tense, the deliverable generation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the deliverable generation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the deliverable generation unit can also provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, visibility is improved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the deliverable generation unit may be performed using AI, for example, or not using AI. For example, the deliverable generation unit can input user emotion data into the generation AI and have the generation AI perform the adjustment of the display method.

[0094] The deliverable generation unit can determine the priority of deliverable generation by considering the geographical location information of each member. For example, the deliverable generation unit sets the priority of deliverable generation based on the geographical location information of each member. For example, the deliverable generation unit can prioritize the generation of deliverables for geographically distant members to prevent communication delays. For example, the deliverable generation unit can postpone the generation of deliverables for geographically closer members to efficiently allocate resources. This makes it possible to efficiently allocate resources by considering geographical location information. Some or all of the above processing in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input the geographical location data of each member into a generation AI and have the generation AI perform the priority determination.

[0095] The deliverable generation unit can analyze each member's social media activity and improve the accuracy of deliverable generation. For example, the deliverable generation unit can analyze each member's social media activity and generate the optimal deliverable. For example, the deliverable generation unit can also infer a member's motivation from their social media activity and optimize the alert settings for deliverable generation. For example, the deliverable generation unit can predict the risk of delays in a member's task progress based on their social media activity and generate deliverables accordingly. This improves the accuracy of deliverable generation by analyzing social media activity. Some or all of the above processes in the deliverable generation unit may be performed using AI, for example, or without AI. For example, the deliverable generation unit can input each member's social media data into a generation AI and have the generation AI perform improvements to the generation accuracy.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The agent AI tool can estimate the stress level of a project leader and adjust task assignments based on that estimate. For example, if the leader is experiencing high stress, the agent AI tool can reduce task assignments to alleviate the leader's burden. Conversely, if the leader is experiencing low stress, the agent AI tool can increase task assignments to accelerate project progress. Furthermore, if the leader is experiencing moderate stress, the agent AI tool can appropriately adjust task assignments to maintain a balanced pace. This allows for task assignment adjustments tailored to the leader's stress level, maximizing project efficiency and results.

[0098] The agent AI tool not only monitors project progress but also monitors the health status of each member and adjusts task assignments based on their health. For example, if a member reports feeling unwell, the agent AI tool can reassign that member's tasks to other members to reduce their burden. If a member is healthy, the agent AI tool can assign them additional tasks to accelerate project progress. Furthermore, if a member's health is moderate, the agent AI tool can appropriately adjust task assignments to maintain a balanced pace. This allows for task assignment adjustments based on member health status, maximizing project efficiency and results.

[0099] The agent AI tool can estimate the project leader's emotions and adjust its communication style based on those emotions. For example, if the leader is stressed, the agent AI tool will communicate simply and clearly to reduce the leader's burden. If the leader is relaxed, the agent AI tool can provide detailed information to help the leader understand more deeply. Furthermore, if the leader is in a hurry, the agent AI tool can communicate quickly and to the point, encouraging a swift response. By adjusting communication styles according to the leader's emotions, the efficiency and results of the project can be maximized.

[0100] Agent AI tools can not only monitor project progress but also evaluate each member's skill level and adjust task assignments based on that skill level. For example, if a member has a high skill level, the agent AI tool can assign them more challenging tasks to accelerate project progress. Conversely, if a member has a low skill level, the agent AI tool can assign them easier tasks to help them improve their skills. Furthermore, if a member has a moderate skill level, the agent AI tool can appropriately adjust task assignments to maintain a balanced progress. This allows for task assignment adjustments tailored to each member's skill level, maximizing project efficiency and results.

[0101] The agent AI tool can estimate the project leader's emotions and tailor the feedback based on those emotions. For example, if the leader is stressed, the agent AI tool can provide positive feedback to boost their motivation. If the leader is relaxed, the agent AI tool can provide detailed feedback to help them understand the situation better. Furthermore, if the leader is in a hurry, the agent AI tool can provide concise and rapid feedback to encourage quick action. By tailoring feedback to the leader's emotions, the efficiency and results of the project can be maximized.

[0102] Agent AI tools can not only monitor project progress but also manage each member's working hours and adjust task assignments based on those hours. For example, if a member's working hours are long, the agent AI tool can reduce their workload to prevent overwork. Conversely, if a member's working hours are short, the agent AI tool can assign additional tasks to that member to accelerate project progress. Furthermore, if a member's working hours are moderate, the agent AI tool can appropriately adjust task assignments to maintain a balanced progress. This allows for task assignment adjustments based on members' working hours, maximizing project efficiency and results.

[0103] The agent AI tool can estimate the project leader's emotions and adjust meeting schedules based on those estimates. For example, if the leader is stressed, the agent AI tool can reduce the frequency of meetings to alleviate their burden. Conversely, if the leader is relaxed, the agent AI tool can increase the frequency of meetings to facilitate detailed information sharing. Furthermore, if the leader is in a hurry, the agent AI tool can schedule short, focused meetings to encourage quick responses. This allows for meeting scheduling tailored to the leader's emotions, maximizing project efficiency and results.

[0104] The agent AI tool not only monitors project progress but also analyzes each member's learning history and adjusts task assignments based on that history. For example, if a member is learning a specific skill, the agent AI tool will assign tasks related to that skill to enhance learning effectiveness. If a member has just learned a new skill, the agent AI tool can assign tasks that utilize that skill, allowing them to gain practical experience. Furthermore, once a member completes their learning, the agent AI tool can assign more advanced tasks that utilize that skill, accelerating project progress. This allows for task assignment adjustments based on members' learning history, maximizing project efficiency and results.

[0105] The agent AI tool can estimate the project leader's emotions and adjust the support provided based on those emotions. For example, if the leader is stressed, the agent AI tool can suggest relaxing activities to reduce stress. If the leader is relaxed, the agent AI tool can also suggest new challenges to boost motivation. Furthermore, if the leader is in a hurry, the agent AI tool can provide quick support to ensure smooth project progress. In this way, by adjusting support based on the leader's emotions, project efficiency and results can be maximized.

[0106] The agent AI tool not only monitors project progress but also analyzes each member's communication style and adjusts task assignments based on that style. For example, if a member prefers face-to-face communication, the agent AI tool will assign them tasks that require face-to-face meetings. If a member prefers remote communication, the agent AI tool can assign them tasks that can be completed remotely. Furthermore, if a member prefers a hybrid communication style, the agent AI tool can assign them tasks that require both face-to-face and remote interaction. This allows for task assignment adjustments tailored to each member's communication style, maximizing project efficiency and results.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The progress monitoring unit monitors the project's progress in real time. For example, it can track each member's task progress down to the second or minute, and immediately detect delay risks. It can also monitor progress within a specific delay tolerance range and notify the leader. Step 2: The delay risk notification unit analyzes the progress of the task monitored by the progress monitoring unit and notifies of the delay risk. For example, it can immediately notify of the delay risk if the task is behind schedule, and can notify if the delay is 10% or more. It can also issue a warning if the delay is 20% or more, and an emergency notification if it is 30% or more. Step 3: The Adjustment Proposal Unit analyzes task dependencies based on the delay risk notified by the Delay Risk Notification Unit and proposes changes to assignments and priorities. For example, it analyzes task dependencies and proposes optimal task assignments and efficient resource utilization. It can also propose changes to priorities and methods for reallocating resources based on the importance and urgency of tasks. Step 4: The deliverable generation unit automatically generates draft reports and deliverables based on team data, using the task assignment changes and priority changes proposed by the adjustment proposal unit. For example, it can automatically generate reports summarizing project progress and deliverable content, automatically generate report items, and automatically classify deliverable types. It can also generate reports based on project progress rates and task completion status, and automatically generate draft deliverables based on team data.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the progress monitoring unit, delay risk notification unit, adjustment proposal unit, and deliverable generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the progress monitoring unit is implemented by the control unit 46A of the smart device 14 and tracks the task progress of each member in real time. The delay risk notification unit is implemented by the specific processing unit 290 of the data processing device 12 and immediately notifies of delay risks. The adjustment proposal unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes task dependencies and proposes the optimal task assignment. The deliverable generation unit is implemented by the control unit 46A of the smart device 14 and automatically generates drafts of reports and deliverables based on the team's data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the progress monitoring unit, delay risk notification unit, adjustment proposal unit, and deliverable generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the progress monitoring unit is implemented by the control unit 46A of the smart glasses 214 and tracks the task progress of each member in real time. The delay risk notification unit is implemented by the identification processing unit 290 of the data processing device 12 and immediately notifies of delay risks. The adjustment proposal unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes task dependencies and proposes the optimal task assignment. The deliverable generation unit is implemented by the control unit 46A of the smart glasses 214 and automatically generates draft reports and deliverables based on the team's data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the progress monitoring unit, delay risk notification unit, adjustment proposal unit, and deliverable generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the progress monitoring unit is implemented by the control unit 46A of the headset terminal 314 and tracks the task progress of each member in real time. The delay risk notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately notifies of delay risks. The adjustment proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes task dependencies and proposes the optimal task assignment. The deliverable generation unit is implemented by the control unit 46A of the headset terminal 314 and automatically generates draft reports and deliverables based on the team's data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the progress monitoring unit, delay risk notification unit, adjustment proposal unit, and deliverable generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the progress monitoring unit is implemented by the control unit 46A of the robot 414 and tracks the task progress of each member in real time. The delay risk notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately notifies of delay risks. The adjustment proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes task dependencies and proposes the optimal task assignment. The deliverable generation unit is implemented by the control unit 46A of the robot 414 and automatically generates draft reports and deliverables based on the team's data. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The progress monitoring department monitors the project's progress in real time, A delay risk notification unit analyzes the progress of tasks monitored by the aforementioned progress monitoring unit and notifies of delay risks, Based on the delay risk notified by the aforementioned delay risk notification unit, the adjustment proposal unit analyzes the task dependencies and proposes changes to assignments and priorities. The system includes a deliverable generation unit that automatically generates draft reports and deliverables based on team data, based on the changes in task assignments and priorities proposed by the aforementioned adjustment proposal unit. A system characterized by the following features. (Note 2) The aforementioned progress monitoring unit, Track each member's task progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned delay risk notification unit, Immediately notify of delay risks if task progress is behind schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned adjustment proposal unit, It analyzes task dependencies and proposes the optimal task assignment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned output generation unit, Generates a report that automatically summarizes the project's progress and deliverables. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned progress monitoring unit, It estimates the user's emotions and adjusts the frequency of progress monitoring based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress monitoring unit, Analyze each member's past task progress data to improve the accuracy of progress monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned progress monitoring unit, Adjust the level of detail in progress monitoring according to the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned progress monitoring unit, It estimates the user's emotions and adjusts how progress monitoring is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned progress monitoring unit, Prioritize progress monitoring by considering each member's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned progress monitoring unit, Analyze each member's social media activity to improve the accuracy of progress monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned delay risk notification unit, The system estimates the user's emotions and adjusts the timing of delay risk notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned delay risk notification unit, By analyzing past delay data, we can improve the accuracy of delay risk notifications. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned delay risk notification unit, Adjust the level of detail in delay risk notifications based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned delay risk notification unit, The system estimates the user's emotions and adjusts how delayed risk notifications are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned delay risk notification unit, Prioritize delay risk notifications by considering each member's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned delay risk notification unit, Analyze each member's social media activity to improve the accuracy of delayed risk notifications. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned adjustment proposal unit, The system estimates the user's emotions and adjusts the content of the adjustment suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned adjustment proposal unit, Analyze past task dependency data to improve the accuracy of adjustment suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned adjustment proposal unit, Adjust the level of detail in the adjustment proposals according to the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned adjustment proposal unit, It estimates the user's emotions and adjusts how adjustment suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned adjustment proposal unit, Prioritize coordination proposals by considering each member's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned adjustment proposal unit, Analyze each member's social media activity to improve the accuracy of coordination proposals. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned output generation unit, It estimates the user's emotions and adjusts the content of the deliverables based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned output generation unit, Analyze past project data to improve the accuracy of deliverable generation. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned output generation unit, Adjust the level of detail in deliverables based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned output generation unit, It estimates the user's emotions and adjusts how deliverables are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned output generation unit, Prioritize the creation of deliverables by considering the geographical location of each member. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned output generation unit, Analyze each member's social media activity to improve the accuracy of deliverable generation. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The progress monitoring department monitors the project's progress in real time, A delay risk notification unit analyzes the progress of tasks monitored by the aforementioned progress monitoring unit and notifies of delay risks, Based on the delay risk notified by the aforementioned delay risk notification unit, the adjustment proposal unit analyzes the task dependencies and proposes changes to assignments and priorities. The system includes a deliverable generation unit that automatically generates draft reports and deliverables based on team data, based on the changes in task assignments and priorities proposed by the aforementioned adjustment proposal unit. A system characterized by the following features.

2. The aforementioned progress monitoring unit, Track each member's task progress in real time. The system according to feature 1.

3. The aforementioned delay risk notification unit, Immediately notify of delay risks if task progress is behind schedule. The system according to feature 1.

4. The aforementioned adjustment proposal unit, It analyzes task dependencies and proposes the optimal task assignment. The system according to feature 1.

5. The aforementioned output generation unit, Generates a report that automatically summarizes the project's progress and deliverables. The system according to feature 1.

6. The aforementioned progress monitoring unit, It estimates the user's emotions and adjusts the frequency of progress monitoring based on the estimated user emotions. The system according to feature 1.

7. The aforementioned progress monitoring unit, Analyze each member's past task progress data to improve the accuracy of progress monitoring. The system according to feature 1.

8. The aforementioned progress monitoring unit, Adjust the level of detail in progress monitoring according to the importance of the project. The system according to feature 1.

9. The aforementioned progress monitoring unit, It estimates the user's emotions and adjusts how progress monitoring is displayed based on those estimated emotions. The system according to feature 1.

10. The aforementioned progress monitoring unit, Prioritize progress monitoring by considering each member's geographical location. The system according to feature 1.

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