system

Abstract

The system according to this embodiment aims to automate project progress and task progress management, new idea proposal, and risk and problem identification. [Solution] The system according to the embodiment comprises a monitoring unit, an update unit, a proposal unit, and a specification unit. The monitoring unit monitors the progress of the project. The update unit updates the progress of tasks based on the progress monitored by the monitoring unit. The proposal unit proposes new ideas based on the progress updated by the update unit. The specification unit identifies risks and problems based on the ideas proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, since the progress of a project, the progress management of tasks, the proposal of new ideas, and the identification of risks and problems are carried out manually, there is a problem that the efficiency is low and it is difficult to respond quickly.

[0005] The system according to the embodiment aims to automate the progress of a project, the progress management of tasks, the proposal of new ideas, and the identification of risks and problems.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an update unit, a proposal unit, and a specification unit. The monitoring unit monitors the progress of the project. The update unit updates the progress of tasks based on the progress monitored by the monitoring unit. The proposal unit proposes new ideas based on the progress updated by the update unit. The specification unit identifies risks and problems based on the ideas proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate project progress and task progress management, new idea proposal, and risk and problem identification. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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 project management system according to an embodiment of the present invention provides multiple systems for streamlining project management using AI. The project management system provides a chatbot that automates the overall progress of a project, task assignment, and progress management. This chatbot has the function of suggesting new ideas and quickly identifying risks and problems using AI. For example, it monitors the project progress in real time and automatically updates the progress of tasks. It also provides appropriate answers to questions from project members and suggests new ideas as needed. Next, it provides a system that manages schedules and makes plans using AI. This system considers each member's tasks and schedules and suggests efficient dates and meeting times. For example, it analyzes each member's calendar and automatically suggests the optimal meeting time. It also creates an efficient schedule considering task priorities. Furthermore, it provides a system that uses AI to predict project risks and suggest appropriate countermeasures. This system analyzes the project progress and past data to identify potential risks. For example, based on past project data, it predicts that a particular task is likely to be delayed and suggests countermeasures. Finally, it provides an AI system that fairly evaluates the performance of project members. This system analyzes the amount of work, quality, and level of cooperation of each member and evaluates their performance. For example, it analyzes each member's task completion rate and level of cooperation to provide fair evaluations. Furthermore, it offers a fully AI-powered document management system. This system automatically classifies and organizes documents, allowing users to instantly find the documents they need. For example, it automatically classifies project-related documents and provides a search function. It also provides a system where AI checks whether projects comply with all laws, regulations, and company policies, and reports any violations. For example, it monitors project progress and issues alerts if there are legal or regulatory violations. Finally, it provides a system that uses AI to analyze data in real time and automatically generates reports based on that analysis. This enables rapid, data-driven decision-making.For example, the system analyzes project progress in real time and generates reports periodically. These systems are expected to streamline project management, reduce risks, and improve performance. This allows the project management system to efficiently monitor, update, propose, and identify project progress.

[0029] The project management system according to this embodiment comprises a monitoring unit, an update unit, a proposal unit, and a specification unit. The monitoring unit monitors the progress of the project. For example, the monitoring unit monitors the progress of the project in real time and automatically updates the progress of tasks. The monitoring unit can also provide appropriate answers to questions from project members and propose new ideas as needed. The update unit updates the progress of tasks based on the progress monitored by the monitoring unit. For example, the update unit automatically updates the progress of tasks and grasps the progress of the project in real time. The update unit can also create an efficient schedule considering the priority of tasks. The proposal unit proposes new ideas based on the progress updated by the update unit. For example, the proposal unit analyzes the progress of the project and proposes new ideas. The proposal unit can also propose new ideas based on feedback from project members. The specification unit identifies risks and problems based on the ideas proposed by the proposal unit. For example, the specification unit analyzes the progress of the project and identifies potential risks. Based on past project data, the specification unit can also predict that a particular task is likely to be delayed and propose countermeasures. As a result, the project management system according to this embodiment can efficiently monitor, update, propose, and identify the progress of the project.

[0030] The monitoring department monitors the progress of the project. For example, the monitoring department monitors the project's progress in real time and automatically updates task progress. Specifically, the monitoring department uses project management tools and software to regularly check the progress of each task and collect progress data. This includes the task's start date, end date, progress rate, and comments from the person in charge. Based on this data, the monitoring department visualizes the overall project progress and provides it to project members and managers. The monitoring department can also provide appropriate answers to questions from project members and propose new ideas as needed. For example, if a project member has a question about the progress of a task, the monitoring department can refer to past data and best practices to provide a quick and accurate answer. Furthermore, the monitoring department can analyze the project's progress and propose new ideas and improvements to promote efficient progress. In this way, the monitoring department can grasp the project's progress in real time and provide support for the project's success.

[0031] The update unit updates task progress based on the progress monitored by the monitoring unit. For example, the update unit automatically updates task progress, allowing for real-time monitoring of project progress. Specifically, the update unit reflects the progress of each task in the project management tool based on data provided by the monitoring unit. This allows project members and managers to check the latest progress in real time. The update unit can also create efficient schedules by considering task priorities. For example, it supports efficient project progress by re-evaluating task priorities according to progress and optimizing resource allocation. Furthermore, the update unit has the function to automatically adjust the project schedule and resource plan in conjunction with progress updates. This allows for quick and accurate changes and adjustments as the project progresses. In addition, based on progress updates, the update unit can identify project risks and problems early and take appropriate measures. This enables the update unit to monitor project progress in real time and achieve efficient and effective project management.

[0032] The proposal department proposes new ideas based on the progress status updated by the update department. For example, the proposal department analyzes the project's progress and proposes new ideas. Specifically, based on the progress data provided by the update department, the proposal department conducts a detailed analysis of the project's current status and identifies areas for improvement and new approaches. The proposal department can also propose new ideas based on feedback from project members. For example, if a project member suggests improvements to the current process, the proposal department considers that feedback and materializes it into a feasible idea. Furthermore, the proposal department can utilize AI to analyze past project data, learn from successes and failures, and propose new strategies to optimize project progress. This allows the proposal department to constantly optimize project progress and provide new ideas and improvements for project success. In addition, the proposal department evaluates the feasibility and effectiveness of proposed ideas and supports project progress by making revisions and improvements as needed. In this way, the proposal department can contribute to project success by analyzing project progress and proposing new ideas.

[0033] The Specialist Department identifies risks and problems based on ideas proposed by the Proposal Department. For example, the Specialist Department analyzes the project's progress and identifies potential risks. Specifically, the Specialist Department conducts a detailed analysis of risks and problems associated with the project's progress, based on ideas and improvement measures provided by the Proposal Department. The Specialist Department can also predict that certain tasks are likely to be delayed based on past project data and propose countermeasures. For example, by analyzing past data, they can identify patterns and causes that make certain tasks prone to delays and take preventative measures to ensure smooth project progress. Furthermore, the Specialist Department can utilize AI to analyze data in real time and perform anomaly detection and risk prediction. This allows the Specialist Department to constantly monitor the project's progress, detect potential risks and problems early, and take appropriate measures. In addition, based on the identification of risks and problems, the Specialist Department can propose concrete action plans to optimize project progress. This allows the Specialist Department to provide support for project success by analyzing the project's progress and identifying potential risks and problems.

[0034] The data collection unit can collect each member's tasks and schedules. For example, the data collection unit can analyze each member's calendar and automatically suggest optimal meeting times. The data collection unit can also create efficient schedules by considering task priorities. The data collection unit can collect each member's tasks and schedules in real time, allowing for a grasp of project progress. This enables efficient collection of each member's tasks and schedules. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input each member's calendar into the AI ​​and have the AI ​​suggest optimal meeting times.

[0035] The analysis unit can analyze project progress and historical data. For example, the analysis unit can analyze project progress in real time and identify potential risks. Based on historical project data, the analysis unit can also predict that a particular task is likely to be delayed and propose countermeasures. The analysis unit can analyze project progress and achieve efficient task management. This allows for efficient analysis of project progress and historical data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input project progress data into AI and have the AI ​​identify risks and propose countermeasures.

[0036] The analysis department can analyze the workload, quality, and level of cooperation of each member. For example, the analysis department can analyze each member's task completion rate and level of cooperation to provide a fair evaluation. The analysis department can analyze each member's workload in real time to achieve efficient task management. The analysis department can analyze each member's level of cooperation to improve the overall team performance. This allows for efficient analysis of each member's workload, quality, and level of cooperation. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input each member's work data into AI and have the AI ​​perform a performance evaluation.

[0037] The classification unit can automatically classify and organize documents. For example, the classification unit can automatically classify documents related to a project and provide a search function. The classification unit can classify documents in real time, allowing users to instantly find the documents they need. The classification unit can efficiently organize documents and support project management. This enables efficient classification and organization of documents. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input document data into AI and have the AI ​​perform the classification and organization.

[0038] The checking unit can verify that the project complies with legal regulations and the company's own policies. For example, the checking unit can monitor the project's progress and issue alerts if there are violations of legal regulations. The checking unit can also check the project's progress based on the company's own policies and report any violations. The checking unit can monitor legal regulations and the company's own policies in real time and understand the project's compliance status. This allows for efficient checking of whether the project complies with legal regulations and the company's own policies. Some or all of the above processes in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input project progress data into AI and have the AI ​​perform checks on compliance with legal regulations and company policies.

[0039] The generation unit can analyze data in real time and automatically generate reports based on that analysis. For example, the generation unit can analyze the progress of a project in real time and generate reports periodically. The generation unit can generate reports in real time to support rapid data-driven decision-making. The generation unit can grasp the progress of a project in real time and achieve efficient report generation. This enables rapid data-driven decision-making. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project progress data into AI and have the AI ​​generate reports.

[0040] The monitoring unit can detect anomalies by referring to past project data when monitoring the progress of a project. For example, the monitoring unit can automatically detect tasks that are behind schedule based on past project data. The monitoring unit can also refer to past data and predict which tasks are likely to be delayed. The monitoring unit can analyze past project data, detect anomalous patterns, and issue alerts. This allows for efficient detection of anomalies by referring to past project data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input past project data into an AI and have the AI ​​perform anomaly detection.

[0041] The monitoring unit can identify and focus on key project milestones during monitoring. For example, the monitoring unit can automatically identify key project milestones and focus on monitoring their progress. The monitoring unit can also monitor the achievement status of milestones in real time and issue alerts if delays occur. The monitoring unit can periodically update the progress of milestones and understand the overall progress of the project. This allows for efficient understanding of project progress by focusing on key milestones. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input project milestone data into AI and have the AI ​​perform focused monitoring.

[0042] The monitoring unit can set the monitoring scope considering the geographical distribution of the project during monitoring. For example, the monitoring unit can focus on monitoring important areas based on the geographical distribution of the project. The monitoring unit can also compare and monitor the progress of different areas, taking geographical distribution into consideration. The monitoring unit can monitor the progress of specific areas in real time based on geographical distribution. This allows for focused monitoring of important areas by considering geographical distribution. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​perform the setting of the monitoring scope.

[0043] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit sets progress monitoring criteria based on relevant project literature. The monitoring unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The monitoring unit can analyze relevant literature and propose new methods to improve monitoring accuracy. In this way, monitoring accuracy can be improved by referring to relevant literature. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input project relevant literature data into AI and have the AI ​​perform the improvement of monitoring accuracy.

[0044] The update unit can re-evaluate task priorities based on project progress during updates. For example, the update unit can automatically re-evaluate task priorities based on project progress. The update unit can also highlight and display high-priority tasks according to progress. The update unit can monitor task progress in real time and dynamically adjust priorities. This enables efficient task management by re-evaluating task priorities based on progress. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input project progress data into AI and have the AI ​​perform the task priority re-evaluation.

[0045] The update unit can collect data to optimize the project's resource allocation during the update process. For example, the update unit can collect resource allocation optimization data based on the project's progress. The update unit can also analyze the resource allocation data and propose the optimal allocation method. The update unit can update the resource allocation data in real time, enabling efficient allocation. This allows for efficient resource allocation by collecting resource allocation optimization data. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the project's resource allocation data into AI and have the AI ​​propose the optimal allocation method.

[0046] The update unit can set the scope of the update considering the geographical distribution of the project during the update process. For example, the update unit can focus on updating important areas based on the geographical distribution of the project. The update unit can also compare and update the progress of different areas, taking geographical distribution into consideration. The update unit can update the progress of specific areas in real time based on geographical distribution. This allows for focused updates of important areas by considering geographical distribution. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input the geographical distribution data of the project into AI and have AI perform the setting of the update scope.

[0047] The update unit can improve the accuracy of updates by referring to relevant project literature during the update process. For example, the update unit can set progress update criteria based on relevant project literature. The update unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The update unit can analyze relevant literature and propose new methods to improve update accuracy. This allows for improved update accuracy by referring to relevant literature. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input project literature data into AI and have the AI ​​perform the update accuracy improvement.

[0048] The proposal department can determine the priority of proposals based on the project's progress at the time of proposal submission. For example, the proposal department can automatically determine the priority of proposals based on the project's progress. The proposal department can also highlight and display high-priority proposals according to the progress. The proposal department can monitor the progress of proposals in real time and dynamically adjust priorities. This enables efficient proposals by determining the priority of proposals based on progress. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input project progress data into AI and have the AI ​​perform the determination of proposal priorities.

[0049] The proposal unit can generate optimal proposals by referring to past project data when making proposals. For example, the proposal unit can automatically generate optimal proposals based on past project data. The proposal unit can also refer to past data and provide optimal proposals for specific tasks. The proposal unit can analyze past project data and propose new methods to improve the accuracy of proposals. This allows for the generation of optimal proposals by referring to past data. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input past project data into AI and have the AI ​​generate optimal proposals.

[0050] The proposal function can set the scope of its proposals by considering the geographical distribution of the project. For example, the proposal function can focus on proposing important areas based on the geographical distribution of the project. The proposal function can also compare the progress of different areas, taking geographical distribution into consideration, and make proposals. The proposal function can propose the progress of specific areas in real time based on geographical distribution. This allows for focusing on important areas by considering geographical distribution. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the proposal scope.

[0051] The proposal department can improve the accuracy of its proposals by referring to relevant project literature during the proposal process. For example, the proposal department can set criteria for proposals based on relevant project literature. The proposal department can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The proposal department can analyze relevant literature and propose new methods to improve the accuracy of proposals. This allows for improved proposal accuracy by referring to relevant literature. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input project-related literature data into AI and have the AI ​​perform the improvement of proposal accuracy.

[0052] The identification unit can determine the priority of risks and problems based on the project's progress at the time of identification. For example, the identification unit can automatically determine the priority of risks and problems based on the project's progress. The identification unit can also highlight and display high-priority risks and problems according to the progress. The identification unit can monitor the progress of risks and problems in real time and dynamically adjust priorities. This enables efficient identification by determining the priority of risks and problems based on progress. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input project progress data into AI and have the AI ​​perform the determination of risk and problem priorities.

[0053] The identification unit can select the optimal identification method by referring to past project data at the time of identification. For example, the identification unit can automatically select the optimal identification method based on past project data. The identification unit can also refer to past data and provide the optimal identification method for specific risks or problems. The identification unit can analyze past project data and propose new methods to improve the accuracy of identification. This allows the optimal identification method to be selected by referring to past data. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input past project data into AI and have the AI ​​perform the selection of the optimal identification method.

[0054] The identification unit can set a specific range when identifying a project, taking into account the geographical distribution of the project. For example, the identification unit can focus on identifying important areas based on the geographical distribution of the project. The identification unit can also compare and identify the progress of different areas, taking geographical distribution into consideration. The identification unit can identify the progress of specific areas in real time based on geographical distribution. This allows for the focus on identifying important areas by considering geographical distribution. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the specific range.

[0055] The identification unit can improve the accuracy of identification by referring to relevant project literature at the time of identification. For example, the identification unit sets criteria for identification based on relevant project literature. The identification unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The identification unit can analyze relevant literature and propose new methods for improving the accuracy of identification. This allows for improvement in the accuracy of identification by referring to relevant literature. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input project relevant literature data into AI and have the AI ​​perform the improvement of the accuracy of identification.

[0056] The data collection unit can select the optimal collection method by referring to each member's past task history during collection. For example, the data collection unit can automatically select the optimal collection method based on each member's past task history. The data collection unit can also refer to past task history and provide the optimal collection method for a specific task. The data collection unit can analyze each member's past task history and propose new methods to improve the accuracy of collection. This allows the optimal collection method to be selected by referring to past task history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input each member's past task history data into AI and have the AI ​​select the optimal collection method.

[0057] The data collection unit can prioritize the collection of highly relevant tasks and appointments by considering each member's geographical location information during the collection process. For example, the data collection unit can automatically collect highly relevant tasks and appointments based on each member's geographical location information. The data collection unit can also prioritize the collection of tasks and appointments in specific regions by considering geographical location information. The data collection unit can analyze each member's geographical location information and propose new methods to improve the accuracy of the collection. This allows for the priority collection of highly relevant tasks and appointments by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input each member's geographical location data into an AI and have the AI ​​determine the collection priority.

[0058] The data collection unit can select the optimal collection method by referring to each member's past task history during collection. For example, the data collection unit can automatically select the optimal collection method based on each member's past task history. The data collection unit can also refer to past task history and provide the optimal collection method for a specific task. The data collection unit can analyze each member's past task history and propose new methods to improve the accuracy of collection. This allows the optimal collection method to be selected by referring to past task history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input each member's past task history data into AI and have the AI ​​select the optimal collection method.

[0059] The data collection unit can prioritize the collection of highly relevant tasks and appointments by considering each member's geographical location information during the collection process. For example, the data collection unit can automatically collect highly relevant tasks and appointments based on each member's geographical location information. The data collection unit can also prioritize the collection of tasks and appointments in specific regions by considering geographical location information. The data collection unit can analyze each member's geographical location information and propose new methods to improve the accuracy of the collection. This allows for the priority collection of highly relevant tasks and appointments by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input each member's geographical location data into an AI and have the AI ​​determine the collection priority.

[0060] The analysis unit can select the optimal analysis method by referring to past project data during the analysis. For example, the analysis unit can automatically select the optimal analysis method based on past project data. The analysis unit can also refer to past data and provide the optimal analysis method for a specific task. The analysis unit can analyze past project data and propose new methods to improve the accuracy of the analysis. This allows the optimal analysis method to be selected by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into AI and have the AI ​​perform the selection of the optimal analysis method.

[0061] The analysis unit can determine the priority of analyses based on the project's progress during the analysis process. For example, the analysis unit can automatically determine the priority of analyses based on the project's progress. The analysis unit can also highlight and display analyses with higher priority according to the progress. The analysis unit can monitor the progress of analyses in real time and dynamically adjust the priority. This enables efficient analysis by determining the priority of analyses based on the progress. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project progress data into the AI ​​and have the AI ​​determine the priority of analyses.

[0062] The analysis unit can set the scope of the analysis while considering the geographical distribution of the project. For example, the analysis unit can focus its analysis on important areas based on the geographical distribution of the project. The analysis unit can also compare and analyze the progress of different areas while considering the geographical distribution. The analysis unit can analyze the progress of a specific area in real time based on the geographical distribution. This allows for focused analysis of important areas by considering the geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​set the scope of the analysis.

[0063] The analysis unit can improve the accuracy of its analysis by referring to relevant project literature during the analysis process. For example, the analysis unit can set analysis criteria based on relevant project literature. The analysis unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The analysis unit can analyze relevant literature and propose new methods to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project-related literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0064] The analysis department can select the optimal analysis method by referring to each member's past work history during the analysis. For example, the analysis department can automatically select the optimal analysis method based on each member's past work history. The analysis department can also refer to past work history and provide the optimal analysis method for a specific task. The analysis department can analyze each member's past work history and propose new methods to improve the accuracy of the analysis. This allows for the selection of the optimal analysis method by referring to past work history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input each member's past work history data into AI and have the AI ​​select the optimal analysis method.

[0065] The analysis unit can determine the priority of analyses based on the project's progress during the analysis process. For example, the analysis unit can automatically determine the priority of analyses based on the project's progress. The analysis unit can also highlight and display analyses with higher priority according to the progress. The analysis unit can monitor the progress of analyses in real time and dynamically adjust priorities. This enables efficient analysis by determining the priority of analyses based on progress. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project progress data into AI and have the AI ​​determine the priority of analyses.

[0066] The analysis unit can set the scope of its analysis by considering the geographical distribution of each member. For example, the analysis unit can focus its analysis on important regions based on the geographical distribution of each member. The analysis unit can also compare and analyze the progress of different regions by considering geographical distribution. The analysis unit can analyze the progress of specific regions in real time based on geographical distribution. This allows for focused analysis of important regions by considering geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the geographical distribution data of each member into the AI ​​and have the AI ​​set the scope of the analysis.

[0067] The analysis unit can improve the accuracy of its analysis by referring to relevant project literature during the analysis process. For example, the analysis unit can set analysis criteria based on relevant project literature. The analysis unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The analysis unit can analyze relevant literature and propose new methods to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project-related literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0068] The evaluation unit can select the optimal evaluation method by referring to each member's past performance data during the evaluation process. For example, the evaluation unit can automatically select the optimal evaluation method based on each member's past performance data. The evaluation unit can also refer to past performance data and provide the optimal evaluation method for a specific task. The evaluation unit can analyze each member's past performance data and propose new methods to improve the accuracy of the evaluation. This allows the optimal evaluation method to be selected by referring to past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input each member's past performance data into an AI and have the AI ​​select the optimal evaluation method.

[0069] The evaluation unit can determine evaluation priorities based on the project's progress during the evaluation process. For example, the evaluation unit can automatically determine evaluation priorities based on the project's progress. The evaluation unit can also highlight and display evaluations with higher priority according to the progress. The evaluation unit can monitor the evaluation progress in real time and dynamically adjust priorities. This enables efficient evaluation by determining evaluation priorities based on progress. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input project progress data into AI and have the AI ​​determine the evaluation priorities.

[0070] The evaluation unit can set the scope of evaluation while considering the geographical distribution of each member. For example, the evaluation unit can focus on evaluating important regions based on the geographical distribution of each member. The evaluation unit can also compare and evaluate the progress of different regions while considering geographical distribution. The evaluation unit can evaluate the progress of specific regions in real time based on geographical distribution. This allows for focused evaluation of important regions by considering geographical distribution. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical distribution data of each member into the AI ​​and have the AI ​​set the evaluation scope.

[0071] The evaluation unit can improve the accuracy of its evaluation by referring to relevant project literature during the evaluation process. For example, the evaluation unit can set evaluation criteria based on relevant project literature. The evaluation unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The evaluation unit can analyze relevant literature and propose new methods to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input project-related literature data into AI and have the AI ​​perform the evaluation accuracy improvement.

[0072] The classification unit can select the optimal classification method by referring to the project's past document data during classification. For example, the classification unit can automatically select the optimal classification method based on past project document data. The classification unit can also refer to past document data and provide the optimal classification method for a specific document. The classification unit can analyze past project document data and propose new methods to improve classification accuracy. This allows the optimal classification method to be selected by referring to past document data. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input past project document data into AI and have the AI ​​perform the selection of the optimal classification method.

[0073] The classification unit can determine classification priorities based on the project's progress during the classification process. For example, the classification unit can automatically determine classification priorities based on the project's progress. The classification unit can also highlight and display classifications with higher priority according to the progress. The classification unit can monitor the classification progress in real time and dynamically adjust priorities. This enables efficient classification by determining classification priorities based on progress. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input project progress data into AI and have the AI ​​determine the classification priorities.

[0074] The classification unit can set the scope of classification while considering the geographical distribution of the project. For example, the classification unit can prioritize classification of important regions based on the geographical distribution of the project. The classification unit can also consider geographical distribution and compare the progress of different regions for classification. The classification unit can classify the progress of specific regions in real time based on geographical distribution. This allows for priority classification of important regions by considering geographical distribution. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the classification scope.

[0075] The classification unit can improve the accuracy of its classification by referring to relevant project literature during the classification process. For example, the classification unit sets classification criteria based on relevant project literature. The classification unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The classification unit can analyze relevant literature and propose new methods to improve classification accuracy. This allows for improved classification accuracy by referring to relevant literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input project-related literature data into AI and have the AI ​​perform the classification accuracy improvement.

[0076] The search unit can select the optimal search method by referring to past project document data during a search. For example, the search unit can automatically select the optimal search method based on past project document data. The search unit can also refer to past document data and provide the optimal search method for a specific document. The search unit can analyze past project document data and propose new methods to improve search accuracy. This allows the optimal search method to be selected by referring to past document data. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past project document data into AI and have the AI ​​select the optimal search method.

[0077] The search unit can determine search priorities based on the project's progress during a search. For example, the search unit can automatically determine search priorities based on the project's progress. The search unit can also highlight and display high-priority search results according to the progress. The search unit can monitor the search progress in real time and dynamically adjust priorities. This enables efficient searching by determining search priorities based on progress. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input project progress data into AI and have the AI ​​determine search priorities.

[0078] The search unit can set the search scope considering the geographical distribution of the project during the search. For example, the search unit can focus its search on important areas based on the geographical distribution of the project. The search unit can also compare the progress of different areas while considering the geographical distribution. The search unit can search for the progress of a specific area in real time based on the geographical distribution. This allows for a focused search on important areas by considering the geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​set the search scope.

[0079] The search unit can improve search accuracy by referring to relevant project literature during the search process. For example, the search unit can set search criteria based on relevant project literature. The search unit can also adjust criteria for detecting abnormal progress by referring to relevant literature. The search unit can analyze relevant literature and propose new methods to improve search accuracy. This allows for improved search accuracy by referring to relevant literature. Some or all of the above processes in the search unit may be performed using AI, for example, or not. For example, the search unit can input project-related literature data into AI and have the AI ​​perform the search accuracy improvement.

[0080] The checking unit can select the optimal checking method by referring to past project data during the checking process. For example, the checking unit can automatically select the optimal checking method based on past project data. The checking unit can also refer to past data and provide the optimal checking method for a specific task. The checking unit can analyze past project data and propose new methods to improve the accuracy of the checks. This allows the optimal checking method to be selected by referring to past data. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past project data into AI and have the AI ​​perform the selection of the optimal checking method.

[0081] The checking unit can determine the priority of checks based on the project's progress during the checking process. For example, the checking unit can automatically determine the priority of checks based on the project's progress. The checking unit can also highlight and display high-priority checks according to the progress. The checking unit can monitor the progress of checks in real time and dynamically adjust priorities. This enables efficient checking by determining the priority of checks based on the progress. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input project progress data into AI and have the AI ​​determine the priority of checks.

[0082] The checking unit can set the scope of the check while considering the geographical distribution of the project. For example, the checking unit can focus on checking important areas based on the geographical distribution of the project. The checking unit can also compare and check the progress of different areas while considering the geographical distribution. The checking unit can check the progress of specific areas in real time based on the geographical distribution. This allows for focused checking of important areas by considering the geographical distribution. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the check scope.

[0083] The checking unit can improve the accuracy of its checks by referring to relevant project literature during the checking process. For example, the checking unit sets checking criteria based on relevant project literature. The checking unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The checking unit can analyze relevant literature and propose new methods to improve the accuracy of the checks. In this way, the accuracy of the checks can be improved by referring to relevant literature. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input project-related literature data into AI and have the AI ​​perform the task of improving the accuracy of the checks.

[0084] The reporting unit can select the optimal reporting method by referring to past project data when reporting. For example, the reporting unit can automatically select the optimal reporting method based on past project data. The reporting unit can also refer to past data and provide the optimal reporting method for a specific task. The reporting unit can analyze past project data and propose new methods to improve the accuracy of reporting. This allows the optimal reporting method to be selected by referring to past data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input past project data into AI and have the AI ​​perform the selection of the optimal reporting method.

[0085] The reporting unit can determine the priority of reports based on the project's progress when reporting. For example, the reporting unit can automatically determine the priority of reports based on the project's progress. The reporting unit can also highlight and display high-priority reports according to their progress. The reporting unit can monitor the progress of reports in real time and dynamically adjust priorities. This enables efficient reporting by determining the priority of reports based on their progress. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input project progress data into AI and have the AI ​​determine the priority of reports.

[0086] The reporting unit can set the scope of its reports considering the geographical distribution of the project. For example, the reporting unit can focus its reports on important regions based on the geographical distribution of the project. The reporting unit can also compare and report on the progress of different regions, taking geographical distribution into consideration. The reporting unit can report on the progress of specific regions in real time based on geographical distribution. This allows for focused reporting on important regions by considering geographical distribution. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the reporting scope.

[0087] The reporting department can improve the accuracy of its reports by referring to relevant project literature during the reporting process. For example, the reporting department can set reporting criteria based on relevant project literature. The reporting department can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The reporting department can analyze relevant literature and propose new methods to improve the accuracy of reports. This allows for improved reporting accuracy by referring to relevant literature. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project-related literature data into AI and have the AI ​​perform the task of improving the accuracy of reports.

[0088] The generation unit can select the optimal generation method by referring to past project data when generating reports. For example, the generation unit can automatically select the optimal generation method based on past project data. The generation unit can also refer to past data and provide the optimal generation method for a specific task. The generation unit can analyze past project data and propose new methods to improve generation accuracy. This allows the optimal generation method to be selected by referring to past data. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input past project data into AI and have the AI ​​perform the selection of the optimal generation method.

[0089] The generation unit can determine the priority of reports based on the project's progress when generating reports. For example, the generation unit can automatically determine the priority of reports based on the project's progress. The generation unit can also highlight and display reports with higher priority according to the progress. The generation unit can monitor the progress of reports in real time and dynamically adjust the priority. This enables efficient report generation by determining the priority of reports based on the progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project progress data into AI and have the AI ​​perform the determination of report priorities.

[0090] The generation unit can set the report scope considering the geographical distribution of the project when generating reports. For example, the generation unit can focus on reporting on important regions based on the geographical distribution of the project. The generation unit can also compare and report on the progress of different regions, taking geographical distribution into consideration. The generation unit can report on the progress of specific regions in real time based on geographical distribution. This allows for focusing on reporting on important regions by considering geographical distribution. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the report scope.

[0091] The generation unit can improve the accuracy of the report by referring to relevant project literature during report generation. For example, the generation unit sets report criteria based on relevant project literature. The generation unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The generation unit can analyze relevant literature and propose new methods to improve the accuracy of the report. In this way, the accuracy of the report can be improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input project relevant literature data into AI and have the AI ​​perform the report accuracy improvement.

[0092] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0093] The project management system also includes a notification function. This function can notify members in real time about project progress and task changes. For example, if a task is behind schedule, the notification function can send an alert to members to encourage prompt action. Furthermore, if a task's priority changes, the notification function can immediately notify members of the change. Additionally, when a key project milestone is achieved, the notification function can send a notification to all members to share the achievement. This allows for quick sharing of project progress and important changes, facilitating smoother communication among members.

[0094] The project management system also includes a feedback department. This department collects feedback from project members and uses it to improve the project. For example, members can report problems and areas for improvement they encountered during the project's progress. The feedback department can also conduct regular surveys to gather opinions on the project's progress and team performance. Furthermore, the feedback department can analyze the collected feedback and propose improvements to the project. This allows for project improvements that reflect member feedback, leading to improved team performance.

[0095] The project management system also includes a learning section. This section records problems and successes that occur during a project, which can then be used for future projects. For example, the causes of delays in specific tasks and the countermeasures taken can be recorded in the learning section. Furthermore, project success stories and effective methods can be saved in the learning section and reused in other projects. In addition, the learning section can analyze past project data to identify common problems and areas for improvement. This allows for the accumulation of project experience and increases the success rate of future projects.

[0096] The project management system also includes a forecasting unit. Based on project progress and historical data, the forecasting unit can predict future risks and problems. For example, if a particular task is likely to be delayed, the forecasting unit can notify the user of the risk in advance and propose countermeasures. Furthermore, by monitoring project progress in real time, the forecasting unit can detect potential problems early. In addition, the forecasting unit can analyze past project data and identify common risk patterns. This strengthens project risk management and helps prevent problems from occurring.

[0097] The project management system also includes a compensation department. This department can determine compensation based on the performance of project members. For example, it can evaluate task completion rates and contributions, and reward members who demonstrate outstanding performance. The compensation department can also provide rewards to the entire team when key project milestones are achieved. Furthermore, the compensation department can improve the compensation system based on member feedback, thereby increasing motivation. This is expected to improve member performance and contribute to the success of the project.

[0098] The following briefly describes the processing flow for example form 1.

[0099] Step 1: The monitoring department monitors the project's progress. For example, the monitoring department monitors the project's progress in real time and automatically updates task progress. It can also provide appropriate answers to questions from project members and suggest new ideas as needed. Step 2: The update unit updates the task progress based on the progress monitored by the monitoring unit. The update unit can, for example, automatically update task progress and provide real-time visibility into project progress. Furthermore, it can create an efficient schedule by considering task priorities. Step 3: The proposal team proposes new ideas based on the progress updated by the update team. For example, the proposal team analyzes the project's progress and proposes new ideas. Furthermore, they can also propose new ideas based on feedback from project members. Step 4: The Identification Department identifies risks and problems based on the ideas proposed by the Proposal Department. For example, the Identification Department may analyze the project's progress and identify potential risks. Furthermore, based on past project data, they may predict that certain tasks are likely to be delayed and propose countermeasures.

[0100] (Example of form 2) The project management system according to an embodiment of the present invention provides multiple systems for streamlining project management using AI. The project management system provides a chatbot that automates the overall progress of a project, task assignment, and progress management. This chatbot has the function of suggesting new ideas and quickly identifying risks and problems using AI. For example, it monitors the project progress in real time and automatically updates the progress of tasks. It also provides appropriate answers to questions from project members and suggests new ideas as needed. Next, it provides a system that manages schedules and makes plans using AI. This system considers each member's tasks and schedules and suggests efficient dates and meeting times. For example, it analyzes each member's calendar and automatically suggests the optimal meeting time. It also creates an efficient schedule considering task priorities. Furthermore, it provides a system that uses AI to predict project risks and suggest appropriate countermeasures. This system analyzes the project progress and past data to identify potential risks. For example, based on past project data, it predicts that a particular task is likely to be delayed and suggests countermeasures. Finally, it provides an AI system that fairly evaluates the performance of project members. This system analyzes the amount of work, quality, and level of cooperation of each member and evaluates their performance. For example, it analyzes each member's task completion rate and level of cooperation to provide fair evaluations. Furthermore, it offers a fully AI-powered document management system. This system automatically classifies and organizes documents, allowing users to instantly find the documents they need. For example, it automatically classifies project-related documents and provides a search function. It also provides a system where AI checks whether projects comply with all laws, regulations, and company policies, and reports any violations. For example, it monitors project progress and issues alerts if there are legal or regulatory violations. Finally, it provides a system that uses AI to analyze data in real time and automatically generates reports based on that analysis. This enables rapid, data-driven decision-making.For example, the system analyzes project progress in real time and generates reports periodically. These systems are expected to streamline project management, reduce risks, and improve performance. This allows the project management system to efficiently monitor, update, propose, and identify project progress.

[0101] The project management system according to this embodiment comprises a monitoring unit, an update unit, a proposal unit, and a specification unit. The monitoring unit monitors the progress of the project. For example, the monitoring unit monitors the progress of the project in real time and automatically updates the progress of tasks. The monitoring unit can also provide appropriate answers to questions from project members and propose new ideas as needed. The update unit updates the progress of tasks based on the progress monitored by the monitoring unit. For example, the update unit automatically updates the progress of tasks and grasps the progress of the project in real time. The update unit can also create an efficient schedule considering the priority of tasks. The proposal unit proposes new ideas based on the progress updated by the update unit. For example, the proposal unit analyzes the progress of the project and proposes new ideas. The proposal unit can also propose new ideas based on feedback from project members. The specification unit identifies risks and problems based on the ideas proposed by the proposal unit. For example, the specification unit analyzes the progress of the project and identifies potential risks. Based on past project data, the specification unit can also predict that a particular task is likely to be delayed and propose countermeasures. As a result, the project management system according to this embodiment can efficiently monitor, update, propose, and identify the progress of the project.

[0102] The monitoring department monitors the progress of the project. For example, the monitoring department monitors the project's progress in real time and automatically updates task progress. Specifically, the monitoring department uses project management tools and software to regularly check the progress of each task and collect progress data. This includes the task's start date, end date, progress rate, and comments from the person in charge. Based on this data, the monitoring department visualizes the overall project progress and provides it to project members and managers. The monitoring department can also provide appropriate answers to questions from project members and propose new ideas as needed. For example, if a project member has a question about the progress of a task, the monitoring department can refer to past data and best practices to provide a quick and accurate answer. Furthermore, the monitoring department can analyze the project's progress and propose new ideas and improvements to promote efficient progress. In this way, the monitoring department can grasp the project's progress in real time and provide support for the project's success.

[0103] The update unit updates task progress based on the progress monitored by the monitoring unit. For example, the update unit automatically updates task progress, allowing for real-time monitoring of project progress. Specifically, the update unit reflects the progress of each task in the project management tool based on data provided by the monitoring unit. This allows project members and managers to check the latest progress in real time. The update unit can also create efficient schedules by considering task priorities. For example, it supports efficient project progress by re-evaluating task priorities according to progress and optimizing resource allocation. Furthermore, the update unit has the function to automatically adjust the project schedule and resource plan in conjunction with progress updates. This allows for quick and accurate changes and adjustments as the project progresses. In addition, based on progress updates, the update unit can identify project risks and problems early and take appropriate measures. This enables the update unit to monitor project progress in real time and achieve efficient and effective project management.

[0104] The proposal department proposes new ideas based on the progress status updated by the update department. For example, the proposal department analyzes the project's progress and proposes new ideas. Specifically, based on the progress data provided by the update department, the proposal department conducts a detailed analysis of the project's current status and identifies areas for improvement and new approaches. The proposal department can also propose new ideas based on feedback from project members. For example, if a project member suggests improvements to the current process, the proposal department considers that feedback and materializes it into a feasible idea. Furthermore, the proposal department can utilize AI to analyze past project data, learn from successes and failures, and propose new strategies to optimize project progress. This allows the proposal department to constantly optimize project progress and provide new ideas and improvements for project success. In addition, the proposal department evaluates the feasibility and effectiveness of proposed ideas and supports project progress by making revisions and improvements as needed. In this way, the proposal department can contribute to project success by analyzing project progress and proposing new ideas.

[0105] The Specialist Department identifies risks and problems based on ideas proposed by the Proposal Department. For example, the Specialist Department analyzes the project's progress and identifies potential risks. Specifically, the Specialist Department conducts a detailed analysis of risks and problems associated with the project's progress, based on ideas and improvement measures provided by the Proposal Department. The Specialist Department can also predict that certain tasks are likely to be delayed based on past project data and propose countermeasures. For example, by analyzing past data, they can identify patterns and causes that make certain tasks prone to delays and take preventative measures to ensure smooth project progress. Furthermore, the Specialist Department can utilize AI to analyze data in real time and perform anomaly detection and risk prediction. This allows the Specialist Department to constantly monitor the project's progress, detect potential risks and problems early, and take appropriate measures. In addition, based on the identification of risks and problems, the Specialist Department can propose concrete action plans to optimize project progress. This allows the Specialist Department to provide support for project success by analyzing the project's progress and identifying potential risks and problems.

[0106] The data collection unit can collect each member's tasks and schedules. For example, the data collection unit can analyze each member's calendar and automatically suggest optimal meeting times. The data collection unit can also create efficient schedules by considering task priorities. The data collection unit can collect each member's tasks and schedules in real time, allowing for a grasp of project progress. This enables efficient collection of each member's tasks and schedules. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input each member's calendar into the AI ​​and have the AI ​​suggest optimal meeting times.

[0107] The analysis unit can analyze project progress and historical data. For example, the analysis unit can analyze project progress in real time and identify potential risks. Based on historical project data, the analysis unit can also predict that a particular task is likely to be delayed and propose countermeasures. The analysis unit can analyze project progress and achieve efficient task management. This allows for efficient analysis of project progress and historical data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input project progress data into AI and have the AI ​​identify risks and propose countermeasures.

[0108] The analysis department can analyze the workload, quality, and level of cooperation of each member. For example, the analysis department can analyze each member's task completion rate and level of cooperation to provide a fair evaluation. The analysis department can analyze each member's workload in real time to achieve efficient task management. The analysis department can analyze each member's level of cooperation to improve the overall team performance. This allows for efficient analysis of each member's workload, quality, and level of cooperation. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input each member's work data into AI and have the AI ​​perform a performance evaluation.

[0109] The classification unit can automatically classify and organize documents. For example, the classification unit can automatically classify documents related to a project and provide a search function. The classification unit can classify documents in real time, allowing users to instantly find the documents they need. The classification unit can efficiently organize documents and support project management. This enables efficient classification and organization of documents. Some or all of the above-described processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input document data into AI and have the AI ​​perform the classification and organization.

[0110] The checking unit can verify that the project complies with legal regulations and the company's own policies. For example, the checking unit can monitor the project's progress and issue alerts if there are violations of legal regulations. The checking unit can also check the project's progress based on the company's own policies and report any violations. The checking unit can monitor legal regulations and the company's own policies in real time and understand the project's compliance status. This allows for efficient checking of whether the project complies with legal regulations and the company's own policies. Some or all of the above processes in the checking unit may be performed using AI, for example, or not. For example, the checking unit can input project progress data into AI and have the AI ​​perform checks on compliance with legal regulations and company policies.

[0111] The generation unit can analyze data in real time and automatically generate reports based on that analysis. For example, the generation unit can analyze the progress of a project in real time and generate reports periodically. The generation unit can generate reports in real time to support rapid data-driven decision-making. The generation unit can grasp the progress of a project in real time and achieve efficient report generation. This enables rapid data-driven decision-making. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project progress data into AI and have the AI ​​generate reports.

[0112] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the monitoring frequency and monitor only important points. If the user is relaxed, the monitoring unit can also increase the monitoring frequency and provide detailed progress updates. If the user is in a hurry, the monitoring unit can optimize the monitoring frequency and provide quick feedback. This allows for more appropriate monitoring by adjusting the monitoring frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI or not using AI. For example, the monitoring unit can input user emotion data into AI and have the AI ​​adjust the monitoring frequency.

[0113] The monitoring unit can detect anomalies by referring to past project data when monitoring the progress of a project. For example, the monitoring unit can automatically detect tasks that are behind schedule based on past project data. The monitoring unit can also refer to past data and predict which tasks are likely to be delayed. The monitoring unit can analyze past project data, detect anomalous patterns, and issue alerts. This allows for efficient detection of anomalies by referring to past project data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input past project data into an AI and have the AI ​​perform anomaly detection.

[0114] The monitoring unit can identify and focus on key project milestones during monitoring. For example, the monitoring unit can automatically identify key project milestones and focus on monitoring their progress. The monitoring unit can also monitor the achievement status of milestones in real time and issue alerts if delays occur. The monitoring unit can periodically update the progress of milestones and understand the overall progress of the project. This allows for efficient understanding of project progress by focusing on key milestones. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not. For example, the monitoring unit can input project milestone data into AI and have the AI ​​perform focused monitoring.

[0115] The monitoring unit can estimate the user's emotions and adjust the display method of the monitoring results based on the estimated user emotions. For example, if the user is tense, the monitoring unit can provide a simple and highly visible display method. If the user is relaxed, the monitoring unit can also provide a display method that includes detailed information. If the user is in a hurry, the monitoring unit can also provide a display method that gets straight to the point. By adjusting the display method of the monitoring results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0116] The monitoring unit can set the monitoring scope considering the geographical distribution of the project during monitoring. For example, the monitoring unit can focus on monitoring important areas based on the geographical distribution of the project. The monitoring unit can also compare and monitor the progress of different areas, taking geographical distribution into consideration. The monitoring unit can monitor the progress of specific areas in real time based on geographical distribution. This allows for focused monitoring of important areas by considering geographical distribution. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​perform the setting of the monitoring scope.

[0117] The monitoring unit can improve the accuracy of its monitoring by referring to relevant project literature during monitoring. For example, the monitoring unit sets progress monitoring criteria based on relevant project literature. The monitoring unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The monitoring unit can analyze relevant literature and propose new methods to improve monitoring accuracy. In this way, monitoring accuracy can be improved by referring to relevant literature. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input project relevant literature data into AI and have the AI ​​perform the improvement of monitoring accuracy.

[0118] The update unit can estimate the user's emotions and adjust the timing of updates based on the estimated emotions. For example, if the user is stressed, the update unit can reduce the frequency of updates and only provide important updates. If the user is relaxed, the update unit can increase the frequency of updates and provide more detailed information. If the user is in a hurry, the update unit can optimize the timing of updates and provide quick feedback. This allows for more appropriate updates by adjusting the timing of updates 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 update unit may be performed using AI or not using AI. For example, the update unit can input user emotion data into AI and have the AI ​​adjust the timing of updates.

[0119] The update unit can re-evaluate task priorities based on project progress during updates. For example, the update unit can automatically re-evaluate task priorities based on project progress. The update unit can also highlight and display high-priority tasks according to progress. The update unit can monitor task progress in real time and dynamically adjust priorities. This enables efficient task management by re-evaluating task priorities based on progress. Some or all of the above processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input project progress data into AI and have the AI ​​perform the task priority re-evaluation.

[0120] The update unit can collect data to optimize the project's resource allocation during the update process. For example, the update unit can collect resource allocation optimization data based on the project's progress. The update unit can also analyze the resource allocation data and propose the optimal allocation method. The update unit can update the resource allocation data in real time, enabling efficient allocation. This allows for efficient resource allocation by collecting resource allocation optimization data. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the project's resource allocation data into AI and have the AI ​​propose the optimal allocation method.

[0121] The update unit can estimate the user's emotions and adjust how the update content is displayed based on the estimated emotions. For example, if the user is nervous, the update unit can provide a simple and highly visible display. If the user is relaxed, the update unit can also provide a display that includes detailed information. If the user is in a hurry, the update unit can also provide a display that gets straight to the point. By adjusting how the update content is displayed according to the user's emotions, a more appropriate display becomes possible. 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 update unit may be performed using AI, for example, or not using AI. For example, the update unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0122] The update unit can set the scope of the update considering the geographical distribution of the project during the update process. For example, the update unit can focus on updating important areas based on the geographical distribution of the project. The update unit can also compare and update the progress of different areas, taking geographical distribution into consideration. The update unit can update the progress of specific areas in real time based on geographical distribution. This allows for focused updates of important areas by considering geographical distribution. Some or all of the above processes in the update unit may be performed using AI, for example, or not using AI. For example, the update unit can input the geographical distribution data of the project into AI and have AI perform the setting of the update scope.

[0123] The update unit can improve the accuracy of updates by referring to relevant project literature during the update process. For example, the update unit can set progress update criteria based on relevant project literature. The update unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The update unit can analyze relevant literature and propose new methods to improve update accuracy. This allows for improved update accuracy by referring to relevant literature. Some or all of the above processes in the update unit may be performed using AI, for example, or not. For example, the update unit can input project literature data into AI and have the AI ​​perform the update accuracy improvement.

[0124] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide suggestions that include more detailed information. If the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the presentation.

[0125] The proposal department can determine the priority of proposals based on the project's progress at the time of proposal submission. For example, the proposal department can automatically determine the priority of proposals based on the project's progress. The proposal department can also highlight and display high-priority proposals according to the progress. The proposal department can monitor the progress of proposals in real time and dynamically adjust priorities. This enables efficient proposals by determining the priority of proposals based on progress. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input project progress data into AI and have the AI ​​perform the determination of proposal priorities.

[0126] The proposal unit can generate optimal proposals by referring to past project data when making proposals. For example, the proposal unit can automatically generate optimal proposals based on past project data. The proposal unit can also refer to past data and provide optimal proposals for specific tasks. The proposal unit can analyze past project data and propose new methods to improve the accuracy of proposals. This allows for the generation of optimal proposals by referring to past data. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input past project data into AI and have the AI ​​generate optimal proposals.

[0127] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can also provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the length of suggestions.

[0128] The proposal function can set the scope of its proposals by considering the geographical distribution of the project. For example, the proposal function can focus on proposing important areas based on the geographical distribution of the project. The proposal function can also compare the progress of different areas, taking geographical distribution into consideration, and make proposals. The proposal function can propose the progress of specific areas in real time based on geographical distribution. This allows for focusing on important areas by considering geographical distribution. Some or all of the above processing in the proposal function may be performed using AI, for example, or not using AI. For example, the proposal function can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the proposal scope.

[0129] The proposal department can improve the accuracy of its proposals by referring to relevant project literature during the proposal process. For example, the proposal department can set criteria for proposals based on relevant project literature. The proposal department can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The proposal department can analyze relevant literature and propose new methods to improve the accuracy of proposals. This allows for improved proposal accuracy by referring to relevant literature. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input project-related literature data into AI and have the AI ​​perform the improvement of proposal accuracy.

[0130] The identification unit can estimate the user's emotions and adjust specific criteria based on the estimated emotions. For example, if the user is nervous, the identification unit provides simple and easily visible criteria. If the user is relaxed, the identification unit can also provide criteria that include more detailed information. If the user is in a hurry, the identification unit can also provide concise criteria. This allows for more accurate identification by adjusting specific criteria 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 identification unit may be performed using AI or not using AI. For example, the identification unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the criteria.

[0131] The identification unit can determine the priority of risks and problems based on the project's progress at the time of identification. For example, the identification unit can automatically determine the priority of risks and problems based on the project's progress. The identification unit can also highlight and display high-priority risks and problems according to the progress. The identification unit can monitor the progress of risks and problems in real time and dynamically adjust priorities. This enables efficient identification by determining the priority of risks and problems based on progress. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input project progress data into AI and have the AI ​​perform the determination of risk and problem priorities.

[0132] The identification unit can select the optimal identification method by referring to past project data at the time of identification. For example, the identification unit can automatically select the optimal identification method based on past project data. The identification unit can also refer to past data and provide the optimal identification method for specific risks or problems. The identification unit can analyze past project data and propose new methods to improve the accuracy of identification. This allows the optimal identification method to be selected by referring to past data. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input past project data into AI and have the AI ​​perform the selection of the optimal identification method.

[0133] The identification unit can estimate the user's emotions and adjust the display method of the identification results based on the estimated user emotions. For example, if the user is nervous, the identification unit can provide a simple and highly visible display method. If the user is relaxed, the identification unit can also provide a display method that includes detailed information. If the user is in a hurry, the identification unit can also provide a display method that gets straight to the point. By adjusting the display method of the identification results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0134] The identification unit can set a specific range when identifying a project, taking into account the geographical distribution of the project. For example, the identification unit can focus on identifying important areas based on the geographical distribution of the project. The identification unit can also compare and identify the progress of different areas, taking geographical distribution into consideration. The identification unit can identify the progress of specific areas in real time based on geographical distribution. This allows for the focus on identifying important areas by considering geographical distribution. Some or all of the above-described processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the specific range.

[0135] The identification unit can improve the accuracy of identification by referring to relevant project literature at the time of identification. For example, the identification unit sets criteria for identification based on relevant project literature. The identification unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The identification unit can analyze relevant literature and propose new methods for improving the accuracy of identification. This allows for improvement in the accuracy of identification by referring to relevant literature. Some or all of the above processes in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input project relevant literature data into AI and have the AI ​​perform the improvement of the accuracy of identification.

[0136] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of collection and collect only important information. If the user is relaxed, the data collection unit can increase the frequency of collection and provide more detailed information. If the user is in a hurry, the data collection unit can optimize the timing of collection and provide quick feedback. This allows for more appropriate data collection by adjusting the timing of collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0137] The data collection unit can select the optimal collection method by referring to each member's past task history during collection. For example, the data collection unit can automatically select the optimal collection method based on each member's past task history. The data collection unit can also refer to past task history and provide the optimal collection method for a specific task. The data collection unit can analyze each member's past task history and propose new methods to improve the accuracy of collection. This allows the optimal collection method to be selected by referring to past task history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input each member's past task history data into AI and have the AI ​​select the optimal collection method.

[0138] The data collection unit can prioritize the collection of highly relevant tasks and appointments by considering each member's geographical location information during the collection process. For example, the data collection unit can automatically collect highly relevant tasks and appointments based on each member's geographical location information. The data collection unit can also prioritize the collection of tasks and appointments in specific regions by considering geographical location information. The data collection unit can analyze each member's geographical location information and propose new methods to improve the accuracy of the collection. This allows for the priority collection of highly relevant tasks and appointments by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input each member's geographical location data into an AI and have the AI ​​determine the collection priority.

[0139] The data collection unit can estimate the user's emotions and determine the priority of tasks and appointments to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important tasks and appointments. If the user is relaxed, the data collection unit can also collect detailed tasks and appointments. If the user is in a hurry, the data collection unit can also collect concise tasks and appointments. This allows for more appropriate data collection by prioritizing tasks and appointments 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 processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of tasks and appointments.

[0140] The data collection unit can select the optimal collection method by referring to each member's past task history during collection. For example, the data collection unit can automatically select the optimal collection method based on each member's past task history. The data collection unit can also refer to past task history and provide the optimal collection method for a specific task. The data collection unit can analyze each member's past task history and propose new methods to improve the accuracy of collection. This allows the optimal collection method to be selected by referring to past task history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input each member's past task history data into AI and have the AI ​​select the optimal collection method.

[0141] The data collection unit can prioritize the collection of highly relevant tasks and appointments by considering each member's geographical location information during the collection process. For example, the data collection unit can automatically collect highly relevant tasks and appointments based on each member's geographical location information. The data collection unit can also prioritize the collection of tasks and appointments in specific regions by considering geographical location information. The data collection unit can analyze each member's geographical location information and propose new methods to improve the accuracy of the collection. This allows for the priority collection of highly relevant tasks and appointments by considering geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input each member's geographical location data into an AI and have the AI ​​determine the collection priority.

[0142] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit provides a simple and easy-to-understand analysis method. If the user is relaxed, the analysis unit can also provide an analysis method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise analysis method. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis method.

[0143] The analysis unit can select the optimal analysis method by referring to past project data during the analysis. For example, the analysis unit can automatically select the optimal analysis method based on past project data. The analysis unit can also refer to past data and provide the optimal analysis method for a specific task. The analysis unit can analyze past project data and propose new methods to improve the accuracy of the analysis. This allows the optimal analysis method to be selected by referring to past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past project data into AI and have the AI ​​perform the selection of the optimal analysis method.

[0144] The analysis unit can determine the priority of analyses based on the project's progress during the analysis process. For example, the analysis unit can automatically determine the priority of analyses based on the project's progress. The analysis unit can also highlight and display analyses with higher priority according to the progress. The analysis unit can monitor the progress of analyses in real time and dynamically adjust the priority. This enables efficient analysis by determining the priority of analyses based on the progress. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project progress data into the AI ​​and have the AI ​​determine the priority of analyses.

[0145] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0146] The analysis unit can set the scope of the analysis while considering the geographical distribution of the project. For example, the analysis unit can focus its analysis on important areas based on the geographical distribution of the project. The analysis unit can also compare and analyze the progress of different areas while considering the geographical distribution. The analysis unit can analyze the progress of a specific area in real time based on the geographical distribution. This allows for focused analysis of important areas by considering the geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​set the scope of the analysis.

[0147] The analysis unit can improve the accuracy of its analysis by referring to relevant project literature during the analysis process. For example, the analysis unit can set analysis criteria based on relevant project literature. The analysis unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The analysis unit can analyze relevant literature and propose new methods to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project-related literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0148] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-understand analysis method. If the user is relaxed, the analysis unit can also provide an analysis method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a concise analysis method. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the analysis method.

[0149] The analysis department can select the optimal analysis method by referring to each member's past work history during the analysis. For example, the analysis department can automatically select the optimal analysis method based on each member's past work history. The analysis department can also refer to past work history and provide the optimal analysis method for a specific task. The analysis department can analyze each member's past work history and propose new methods to improve the accuracy of the analysis. This allows for the selection of the optimal analysis method by referring to past work history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input each member's past work history data into AI and have the AI ​​select the optimal analysis method.

[0150] The analysis unit can determine the priority of analyses based on the project's progress during the analysis process. For example, the analysis unit can automatically determine the priority of analyses based on the project's progress. The analysis unit can also highlight and display analyses with higher priority according to the progress. The analysis unit can monitor the progress of analyses in real time and dynamically adjust priorities. This enables efficient analysis by determining the priority of analyses based on progress. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project progress data into AI and have the AI ​​determine the priority of analyses.

[0151] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. 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 analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0152] The analysis unit can set the scope of its analysis by considering the geographical distribution of each member. For example, the analysis unit can focus its analysis on important regions based on the geographical distribution of each member. The analysis unit can also compare and analyze the progress of different regions by considering geographical distribution. The analysis unit can analyze the progress of specific regions in real time based on geographical distribution. This allows for focused analysis of important regions by considering geographical distribution. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the geographical distribution data of each member into the AI ​​and have the AI ​​set the scope of the analysis.

[0153] The analysis unit can improve the accuracy of its analysis by referring to relevant project literature during the analysis process. For example, the analysis unit can set analysis criteria based on relevant project literature. The analysis unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The analysis unit can analyze relevant literature and propose new methods to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by referring to relevant literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project-related literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0154] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit provides simple and easy-to-understand evaluation criteria. If the user is relaxed, the evaluation unit can also provide evaluation criteria that include detailed information. If the user is in a hurry, the evaluation unit can also provide concise evaluation criteria. This allows for more appropriate evaluations by adjusting the evaluation criteria 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 evaluation unit may be performed using AI or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​perform the adjustment of evaluation criteria.

[0155] The evaluation unit can select the optimal evaluation method by referring to each member's past performance data during the evaluation process. For example, the evaluation unit can automatically select the optimal evaluation method based on each member's past performance data. The evaluation unit can also refer to past performance data and provide the optimal evaluation method for a specific task. The evaluation unit can analyze each member's past performance data and propose new methods to improve the accuracy of the evaluation. This allows the optimal evaluation method to be selected by referring to past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input each member's past performance data into an AI and have the AI ​​select the optimal evaluation method.

[0156] The evaluation unit can determine evaluation priorities based on the project's progress during the evaluation process. For example, the evaluation unit can automatically determine evaluation priorities based on the project's progress. The evaluation unit can also highlight and display evaluations with higher priority according to the progress. The evaluation unit can monitor the evaluation progress in real time and dynamically adjust priorities. This enables efficient evaluation by determining evaluation priorities based on progress. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input project progress data into AI and have the AI ​​determine the evaluation priorities.

[0157] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, the evaluation unit can also provide a display method that includes detailed information. If the user is in a hurry, the evaluation unit can also provide a display method that gets straight to the point. By adjusting the display method of the evaluation results according to the user's emotions, a more appropriate display becomes possible. 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 evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0158] The evaluation unit can set the scope of evaluation while considering the geographical distribution of each member. For example, the evaluation unit can focus on evaluating important regions based on the geographical distribution of each member. The evaluation unit can also compare and evaluate the progress of different regions while considering geographical distribution. The evaluation unit can evaluate the progress of specific regions in real time based on geographical distribution. This allows for focused evaluation of important regions by considering geographical distribution. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the geographical distribution data of each member into the AI ​​and have the AI ​​set the evaluation scope.

[0159] The evaluation unit can improve the accuracy of its evaluation by referring to relevant project literature during the evaluation process. For example, the evaluation unit can set evaluation criteria based on relevant project literature. The evaluation unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The evaluation unit can analyze relevant literature and propose new methods to improve the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input project-related literature data into AI and have the AI ​​perform the evaluation accuracy improvement.

[0160] The classification unit can estimate the user's emotions and adjust the classification method based on the estimated emotions. For example, if the user is tense, the classification unit provides a simple and highly visual classification method. If the user is relaxed, the classification unit can also provide a classification method that includes detailed information. If the user is in a hurry, the classification unit can also provide a concise classification method. This allows for more appropriate classification by adjusting the classification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input user emotion data into an AI and have the AI ​​adjust the classification method.

[0161] The classification unit can select the optimal classification method by referring to the project's past document data during classification. For example, the classification unit can automatically select the optimal classification method based on past project document data. The classification unit can also refer to past document data and provide the optimal classification method for a specific document. The classification unit can analyze past project document data and propose new methods to improve classification accuracy. This allows the optimal classification method to be selected by referring to past document data. Some or all of the above processes in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input past project document data into AI and have the AI ​​perform the selection of the optimal classification method.

[0162] The classification unit can determine classification priorities based on the project's progress during the classification process. For example, the classification unit can automatically determine classification priorities based on the project's progress. The classification unit can also highlight and display classifications with higher priority according to the progress. The classification unit can monitor the classification progress in real time and dynamically adjust priorities. This enables efficient classification by determining classification priorities based on progress. Some or all of the above processes in the classification unit may be performed using AI, for example, or without AI. For example, the classification unit can input project progress data into AI and have the AI ​​determine the classification priorities.

[0163] The classification unit can estimate the user's emotions and adjust the display method of the classification results based on the estimated emotions. For example, if the user is nervous, the classification unit can provide a simple and highly visible display method. If the user is relaxed, the classification unit can also provide a display method that includes detailed information. If the user is in a hurry, the classification unit can also provide a display method that gets straight to the point. By adjusting the display method of the classification results according to the user's emotions, a more appropriate display becomes possible. 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 classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0164] The classification unit can set the scope of classification while considering the geographical distribution of the project. For example, the classification unit can prioritize classification of important regions based on the geographical distribution of the project. The classification unit can also consider geographical distribution and compare the progress of different regions for classification. The classification unit can classify the progress of specific regions in real time based on geographical distribution. This allows for priority classification of important regions by considering geographical distribution. Some or all of the above processing in the classification unit may be performed using AI, for example, or not using AI. For example, the classification unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the classification scope.

[0165] The classification unit can improve the accuracy of its classification by referring to relevant project literature during the classification process. For example, the classification unit sets classification criteria based on relevant project literature. The classification unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The classification unit can analyze relevant literature and propose new methods to improve classification accuracy. This allows for improved classification accuracy by referring to relevant literature. Some or all of the above processes in the classification unit may be performed using AI, for example, or not. For example, the classification unit can input project-related literature data into AI and have the AI ​​perform the classification accuracy improvement.

[0166] The search unit can estimate the user's emotions and adjust the search method based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible search method. If the user is relaxed, the search unit can also provide a search method that includes detailed information. If the user is in a hurry, the search unit can also provide a concise search method. By adjusting the search method according to the user's emotions, more appropriate searches become possible. 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 search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into AI and have the AI ​​perform the adjustment of the search method.

[0167] The search unit can select the optimal search method by referring to past project document data during a search. For example, the search unit can automatically select the optimal search method based on past project document data. The search unit can also refer to past document data and provide the optimal search method for a specific document. The search unit can analyze past project document data and propose new methods to improve search accuracy. This allows the optimal search method to be selected by referring to past document data. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input past project document data into AI and have the AI ​​select the optimal search method.

[0168] The search unit can determine search priorities based on the project's progress during a search. For example, the search unit can automatically determine search priorities based on the project's progress. The search unit can also highlight and display high-priority search results according to the progress. The search unit can monitor the search progress in real time and dynamically adjust priorities. This enables efficient searching by determining search priorities based on progress. Some or all of the above processes in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input project progress data into AI and have the AI ​​determine search priorities.

[0169] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is nervous, the search unit can provide a simple and highly visible display. If the user is relaxed, the search unit can also provide a display that includes detailed information. If the user is in a hurry, the search unit can also provide a display that gets straight to the point. By adjusting how search results are displayed according to the user's emotions, a more appropriate display becomes possible. 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 search unit may be performed using AI, for example, or not using AI. For example, the search unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0170] The search unit can set the search scope considering the geographical distribution of the project during the search. For example, the search unit can focus its search on important areas based on the geographical distribution of the project. The search unit can also compare the progress of different areas while considering the geographical distribution. The search unit can search for the progress of a specific area in real time based on the geographical distribution. This allows for a focused search on important areas by considering the geographical distribution. Some or all of the above processing in the search unit may be performed using AI, for example, or not using AI. For example, the search unit can input the geographical distribution data of the project into the AI ​​and have the AI ​​set the search scope.

[0171] The search unit can improve search accuracy by referring to relevant project literature during the search process. For example, the search unit can set search criteria based on relevant project literature. The search unit can also adjust criteria for detecting abnormal progress by referring to relevant literature. The search unit can analyze relevant literature and propose new methods to improve search accuracy. This allows for improved search accuracy by referring to relevant literature. Some or all of the above processes in the search unit may be performed using AI, for example, or not. For example, the search unit can input project-related literature data into AI and have the AI ​​perform the search accuracy improvement.

[0172] The checking unit can estimate the user's emotions and adjust the checking criteria based on the estimated emotions. For example, if the user is nervous, the checking unit provides simple and easily visible checking criteria. If the user is relaxed, the checking unit can also provide checking criteria that include detailed information. If the user is in a hurry, the checking unit can also provide concise checking criteria. This allows for more appropriate checking by adjusting the checking criteria 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 checking unit may be performed using AI or not using AI. For example, the checking unit can input user emotion data into AI and have the AI ​​perform the adjustment of the checking criteria.

[0173] The checking unit can select the optimal checking method by referring to past project data during the checking process. For example, the checking unit can automatically select the optimal checking method based on past project data. The checking unit can also refer to past data and provide the optimal checking method for a specific task. The checking unit can analyze past project data and propose new methods to improve the accuracy of the checks. This allows the optimal checking method to be selected by referring to past data. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input past project data into AI and have the AI ​​perform the selection of the optimal checking method.

[0174] The checking unit can determine the priority of checks based on the project's progress during the checking process. For example, the checking unit can automatically determine the priority of checks based on the project's progress. The checking unit can also highlight and display high-priority checks according to the progress. The checking unit can monitor the progress of checks in real time and dynamically adjust priorities. This enables efficient checking by determining the priority of checks based on the progress. Some or all of the above processes in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input project progress data into AI and have the AI ​​determine the priority of checks.

[0175] The checking unit can estimate the user's emotions and adjust the display method of the check results based on the estimated emotions. For example, if the user is nervous, the checking unit can provide a simple and highly visible display method. If the user is relaxed, the checking unit can also provide a display method that includes detailed information. If the user is in a hurry, the checking unit can also provide a display method that gets straight to the point. By adjusting the display method of the check results according to the user's emotions, a more appropriate display becomes possible. 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 checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method.

[0176] The checking unit can set the scope of the check while considering the geographical distribution of the project. For example, the checking unit can focus on checking important areas based on the geographical distribution of the project. The checking unit can also compare and check the progress of different areas while considering the geographical distribution. The checking unit can check the progress of specific areas in real time based on the geographical distribution. This allows for focused checking of important areas by considering the geographical distribution. Some or all of the above processing in the checking unit may be performed using AI, for example, or without AI. For example, the checking unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the check scope.

[0177] The checking unit can improve the accuracy of its checks by referring to relevant project literature during the checking process. For example, the checking unit sets checking criteria based on relevant project literature. The checking unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The checking unit can analyze relevant literature and propose new methods to improve the accuracy of the checks. In this way, the accuracy of the checks can be improved by referring to relevant literature. Some or all of the above processes in the checking unit may be performed using AI, for example, or not using AI. For example, the checking unit can input project-related literature data into AI and have the AI ​​perform the task of improving the accuracy of the checks.

[0178] The reporting unit can estimate the user's emotions and adjust the reporting method based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and easy-to-read reporting method. If the user is relaxed, the reporting unit can also provide a reporting method that includes detailed information. If the user is in a hurry, the reporting unit can also provide a concise reporting method. This allows for more appropriate reporting by adjusting the reporting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into AI and have the AI ​​adjust the reporting method.

[0179] The reporting unit can select the optimal reporting method by referring to past project data when reporting. For example, the reporting unit can automatically select the optimal reporting method based on past project data. The reporting unit can also refer to past data and provide the optimal reporting method for a specific task. The reporting unit can analyze past project data and propose new methods to improve the accuracy of reporting. This allows the optimal reporting method to be selected by referring to past data. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not using AI. For example, the reporting unit can input past project data into AI and have the AI ​​perform the selection of the optimal reporting method.

[0180] The reporting unit can determine the priority of reports based on the project's progress when reporting. For example, the reporting unit can automatically determine the priority of reports based on the project's progress. The reporting unit can also highlight and display high-priority reports according to their progress. The reporting unit can monitor the progress of reports in real time and dynamically adjust priorities. This enables efficient reporting by determining the priority of reports based on their progress. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input project progress data into AI and have the AI ​​determine the priority of reports.

[0181] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is nervous, the reporting unit can provide a simple and highly visible display. If the user is relaxed, the reporting unit can also provide a display that includes detailed information. If the user is in a hurry, the reporting unit can also provide a display that gets straight to the point. By adjusting how the report is displayed according to the user's emotions, a more appropriate display becomes possible. 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 reporting unit may be performed using AI, or not using AI. For example, the reporting unit can input user emotion data into AI and have the AI ​​adjust the display method.

[0182] The reporting unit can set the scope of its reports considering the geographical distribution of the project. For example, the reporting unit can focus its reports on important regions based on the geographical distribution of the project. The reporting unit can also compare and report on the progress of different regions, taking geographical distribution into consideration. The reporting unit can report on the progress of specific regions in real time based on geographical distribution. This allows for focused reporting on important regions by considering geographical distribution. Some or all of the above processes in the reporting unit may be performed using AI, for example, or not. For example, the reporting unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the reporting scope.

[0183] The reporting department can improve the accuracy of its reports by referring to relevant project literature during the reporting process. For example, the reporting department can set reporting criteria based on relevant project literature. The reporting department can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The reporting department can analyze relevant literature and propose new methods to improve the accuracy of reports. This allows for improved reporting accuracy by referring to relevant literature. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input project-related literature data into AI and have the AI ​​perform the task of improving the accuracy of reports.

[0184] The generation unit can estimate the user's emotions and adjust the report generation method based on the estimated emotions. For example, if the user is tense, the generation unit will generate a simple and easy-to-read report. If the user is relaxed, the generation unit can also generate a report with detailed information. If the user is in a hurry, the generation unit can also generate a report that gets straight to the point. This allows for more appropriate reports by adjusting the report generation method according to the user's emotions. 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the report generation method.

[0185] The generation unit can select the optimal generation method by referring to past project data when generating reports. For example, the generation unit can automatically select the optimal generation method based on past project data. The generation unit can also refer to past data and provide the optimal generation method for a specific task. The generation unit can analyze past project data and propose new methods to improve generation accuracy. This allows the optimal generation method to be selected by referring to past data. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input past project data into AI and have the AI ​​perform the selection of the optimal generation method.

[0186] The generation unit can determine the priority of reports based on the project's progress when generating reports. For example, the generation unit can automatically determine the priority of reports based on the project's progress. The generation unit can also highlight and display reports with higher priority according to the progress. The generation unit can monitor the progress of reports in real time and dynamically adjust the priority. This enables efficient report generation by determining the priority of reports based on the progress. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input project progress data into AI and have the AI ​​perform the determination of report priorities.

[0187] The generation unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is stressed, the generation unit can provide a simple and highly visible display. If the user is relaxed, the generation unit can also provide a display that includes detailed information. If the user is in a hurry, the generation unit can also provide a display that gets straight to the point. By adjusting how the report is displayed according to the user's emotions, a more appropriate display becomes possible. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​perform the adjustment of the display method.

[0188] The generation unit can set the report scope considering the geographical distribution of the project when generating reports. For example, the generation unit can focus on reporting on important regions based on the geographical distribution of the project. The generation unit can also compare and report on the progress of different regions, taking geographical distribution into consideration. The generation unit can report on the progress of specific regions in real time based on geographical distribution. This allows for focusing on reporting on important regions by considering geographical distribution. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the geographical distribution data of the project into AI and have the AI ​​perform the setting of the report scope.

[0189] The generation unit can improve the accuracy of the report by referring to relevant project literature during report generation. For example, the generation unit sets report criteria based on relevant project literature. The generation unit can also adjust the criteria for detecting abnormal progress by referring to relevant literature. The generation unit can analyze relevant literature and propose new methods to improve the accuracy of the report. In this way, the accuracy of the report can be improved by referring to relevant literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input project relevant literature data into AI and have the AI ​​perform the report accuracy improvement.

[0190] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0191] The project management system also includes a notification function. This function can notify members in real time about project progress and task changes. For example, if a task is behind schedule, the notification function can send an alert to members to encourage prompt action. Furthermore, if a task's priority changes, the notification function can immediately notify members of the change. Additionally, when a key project milestone is achieved, the notification function can send a notification to all members to share the achievement. This allows for quick sharing of project progress and important changes, facilitating smoother communication among members.

[0192] The project management system also includes a feedback department. This department collects feedback from project members and uses it to improve the project. For example, members can report problems and areas for improvement they encountered during the project's progress. The feedback department can also conduct regular surveys to gather opinions on the project's progress and team performance. Furthermore, the feedback department can analyze the collected feedback and propose improvements to the project. This allows for project improvements that reflect member feedback, leading to improved team performance.

[0193] The project management system also includes a learning section. This section records problems and successes that occur during a project, which can then be used for future projects. For example, the causes of delays in specific tasks and the countermeasures taken can be recorded in the learning section. Furthermore, project success stories and effective methods can be saved in the learning section and reused in other projects. In addition, the learning section can analyze past project data to identify common problems and areas for improvement. This allows for the accumulation of project experience and increases the success rate of future projects.

[0194] The project management system also includes a forecasting unit. Based on project progress and historical data, the forecasting unit can predict future risks and problems. For example, if a particular task is likely to be delayed, the forecasting unit can notify the user of the risk in advance and propose countermeasures. Furthermore, by monitoring project progress in real time, the forecasting unit can detect potential problems early. In addition, the forecasting unit can analyze past project data and identify common risk patterns. This strengthens project risk management and helps prevent problems from occurring.

[0195] The project management system also includes a compensation department. This department can determine compensation based on the performance of project members. For example, it can evaluate task completion rates and contributions, and reward members who demonstrate outstanding performance. The compensation department can also provide rewards to the entire team when key project milestones are achieved. Furthermore, the compensation department can improve the compensation system based on member feedback, thereby increasing motivation. This is expected to improve member performance and contribute to the success of the project.

[0196] The project management system also includes an emotion estimation unit. This unit estimates the emotions of project members and can adjust project progress based on those estimates. For example, if a member is feeling stressed, the emotion estimation unit can reduce their workload and provide a more relaxed environment. Conversely, if a member is highly motivated, the unit can assign challenging tasks and provide opportunities for growth. Furthermore, the emotion estimation unit can analyze member emotion data to understand the overall emotional state of the team. This enables project management tailored to the emotions of the members, leading to improved team performance.

[0197] The project management system also includes a communications department. This department can provide functions to facilitate communication among project members. For example, it can offer chat and video conferencing features to enable real-time information sharing. Furthermore, the communications department can estimate members' emotions and suggest appropriate communication methods. For instance, it can provide simple and clear messages if a member is stressed, and share detailed information if they are relaxed. Additionally, the communications department can collect feedback from members and suggest areas for communication improvement. This leads to smoother communication among members and a more efficient project progress.

[0198] The project management system also includes a motivation section. This section can provide functions to boost the motivation of project members. For example, it can estimate a member's emotions and send encouraging messages if their motivation is low. The motivation section can also visualize the tasks and milestones completed by members, allowing them to share their sense of accomplishment. Furthermore, the motivation section can analyze members' emotional data and suggest specific actions to increase motivation. This can improve member motivation and contribute to the success of the project.

[0199] The project management system also includes a stress management department. This department can monitor the stress levels of project members and propose appropriate measures. For example, it can estimate members' emotions and assign relaxing tasks if stress levels are high. The stress management department can also provide members with activities and resources to reduce stress. Furthermore, it can analyze members' emotional data, identify the causes of stress, and propose solutions. This helps reduce stress among members and provides a healthy project environment.

[0200] The project management system also includes an engagement section. This section can provide functions to enhance project member engagement. For example, it can estimate members' emotions and, if engagement is low, assign them to tasks or projects that will interest them. Furthermore, the engagement section can actively incorporate members' opinions and ideas, increasing their sense of participation in the project. In addition, the engagement section can analyze members' emotional data and propose specific actions to improve engagement. This, in turn, can improve member engagement and contribute to the success of the project.

[0201] The following briefly describes the processing flow for example form 2.

[0202] Step 1: The monitoring department monitors the project's progress. For example, the monitoring department monitors the project's progress in real time and automatically updates task progress. It can also provide appropriate answers to questions from project members and suggest new ideas as needed. Step 2: The update unit updates the task progress based on the progress monitored by the monitoring unit. The update unit can, for example, automatically update task progress and provide real-time visibility into project progress. Furthermore, it can create an efficient schedule by considering task priorities. Step 3: The proposal team proposes new ideas based on the progress updated by the update team. For example, the proposal team analyzes the project's progress and proposes new ideas. Furthermore, they can also propose new ideas based on feedback from project members. Step 4: The Identification Department identifies risks and problems based on the ideas proposed by the Proposal Department. For example, the Identification Department may analyze the project's progress and identify potential risks. Furthermore, based on past project data, they may predict that certain tasks are likely to be delayed and propose countermeasures.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] Each of the multiple elements described above, including the monitoring unit, update unit, proposal unit, identification unit, collection unit, analysis unit, classification unit, check unit, and generation unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit monitors the progress of the project using the camera 42 and microphone 38B of the smart device 14, and the control unit 46A grasps the progress in real time. The update unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and automatically updates the progress of tasks based on the progress. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and proposes new ideas. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies risks and problems. The collection unit is implemented, for example, by the control unit 46A of the smart device 14, and collects the tasks and schedules of each member. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and past data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the workload, quality, and level of cooperation of each member. The classification unit is implemented, for example, by the control unit 46A of the smart device 14, and automatically classifies and organizes documents. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the project complies with legal regulations and the company's own policies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data in real time and automatically generates reports based on that analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0207] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.).

[0219] 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.

[0220] 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 (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 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.

[0221] 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.

[0222] Each of the multiple elements described above, including the monitoring unit, update unit, proposal unit, identification unit, collection unit, analysis unit, classification unit, check unit, and generation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit monitors the progress of the project using the camera 42 and microphone 238 of the smart glasses 214, and the control unit 46A grasps the progress in real time. The update unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and automatically updates the progress of tasks based on the progress. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and proposes new ideas. The identification unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and identifies risks and problems. The collection unit is implemented, for example, in the control unit 46A of the smart glasses 214, and collects the tasks and schedules of each member. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and past data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the workload, quality, and level of cooperation of each member. The classification unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automatically classifies and organizes documents. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the project complies with legal regulations and the company's own policies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data in real time and automatically generates reports based on that analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0223] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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).

[0229] 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.

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.).

[0235] 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.

[0236] 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 (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 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.

[0237] 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.

[0238] Each of the multiple elements described above, including the monitoring unit, update unit, proposal unit, identification unit, collection unit, analysis unit, classification unit, check unit, and generation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit monitors the progress of the project using the camera 42 and microphone 238 of the headset terminal 314, and the control unit 46A grasps the progress in real time. The update unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and automatically updates the progress of tasks based on the progress. The proposal unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and proposes new ideas. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and identifies risks and problems. The collection unit is implemented by, for example, the control unit 46A of the headset terminal 314, and collects the tasks and schedules of each member. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the project's progress and past data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the workload, quality, and level of cooperation of each member. The classification unit is implemented, for example, by the control unit 46A of the headset terminal 314, and automatically classifies and organizes documents. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the project complies with legal regulations and the company's own policies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data in real time and automatically generates reports based on that analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0239] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0240] 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.

[0241] 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.

[0242] 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.

[0243] 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.

[0244] 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).

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.).

[0252] 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.

[0253] 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 (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 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.

[0254] 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 external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0255] Each of the multiple elements described above, including the monitoring unit, update unit, proposal unit, identification unit, collection unit, analysis unit, classification unit, check unit, and generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit monitors the progress of the project using the camera 42 and microphone 238 of the robot 414, and the control unit 46A grasps the progress in real time. The update unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and automatically updates the progress of tasks based on the progress. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and proposes new ideas. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and identifies risks and problems. The collection unit is implemented, for example, by the control unit 46A of the robot 414, and collects the tasks and schedules of each member. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the progress of the project and past data. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the workload, quality, and level of cooperation of each member. The classification unit is implemented, for example, by the control unit 46A of the robot 414, and automatically classifies and organizes documents. The checking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and checks whether the project complies with legal regulations and the company's own policies. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and analyzes data in real time and automatically generates reports based on that analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0256] 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.

[0257] 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.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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."

[0262] 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.

[0263] 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.

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] (Note 1) The monitoring department monitors the progress of the project, An update unit that updates the progress status of a task based on the progress monitored by the monitoring unit; A proposal unit that proposes new ideas based on the progress status updated by the update unit; And a specifying unit that specifies risks and problems based on the ideas proposed by the proposal unit. A system characterized by this. (Appendix 2) Comprising a collection unit that collects the tasks and schedules of each member. The system according to Appendix 1, characterized by this. (Appendix 3) Comprising an analysis unit that analyzes the progress status and past data of the project. The system according to Appendix 1, characterized by this. (Appendix 4) Comprising an analysis unit that analyzes the workload, quality, and cooperation degree of each member. The system according to Appendix 1, characterized by this. (Appendix 5) Comprising a classification unit that automatically classifies and organizes documents. The system according to Appendix 1, characterized by this. (Appendix 6) Comprising a checking unit that checks whether the project complies with laws and regulations and the company's own policies. The system according to Appendix 1, characterized by this. (Appendix 7) Comprising a generating unit that analyzes data in real time and automatically generates a report based on it. The system according to Appendix 1, characterized by this. (Appendix 8) The monitoring unit Estimates the user's sentiment and adjusts the monitoring frequency based on the estimated user's sentiment. The system according to Appendix 1, characterized by this. (Appendix 9) The monitoring unit When monitoring the progress of the project, refers to past project data to detect anomalies. The system according to Appendix 1, characterized by this. (Appendix 10) The monitoring unit identifies important project milestones during monitoring and focuses on monitoring them The system according to Appendix 1, characterized in that (Appendix 11) The monitoring unit estimates the user's emotion and adjusts the display method of the monitoring result based on the estimated user's emotion The system according to Appendix 1, characterized in that (Appendix 12) The monitoring unit sets the monitoring scope considering the geographical distribution of the project during monitoring The system according to Appendix 1, characterized in that (Appendix 13) The monitoring unit refers to relevant project literature during monitoring to improve the accuracy of monitoring The system according to Appendix 1, characterized in that (Appendix 14) The updating unit estimates the user's emotion and adjusts the updating timing based on the estimated user's emotion The system according to Appendix 1, characterized in that (Appendix 15) The updating unit re - evaluates the priority of tasks based on the progress of the project during updating The system according to Appendix 1, characterized in that (Appendix 16) The updating unit collects data for optimizing the resource allocation of the project during updating The system according to Appendix 1, characterized in that (Appendix 17) The updating unit estimates the user's emotion and adjusts the display method of the updated content based on the estimated user's emotion The system according to Appendix 1, characterized in that (Appendix 18) The update unit sets the update range considering the geographical distribution of the project at the time of update The system according to Appendix 1, characterized in that. (Appendix 19) The update unit improves the update accuracy by referring to the related documents of the project at the time of update The system according to Appendix 1, characterized in that. (Appendix 20) The proposal unit estimates the user's emotion and adjusts the expression method of the proposal based on the estimated user's emotion The system according to Appendix 1, characterized in that. (Appendix 21) The proposal unit determines the priority of the proposal based on the progress of the project at the time of proposal The system according to Appendix 1, characterized in that. (Appendix 22) The proposal unit generates an optimal proposal by referring to the past data of the project at the time of proposal The system according to Appendix 1, characterized in that. (Appendix 23) The proposal unit estimates the user's emotion and adjusts the length of the proposal based on the estimated user's emotion The system according to Appendix 1, characterized in that. (Appendix 24) The proposal unit sets the range of the proposal considering the geographical distribution of the project at the time of proposal The system according to Appendix 1, characterized in that. (Appendix 25) The proposal unit improves the accuracy of the proposal by referring to the related documents of the project at the time of proposal The system according to Appendix 1, characterized in that. (Appendix 26) The identification unit It estimates the user's emotions and adjusts certain criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The specified part is, Prioritize risks and issues based on the project's progress at specific points in time. The system described in Appendix 1, characterized by the features described herein. (Note 28) The specified part is, At specific times, the project's historical data is referenced to select the most suitable identification method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The specified part is, It estimates the user's emotions and adjusts how specific results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The specified part is, At specific times, set a specific range considering the geographical distribution of the project. The system described in Appendix 1, characterized by the features described herein. (Note 31) The specified part is, At specific times, refer to relevant project literature to improve specific accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of data collection based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned collection unit is During data collection, the optimal collection method is selected by referring to each member's past task history. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant tasks and appointments, taking into account each member's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned collection unit is It estimates the user's emotions and determines the priority of tasks and appointments to collect based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned collection unit is During data collection, the optimal collection method is selected by referring to each member's past task history. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant tasks and appointments, taking into account each member's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 38) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned analysis unit, During the analysis, the optimal analysis method is selected by referring to the project's historical data. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned analysis unit, During the analysis, prioritize the analysis based on the project's progress. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned analysis unit, During the analysis, the scope of the analysis is set considering the geographical distribution of the projects. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned analysis unit, During analysis, we refer to relevant project literature to improve the accuracy of the analysis. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 45) The aforementioned analysis unit is During the analysis, the most suitable analysis method is selected by referring to each member's past work history. The system described in Appendix 4, characterized by the features described herein. (Note 46) The aforementioned analysis unit is During the analysis, prioritize the analysis based on the project's progress. The system described in Appendix 4, characterized by the features described herein. (Note 47) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 48) The aforementioned analysis unit is When conducting the analysis, the scope of the analysis should be set considering the geographical distribution of each member. The system described in Appendix 4, characterized by the features described herein. (Note 49) The aforementioned analysis unit is During analysis, refer to relevant project literature to improve the accuracy of the analysis. The system described in Appendix 4, characterized by the features described herein. (Note 50) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 51) The evaluation unit, During the evaluation process, the optimal evaluation method is selected by referring to each member's past performance data. The system described in Appendix 5, characterized by the features described herein. (Note 52) The evaluation unit, During the evaluation process, prioritization of evaluations will be determined based on the project's progress. The system described in Appendix 5, characterized by the features described herein. (Note 53) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 5, characterized by the features described herein. (Note 54) The evaluation unit, When evaluating, the scope of evaluation should be set considering the geographical distribution of each member. The system described in Appendix 5, characterized by the features described herein. (Note 55) The evaluation unit, During the evaluation process, refer to relevant project literature to improve the accuracy of the evaluation. The system described in Appendix 5, characterized by the features described herein. (Note 56) The aforementioned classification unit is It estimates the user's emotions and adjusts the classification method based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 57) The aforementioned classification unit is During classification, the optimal classification method is selected by referring to the project's past document data. The system described in Appendix 6, characterized by the features described herein. (Note 58) The aforementioned classification unit is When classifying, prioritize classifications based on the project's progress. The system described in Appendix 6, characterized by the features described herein. (Note 59) The aforementioned classification unit is It estimates the user's emotions and adjusts how the classification results are displayed based on the estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 60) The aforementioned classification unit is When classifying, set the scope of classification considering the geographical distribution of the projects. The system described in Appendix 6, characterized by the features described herein. (Note 61) The aforementioned classification unit is During classification, refer to relevant project literature to improve classification accuracy. The system described in Appendix 6, characterized by the features described herein. (Note 62) The aforementioned search unit, It estimates the user's sentiment and adjusts the search method based on the estimated user sentiment. The system described in Appendix 7, characterized by the features described herein. (Note 63) The aforementioned search unit, During the search, the project's past documentation data is referenced to select the most suitable search method. The system described in Appendix 7, characterized by the features described herein. (Note 64) The aforementioned search unit, When searching, prioritize your search based on the project's progress. The system described in Appendix 7, characterized by the features described herein. (Note 65) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 7, characterized by the features described herein. (Note 66) The aforementioned search unit, When searching, set the search range considering the geographical distribution of projects. The system described in Appendix 7, characterized by the features described herein. (Note 67) The aforementioned search unit, When searching, refer to relevant project literature to improve search accuracy. The system described in Appendix 7, characterized by the features described herein. (Note 68) The aforementioned checking unit is The system estimates the user's emotions and adjusts the check criteria based on those estimated emotions. The system described in Appendix 8, characterized by the features described herein. (Note 69) The aforementioned checking unit is During the check, the most suitable checking method is selected by referring to the project's past data. The system described in Appendix 8, characterized by the features described herein. (Note 70) The aforementioned checking unit is During the check, prioritize the checks based on the project's progress. The system described in Appendix 8, characterized by the features described herein. (Note 71) The aforementioned checking unit is The system estimates the user's emotions and adjusts how the check results are displayed based on those estimated emotions. The system described in Appendix 8, characterized by the features described herein. (Note 72) The aforementioned checking unit is When checking, set the scope of the check considering the geographical distribution of the projects. The system described in Appendix 8, characterized by the features described herein. (Note 73) The aforementioned checking unit is During the check, refer to relevant project literature to improve the accuracy of the check. The system described in Appendix 8, characterized by the features described herein. (Note 74) The aforementioned reporting department, We estimate the user's emotions and adjust the reporting method based on the estimated user emotions. The system described in Appendix 9, characterized by the features described herein. (Note 75) The aforementioned reporting department, When reporting, refer to the project's past data to select the most appropriate reporting method. The system described in Appendix 9, characterized by the features described herein. (Note 76) The aforementioned reporting department, When reporting, prioritize reports based on the project's progress. The system described in Appendix 9, characterized by the features described herein. (Note 77) The aforementioned reporting department, The system estimates the user's emotions and adjusts how the report content is displayed based on those estimated emotions. The system described in Appendix 9, characterized by the features described herein. (Note 78) The aforementioned reporting department, When reporting, set the scope of the report considering the geographical distribution of the project. The system described in Appendix 9, characterized by the features described herein. (Note 79) The aforementioned reporting department, When reporting, refer to relevant project literature to improve the accuracy of the report. The system described in Appendix 9, characterized by the features described herein. (Note 80) The generating unit is We estimate user sentiment and adjust how reports are generated based on the estimated user sentiment. The system described in Appendix 10, characterized by the features described herein. (Note 81) The generating unit is When generating reports, the optimal generation method is selected by referring to the project's historical data. The system described in Appendix 10, characterized by the features described herein. (Note 82) The generating unit is When generating reports, prioritize reports based on the project's progress. The system described in Appendix 10, characterized by the features described herein. (Note 83) The generating unit is It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 10, characterized by the features described herein. (Note 84) The generating unit is When generating a report, set the report scope considering the geographical distribution of the project. The system described in Appendix 10, characterized by the features described herein. (Note 85) The generating unit is When generating reports, we refer to relevant project literature to improve the accuracy of the reports. The system described in Appendix 10, characterized by the features described herein. [Explanation of Symbols]

[0275] 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 monitoring department monitors the progress of the project, An update unit updates the progress status of a task based on the progress status monitored by the aforementioned monitoring unit, A proposal unit that proposes new ideas based on the progress status updated by the aforementioned update unit, The system includes an identification unit that identifies risks and problems based on the ideas proposed by the aforementioned proposal unit. A system characterized by the following features.

2. It includes a collection unit that gathers each member's tasks and schedules. The system according to feature 1.

3. It includes an analysis unit that analyzes the project's progress and historical data. The system according to feature 1.

4. The department includes an analysis section that analyzes the workload, quality, and level of cooperation of each member. The system according to feature 1.

5. It includes a classification unit that automatically classifies and organizes documents. The system according to feature 1.

6. It includes a checklist to ensure the project complies with legal regulations and the company's own policies. The system according to feature 1.

7. It features a generation unit that analyzes data in real time and automatically generates reports based on that analysis. The system according to feature 1.

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

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