system

Abstract

The system according to this embodiment aims to manage the progress of a project in real time and propose the optimal schedule and resource allocation. [Solution] The system according to the embodiment comprises a management unit, a data collection unit, an analysis unit, a proposal unit, and a reporting unit. The management unit manages the progress of the project in real time. The data collection unit collects project data managed by the management unit. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis unit. The reporting unit reports the content 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the progress of a project has not been sufficiently managed in real time, and an optimal schedule and resource allocation have not been proposed, leaving room for improvement.

[0005] The system according to the embodiment aims to manage the progress of a project in real time and propose an optimal schedule and resource allocation.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a management unit, a data collection unit, an analysis unit, a proposal unit, and a reporting unit. The management unit manages the progress of the project in real time. The data collection unit collects project data managed by the management unit. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis unit. The reporting unit reports the content proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage the progress of a project in real time and propose the optimal schedule and resource allocation. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that manages the progress of a project in real time and automatically analyzes and reports on task allocation and progress. Furthermore, it proposes an optimal schedule and resource allocation to support project success. For example, the project management system grasps the progress of each task in detail and updates the progress in real time. The project management system collects information such as the start and end dates and progress of tasks, and visualizes the overall progress of the project. Next, the project management system uses an AI agent to automatically analyze task allocation and progress. The AI ​​agent analyzes project data and determines the progress and priority of each task. For example, it identifies problems such as delays in task progress or insufficient resources and proposes appropriate countermeasures. Furthermore, the project management system uses the AI ​​agent to propose an optimal schedule and resource allocation. Based on project data, the AI ​​agent optimizes the priority and resource allocation of each task. For example, it makes optimal suggestions for project success, such as reallocating resources or changing task schedules. This system allows for real-time management of project progress, automatic analysis and reporting of task allocation and progress. This reduces the burden on project managers and improves project success rates. For example, if a project manager manages multiple projects, they can grasp the progress of each project in real time and appropriately prioritize tasks. Furthermore, optimizing resource allocation improves project efficiency. Thus, by using an AI agent, project progress can be managed in real time, and task allocation and progress can be automatically analyzed and reported. This reduces the burden on project managers and improves project success rates.This allows the project management system to manage project progress in real time, automatically analyze and report on task assignments and progress.

[0029] The project management system according to this embodiment comprises a management unit, a data collection unit, an analysis unit, a proposal unit, and a reporting unit. The management unit manages the progress of the project in real time. For example, the management unit grasps the progress of each task in the project in detail and updates the progress in real time. The management unit collects information such as the start date, end date, and progress of tasks and visualizes the progress of the entire project. The data collection unit collects project data managed by the management unit. For example, the data collection unit collects data such as the progress of each task in the project and the usage of resources. The data collection unit centrally manages the project data and provides it to the analysis unit. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the project data and determines the progress and priority of each task. The analysis unit identifies problems, such as when task progress is behind schedule or when resources are insufficient, and proposes appropriate countermeasures. The proposal unit proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis unit. The proposal department makes optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Based on project data, the proposal department optimizes the prioritization of each task and the allocation of resources. The reporting department reports on the proposals made by the proposal department. The reporting department reports so that the project manager can understand the progress in real time. The reporting department provides information such as the project progress, task prioritization, and resource allocation. As a result, the project management system according to this embodiment can manage the project progress in real time and automatically analyze and report on task allocation and progress.

[0030] The management department manages the project's progress in real time. For example, the management department has a detailed understanding of the progress of each project task and updates the progress in real time. Specifically, the management department uses project management software to collect information such as the start date, end date, progress, and assigned person for each task, and manages this information centrally. The management department uses visual tools such as Gantt charts and burn-down charts to visualize the project's progress. This allows project managers and team members to grasp the project's progress at a glance. Furthermore, the management department collects feedback from each team member and regularly checks the progress to update task progress in real time. For example, they check the progress of each task through weekly meetings and daily stand-up meetings and adjust the task schedule as needed. This allows the management department to keep the project progress up to date and detect project delays and resource shortages early. In addition, the management department can use dashboards to visualize the project's progress. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This allows the management department to efficiently manage the project's progress and take appropriate measures to ensure its success.

[0031] The data collection unit collects project data managed by the management unit. For example, the collection unit collects data such as the progress of each project task and resource usage. Specifically, the collection unit automatically collects data from project management software, various sensors, and devices, and centrally manages this data. The collection unit uses a database to centrally manage project data and provide it to the analysis unit. The database stores information such as the progress of each task, resource usage, and project progress, and allows the analysis unit to access it as needed. The collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if the project progress changes rapidly or resource usage increases abnormally, the collection unit can increase the data collection frequency to grasp the situation in real time. This allows the collection unit to efficiently collect project progress and resource usage, improving the overall system performance. Furthermore, the collection unit performs data verification and cleaning to ensure data quality. For example, it checks for errors or omissions in the collected data and corrects the data as needed. This allows the data collection unit to provide accurate and reliable data, and to provide the foundation for the analysis unit to perform accurate analysis.

[0032] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes project data to determine the progress and priority of each task. Specifically, the analysis department uses AI and machine learning algorithms to analyze the collected data and evaluate the project's progress and resource usage. For example, AI analyzes the progress of each task and identifies tasks that are behind schedule or lacking resources. AI also determines the priority of each task based on historical data and statistical information and proposes an optimal schedule for project success. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a task's progress suddenly slows down or resource usage increases abnormally, the analysis department will detect the anomaly and issue a warning to the project manager. This allows the analysis department to accurately understand the project's progress and resource usage and identify problems early. In addition, the analysis department can use dashboards to visualize the project's progress and resource usage. The dashboard displays information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This enables the analytics department to efficiently analyze project progress and resource usage, and take appropriate actions to ensure project success.

[0033] The proposal department proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis department. The proposal department makes optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Specifically, the proposal department uses AI and machine learning algorithms to analyze collected data and evaluate project progress and resource usage. For example, AI analyzes the progress of each task, identifying tasks that are behind schedule or lacking resources. AI also determines the priority of each task based on historical data and statistics, proposing the optimal schedule for project success. Furthermore, the proposal department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. For example, if task progress falls sharply or resource usage increases abnormally, the proposal department detects the anomaly and warns the project manager. This allows the proposal department to accurately understand project progress and resource usage, and identify problems early. Additionally, the proposal department can use dashboards to visualize project progress and resource usage. The dashboard displays information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This enables the proposal department to efficiently analyze project progress and resource usage and take appropriate actions to ensure project success.

[0034] The reporting department reports on the proposals made by the proposal department. For example, the reporting department reports so that the project manager can understand the progress in real time. Specifically, the reporting department uses project management software to display information such as the progress of each task and resource usage in real time. The reporting department uses dashboards to provide information such as project progress, task priorities, and resource allocation. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly understand the situation. Furthermore, the reporting department uses visual tools such as Gantt charts and burn-down charts to visualize project progress and resource usage. This allows project managers and team members to grasp the project progress at a glance. Additionally, the reporting department can use dashboards to visualize project progress and resource usage. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly understand the situation. This enables the reporting department to efficiently report on project progress and resource usage, and to take appropriate actions toward project success.

[0035] The management department can gain a detailed understanding of the progress of each task in the project and update the progress status in real time. For example, the management department collects information such as the start and end dates and progress of tasks to visualize the overall project progress. To gain a detailed understanding of the progress of each task in the project, the management department needs to clearly define how progress is recorded and what types of information it needs to collect. For example, the management department can use a task management tool to record the progress of tasks. To update the task progress in real time, the management department needs to set the update frequency and delay time. For example, the management department can update the task progress daily. This allows the management department to gain a detailed understanding of the progress of each task in the project and update the progress status in real time.

[0036] The analysis department can analyze project data and determine the progress and priority of each task. For example, the analysis department can analyze project data and determine the progress and priority of each task. To analyze project data, the analysis department needs to clearly define the algorithms to be used and the data to be analyzed. For example, to evaluate the progress of a task, the analysis department can analyze progress data. To determine the priority of a task, the analysis department needs to evaluate the importance and urgency of the task. For example, to evaluate the importance of a task, the analysis department can consider the impact and dependencies of the task. This allows the analysis department to analyze project data and determine the progress and priority of each task.

[0037] The proposal department can make optimal suggestions for project success, such as reallocating resources or rescheduling tasks. To make optimal suggestions, the proposal department needs to clarify the evaluation criteria and content of the suggestions. For example, to propose resource reallocation, the proposal department can evaluate resource usage and project progress. To propose task rescheduling, the proposal department needs to evaluate task progress and dependencies. For example, to propose task rescheduling, the proposal department can consider task progress and dependencies. This allows the proposal department to make optimal suggestions for project success, such as reallocating resources or rescheduling tasks.

[0038] The reporting department can provide reports that allow the project manager to understand the progress in real time. For example, the reporting department can provide reports that allow the project manager to understand the progress in real time. To ensure real-time understanding, the reporting department needs to clearly define the types of information to be understood and the frequency of updates. For example, the reporting department can provide information such as project progress, task priorities, and resource allocation. The reporting department needs to set the frequency and method of reporting so that the project manager can understand the progress in real time. For example, the reporting department can provide regular reports so that the project manager can understand the progress in real time. This allows the reporting department to provide reports that allow the project manager to understand the progress in real time.

[0039] The management department can gain a detailed understanding of the progress of each task in the project and, when updating progress in real time, can adjust the update frequency while considering task dependencies. For example, the management department can analyze task dependencies and prioritize updating the progress of important tasks. The management department can dynamically adjust the update frequency of progress based on task dependencies. The management department can optimize the timing of progress updates while considering task dependencies. This allows the management department to adjust the update frequency of progress while considering task dependencies. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input task dependency data into a generating AI and have the generating AI perform the adjustment of the update frequency.

[0040] The management department can set priorities based on the importance and urgency of tasks when monitoring their progress. For example, the management department can evaluate the importance of tasks and display important tasks first. The management department can evaluate the urgency of tasks and display urgent tasks first. The management department can comprehensively evaluate the importance and urgency of tasks and set the optimal priority. This allows the management department to set priorities based on the importance and urgency of tasks. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input task importance and urgency data into a generating AI and have the generating AI perform the priority setting.

[0041] The management department can optimize task allocation by considering the skill sets and experience of project members when monitoring task progress. For example, the management department can evaluate the skill sets of project members and assign appropriate tasks. The management department can optimize task allocation by considering the experience of project members. The management department can comprehensively evaluate the skill sets and experience of project members and make optimal task allocations. This allows the management department to optimize task allocation by considering the skill sets and experience of project members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input project members' skill set and experience data into a generating AI and have the generating AI perform task allocation.

[0042] The management department can update progress status while considering the geographical distribution of the project when monitoring task progress. For example, the management department can evaluate the geographical distribution of the project and adjust the frequency of progress status updates. The management department can optimize the timing of progress status updates based on the geographical distribution. The management department can adjust the method of updating progress status while considering the geographical distribution. This allows the management department to update progress status while considering the geographical distribution of the project. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input geographical distribution data of the project into a generating AI and have the generating AI perform progress status updates.

[0043] The data collection unit can evaluate the reliability and accuracy of project data and optimize the collection method. For example, the data collection unit can evaluate the reliability of the data and prioritize the collection of reliable data. The data collection unit can evaluate the accuracy of the data and prioritize the collection of accurate data. The data collection unit can comprehensively evaluate the reliability and accuracy of the data and select the optimal collection method. In this way, the data collection unit can evaluate the reliability and accuracy of the data and optimize the collection method. 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 data reliability and accuracy data into a generating AI and have the generating AI perform the optimization of the collection method.

[0044] The data collection unit can adjust the type and amount of data collected according to the project's progress. For example, the data collection unit can evaluate the project's progress and select the necessary data types. The data collection unit can adjust the amount of data collected according to the project's progress. The data collection unit can comprehensively evaluate the project's progress and select the optimal data collection method. This allows the data collection unit to adjust the type and amount of data collected according to the project's progress. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input project progress data into a generating AI and have the generating AI perform the data collection adjustments.

[0045] The data collection unit can collect feedback from project members during data collection and improve the quality of the collected data. For example, the data collection unit can collect feedback from project members and improve the data collection method. The data collection unit can improve the quality of the collected data by reflecting the opinions of project members. The data collection unit can optimize the data collection process based on feedback from project members. This allows the data collection unit to collect feedback from project members and improve the quality of the collected data. 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 project member feedback data into a generating AI and have the generating AI implement improvements to the data collection method.

[0046] The data collection unit can select data to collect while considering the project's external environment (such as market trends and competitive landscape). For example, the data collection unit can evaluate market trends and select the necessary data. The data collection unit can evaluate the competitive landscape and select the data to collect. The data collection unit can comprehensively evaluate market trends and the competitive landscape and select the optimal data collection method. This allows the data collection unit to select data while considering the project's external environment. 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 market trend and competitive landscape data into a generating AI and have the generating AI perform the selection of data to collect.

[0047] The analysis unit can optimize algorithms for determining task progress and priority when analyzing project data. For example, the analysis unit can optimize an algorithm for evaluating task progress. The analysis unit can optimize an algorithm for determining task priority. The analysis unit can optimize an algorithm for comprehensively evaluating task progress and priority. This allows the analysis unit to optimize algorithms for determining task progress and priority. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input task progress and priority data into a generating AI and have the generating AI perform algorithm optimization.

[0048] The analysis unit can improve prediction accuracy by referring to the project's past data during data analysis. For example, the analysis unit can improve prediction accuracy by referring to the project's past data. The analysis unit can improve the prediction model based on past data. The analysis unit can improve prediction accuracy by comprehensively evaluating past data. Thus, the analysis unit can improve prediction accuracy by referring to the project's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the project's past data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0049] The analysis department can customize the analysis results when analyzing data, taking into account the skill sets and experience of project members. For example, the analysis department can evaluate the skill sets of project members and provide appropriate analysis results. The analysis department can customize the analysis results by taking into account the experience of project members. The analysis department can comprehensively evaluate the skill sets and experience of project members and provide optimal analysis results. This allows the analysis department to customize the analysis results by taking into account the skill sets and experience of project members. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input project member skill set and experience data into a generating AI and have the generating AI perform the customization of the analysis results.

[0050] The analysis department can provide analysis results while considering the project's external environment (such as market trends and competitive landscape) during data analysis. For example, the analysis department can evaluate market trends and provide appropriate analysis results. The analysis department can evaluate the competitive landscape and provide analysis results. The analysis department can comprehensively evaluate market trends and the competitive landscape and provide optimal analysis results. In this way, the analysis department can provide analysis results while considering the project's external environment. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input market trend and competitive landscape data into a generating AI and have the generating AI perform the task of providing analysis results.

[0051] The proposal department can set priorities for proposals when making optimal suggestions for project success, such as reallocating resources or rescheduling tasks. For example, the proposal department may prioritize resource reallocation. The proposal department may also prioritize task rescheduling. The proposal department can comprehensively evaluate resource reallocation and task rescheduling to make the optimal proposal. This allows the proposal department to set priorities for proposals when making optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input resource reallocation and task rescheduling data into a generating AI and have the generating AI perform the setting of proposal priorities.

[0052] The proposal department can reflect the project progress and resource utilization in real time when making a proposal. For example, the proposal department can evaluate the project progress in real time and reflect it in the proposal. The proposal department can evaluate resource utilization in real time and reflect it in the proposal. The proposal department can comprehensively evaluate the project progress and resource utilization and make the optimal proposal. As a result, the proposal department can reflect the project progress and resource utilization in real time. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input project progress and resource utilization data into a generating AI and have the generating AI perform the reflection of the proposal content.

[0053] The proposal department can customize proposals by considering the skill sets and experience of project members. For example, the proposal department can evaluate the skill sets of project members and provide appropriate proposals. The proposal department can customize proposals by considering the experience of project members. The proposal department can comprehensively evaluate the skill sets and experience of project members and provide optimal proposals. This allows the proposal department to customize proposals by considering the skill sets and experience of project members. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input project member skill set and experience data into a generating AI and have the generating AI perform the customization of proposals.

[0054] The proposal department can provide proposals while considering the external environment of the project (such as market trends and competitive conditions). For example, the proposal department can evaluate market trends and provide appropriate proposals. The proposal department can evaluate the competitive situation and provide proposals. The proposal department can comprehensively evaluate market trends and the competitive situation and provide optimal proposals. In this way, the proposal department can provide proposals while considering the external environment of the project. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input market trend and competitive situation data into a generating AI and have the generating AI perform the task of providing proposals.

[0055] The reporting unit can optimize the frequency of reports so that project managers can stay informed about progress in real time. For example, the reporting unit can evaluate the project's progress and optimize the frequency of reports. The reporting unit can adjust the frequency of reports according to the project manager's needs. The reporting unit can comprehensively evaluate the project's progress and the project manager's needs to set the optimal reporting frequency. This allows the reporting unit to optimize the frequency of reports so that project managers can stay informed about progress in real time. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input project progress and project manager needs data into a generating AI and have the generating AI perform the optimization of the reporting frequency.

[0056] The reporting unit can reflect project progress and resource utilization in real time when reporting. For example, the reporting unit can evaluate project progress in real time and reflect it in the report. The reporting unit can evaluate resource utilization in real time and reflect it in the report. The reporting unit can comprehensively evaluate project progress and resource utilization and provide optimal report content. As a result, the reporting unit can reflect project progress and resource utilization in real time. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input project progress and resource utilization data into a generating AI and have the generating AI perform the task of reflecting the report content.

[0057] The reporting department can customize the content of reports by taking into account the skill sets and experience of project members. For example, the reporting department can evaluate the skill sets of project members and provide appropriate report content. The reporting department can customize the content of reports by taking into account the experience of project members. The reporting department can comprehensively evaluate the skill sets and experience of project members and provide optimal report content. In this way, the reporting department can customize the content of reports by taking into account the skill sets and experience of project members. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input the skill set and experience data of project members into a generating AI and have the generating AI perform the customization of the report content.

[0058] The reporting department can provide reports that take into account the project's external environment (such as market trends and competitive landscape). For example, the reporting department can evaluate market trends and provide appropriate reports. The reporting department can evaluate the competitive landscape and provide reports. The reporting department can comprehensively evaluate market trends and the competitive landscape and provide optimal reports. This allows the reporting department to provide reports that take into account the project's external environment. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input market trend and competitive landscape data into a generating AI and have the generating AI perform the task of providing reports.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The management department can adjust task assignments in real time, taking into account the health status of project members when managing project progress. For example, if a project member is fatigued, the management department can assign them less demanding tasks. Conversely, if a project member is in good health, the management department can prioritize assigning them important tasks. Furthermore, the management department can regularly monitor the health status of project members and dynamically adjust task assignments. This allows the management department to optimize task assignments while considering the health status of project members.

[0061] The data collection unit can change its data collection method according to the project's progress. For example, it can collect a wide range of data in the initial stages of the project, and then narrow down the types and amount of data collected as the project progresses. It can also adjust the frequency of data collection according to the project's progress. Furthermore, the data collection unit can set data collection priorities based on the project's progress. This allows the data collection unit to optimize its data collection method according to the project's progress.

[0062] The analysis department can identify project risk factors and propose risk management strategies when analyzing project data. For example, it can analyze project progress and resource usage to identify potential risks. It can also evaluate the impact and probability of these risks occurring and prioritize risk management measures. Furthermore, the analysis department can propose specific countermeasures for risk management. In this way, the analysis department can identify project risk factors and propose risk management strategies.

[0063] The proposal department can re-evaluate the project's objectives and scope as the project progresses and adjust them as necessary. For example, the proposal department can assess the project's progress and determine whether the objectives are achievable. It can also re-evaluate the project's scope and reduce or expand it as needed. Furthermore, the proposal department can assess the impact of changes to the project's objectives and scope and propose appropriate countermeasures. This allows the proposal department to re-evaluate the objectives and scope as the project progresses and adjust them as necessary.

[0064] The reporting department can customize the content of its reports to meet the needs of project stakeholders when reporting on project progress. For example, it can provide concise, to-the-point reports to management and detailed reports to project teams. It can also adjust the content of reports according to the interests of stakeholders. Furthermore, the reporting department can collect stakeholder feedback and incorporate it into improving the reports. This allows the reporting department to customize the content of reports to meet the needs of project stakeholders.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The management department manages the project's progress in real time. The management department has a detailed understanding of the progress of each task in the project and updates the progress status in real time. They collect information such as the start and end dates of tasks and the degree of progress, and visualize the overall progress of the project. Step 2: The Collection Department collects project data managed by the Management Department. The Collection Department collects data such as the progress of each project task and resource usage, centrally manages the project data, and provides it to the Analysis Department. Step 3: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the project data to determine the progress and priority of each task. They identify problems, such as delays in task progress or insufficient resources, and propose appropriate solutions. Step 4: The proposal team proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis team. The proposal team makes optimal suggestions for project success, such as reallocating resources and rescheduling tasks. Based on project data, they optimize the prioritization of each task and the allocation of resources. Step 5: The reporting team reports on the proposals made by the proposal team. The reporting team reports so that the project manager can understand the progress in real time. They provide information such as project progress, task priorities, and resource allocation.

[0067] (Example of form 2) The project management system according to an embodiment of the present invention is a system that manages the progress of a project in real time and automatically analyzes and reports on task allocation and progress. Furthermore, it proposes an optimal schedule and resource allocation to support project success. For example, the project management system grasps the progress of each task in detail and updates the progress in real time. The project management system collects information such as the start and end dates and progress of tasks, and visualizes the overall progress of the project. Next, the project management system uses an AI agent to automatically analyze task allocation and progress. The AI ​​agent analyzes project data and determines the progress and priority of each task. For example, it identifies problems such as delays in task progress or insufficient resources and proposes appropriate countermeasures. Furthermore, the project management system uses the AI ​​agent to propose an optimal schedule and resource allocation. Based on project data, the AI ​​agent optimizes the priority and resource allocation of each task. For example, it makes optimal suggestions for project success, such as reallocating resources or changing task schedules. This system allows for real-time management of project progress, automatic analysis and reporting of task allocation and progress. This reduces the burden on project managers and improves project success rates. For example, if a project manager manages multiple projects, they can grasp the progress of each project in real time and appropriately prioritize tasks. Furthermore, optimizing resource allocation improves project efficiency. Thus, by using an AI agent, project progress can be managed in real time, and task allocation and progress can be automatically analyzed and reported. This reduces the burden on project managers and improves project success rates.This allows the project management system to manage project progress in real time, automatically analyze and report on task assignments and progress.

[0068] The project management system according to this embodiment comprises a management unit, a data collection unit, an analysis unit, a proposal unit, and a reporting unit. The management unit manages the progress of the project in real time. For example, the management unit grasps the progress of each task in the project in detail and updates the progress in real time. The management unit collects information such as the start date, end date, and progress of tasks and visualizes the progress of the entire project. The data collection unit collects project data managed by the management unit. For example, the data collection unit collects data such as the progress of each task in the project and the usage of resources. The data collection unit centrally manages the project data and provides it to the analysis unit. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the project data and determines the progress and priority of each task. The analysis unit identifies problems, such as when task progress is behind schedule or when resources are insufficient, and proposes appropriate countermeasures. The proposal unit proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis unit. The proposal department makes optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Based on project data, the proposal department optimizes the prioritization of each task and the allocation of resources. The reporting department reports on the proposals made by the proposal department. The reporting department reports so that the project manager can understand the progress in real time. The reporting department provides information such as the project progress, task prioritization, and resource allocation. As a result, the project management system according to this embodiment can manage the project progress in real time and automatically analyze and report on task allocation and progress.

[0069] The management department manages the project's progress in real time. For example, the management department has a detailed understanding of the progress of each project task and updates the progress in real time. Specifically, the management department uses project management software to collect information such as the start date, end date, progress, and assigned person for each task, and manages this information centrally. The management department uses visual tools such as Gantt charts and burn-down charts to visualize the project's progress. This allows project managers and team members to grasp the project's progress at a glance. Furthermore, the management department collects feedback from each team member and regularly checks the progress to update task progress in real time. For example, they check the progress of each task through weekly meetings and daily stand-up meetings and adjust the task schedule as needed. This allows the management department to keep the project progress up to date and detect project delays and resource shortages early. In addition, the management department can use dashboards to visualize the project's progress. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This allows the management department to efficiently manage the project's progress and take appropriate measures to ensure its success.

[0070] The data collection unit collects project data managed by the management unit. For example, the collection unit collects data such as the progress of each project task and resource usage. Specifically, the collection unit automatically collects data from project management software, various sensors, and devices, and centrally manages this data. The collection unit uses a database to centrally manage project data and provide it to the analysis unit. The database stores information such as the progress of each task, resource usage, and project progress, and allows the analysis unit to access it as needed. The collection unit can flexibly respond to specific situations and conditions by adjusting the data collection frequency and accuracy. For example, if the project progress changes rapidly or resource usage increases abnormally, the collection unit can increase the data collection frequency to grasp the situation in real time. This allows the collection unit to efficiently collect project progress and resource usage, improving the overall system performance. Furthermore, the collection unit performs data verification and cleaning to ensure data quality. For example, it checks for errors or omissions in the collected data and corrects the data as needed. This allows the data collection unit to provide accurate and reliable data, and to provide the foundation for the analysis unit to perform accurate analysis.

[0071] The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes project data to determine the progress and priority of each task. Specifically, the analysis department uses AI and machine learning algorithms to analyze the collected data and evaluate the project's progress and resource usage. For example, AI analyzes the progress of each task and identifies tasks that are behind schedule or lacking resources. AI also determines the priority of each task based on historical data and statistical information and proposes an optimal schedule for project success. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. For example, if a task's progress suddenly slows down or resource usage increases abnormally, the analysis department will detect the anomaly and issue a warning to the project manager. This allows the analysis department to accurately understand the project's progress and resource usage and identify problems early. In addition, the analysis department can use dashboards to visualize the project's progress and resource usage. The dashboard displays information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This enables the analytics department to efficiently analyze project progress and resource usage, and take appropriate actions to ensure project success.

[0072] The proposal department proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis department. The proposal department makes optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Specifically, the proposal department uses AI and machine learning algorithms to analyze collected data and evaluate project progress and resource usage. For example, AI analyzes the progress of each task, identifying tasks that are behind schedule or lacking resources. AI also determines the priority of each task based on historical data and statistics, proposing the optimal schedule for project success. Furthermore, the proposal department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. For example, if task progress falls sharply or resource usage increases abnormally, the proposal department detects the anomaly and warns the project manager. This allows the proposal department to accurately understand project progress and resource usage, and identify problems early. Additionally, the proposal department can use dashboards to visualize project progress and resource usage. The dashboard displays information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly grasp the situation. This enables the proposal department to efficiently analyze project progress and resource usage and take appropriate actions to ensure project success.

[0073] The reporting department reports on the proposals made by the proposal department. For example, the reporting department reports so that the project manager can understand the progress in real time. Specifically, the reporting department uses project management software to display information such as the progress of each task and resource usage in real time. The reporting department uses dashboards to provide information such as project progress, task priorities, and resource allocation. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly understand the situation. Furthermore, the reporting department uses visual tools such as Gantt charts and burn-down charts to visualize project progress and resource usage. This allows project managers and team members to grasp the project progress at a glance. Additionally, the reporting department can use dashboards to visualize project progress and resource usage. Dashboards display information such as project progress, task progress, and resource usage in real time, allowing project managers and team members to quickly understand the situation. This enables the reporting department to efficiently report on project progress and resource usage, and to take appropriate actions toward project success.

[0074] The management department can gain a detailed understanding of the progress of each task in the project and update the progress status in real time. For example, the management department collects information such as the start and end dates and progress of tasks to visualize the overall project progress. To gain a detailed understanding of the progress of each task in the project, the management department needs to clearly define how progress is recorded and what types of information it needs to collect. For example, the management department can use a task management tool to record the progress of tasks. To update the task progress in real time, the management department needs to set the update frequency and delay time. For example, the management department can update the task progress daily. This allows the management department to gain a detailed understanding of the progress of each task in the project and update the progress status in real time.

[0075] The analysis department can analyze project data and determine the progress and priority of each task. For example, the analysis department can analyze project data and determine the progress and priority of each task. To analyze project data, the analysis department needs to clearly define the algorithms to be used and the data to be analyzed. For example, to evaluate the progress of a task, the analysis department can analyze progress data. To determine the priority of a task, the analysis department needs to evaluate the importance and urgency of the task. For example, to evaluate the importance of a task, the analysis department can consider the impact and dependencies of the task. This allows the analysis department to analyze project data and determine the progress and priority of each task.

[0076] The proposal department can make optimal suggestions for project success, such as reallocating resources or rescheduling tasks. To make optimal suggestions, the proposal department needs to clarify the evaluation criteria and content of the suggestions. For example, to propose resource reallocation, the proposal department can evaluate resource usage and project progress. To propose task rescheduling, the proposal department needs to evaluate task progress and dependencies. For example, to propose task rescheduling, the proposal department can consider task progress and dependencies. This allows the proposal department to make optimal suggestions for project success, such as reallocating resources or rescheduling tasks.

[0077] The reporting department can provide reports that allow the project manager to understand the progress in real time. For example, the reporting department can provide reports that allow the project manager to understand the progress in real time. To ensure real-time understanding, the reporting department needs to clearly define the types of information to be understood and the frequency of updates. For example, the reporting department can provide information such as project progress, task priorities, and resource allocation. The reporting department needs to set the frequency and method of reporting so that the project manager can understand the progress in real time. For example, the reporting department can provide regular reports so that the project manager can understand the progress in real time. This allows the reporting department to provide reports that allow the project manager to understand the progress in real time.

[0078] The management unit can estimate the user's emotions and adjust the display method of task progress based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible display method. If the user is relaxed, the management unit can provide a display method that includes detailed information. If the user is in a hurry, the management unit can provide a display method that gets straight to the point. In this way, the management unit can adjust the display method of task progress 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The management department can gain a detailed understanding of the progress of each task in the project and, when updating progress in real time, can adjust the update frequency while considering task dependencies. For example, the management department can analyze task dependencies and prioritize updating the progress of important tasks. The management department can dynamically adjust the update frequency of progress based on task dependencies. The management department can optimize the timing of progress updates while considering task dependencies. This allows the management department to adjust the update frequency of progress while considering task dependencies. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input task dependency data into a generating AI and have the generating AI perform the adjustment of the update frequency.

[0080] The management department can set priorities based on the importance and urgency of tasks when monitoring their progress. For example, the management department can evaluate the importance of tasks and display important tasks first. The management department can evaluate the urgency of tasks and display urgent tasks first. The management department can comprehensively evaluate the importance and urgency of tasks and set the optimal priority. This allows the management department to set priorities based on the importance and urgency of tasks. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input task importance and urgency data into a generating AI and have the generating AI perform the priority setting.

[0081] The management unit can estimate the user's emotions and adjust the notification method for task progress based on the estimated user emotions. For example, if the user is stressed, the management unit can provide a simple notification method. If the user is relaxed, the management unit can provide a detailed notification method. If the user is in a hurry, the management unit can provide a rapid notification method. This allows the management unit to adjust the notification method for task progress 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The management department can optimize task allocation by considering the skill sets and experience of project members when monitoring task progress. For example, the management department can evaluate the skill sets of project members and assign appropriate tasks. The management department can optimize task allocation by considering the experience of project members. The management department can comprehensively evaluate the skill sets and experience of project members and make optimal task allocations. This allows the management department to optimize task allocation by considering the skill sets and experience of project members. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input project members' skill set and experience data into a generating AI and have the generating AI perform task allocation.

[0083] The management department can update progress status while considering the geographical distribution of the project when monitoring task progress. For example, the management department can evaluate the geographical distribution of the project and adjust the frequency of progress status updates. The management department can optimize the timing of progress status updates based on the geographical distribution. The management department can adjust the method of updating progress status while considering the geographical distribution. This allows the management department to update progress status while considering the geographical distribution of the project. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input geographical distribution data of the project into a generating AI and have the generating AI perform progress status updates.

[0084] 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 delay the timing of data collection. If the user is relaxed, the data collection unit can speed up the timing of data collection. If the user is in a hurry, the data collection unit can collect data quickly. In this way, the data collection unit can adjust the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into the generative AI and have the generative AI adjust the timing of data collection.

[0085] The data collection unit can evaluate the reliability and accuracy of project data and optimize the collection method. For example, the data collection unit can evaluate the reliability of the data and prioritize the collection of reliable data. The data collection unit can evaluate the accuracy of the data and prioritize the collection of accurate data. The data collection unit can comprehensively evaluate the reliability and accuracy of the data and select the optimal collection method. In this way, the data collection unit can evaluate the reliability and accuracy of the data and optimize the collection method. 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 data reliability and accuracy data into a generating AI and have the generating AI perform the optimization of the collection method.

[0086] The data collection unit can adjust the type and amount of data collected according to the project's progress. For example, the data collection unit can evaluate the project's progress and select the necessary data types. The data collection unit can adjust the amount of data collected according to the project's progress. The data collection unit can comprehensively evaluate the project's progress and select the optimal data collection method. This allows the data collection unit to adjust the type and amount of data collected according to the project's progress. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input project progress data into a generating AI and have the generating AI perform the data collection adjustments.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting important data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, the data collection unit can determine the priority of data to collect 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 a generative AI and have the generative AI determine the priority of the data.

[0088] The data collection unit can collect feedback from project members during data collection and improve the quality of the collected data. For example, the data collection unit can collect feedback from project members and improve the data collection method. The data collection unit can improve the quality of the collected data by reflecting the opinions of project members. The data collection unit can optimize the data collection process based on feedback from project members. This allows the data collection unit to collect feedback from project members and improve the quality of the collected data. 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 project member feedback data into a generating AI and have the generating AI implement improvements to the data collection method.

[0089] The data collection unit can select data to collect while considering the project's external environment (such as market trends and competitive landscape). For example, the data collection unit can evaluate market trends and select the necessary data. The data collection unit can evaluate the competitive landscape and select the data to collect. The data collection unit can comprehensively evaluate market trends and the competitive landscape and select the optimal data collection method. This allows the data collection unit to select data while considering the project's external environment. 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 market trend and competitive landscape data into a generating AI and have the generating AI perform the selection of data to collect.

[0090] 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 stressed, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. If the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, the analysis unit can adjust the display method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0091] The analysis unit can optimize algorithms for determining task progress and priority when analyzing project data. For example, the analysis unit can optimize an algorithm for evaluating task progress. The analysis unit can optimize an algorithm for determining task priority. The analysis unit can optimize an algorithm for comprehensively evaluating task progress and priority. This allows the analysis unit to optimize algorithms for determining task progress and priority. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input task progress and priority data into a generating AI and have the generating AI perform algorithm optimization.

[0092] The analysis unit can improve prediction accuracy by referring to the project's past data during data analysis. For example, the analysis unit can improve prediction accuracy by referring to the project's past data. The analysis unit can improve the prediction model based on past data. The analysis unit can improve prediction accuracy by comprehensively evaluating past data. Thus, the analysis unit can improve prediction accuracy by referring to the project's past data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the project's past data into a generating AI and have the generating AI perform the improvement of prediction accuracy.

[0093] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple notification method. If the user is relaxed, the analysis unit can provide a detailed notification method. If the user is in a hurry, the analysis unit can provide a rapid notification method. In this way, the analysis unit can adjust the notification method of the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the notification method.

[0094] The analysis department can customize the analysis results when analyzing data, taking into account the skill sets and experience of project members. For example, the analysis department can evaluate the skill sets of project members and provide appropriate analysis results. The analysis department can customize the analysis results by taking into account the experience of project members. The analysis department can comprehensively evaluate the skill sets and experience of project members and provide optimal analysis results. This allows the analysis department to customize the analysis results by taking into account the skill sets and experience of project members. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input project member skill set and experience data into a generating AI and have the generating AI perform the customization of the analysis results.

[0095] The analysis department can provide analysis results while considering the project's external environment (such as market trends and competitive landscape) during data analysis. For example, the analysis department can evaluate market trends and provide appropriate analysis results. The analysis department can evaluate the competitive landscape and provide analysis results. The analysis department can comprehensively evaluate market trends and the competitive landscape and provide optimal analysis results. In this way, the analysis department can provide analysis results while considering the project's external environment. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input market trend and competitive landscape data into a generating AI and have the generating AI perform the task of providing analysis results.

[0096] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are presented based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can provide suggestions that include detailed information. If the user is in a hurry, the suggestion unit can provide suggestions that get straight to the point. In this way, the suggestion unit can adjust the way the suggestions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way the suggestions are presented.

[0097] The proposal department can set priorities for proposals when making optimal suggestions for project success, such as reallocating resources or rescheduling tasks. For example, the proposal department may prioritize resource reallocation. The proposal department may also prioritize task rescheduling. The proposal department can comprehensively evaluate resource reallocation and task rescheduling to make the optimal proposal. This allows the proposal department to set priorities for proposals when making optimal suggestions for project success, such as reallocating resources or rescheduling tasks. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input resource reallocation and task rescheduling data into a generating AI and have the generating AI perform the setting of proposal priorities.

[0098] The proposal department can reflect the project progress and resource utilization in real time when making a proposal. For example, the proposal department can evaluate the project progress in real time and reflect it in the proposal. The proposal department can evaluate resource utilization in real time and reflect it in the proposal. The proposal department can comprehensively evaluate the project progress and resource utilization and make the optimal proposal. As a result, the proposal department can reflect the project progress and resource utilization in real time. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input project progress and resource utilization data into a generating AI and have the generating AI perform the reflection of the proposal content.

[0099] The suggestion unit can estimate the user's emotions and adjust the notification method of the suggestion based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide a simple notification method. If the user is relaxed, the suggestion unit can provide a detailed notification method. If the user is in a hurry, the suggestion unit can provide a rapid notification method. In this way, the suggestion unit can adjust the notification method of the suggestion 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 suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the notification method.

[0100] The proposal department can customize proposals by considering the skill sets and experience of project members. For example, the proposal department can evaluate the skill sets of project members and provide appropriate proposals. The proposal department can customize proposals by considering the experience of project members. The proposal department can comprehensively evaluate the skill sets and experience of project members and provide optimal proposals. This allows the proposal department to customize proposals by considering the skill sets and experience of project members. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input project member skill set and experience data into a generating AI and have the generating AI perform the customization of proposals.

[0101] The proposal department can provide proposals while considering the external environment of the project (such as market trends and competitive conditions). For example, the proposal department can evaluate market trends and provide appropriate proposals. The proposal department can evaluate the competitive situation and provide proposals. The proposal department can comprehensively evaluate market trends and the competitive situation and provide optimal proposals. In this way, the proposal department can provide proposals while considering the external environment of the project. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input market trend and competitive situation data into a generating AI and have the generating AI perform the task of providing proposals.

[0102] 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 stressed, the reporting unit can provide a simple and highly visible display. If the user is relaxed, the reporting unit can provide a display that includes detailed information. If the user is in a hurry, the reporting unit can provide a display that gets straight to the point. In this way, the reporting unit can adjust how the report is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0103] The reporting unit can optimize the frequency of reports so that project managers can stay informed about progress in real time. For example, the reporting unit can evaluate the project's progress and optimize the frequency of reports. The reporting unit can adjust the frequency of reports according to the project manager's needs. The reporting unit can comprehensively evaluate the project's progress and the project manager's needs to set the optimal reporting frequency. This allows the reporting unit to optimize the frequency of reports so that project managers can stay informed about progress in real time. Some or all of the above processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input project progress and project manager needs data into a generating AI and have the generating AI perform the optimization of the reporting frequency.

[0104] The reporting unit can reflect project progress and resource utilization in real time when reporting. For example, the reporting unit can evaluate project progress in real time and reflect it in the report. The reporting unit can evaluate resource utilization in real time and reflect it in the report. The reporting unit can comprehensively evaluate project progress and resource utilization and provide optimal report content. As a result, the reporting unit can reflect project progress and resource utilization in real time. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input project progress and resource utilization data into a generating AI and have the generating AI perform the task of reflecting the report content.

[0105] The reporting unit can estimate the user's emotions and adjust the notification method of the report based on the estimated user emotions. For example, if the user is stressed, the reporting unit can provide a simple notification method. If the user is relaxed, the reporting unit can provide a detailed notification method. If the user is in a hurry, the reporting unit can provide a rapid notification method. In this way, the reporting unit can adjust the notification method of the report 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 reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the notification method.

[0106] The reporting department can customize the content of reports by taking into account the skill sets and experience of project members. For example, the reporting department can evaluate the skill sets of project members and provide appropriate report content. The reporting department can customize the content of reports by taking into account the experience of project members. The reporting department can comprehensively evaluate the skill sets and experience of project members and provide optimal report content. In this way, the reporting department can customize the content of reports by taking into account the skill sets and experience of project members. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input the skill set and experience data of project members into a generating AI and have the generating AI perform the customization of the report content.

[0107] The reporting department can provide reports that take into account the project's external environment (such as market trends and competitive landscape). For example, the reporting department can evaluate market trends and provide appropriate reports. The reporting department can evaluate the competitive landscape and provide reports. The reporting department can comprehensively evaluate market trends and the competitive landscape and provide optimal reports. This allows the reporting department to provide reports that take into account the project's external environment. Some or all of the above processes in the reporting department may be performed using AI or not. For example, the reporting department can input market trend and competitive landscape data into a generating AI and have the generating AI perform the task of providing reports.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The management department can adjust task assignments in real time, taking into account the health status of project members when managing project progress. For example, if a project member is fatigued, the management department can assign them less demanding tasks. Conversely, if a project member is in good health, the management department can prioritize assigning them important tasks. Furthermore, the management department can regularly monitor the health status of project members and dynamically adjust task assignments. This allows the management department to optimize task assignments while considering the health status of project members.

[0110] The data collection unit can change its data collection method according to the project's progress. For example, it can collect a wide range of data in the initial stages of the project, and then narrow down the types and amount of data collected as the project progresses. It can also adjust the frequency of data collection according to the project's progress. Furthermore, the data collection unit can set data collection priorities based on the project's progress. This allows the data collection unit to optimize its data collection method according to the project's progress.

[0111] The analysis department can identify project risk factors and propose risk management strategies when analyzing project data. For example, it can analyze project progress and resource usage to identify potential risks. It can also evaluate the impact and probability of these risks occurring and prioritize risk management measures. Furthermore, the analysis department can propose specific countermeasures for risk management. In this way, the analysis department can identify project risk factors and propose risk management strategies.

[0112] The proposal department can re-evaluate the project's objectives and scope as the project progresses and adjust them as necessary. For example, the proposal department can assess the project's progress and determine whether the objectives are achievable. It can also re-evaluate the project's scope and reduce or expand it as needed. Furthermore, the proposal department can assess the impact of changes to the project's objectives and scope and propose appropriate countermeasures. This allows the proposal department to re-evaluate the objectives and scope as the project progresses and adjust them as necessary.

[0113] The reporting department can customize the content of its reports to meet the needs of project stakeholders when reporting on project progress. For example, it can provide concise, to-the-point reports to management and detailed reports to project teams. It can also adjust the content of reports according to the interests of stakeholders. Furthermore, the reporting department can collect stakeholder feedback and incorporate it into improving the reports. This allows the reporting department to customize the content of reports to meet the needs of project stakeholders.

[0114] The management department can estimate the user's emotions and adjust how task progress is displayed based on those emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, the management department can adjust how task progress is displayed according to the user's emotions.

[0115] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the timing of data collection can be delayed. If the user is relaxed, the timing of data collection can be accelerated. If the user is in a hurry, data can be collected quickly. In this way, the data collection unit can adjust the timing of data collection according to the user's emotions.

[0116] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can provide a display that gets straight to the point. In this way, the analysis unit can adjust how the analysis results are displayed according to the user's emotions.

[0117] The proposal function can estimate the user's emotions and adjust the way the proposal is presented based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible proposal. If the user is relaxed, it can provide a proposal that includes detailed information. If the user is in a hurry, it can provide a proposal that gets straight to the point. In this way, the proposal function can adjust the way the proposal is presented according to the user's emotions.

[0118] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. If the user is in a hurry, it can provide a display that gets straight to the point. In this way, the reporting unit can adjust how the report is displayed according to the user's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The management department manages the project's progress in real time. The management department has a detailed understanding of the progress of each task in the project and updates the progress status in real time. They collect information such as the start and end dates of tasks and the degree of progress, and visualize the overall progress of the project. Step 2: The Collection Department collects project data managed by the Management Department. The Collection Department collects data such as the progress of each project task and resource usage, centrally manages the project data, and provides it to the Analysis Department. Step 3: The analysis department analyzes the data collected by the data collection department. The analysis department analyzes the project data to determine the progress and priority of each task. They identify problems, such as delays in task progress or insufficient resources, and propose appropriate solutions. Step 4: The proposal team proposes the optimal schedule and resource allocation based on the analysis results obtained by the analysis team. The proposal team makes optimal suggestions for project success, such as reallocating resources and rescheduling tasks. Based on project data, they optimize the prioritization of each task and the allocation of resources. Step 5: The reporting team reports on the proposals made by the proposal team. The reporting team reports so that the project manager can understand the progress in real time. They provide information such as project progress, task priorities, and resource allocation.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the management unit, collection unit, analysis unit, proposal unit, and reporting unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart device 14 and manages the project progress in real time. The collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and collects project data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal schedule and resource allocation. The reporting unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 and reports the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0139] 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.

[0140] Each of the multiple elements described above, including the management unit, collection unit, analysis unit, proposal unit, and reporting unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the smart glasses 214 and manages the project progress in real time. The collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and collects project data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal schedule and resource allocation. The reporting unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 and reports the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] 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.

[0156] Each of the multiple elements described above, including the management unit, collection unit, analysis unit, proposal unit, and reporting unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the headset terminal 314 and manages the project progress in real time. The collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and collects project data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal schedule and resource allocation. The reporting unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 and reports the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0173] Each of the multiple elements described above, including the management unit, collection unit, analysis unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the management unit is implemented by the control unit 46A of the robot 414 and manages the project progress in real time. The collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and collects project data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal schedule and resource allocation. The reporting unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 and reports the proposed content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The management department manages the project's progress in real time, A collection unit that collects project data managed by the aforementioned management unit, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal schedule and resource allocation. The system comprises a reporting unit that reports the content proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned management department, Gain detailed insights into the progress of each project task and update progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Analyze project data to determine the progress and priority of each task. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We provide optimal suggestions for project success, such as reallocating resources and rescheduling tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reporting department, Reporting so that the project manager can understand the progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, It estimates the user's emotions and adjusts how task progress is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, When tracking the progress of each project task in detail and updating progress in real time, adjust the update frequency while considering task dependencies. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, When tracking the progress of tasks, prioritize them based on their importance and urgency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned management department, It estimates the user's emotions and adjusts the notification method for task progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned management department, When monitoring task progress, optimize task allocation by considering the skill sets and experience of project members. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned management department, When tracking task progress, update the progress status while taking into account the geographical distribution of the project. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting project data, evaluate the reliability and accuracy of the data and optimize the collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, adjust the type and amount of data collected according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is During data collection, we gather feedback from project members to improve the quality of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting data, select the data to be collected while considering the project's external environment. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When analyzing project data, optimize the algorithms used to determine task progress and priorities. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is When analyzing data, refer to historical project data to improve prediction accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts the notification method of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When analyzing data, customize the analysis results by taking into account the skill sets and experience of the project members. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is When analyzing data, we provide analysis results that take into account the project's external environment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way the suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making optimal suggestions for project success, such as reallocating resources or rescheduling tasks, prioritize your suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, reflect the project's progress and resource utilization in real time. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and adjusts the notification method for suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making a proposal, customize the proposal content to take into account the skill sets and experience of the project members. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we will provide the proposal content while taking into account the external environment of the project. The system described in Appendix 1, characterized by the features described herein. (Note 30) 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 1, characterized by the features described herein. (Note 31) The aforementioned reporting department, Optimize the frequency of reports so that project managers can track progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned reporting department, The report should reflect the project's progress and resource utilization in real time. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned reporting department, It estimates the user's emotions and adjusts the notification method for the report based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned reporting department, When reporting, customize the report content to take into account the skill sets and experience of the project members. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned reporting department, When submitting a report, provide content that takes into account the project's external environment. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 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 management department manages the project's progress in real time, A collection unit that collects project data managed by the aforementioned management unit, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal schedule and resource allocation. The system comprises a reporting unit that reports the content proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned management department, Gain detailed insights into the progress of each project task and update progress in real time. The system according to feature 1.

3. The aforementioned analysis unit is Analyze project data to determine the progress and priority of each task. The system according to feature 1.

4. The aforementioned proposal section is, We provide optimal suggestions for project success, such as reallocating resources and rescheduling tasks. The system according to feature 1.

5. The aforementioned reporting department, Reporting so that the project manager can understand the progress in real time. The system according to feature 1.

6. The aforementioned management department, It estimates the user's emotions and adjusts how task progress is displayed based on those estimated emotions. The system according to feature 1.

7. The aforementioned management department, When tracking the progress of each project task in detail and updating progress in real time, adjust the update frequency while considering task dependencies. The system according to feature 1.

8. The aforementioned management department, When tracking the progress of tasks, prioritize them based on their importance and urgency. The system according to feature 1.

9. The aforementioned management department, It estimates the user's emotions and adjusts the notification method for task progress based on the estimated user emotions. The system according to feature 1.

10. The aforementioned management department, When monitoring task progress, optimize task allocation by considering the skill sets and experience of project members. The system according to feature 1.

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