system

Abstract

The system according to this embodiment aims to analyze project progress in real time and adjust resources and timelines accordingly. [Solution] The system according to this embodiment comprises a progress analysis unit, an adjustment unit, and a management unit. The progress analysis unit analyzes the progress of the project in real time. The adjustment unit adjusts resources and timelines based on the progress status analyzed by the progress analysis unit. The management unit manages the progress of the project based on the resources and timelines adjusted by the adjustment unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is frequently found that projects do not progress on time, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze the progress of a project in real time and adjust resources and timelines.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a progress analysis unit, an adjustment unit, and a management unit. The progress analysis unit analyzes the progress of the project in real time. The adjustment unit adjusts resources and timelines based on the progress status analyzed by the progress analysis unit. The management unit manages the progress of the project based on the resources and timelines adjusted by the adjustment unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze project progress in real time and adjust resources and timelines. [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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that uses an AI agent to analyze project progress in real time and adjust resources and timelines. This project management system addresses the common problem of projects frequently not progressing as planned by introducing an AI agent to automatically monitor project progress and adjust resources and timelines. For example, at the start of a project, the AI ​​analyzes past project data and proposes the optimal resource allocation. During project progress, the project management system automatically checks the progress rate of each task periodically and visualizes it on a real-time dashboard. The project management system notifies all stakeholders of the progress. When a problem arises, the project management system detects the risk early using a risk detection algorithm and automatically generates and notifies of adjustment proposals. Applications of the generating AI include analysis of past project data, prediction of future results, real-time progress visualization, and easy sharing among stakeholders via a dashboard. This is expected to improve project success rates, streamline adjustment work, significantly reduce the risk of delivery delays, and improve overall organizational efficiency and satisfaction. For example, at the start of a project, the AI ​​analyzes past project data. At this stage, the success and failure factors of past projects are analyzed to propose the optimal resource allocation. The project management system identifies which resources were most effective for specific tasks from past project data and allocates those resources to the current project. The project management system automatically checks the progress rate of each task periodically during project progress. AI monitors the progress of each task in real time and detects tasks that are behind schedule early. For example, if a task's progress rate is behind schedule, the AI ​​identifies that task and notifies the relevant parties. When issues arise, the project management system detects risks early using a risk detection algorithm. AI analyzes the project's progress in real time and detects risks early if there is a possibility of them occurring.For example, if a particular task is behind schedule, the project management system predicts the impact of that task on the entire project and notifies stakeholders. The AI ​​automatically generates adjustment proposals and notifies stakeholders. The AI ​​analyzes the project's progress and generates optimal resource allocation and schedule adjustment proposals. For example, if a particular task is behind schedule, it suggests allocating additional resources to that task. In this way, using an AI agent allows for real-time analysis of project progress and adjustments to resources and timelines. This is expected to improve the project's success rate, streamline adjustment work, significantly reduce the risk of delivery delays, and enhance overall organizational efficiency and satisfaction. Thus, by analyzing project progress in real time and adjusting resources and timelines, the project management system can increase the project's success rate.

[0029] The project management system according to this embodiment comprises a progress analysis unit, an adjustment unit, and a management unit. The progress analysis unit analyzes the progress of the project in real time. The progress analysis unit monitors the progress of each task in the project using AI, for example, and detects tasks that are behind schedule early. The progress analysis unit can identify a task if its progress rate is behind schedule and notify the relevant parties. The progress analysis unit can display the project progress in real time on a dashboard, for example, so that all relevant parties can understand the progress. The adjustment unit adjusts resources and timelines based on the progress analyzed by the progress analysis unit. The adjustment unit can propose allocating additional resources to tasks that are behind schedule. The adjustment unit can adjust the schedule of tasks that are behind schedule to optimize the overall progress of the project. The adjustment unit can automatically reallocate resources and change schedules to ensure smooth project progress. The management unit manages the progress of the project based on the resources and timelines adjusted by the adjustment unit. The management unit monitors the progress of the project based on the resource allocation and schedule adjusted by the adjustment unit. The management department, for example, monitors the project's progress in real time and makes additional adjustments as needed. The management department also notifies stakeholders of the project's progress to ensure smooth project execution. As a result, the project management system according to this embodiment can analyze project progress in real time and adjust resources and timelines to increase the project's success rate.

[0030] The Progress Analysis Department analyzes project progress in real time. For example, it uses AI to monitor the progress of each project task and detect tasks that are behind schedule early on. Specifically, the AI ​​analyzes data entered into project management tools and evaluates the progress rate, expected completion date, and resource usage of each task. Using natural language processing technology, the AI ​​can analyze task progress reports and comments and automatically extract delays and problems. For example, if a task's progress rate is behind schedule, it can identify that task and notify the relevant parties. Notifications are made in real time via email and chat tools, allowing stakeholders to immediately consider countermeasures. The Progress Analysis Department displays the project progress in real time on a dashboard, allowing all stakeholders to understand the progress. The dashboard visually displays the progress rate, expected completion date, resource usage, delays, and problems for each task. This allows project managers and team members to grasp the overall picture of the project at a glance and take quick countermeasures. Furthermore, the progress analysis department can learn patterns of delays and problems based on past project data, and predict future risks. This allows the progress analysis department to not only analyze project progress in real time, but also predict future risks and provide information to increase the project's success rate.

[0031] The coordination department adjusts resources and timelines based on the progress analyzed by the progress analysis department. For example, the coordination department proposes allocating additional resources to tasks that are behind schedule. Specifically, the coordination department uses AI to calculate the optimal resource allocation and proposes assigning additional personnel or equipment to tasks that are behind schedule. The AI ​​calculates the optimal resource allocation considering the importance, dependencies, and resource usage of each task. For example, it adjusts the schedule of tasks that are behind schedule to optimize the progress of the entire project. The coordination department uses AI to optimize the schedule, recalculates the expected completion date of tasks that are behind schedule, and adjusts the overall project schedule. This allows the project to proceed smoothly. The coordination department automatically reallocates resources and changes schedules to ensure smooth project progress. Specifically, the AI ​​monitors resource usage and task progress in real time and automatically reallocates resources and changes schedules as needed. This allows the coordination department to optimize project progress and increase the project's success rate. Furthermore, the coordination department notifies stakeholders of the project's progress to ensure smooth project progress. For example, if resources are reallocated or the schedule is changed, stakeholders are notified, allowing them to immediately consider countermeasures. This enables the coordination department to provide information that optimizes project progress and increases the project's success rate.

[0032] The management department manages project progress based on resources and timelines coordinated by the coordination department. For example, the management department monitors project progress based on resource allocation and schedules coordinated by the coordination department. Specifically, the management department uses AI to monitor project progress in real time and make additional adjustments as needed. The AI ​​analyzes data entered into project management tools and evaluates the progress rate, expected completion date, and resource usage of each task. This allows the management department to understand project progress in real time and make additional adjustments as needed. The management department notifies stakeholders of project progress to ensure smooth project execution. For example, if project progress is behind schedule, stakeholders are notified, allowing them to immediately consider countermeasures. This enables the management department to ensure smooth project execution and increase the project's success rate. Furthermore, the management department regularly reports on project progress to ensure all stakeholders are aware of its status. For example, weekly and monthly reports are created and distributed to stakeholders to share project progress. This allows the management department to provide information that facilitates project execution and increases the project's success rate. Furthermore, the management department can predict future risks based on the project's progress and take measures to increase the project's success rate. This allows the management department to provide information that facilitates project progress and enhances the project's success rate.

[0033] The Data Analysis Department analyzes past project data. For example, the Data Analysis Department uses AI to analyze past project data and identify factors for project success and failure. For example, the Data Analysis Department identifies which resources were most effective for a particular task from past project data and proposes allocating those resources to the current project. For example, the Data Analysis Department predicts the progress of the current project based on past project data and proposes the optimal resource allocation. For example, the Data Analysis Department monitors the progress of the project in real time based on past project data and detects tasks that are behind schedule early. In this way, the Data Analysis Department can increase the success rate of projects by analyzing past project data. Past project data includes, but is not limited to, project deliverables and progress records. Some or all of the above processing in the Data Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Data Analysis Department can input past project data into generative AI, which can analyze the data and identify factors for success and failure.

[0034] The Risk Detection Unit executes a risk detection algorithm. The Risk Detection Unit analyzes the project's progress in real time, for example, using AI, and detects risks early if they are likely to occur. The Risk Detection Unit predicts the impact of a particular task on the entire project if that task is behind schedule and notifies stakeholders. The Risk Detection Unit monitors the project's progress in real time and detects risks early if they are likely to occur. The Risk Detection Unit analyzes the project's progress using a risk detection algorithm and detects risks early if they are likely to occur. As a result, the Risk Detection Unit can detect project risks early by executing the risk detection algorithm. The risk detection algorithm includes, but is not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the Risk Detection Unit may be performed using, for example, generative AI, or without generative AI. For example, the Risk Detection Unit can input the project's progress into a generative AI, which can then detect risks.

[0035] The Adjustment Proposal Generation Unit generates adjustment proposals. For example, the Adjustment Proposal Generation Unit uses AI to analyze the project's progress and generates optimal resource allocation and schedule adjustment proposals. For example, if a particular task is behind schedule, the Adjustment Proposal Generation Unit suggests allocating additional resources to that task. For example, the Adjustment Proposal Generation Unit generates adjustment proposals that optimize the overall project progress by adjusting the schedule of tasks that are behind schedule. For example, the Adjustment Proposal Generation Unit generates adjustment proposals that automatically reallocate resources or change schedules. In this way, the Adjustment Proposal Generation Unit can optimize the project's progress by generating adjustment proposals. Adjustment proposals include, but are not limited to, resource reallocation proposals and schedule change proposals. Some or all of the above-described processes in the Adjustment Proposal Generation Unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the Adjustment Proposal Generation Unit can input the project's progress into a generating AI, which can then generate the optimal adjustment proposal.

[0036] The Visualization Unit visualizes the progress. For example, the Visualization Unit uses AI to display the project's progress on a dashboard in real time, allowing all stakeholders to understand the progress. For example, the Visualization Unit visualizes the project's progress using graphs and charts, making it easy for stakeholders to understand. For example, the Visualization Unit monitors the project's progress in real time and detects tasks that are behind schedule early. For example, the Visualization Unit displays the project's progress in real time, allowing all stakeholders to understand the progress. In this way, the Visualization Unit can communicate the project's progress to stakeholders in an easy-to-understand manner by visualizing the progress. Visualization includes, but is not limited to, graphs, charts, and dashboards. Some or all of the above-described processes in the Visualization Unit may be performed using, for example, generative AI, or not using generative AI. For example, the Visualization Unit can input the project's progress into generative AI, which can then visualize the progress.

[0037] The notification unit notifies stakeholders. For example, the notification unit uses AI to monitor project progress in real time and notifies stakeholders of tasks that are behind schedule. For example, the notification unit displays project progress on a dashboard in real time so that all stakeholders can understand the progress. For example, the notification unit notifies stakeholders of project progress via email or push notification. For example, the notification unit monitors project progress in real time and detects tasks that are behind schedule early and notifies stakeholders. In this way, the notification unit can quickly share project progress with stakeholders by notifying them. Notifications include, but are not limited to, email notifications, push notifications, and alerts. Some or all of the above processing in the notification unit may be performed using, for example, generative AI, or not using generative AI. For example, the notification unit can input project progress into generative AI, and the generative AI can notify stakeholders of the progress.

[0038] The progress analysis unit can adjust the level of detail of its analysis based on the importance of each task when analyzing the progress of a project. For example, the progress analysis unit can perform a detailed progress analysis for high-importance tasks and provide detailed progress information. For example, the progress analysis unit can perform a simplified progress analysis for low-importance tasks and provide only an overview. For example, the progress analysis unit can perform a progress analysis with an appropriate level of detail for tasks of moderate importance and provide the necessary information. This allows the progress analysis unit to efficiently manage progress by adjusting the level of detail of its analysis according to the importance of each task. Task importance includes, but is not limited to, the degree of impact on the project and the degree of adherence to deadlines. Some or all of the above processing in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input the importance data of each task into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0039] The progress analysis unit can improve the accuracy of its analysis by referring to data from similar past projects when analyzing the progress of a project. For example, the progress analysis unit can analyze the success and failure factors of similar past projects and apply them to the current project. For example, the progress analysis unit can predict the progress of the current project based on the progress data of similar past projects. For example, the progress analysis unit can refer to resource allocation data from similar past projects and propose the optimal resource allocation. In this way, the progress analysis unit can improve the accuracy of its progress analysis by referring to data from similar past projects. Similar projects include, but are not limited to, project size, content, and objectives. Some or all of the above processes in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input data from similar past projects into a generative AI, and the generative AI can analyze the data to improve the accuracy of the progress analysis.

[0040] The progress analysis department can analyze the progress of a project while taking into account the skill level of each task's assignee. For example, the progress analysis department can predict delays in progress based on the skill level of each task's assignee and take early countermeasures. For example, the progress analysis department can perform detailed progress analysis for tasks assigned by highly skilled assignees and simplified progress analysis for tasks assigned by less skilled assignees. For example, the progress analysis department can monitor progress in real time, taking into account the skill level of each task's assignee, and reallocate resources as needed. This allows the progress analysis department to improve the accuracy of progress management by taking into account the skill level of each task's assignee. Skill levels include, but are not limited to, years of experience, qualifications, and past performance. Some or all of the above processes in the progress analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the progress analysis department can input skill level data for each task's assignee into a generative AI, which can then perform progress analysis while taking skill levels into account.

[0041] The progress analysis unit can analyze the project's progress while considering the resource consumption of each task. For example, the progress analysis unit can predict delays in progress based on the resource consumption of each task and take early countermeasures. For example, the progress analysis unit can perform detailed progress analysis for tasks with high resource consumption and simplified progress analysis for tasks with low resource consumption. For example, the progress analysis unit can monitor the progress in real time, considering the resource consumption of each task, and reallocate resources as needed. This enables efficient resource management by considering the resource consumption of each task. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input resource consumption data for each task into a generative AI, which can then perform progress analysis while considering resource consumption.

[0042] The adjustment unit can adjust resources and timelines while considering the dependencies between tasks. For example, the adjustment unit can analyze the dependencies between tasks and prioritize resource and timeline adjustments for tasks with strong dependencies. For example, the adjustment unit can flexibly adjust resources and timelines for tasks with weak dependencies to maintain overall balance. For example, the adjustment unit can optimize the progress of the entire project by adjusting resources and timelines while considering the dependencies between tasks. In this way, the adjustment unit can optimize the progress of the entire project by considering the dependencies between tasks. Task dependencies include, but are not limited to, the order and degree of dependency between tasks. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input dependency data for each task into a generative AI, and the generative AI can perform adjustments while considering the dependencies.

[0043] The adjustment unit can select the optimal adjustment method by referring to past adjustment history when adjusting resources and timelines. For example, the adjustment unit can analyze past adjustment history and apply successful adjustment methods to the current project. For example, the adjustment unit can obtain guidelines from past adjustment history to avoid failed adjustment methods. For example, the adjustment unit can propose the optimal resource and timeline adjustment method based on past adjustment history. In this way, the adjustment unit can select the optimal resource and timeline adjustment method by referring to past adjustment history. The adjustment history includes, but is not limited to, past adjustment content and adjustment results. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input past adjustment history data into a generative AI, and the generative AI can select the optimal adjustment method.

[0044] The management department can monitor and manage the progress of each task in real time when managing the progress of the project. For example, the management department can monitor the progress of each task in real time and take early action for tasks that are behind schedule. For example, the management department can flexibly manage tasks that are progressing on schedule to maintain overall balance. For example, the management department can optimize the progress of the entire project by monitoring the progress of each task in real time. This allows the management department to optimize the progress of the entire project by monitoring the progress of each task in real time. Real-time monitoring includes, but is not limited to, the frequency of monitoring and the tools used. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input progress data for each task into a generative AI, which can then monitor it in real time.

[0045] The management department can improve the accuracy of its management by referring to past project management data when managing the progress of a project. For example, the management department can analyze past project management data and apply successful management methods to the current project. For example, the management department can obtain guidance from past project management data to avoid failed management methods. For example, the management department can propose the optimal management method based on past project management data. In this way, the management department can improve the accuracy of its management by referring to past project management data. Project management data includes, but is not limited to, progress records and management reports. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input past project management data into generative AI, and the generative AI can analyze the data to improve the accuracy of management.

[0046] The data analysis department can analyze past project data to determine the success and failure factors of each project in detail. For example, the data analysis department can analyze past project data, identify success factors, and apply them to current projects. For example, the data analysis department can identify failure factors from past project data and avoid them in current projects. For example, the data analysis department can analyze success and failure factors in detail based on past project data and propose the optimal project management method. In this way, the data analysis department can analyze the success and failure factors of each project in detail and apply them to current projects. Success and failure factors include, but are not limited to, project outcomes and causes of failure. Some or all of the above processing in the data analysis department may be performed using, for example, generative AI, or without generative AI. For example, the data analysis department can input past project data into generative AI, and the generative AI can analyze success and failure factors.

[0047] The Data Analysis Department can analyze past project data while considering the resource consumption of each project. For example, the Data Analysis Department can analyze past project data and perform a detailed analysis for projects with high resource consumption. For example, the Data Analysis Department can perform a simplified analysis for projects with low resource consumption. For example, the Data Analysis Department can propose the optimal data analysis method based on past project data, taking resource consumption into consideration. This enables the Data Analysis Department to perform efficient data analysis by considering the resource consumption of each project. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the Data Analysis Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Data Analysis Department can input past project data into a generative AI, which can then perform the analysis while considering resource consumption.

[0048] The risk detection unit can detect risks early by monitoring the progress of each task in real time when performing risk detection. For example, the risk detection unit monitors the progress of each task in real time and detects risks early for tasks that are behind schedule. For example, the risk detection unit flexibly detects risks for tasks that are progressing smoothly and maintains an overall balance. For example, the risk detection unit monitors the progress of each task in real time to detect risks early and optimize the progress of the entire project. In this way, the risk detection unit can detect risks early by monitoring the progress of each task in real time. Real-time monitoring includes, but is not limited to, the frequency of monitoring and the tools used. Some or all of the above processing in the risk detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk detection unit can input progress data for each task into a generative AI, which can perform real-time monitoring.

[0049] The risk detection unit can detect risks early by monitoring the resource consumption of each task in real time when performing risk detection. For example, the risk detection unit monitors the resource consumption of each task in real time and detects risks early for tasks that are lacking resources. For example, the risk detection unit flexibly detects risks for tasks where resource consumption is on track and maintains an overall balance. For example, the risk detection unit monitors the resource consumption of each task in real time to detect risks early and optimize the progress of the entire project. In this way, the risk detection unit can detect risks early by monitoring the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the risk detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk detection unit can input resource consumption data for each task into a generative AI, which can then monitor it in real time.

[0050] The adjustment plan generation unit can generate adjustment plans by reflecting the progress of each task in real time. For example, the adjustment plan generation unit monitors the progress of each task in real time and prioritizes generating adjustment plans for tasks that are behind schedule. For example, the adjustment plan generation unit flexibly generates adjustment plans for tasks that are progressing smoothly, maintaining an overall balance. For example, the adjustment plan generation unit generates adjustment plans by reflecting the progress of each task in real time, optimizing the progress of the entire project. In this way, the adjustment plan generation unit can generate optimal adjustment plans by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the adjustment plan generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the adjustment plan generation unit can input progress data for each task into a generation AI, and the generation AI can reflect it in real time and generate adjustment plans.

[0051] The adjustment plan generation unit can generate adjustment plans by reflecting the resource consumption of each task in real time. For example, the adjustment plan generation unit monitors the resource consumption of each task in real time and prioritizes generating adjustment plans for tasks that are lacking resources. For example, the adjustment plan generation unit flexibly generates adjustment plans for tasks with sufficient resource consumption to maintain overall balance. For example, the adjustment plan generation unit generates adjustment plans by reflecting the resource consumption of each task in real time and optimizes the progress of the entire project. In this way, the adjustment plan generation unit can generate optimal adjustment plans by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the adjustment plan generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the adjustment plan generation unit can input resource consumption data for each task into a generation AI, and the generation AI can reflect it in real time to generate adjustment plans.

[0052] The visualization unit can visualize progress by reflecting the progress of each task in real time. For example, the visualization unit can monitor the progress of each task in real time and prioritize the visualization of tasks that are behind schedule. For example, the visualization unit can flexibly visualize tasks that are progressing smoothly to maintain an overall balance. For example, the visualization unit can optimize the progress of the entire project by reflecting the progress of each task in real time and performing visualization. In this way, the visualization unit can optimize the progress of the entire project by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input progress data for each task into a generative AI, and the generative AI can reflect and visualize it in real time.

[0053] The visualization unit can visualize progress by reflecting the resource consumption of each task in real time. For example, the visualization unit can monitor the resource consumption of each task in real time and prioritize the visualization of tasks that are lacking resources. For example, the visualization unit can flexibly visualize tasks that are consuming resources on schedule to maintain an overall balance. For example, the visualization unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time and performing visualization. In this way, the visualization unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input resource consumption data for each task into a generative AI, and the generative AI can reflect and visualize it in real time.

[0054] The notification unit can notify stakeholders in real time, reflecting the progress of each task. For example, the notification unit can monitor the progress of each task in real time and prioritize notifications for tasks that are behind schedule. For example, the notification unit can flexibly notify about tasks that are progressing smoothly to maintain overall balance. For example, the notification unit can optimize the progress of the entire project by reflecting the progress of each task in real time and issuing notifications. In this way, the notification unit can optimize the progress of the entire project by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input progress data for each task into a generative AI, and the generative AI can reflect it in real time and issue notifications.

[0055] The notification unit can notify stakeholders in real time, reflecting the resource consumption of each task. For example, the notification unit can monitor the resource consumption of each task in real time and prioritize notifications for tasks that are running low on resources. For example, the notification unit can flexibly notify tasks with sufficient resource consumption to maintain overall balance. For example, the notification unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time and issuing notifications. In this way, the notification unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input resource consumption data for each task into a generative AI, and the generative AI can reflect it in real time and issue notifications.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The progress analysis department can analyze the project's progress while considering the dependencies between tasks. For example, it can perform detailed progress analyses for tasks with strong dependencies and simplified analyses for tasks with weak dependencies. If a task with strong dependencies is behind schedule, the progress analysis department can predict the impact and notify stakeholders. In this way, the progress analysis department can optimize the overall progress of the project by considering the dependencies between tasks.

[0058] The coordination unit can adjust resources and timelines while considering the priority of each task. For example, it can prioritize resource allocation for high-priority tasks and allocate resources flexibly for lower-priority tasks. If a high-priority task is behind schedule, the coordination unit can propose allocating additional resources to that task. In this way, the coordination unit can optimize the overall progress of the project by considering the priority of each task.

[0059] The management department, when managing the progress of a project, can analyze the progress of each task and take early action on tasks that are behind schedule. For example, the management department can propose allocating additional resources to tasks that are behind schedule. For example, the management department can propose reallocating resources to other tasks for tasks that are progressing on time. In this way, the management department can optimize the overall progress of the project by analyzing the progress of each task.

[0060] The Data Analysis Department can analyze past project data while considering the resource consumption of each project. For example, it can perform detailed analyses of projects with high resource consumption and simplified analyses of projects with low resource consumption. The Data Analysis Department can identify the success and failure factors of projects with high resource consumption and apply them to current projects. This allows the Data Analysis Department to perform efficient data analysis by considering the resource consumption of each project.

[0061] The risk detection unit can detect risks early by monitoring the progress of each task in real time. For example, the risk detection unit can detect risks early in tasks that are behind schedule and notify relevant parties. For example, the risk detection unit can flexibly detect risks in tasks that are progressing smoothly, maintaining an overall balance. In this way, the risk detection unit can detect risks early by monitoring the progress of each task in real time.

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

[0063] Step 1: The progress analysis department analyzes the project's progress in real time. The progress analysis department uses AI to monitor the progress of each project task and detect tasks that are behind schedule early on. For example, if a task's progress rate is behind schedule, it can identify that task and notify the relevant parties. In addition, the project's progress is displayed on a dashboard in real time so that all stakeholders can understand the progress. Step 2: The adjustment unit adjusts resources and timelines based on the progress analyzed by the progress analysis unit. For example, it may propose allocating additional resources to tasks that are behind schedule. Furthermore, it adjusts the schedules of tasks that are behind schedule to optimize the overall progress of the project. Resource reallocation and schedule changes are performed automatically to ensure smooth project progress. Step 3: The management department manages the project progress based on the resources and timelines coordinated by the coordination department. For example, they monitor the project progress based on the resource allocation and schedule coordinated by the coordination department. They monitor the project progress in real time and make additional adjustments as needed. They notify stakeholders of the project progress to ensure smooth project progress.

[0064] (Example of form 2) The project management system according to an embodiment of the present invention is a system that uses an AI agent to analyze project progress in real time and adjust resources and timelines. This project management system addresses the common problem of projects frequently not progressing as planned by introducing an AI agent to automatically monitor project progress and adjust resources and timelines. For example, at the start of a project, the AI ​​analyzes past project data and proposes the optimal resource allocation. During project progress, the project management system automatically checks the progress rate of each task periodically and visualizes it on a real-time dashboard. The project management system notifies all stakeholders of the progress. When a problem arises, the project management system detects the risk early using a risk detection algorithm and automatically generates and notifies of adjustment proposals. Applications of the generating AI include analysis of past project data, prediction of future results, real-time progress visualization, and easy sharing among stakeholders via a dashboard. This is expected to improve project success rates, streamline adjustment work, significantly reduce the risk of delivery delays, and improve overall organizational efficiency and satisfaction. For example, at the start of a project, the AI ​​analyzes past project data. At this stage, the success and failure factors of past projects are analyzed to propose the optimal resource allocation. The project management system identifies which resources were most effective for specific tasks from past project data and allocates those resources to the current project. The project management system automatically checks the progress rate of each task periodically during project progress. AI monitors the progress of each task in real time and detects tasks that are behind schedule early. For example, if a task's progress rate is behind schedule, the AI ​​identifies that task and notifies the relevant parties. When issues arise, the project management system detects risks early using a risk detection algorithm. AI analyzes the project's progress in real time and detects risks early if there is a possibility of them occurring.For example, if a particular task is behind schedule, the project management system predicts the impact of that task on the entire project and notifies stakeholders. The AI ​​automatically generates adjustment proposals and notifies stakeholders. The AI ​​analyzes the project's progress and generates optimal resource allocation and schedule adjustment proposals. For example, if a particular task is behind schedule, it suggests allocating additional resources to that task. In this way, using an AI agent allows for real-time analysis of project progress and adjustments to resources and timelines. This is expected to improve the project's success rate, streamline adjustment work, significantly reduce the risk of delivery delays, and enhance overall organizational efficiency and satisfaction. Thus, by analyzing project progress in real time and adjusting resources and timelines, the project management system can increase the project's success rate.

[0065] The project management system according to this embodiment comprises a progress analysis unit, an adjustment unit, and a management unit. The progress analysis unit analyzes the progress of the project in real time. The progress analysis unit monitors the progress of each task in the project using AI, for example, and detects tasks that are behind schedule early. The progress analysis unit can identify a task if its progress rate is behind schedule and notify the relevant parties. The progress analysis unit can display the project progress in real time on a dashboard, for example, so that all relevant parties can understand the progress. The adjustment unit adjusts resources and timelines based on the progress analyzed by the progress analysis unit. The adjustment unit can propose allocating additional resources to tasks that are behind schedule. The adjustment unit can adjust the schedule of tasks that are behind schedule to optimize the overall progress of the project. The adjustment unit can automatically reallocate resources and change schedules to ensure smooth project progress. The management unit manages the progress of the project based on the resources and timelines adjusted by the adjustment unit. The management unit monitors the progress of the project based on the resource allocation and schedule adjusted by the adjustment unit. The management department, for example, monitors the project's progress in real time and makes additional adjustments as needed. The management department also notifies stakeholders of the project's progress to ensure smooth project execution. As a result, the project management system according to this embodiment can analyze project progress in real time and adjust resources and timelines to increase the project's success rate.

[0066] The Progress Analysis Department analyzes project progress in real time. For example, it uses AI to monitor the progress of each project task and detect tasks that are behind schedule early on. Specifically, the AI ​​analyzes data entered into project management tools and evaluates the progress rate, expected completion date, and resource usage of each task. Using natural language processing technology, the AI ​​can analyze task progress reports and comments and automatically extract delays and problems. For example, if a task's progress rate is behind schedule, it can identify that task and notify the relevant parties. Notifications are made in real time via email and chat tools, allowing stakeholders to immediately consider countermeasures. The Progress Analysis Department displays the project progress in real time on a dashboard, allowing all stakeholders to understand the progress. The dashboard visually displays the progress rate, expected completion date, resource usage, delays, and problems for each task. This allows project managers and team members to grasp the overall picture of the project at a glance and take quick countermeasures. Furthermore, the progress analysis department can learn patterns of delays and problems based on past project data, and predict future risks. This allows the progress analysis department to not only analyze project progress in real time, but also predict future risks and provide information to increase the project's success rate.

[0067] The coordination department adjusts resources and timelines based on the progress analyzed by the progress analysis department. For example, the coordination department proposes allocating additional resources to tasks that are behind schedule. Specifically, the coordination department uses AI to calculate the optimal resource allocation and proposes assigning additional personnel or equipment to tasks that are behind schedule. The AI ​​calculates the optimal resource allocation considering the importance, dependencies, and resource usage of each task. For example, it adjusts the schedule of tasks that are behind schedule to optimize the progress of the entire project. The coordination department uses AI to optimize the schedule, recalculates the expected completion date of tasks that are behind schedule, and adjusts the overall project schedule. This allows the project to proceed smoothly. The coordination department automatically reallocates resources and changes schedules to ensure smooth project progress. Specifically, the AI ​​monitors resource usage and task progress in real time and automatically reallocates resources and changes schedules as needed. This allows the coordination department to optimize project progress and increase the project's success rate. Furthermore, the coordination department notifies stakeholders of the project's progress to ensure smooth project progress. For example, if resources are reallocated or the schedule is changed, stakeholders are notified, allowing them to immediately consider countermeasures. This enables the coordination department to provide information that optimizes project progress and increases the project's success rate.

[0068] The management department manages project progress based on resources and timelines coordinated by the coordination department. For example, the management department monitors project progress based on resource allocation and schedules coordinated by the coordination department. Specifically, the management department uses AI to monitor project progress in real time and make additional adjustments as needed. The AI ​​analyzes data entered into project management tools and evaluates the progress rate, expected completion date, and resource usage of each task. This allows the management department to understand project progress in real time and make additional adjustments as needed. The management department notifies stakeholders of project progress to ensure smooth project execution. For example, if project progress is behind schedule, stakeholders are notified, allowing them to immediately consider countermeasures. This enables the management department to ensure smooth project execution and increase the project's success rate. Furthermore, the management department regularly reports on project progress to ensure all stakeholders are aware of its status. For example, weekly and monthly reports are created and distributed to stakeholders to share project progress. This allows the management department to provide information that facilitates project execution and increases the project's success rate. Furthermore, the management department can predict future risks based on the project's progress and take measures to increase the project's success rate. This allows the management department to provide information that facilitates project progress and enhances the project's success rate.

[0069] The Data Analysis Department analyzes past project data. For example, the Data Analysis Department uses AI to analyze past project data and identify factors for project success and failure. For example, the Data Analysis Department identifies which resources were most effective for a particular task from past project data and proposes allocating those resources to the current project. For example, the Data Analysis Department predicts the progress of the current project based on past project data and proposes the optimal resource allocation. For example, the Data Analysis Department monitors the progress of the project in real time based on past project data and detects tasks that are behind schedule early. In this way, the Data Analysis Department can increase the success rate of projects by analyzing past project data. Past project data includes, but is not limited to, project deliverables and progress records. Some or all of the above processing in the Data Analysis Department may be performed using, for example, generative AI, or without generative AI. For example, the Data Analysis Department can input past project data into generative AI, which can analyze the data and identify factors for success and failure.

[0070] The Risk Detection Unit executes a risk detection algorithm. The Risk Detection Unit analyzes the project's progress in real time, for example, using AI, and detects risks early if they are likely to occur. The Risk Detection Unit predicts the impact of a particular task on the entire project if that task is behind schedule and notifies stakeholders. The Risk Detection Unit monitors the project's progress in real time and detects risks early if they are likely to occur. The Risk Detection Unit analyzes the project's progress using a risk detection algorithm and detects risks early if they are likely to occur. As a result, the Risk Detection Unit can detect project risks early by executing the risk detection algorithm. The risk detection algorithm includes, but is not limited to, machine learning algorithms and rule-based algorithms. Some or all of the above processing in the Risk Detection Unit may be performed using, for example, generative AI, or without generative AI. For example, the Risk Detection Unit can input the project's progress into a generative AI, which can then detect risks.

[0071] The Adjustment Proposal Generation Unit generates adjustment proposals. For example, the Adjustment Proposal Generation Unit uses AI to analyze the project's progress and generates optimal resource allocation and schedule adjustment proposals. For example, if a particular task is behind schedule, the Adjustment Proposal Generation Unit suggests allocating additional resources to that task. For example, the Adjustment Proposal Generation Unit generates adjustment proposals that optimize the overall project progress by adjusting the schedule of tasks that are behind schedule. For example, the Adjustment Proposal Generation Unit generates adjustment proposals that automatically reallocate resources or change schedules. In this way, the Adjustment Proposal Generation Unit can optimize the project's progress by generating adjustment proposals. Adjustment proposals include, but are not limited to, resource reallocation proposals and schedule change proposals. Some or all of the above-described processes in the Adjustment Proposal Generation Unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the Adjustment Proposal Generation Unit can input the project's progress into a generating AI, which can then generate the optimal adjustment proposal.

[0072] The Visualization Unit visualizes the progress. For example, the Visualization Unit uses AI to display the project's progress on a dashboard in real time, allowing all stakeholders to understand the progress. For example, the Visualization Unit visualizes the project's progress using graphs and charts, making it easy for stakeholders to understand. For example, the Visualization Unit monitors the project's progress in real time and detects tasks that are behind schedule early. For example, the Visualization Unit displays the project's progress in real time, allowing all stakeholders to understand the progress. In this way, the Visualization Unit can communicate the project's progress to stakeholders in an easy-to-understand manner by visualizing the progress. Visualization includes, but is not limited to, graphs, charts, and dashboards. Some or all of the above-described processes in the Visualization Unit may be performed using, for example, generative AI, or not using generative AI. For example, the Visualization Unit can input the project's progress into generative AI, which can then visualize the progress.

[0073] The notification unit notifies stakeholders. For example, the notification unit uses AI to monitor project progress in real time and notifies stakeholders of tasks that are behind schedule. For example, the notification unit displays project progress on a dashboard in real time so that all stakeholders can understand the progress. For example, the notification unit notifies stakeholders of project progress via email or push notification. For example, the notification unit monitors project progress in real time and detects tasks that are behind schedule early and notifies stakeholders. In this way, the notification unit can quickly share project progress with stakeholders by notifying them. Notifications include, but are not limited to, email notifications, push notifications, and alerts. Some or all of the above processing in the notification unit may be performed using, for example, generative AI, or not using generative AI. For example, the notification unit can input project progress into generative AI, and the generative AI can notify stakeholders of the progress.

[0074] The progress analysis unit can estimate the user's emotions and adjust the frequency of progress analysis based on the estimated emotions. For example, if the user is stressed, the progress analysis unit can reduce the frequency of progress analysis and perform analysis only at important times. For example, if the user is relaxed, the progress analysis unit can increase the frequency of progress analysis and provide detailed progress information. For example, if the user is in a hurry, the progress analysis unit can increase the frequency of progress analysis and provide real-time progress information. In this way, the progress analysis unit can reduce the burden on the user by adjusting the frequency of progress analysis according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the progress analysis unit may be performed using a generative AI, or not using a generative AI. For example, the progress analysis unit can input user emotion data into a generative AI, and the generative AI can estimate the emotions.

[0075] The progress analysis unit can adjust the level of detail of its analysis based on the importance of each task when analyzing the progress of a project. For example, the progress analysis unit can perform a detailed progress analysis for high-importance tasks and provide detailed progress information. For example, the progress analysis unit can perform a simplified progress analysis for low-importance tasks and provide only an overview. For example, the progress analysis unit can perform a progress analysis with an appropriate level of detail for tasks of moderate importance and provide the necessary information. This allows the progress analysis unit to efficiently manage progress by adjusting the level of detail of its analysis according to the importance of each task. Task importance includes, but is not limited to, the degree of impact on the project and the degree of adherence to deadlines. Some or all of the above processing in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input the importance data of each task into a generative AI, and the generative AI can adjust the level of detail of the analysis.

[0076] The progress analysis unit can improve the accuracy of its analysis by referring to data from similar past projects when analyzing the progress of a project. For example, the progress analysis unit can analyze the success and failure factors of similar past projects and apply them to the current project. For example, the progress analysis unit can predict the progress of the current project based on the progress data of similar past projects. For example, the progress analysis unit can refer to resource allocation data from similar past projects and propose the optimal resource allocation. In this way, the progress analysis unit can improve the accuracy of its progress analysis by referring to data from similar past projects. Similar projects include, but are not limited to, project size, content, and objectives. Some or all of the above processes in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input data from similar past projects into a generative AI, and the generative AI can analyze the data to improve the accuracy of the progress analysis.

[0077] The progress analysis unit can estimate the user's emotions and adjust the display method of the progress analysis results based on the estimated user emotions. For example, if the user is nervous, the progress analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the progress analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the progress analysis unit provides a display method that gets straight to the point. In this way, the progress analysis unit can make the display easy for the user to understand by adjusting the display method of the progress analysis results according to the user's emotions. The display method includes, but is not limited to, the type of graph, the use of colors, and the arrangement of information. Some or all of the above processing in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input user emotion data into a generative AI, and the generative AI can adjust the display method.

[0078] The progress analysis department can analyze the progress of a project while taking into account the skill level of each task's assignee. For example, the progress analysis department can predict delays in progress based on the skill level of each task's assignee and take early countermeasures. For example, the progress analysis department can perform detailed progress analysis for tasks assigned by highly skilled assignees and simplified progress analysis for tasks assigned by less skilled assignees. For example, the progress analysis department can monitor progress in real time, taking into account the skill level of each task's assignee, and reallocate resources as needed. This allows the progress analysis department to improve the accuracy of progress management by taking into account the skill level of each task's assignee. Skill levels include, but are not limited to, years of experience, qualifications, and past performance. Some or all of the above processes in the progress analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the progress analysis department can input skill level data for each task's assignee into a generative AI, which can then perform progress analysis while taking skill levels into account.

[0079] The progress analysis unit can analyze the project's progress while considering the resource consumption of each task. For example, the progress analysis unit can predict delays in progress based on the resource consumption of each task and take early countermeasures. For example, the progress analysis unit can perform detailed progress analysis for tasks with high resource consumption and simplified progress analysis for tasks with low resource consumption. For example, the progress analysis unit can monitor the progress in real time, considering the resource consumption of each task, and reallocate resources as needed. This enables efficient resource management by considering the resource consumption of each task. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the progress analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the progress analysis unit can input resource consumption data for each task into a generative AI, which can then perform progress analysis while considering resource consumption.

[0080] The adjustment unit can estimate the user's emotions and select methods for adjusting resources and timelines based on the estimated emotions. For example, if the user is stressed, the adjustment unit will minimize adjustments to resources and timelines, making only essential adjustments. If the user is relaxed, the adjustment unit will make detailed adjustments to resources and timelines and suggest the optimal adjustment method. If the user is in a hurry, the adjustment unit will quickly adjust resources and timelines to provide immediate assistance. In this way, the adjustment unit can reduce the user's burden by selecting methods for adjusting resources and timelines according to the user's emotions. Adjustment methods include, but are not limited to, methods for reallocating resources and methods for changing schedules. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the adjustment unit can input user emotion data into a generative AI, which can then select the adjustment method.

[0081] The adjustment unit can adjust resources and timelines while considering the dependencies between tasks. For example, the adjustment unit can analyze the dependencies between tasks and prioritize resource and timeline adjustments for tasks with strong dependencies. For example, the adjustment unit can flexibly adjust resources and timelines for tasks with weak dependencies to maintain overall balance. For example, the adjustment unit can optimize the progress of the entire project by adjusting resources and timelines while considering the dependencies between tasks. In this way, the adjustment unit can optimize the progress of the entire project by considering the dependencies between tasks. Task dependencies include, but are not limited to, the order and degree of dependency between tasks. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input dependency data for each task into a generative AI, and the generative AI can perform adjustments while considering the dependencies.

[0082] The adjustment unit can select the optimal adjustment method by referring to past adjustment history when adjusting resources and timelines. For example, the adjustment unit can analyze past adjustment history and apply successful adjustment methods to the current project. For example, the adjustment unit can obtain guidelines from past adjustment history to avoid failed adjustment methods. For example, the adjustment unit can propose the optimal resource and timeline adjustment method based on past adjustment history. In this way, the adjustment unit can select the optimal resource and timeline adjustment method by referring to past adjustment history. The adjustment history includes, but is not limited to, past adjustment content and adjustment results. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the adjustment unit can input past adjustment history data into a generative AI, and the generative AI can select the optimal adjustment method.

[0083] The management department can estimate the user's emotions and adjust project management methods based on those estimated emotions. For example, if the user is stressed, the management department can provide a simple and intuitive management method. If the user is relaxed, the management department can provide a management method that includes detailed information. If the user is in a hurry, the management department can provide a management method that requires a quick response. This allows the management department to provide user-friendly management by adjusting project management methods according to the user's emotions. Project management methods include, but are not limited to, the use of management tools and changes to management processes. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not. For example, the management department can input user emotion data into a generative AI, which can then adjust project management methods.

[0084] The management department can monitor and manage the progress of each task in real time when managing the progress of the project. For example, the management department can monitor the progress of each task in real time and take early action for tasks that are behind schedule. For example, the management department can flexibly manage tasks that are progressing on schedule to maintain overall balance. For example, the management department can optimize the progress of the entire project by monitoring the progress of each task in real time. This allows the management department to optimize the progress of the entire project by monitoring the progress of each task in real time. Real-time monitoring includes, but is not limited to, the frequency of monitoring and the tools used. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input progress data for each task into a generative AI, which can then monitor it in real time.

[0085] The management department can improve the accuracy of its management by referring to past project management data when managing the progress of a project. For example, the management department can analyze past project management data and apply successful management methods to the current project. For example, the management department can obtain guidance from past project management data to avoid failed management methods. For example, the management department can propose the optimal management method based on past project management data. In this way, the management department can improve the accuracy of its management by referring to past project management data. Project management data includes, but is not limited to, progress records and management reports. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can input past project management data into generative AI, and the generative AI can analyze the data to improve the accuracy of management.

[0086] The data analysis department can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the data analysis department provides a simple and intuitive data analysis method. For example, if the user is relaxed, the data analysis department provides a data analysis method that includes detailed information. For example, if the user is in a hurry, the data analysis department provides a data analysis method that requires a quick response. In this way, the data analysis department can provide data analysis that is easy for the user to understand by adjusting the data analysis method according to the user's emotions. The data analysis method includes, but is not limited to, the analysis tools used and changes to the analysis process. Some or all of the above processing in the data analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the data analysis department can input user emotion data into generative AI, and the generative AI can adjust the data analysis method.

[0087] The data analysis department can analyze past project data to determine the success and failure factors of each project in detail. For example, the data analysis department can analyze past project data, identify success factors, and apply them to current projects. For example, the data analysis department can identify failure factors from past project data and avoid them in current projects. For example, the data analysis department can analyze success and failure factors in detail based on past project data and propose the optimal project management method. In this way, the data analysis department can analyze the success and failure factors of each project in detail and apply them to current projects. Success and failure factors include, but are not limited to, project outcomes and causes of failure. Some or all of the above processing in the data analysis department may be performed using, for example, generative AI, or without generative AI. For example, the data analysis department can input past project data into generative AI, and the generative AI can analyze success and failure factors.

[0088] The data analysis department can estimate the user's emotions and prioritize data analysis based on those emotions. For example, if the user is stressed, the data analysis department will prioritize important data analysis and postpone other data analysis. If the user is relaxed, the data analysis department will perform all data analysis in a balanced manner and suggest the optimal data analysis method. If the user is in a hurry, the data analysis department will prioritize data analysis that requires immediate attention and respond immediately. In this way, the data analysis department can prioritize important data analysis by determining the priority of data analysis according to the user's emotions. The data analysis priority includes, but is not limited to, the importance of the project and the urgency of the data. Some or all of the above processes in the data analysis department may be performed using, for example, generative AI, or not using generative AI. For example, the data analysis department can input user emotion data into a generative AI, which can then determine the priority of data analysis.

[0089] The Data Analysis Department can analyze past project data while considering the resource consumption of each project. For example, the Data Analysis Department can analyze past project data and perform a detailed analysis for projects with high resource consumption. For example, the Data Analysis Department can perform a simplified analysis for projects with low resource consumption. For example, the Data Analysis Department can propose the optimal data analysis method based on past project data, taking resource consumption into consideration. This enables the Data Analysis Department to perform efficient data analysis by considering the resource consumption of each project. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the Data Analysis Department may be performed using, for example, a generative AI, or without a generative AI. For example, the Data Analysis Department can input past project data into a generative AI, which can then perform the analysis while considering resource consumption.

[0090] The risk detection unit can estimate the user's emotions and adjust the frequency of risk detection based on the estimated emotions. For example, if the user is stressed, the risk detection unit reduces the frequency of risk detection and only detects at critical moments. For example, if the user is relaxed, the risk detection unit increases the frequency of risk detection and provides detailed risk information. For example, if the user is in a hurry, the risk detection unit increases the frequency of risk detection and provides real-time risk information. In this way, the risk detection unit can reduce the burden on the user by adjusting the frequency of risk detection according to the user's emotions. The frequency of risk detection includes, but is not limited to, periodic detection and event-driven detection. Some or all of the above processing in the risk detection unit may be performed using, for example, generative AI, or without generative AI. For example, the risk detection unit can input user emotion data into generative AI, which can then adjust the frequency of risk detection.

[0091] The risk detection unit can detect risks early by monitoring the progress of each task in real time when performing risk detection. For example, the risk detection unit monitors the progress of each task in real time and detects risks early for tasks that are behind schedule. For example, the risk detection unit flexibly detects risks for tasks that are progressing smoothly and maintains an overall balance. For example, the risk detection unit monitors the progress of each task in real time to detect risks early and optimize the progress of the entire project. In this way, the risk detection unit can detect risks early by monitoring the progress of each task in real time. Real-time monitoring includes, but is not limited to, the frequency of monitoring and the tools used. Some or all of the above processing in the risk detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk detection unit can input progress data for each task into a generative AI, which can perform real-time monitoring.

[0092] The risk detection unit can estimate the user's emotions and determine the priority of risk detection based on the estimated emotions. For example, if the user is stressed, the risk detection unit will prioritize important risk detections and postpone other risk detections. For example, if the user is relaxed, the risk detection unit will perform all risk detections in a balanced manner and propose the optimal risk detection method. For example, if the user is in a hurry, the risk detection unit will prioritize risk detections that require immediate attention and respond immediately. In this way, the risk detection unit can prioritize important risk detections by determining the priority of risk detection according to the user's emotions. The priority of risk detection includes, but is not limited to, the severity of the risk and the probability of occurrence. Some or all of the above processing in the risk detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk detection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of risk detection.

[0093] The risk detection unit can detect risks early by monitoring the resource consumption of each task in real time when performing risk detection. For example, the risk detection unit monitors the resource consumption of each task in real time and detects risks early for tasks that are lacking resources. For example, the risk detection unit flexibly detects risks for tasks where resource consumption is on track and maintains an overall balance. For example, the risk detection unit monitors the resource consumption of each task in real time to detect risks early and optimize the progress of the entire project. In this way, the risk detection unit can detect risks early by monitoring the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the risk detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the risk detection unit can input resource consumption data for each task into a generative AI, which can then monitor it in real time.

[0094] The adjustment proposal generation unit can estimate the user's emotions and adjust the method of generating adjustment proposals based on the estimated user emotions. For example, if the user is stressed, the adjustment proposal generation unit generates a simple and intuitive adjustment proposal. For example, if the user is relaxed, the adjustment proposal generation unit generates an adjustment proposal that includes detailed information. For example, if the user is in a hurry, the adjustment proposal generation unit generates an adjustment proposal that requires immediate attention. In this way, the adjustment proposal generation unit generates adjustment proposals that are easy for the user to understand by adjusting the method of generating adjustment proposals according to the user's emotions. The method of generating adjustment proposals includes, but is not limited to, the use of algorithms and user feedback. Some or all of the above processing in the adjustment proposal generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the adjustment proposal generation unit can input user emotion data into a generative AI, and the generative AI can adjust the method of generating adjustment proposals.

[0095] The adjustment plan generation unit can generate adjustment plans by reflecting the progress of each task in real time. For example, the adjustment plan generation unit monitors the progress of each task in real time and prioritizes generating adjustment plans for tasks that are behind schedule. For example, the adjustment plan generation unit flexibly generates adjustment plans for tasks that are progressing smoothly, maintaining an overall balance. For example, the adjustment plan generation unit generates adjustment plans by reflecting the progress of each task in real time, optimizing the progress of the entire project. In this way, the adjustment plan generation unit can generate optimal adjustment plans by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the adjustment plan generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the adjustment plan generation unit can input progress data for each task into a generation AI, and the generation AI can reflect it in real time and generate adjustment plans.

[0096] The adjustment proposal generation unit can estimate the user's emotions and determine the priority of adjustment proposals based on the estimated emotions. For example, if the user is stressed, the adjustment proposal generation unit will prioritize important adjustment proposals and postpone others. For example, if the user is relaxed, the adjustment proposal generation unit will balance all adjustment proposals and propose the optimal one. For example, if the user is in a hurry, the adjustment proposal generation unit will prioritize adjustment proposals that require immediate attention and respond immediately. In this way, the adjustment proposal generation unit can prioritize important adjustment proposals by determining the priority of adjustment proposals according to the user's emotions. The priority of adjustment proposals includes, but is not limited to, the importance of the project and the urgency of the adjustment. Some or all of the above processing in the adjustment proposal generation unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the adjustment proposal generation unit can input user emotion data into a generating AI, and the generating AI can determine the priority of adjustment proposals.

[0097] The adjustment plan generation unit can generate adjustment plans by reflecting the resource consumption of each task in real time. For example, the adjustment plan generation unit monitors the resource consumption of each task in real time and prioritizes generating adjustment plans for tasks that are lacking resources. For example, the adjustment plan generation unit flexibly generates adjustment plans for tasks with sufficient resource consumption to maintain overall balance. For example, the adjustment plan generation unit generates adjustment plans by reflecting the resource consumption of each task in real time and optimizes the progress of the entire project. In this way, the adjustment plan generation unit can generate optimal adjustment plans by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the adjustment plan generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the adjustment plan generation unit can input resource consumption data for each task into a generation AI, and the generation AI can reflect it in real time to generate adjustment plans.

[0098] The visualization unit can estimate the user's emotions and adjust the visualization method based on the estimated emotions. For example, if the user is stressed, the visualization unit provides a simple and intuitive visualization method. For example, if the user is relaxed, the visualization unit provides a visualization method that includes detailed information. For example, if the user is in a hurry, the visualization unit provides a visualization method that requires quick attention. In this way, the visualization unit can provide visualizations that are easy for the user to understand by adjusting the visualization method according to the user's emotions. The visualization method includes, but is not limited to, the type of graph, the use of colors, and the arrangement of information. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input user emotion data into a generative AI, and the generative AI can adjust the visualization method.

[0099] The visualization unit can visualize progress by reflecting the progress of each task in real time. For example, the visualization unit can monitor the progress of each task in real time and prioritize the visualization of tasks that are behind schedule. For example, the visualization unit can flexibly visualize tasks that are progressing smoothly to maintain an overall balance. For example, the visualization unit can optimize the progress of the entire project by reflecting the progress of each task in real time and performing visualization. In this way, the visualization unit can optimize the progress of the entire project by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input progress data for each task into a generative AI, and the generative AI can reflect and visualize it in real time.

[0100] The visualization unit can estimate the user's emotions and determine the priority of visualizations based on the estimated emotions. For example, if the user is stressed, the visualization unit will prioritize important visualizations and postpone others. If the user is relaxed, the visualization unit will balance all visualizations and suggest the optimal visualization method. If the user is in a hurry, the visualization unit will prioritize visualizations that require immediate attention and respond immediately. In this way, the visualization unit can prioritize important visualizations by determining the priority of visualizations according to the user's emotions. The priority of visualizations includes, but is not limited to, the importance of the project or the urgency of the visualization. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input user emotion data into a generative AI, which can then determine the priority of visualizations.

[0101] The visualization unit can visualize progress by reflecting the resource consumption of each task in real time. For example, the visualization unit can monitor the resource consumption of each task in real time and prioritize the visualization of tasks that are lacking resources. For example, the visualization unit can flexibly visualize tasks that are consuming resources on schedule to maintain an overall balance. For example, the visualization unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time and performing visualization. In this way, the visualization unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the visualization unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the visualization unit can input resource consumption data for each task into a generative AI, and the generative AI can reflect and visualize it in real time.

[0102] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit provides a simple and intuitive notification method. For example, if the user is relaxed, the notification unit provides a notification method that includes detailed information. For example, if the user is in a hurry, the notification unit provides a notification method that requires immediate attention. In this way, the notification unit can provide notifications that are easy for the user to understand by adjusting the notification method according to the user's emotions. Notification methods include, but are not limited to, email notifications, push notifications, and alerts. Some or all of the above processing in the notification unit may be performed using, for example, generative AI, or not using generative AI. For example, the notification unit can input user emotion data into generative AI, which can then adjust the notification method.

[0103] The notification unit can notify stakeholders in real time, reflecting the progress of each task. For example, the notification unit can monitor the progress of each task in real time and prioritize notifications for tasks that are behind schedule. For example, the notification unit can flexibly notify about tasks that are progressing smoothly to maintain overall balance. For example, the notification unit can optimize the progress of the entire project by reflecting the progress of each task in real time and issuing notifications. In this way, the notification unit can optimize the progress of the entire project by reflecting the progress of each task in real time. Real-time reflection includes, but is not limited to, the frequency of data updates and the tools used. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input progress data for each task into a generative AI, and the generative AI can reflect it in real time and issue notifications.

[0104] The notification unit can estimate the user's emotions and determine notification priorities based on those emotions. For example, if the user is stressed, the notification unit will prioritize important notifications and postpone others. If the user is relaxed, the notification unit will distribute all notifications evenly and suggest the optimal notification method. If the user is in a hurry, the notification unit will prioritize notifications requiring immediate attention and respond immediately. In this way, the notification unit can prioritize important notifications by determining notification priorities according to the user's emotions. Notification priorities include, but are not limited to, the importance and urgency of the notification. Some or all of the processing described above in the notification unit may be performed using, for example, generative AI, or not using generative AI. For example, the notification unit can input user emotion data into generative AI, which can then determine notification priorities.

[0105] The notification unit can notify stakeholders in real time, reflecting the resource consumption of each task. For example, the notification unit can monitor the resource consumption of each task in real time and prioritize notifications for tasks that are running low on resources. For example, the notification unit can flexibly notify tasks with sufficient resource consumption to maintain overall balance. For example, the notification unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time and issuing notifications. In this way, the notification unit can optimize the progress of the entire project by reflecting the resource consumption of each task in real time. Resource consumption includes, but is not limited to, time, budget, and equipment used. Some or all of the above processing in the notification unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the notification unit can input resource consumption data for each task into a generative AI, and the generative AI can reflect it in real time and issue notifications.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The progress analysis department can analyze the project's progress while considering the dependencies between tasks. For example, it can perform detailed progress analyses for tasks with strong dependencies and simplified analyses for tasks with weak dependencies. If a task with strong dependencies is behind schedule, the progress analysis department can predict the impact and notify stakeholders. In this way, the progress analysis department can optimize the overall progress of the project by considering the dependencies between tasks.

[0108] The coordination unit can adjust resources and timelines while considering the priority of each task. For example, it can prioritize resource allocation for high-priority tasks and allocate resources flexibly for lower-priority tasks. If a high-priority task is behind schedule, the coordination unit can propose allocating additional resources to that task. In this way, the coordination unit can optimize the overall progress of the project by considering the priority of each task.

[0109] The management department, when managing the progress of a project, can analyze the progress of each task and take early action on tasks that are behind schedule. For example, the management department can propose allocating additional resources to tasks that are behind schedule. For example, the management department can propose reallocating resources to other tasks for tasks that are progressing on time. In this way, the management department can optimize the overall progress of the project by analyzing the progress of each task.

[0110] The Data Analysis Department can analyze past project data while considering the resource consumption of each project. For example, it can perform detailed analyses of projects with high resource consumption and simplified analyses of projects with low resource consumption. The Data Analysis Department can identify the success and failure factors of projects with high resource consumption and apply them to current projects. This allows the Data Analysis Department to perform efficient data analysis by considering the resource consumption of each project.

[0111] The risk detection unit can detect risks early by monitoring the progress of each task in real time. For example, the risk detection unit can detect risks early in tasks that are behind schedule and notify relevant parties. For example, the risk detection unit can flexibly detect risks in tasks that are progressing smoothly, maintaining an overall balance. In this way, the risk detection unit can detect risks early by monitoring the progress of each task in real time.

[0112] The progress analysis unit can estimate the user's emotions and adjust the frequency of progress analysis based on those emotions. For example, if the user is stressed, the progress analysis unit will reduce the frequency of progress analysis and only perform analysis at important times. For example, if the user is relaxed, the progress analysis unit will increase the frequency of progress analysis and provide detailed progress information. In this way, the progress analysis unit can reduce the burden on the user by adjusting the frequency of progress analysis according to the user's emotions.

[0113] The adjustment unit can estimate the user's emotions and select how to adjust resources and timelines based on those emotions. For example, if the user is stressed, the adjustment unit will minimize adjustments to resources and timelines, making only essential adjustments. For example, if the user is relaxed, the adjustment unit will make detailed adjustments to resources and timelines and suggest the optimal adjustment method. In this way, the adjustment unit can reduce the user's burden by selecting how to adjust resources and timelines according to the user's emotions.

[0114] The management department can estimate the user's emotions and adjust project management methods based on those estimates. For example, if the user is stressed, the management department can provide a simple and intuitive management method. For example, if the user is relaxed, the management department can provide a management method that includes detailed information. This allows the management department to adjust project management methods according to the user's emotions, making management easier for the user to understand.

[0115] The data analysis department can estimate user emotions and adjust its data analysis methods based on those estimated emotions. For example, if a user is stressed, the data analysis department provides a simple and intuitive data analysis method. For example, if a user is relaxed, the data analysis department provides a data analysis method that includes detailed information. This allows the data analysis department to provide user-friendly data analysis by adjusting its methods according to the user's emotions.

[0116] The risk detection unit can estimate the user's emotions and adjust the frequency of risk detection based on the estimated emotions. For example, if the user is stressed, the risk detection unit will reduce the frequency of risk detection and only detect risks at critical moments. For example, if the user is relaxed, the risk detection unit will increase the frequency of risk detection and provide more detailed risk information. In this way, the risk detection unit can reduce the user's burden by adjusting the frequency of risk detection according to the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The progress analysis department analyzes the project's progress in real time. The progress analysis department uses AI to monitor the progress of each project task and detect tasks that are behind schedule early on. For example, if a task's progress rate is behind schedule, it can identify that task and notify the relevant parties. In addition, the project's progress is displayed on a dashboard in real time so that all stakeholders can understand the progress. Step 2: The adjustment unit adjusts resources and timelines based on the progress analyzed by the progress analysis unit. For example, it may propose allocating additional resources to tasks that are behind schedule. Furthermore, it adjusts the schedules of tasks that are behind schedule to optimize the overall progress of the project. Resource reallocation and schedule changes are performed automatically to ensure smooth project progress. Step 3: The management department manages the project progress based on the resources and timelines coordinated by the coordination department. For example, they monitor the project progress based on the resource allocation and schedule coordinated by the coordination department. They monitor the project progress in real time and make additional adjustments as needed. They notify stakeholders of the project progress to ensure smooth project progress.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the progress analysis unit, adjustment unit, management unit, data analysis unit, risk detection unit, adjustment proposal generation unit, visualization unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the progress analysis unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of each task in the project. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts resources and timelines. The management unit is implemented by the control unit 46A of the smart device 14 and manages the progress of the project. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past project data. The risk detection unit is implemented by the control unit 46A of the smart device 14 and detects risks early. The adjustment proposal generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal adjustment proposals. The visualization unit is implemented by the control unit 46A of the smart device 14 and visualizes the progress status. The notification function is implemented by the specific processing unit 290 of the data processing device 12 and notifies the relevant parties. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the progress analysis unit, adjustment unit, management unit, data analysis unit, risk detection unit, adjustment proposal generation unit, visualization unit, and notification unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the progress analysis unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of each task in the project. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts resources and timelines. The management unit is implemented by the control unit 46A of the smart glasses 214 and manages the progress of the project. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past project data. The risk detection unit is implemented by the control unit 46A of the smart glasses 214 and detects risks early. The adjustment proposal generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal adjustment proposals. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visualizes the progress status. The notification function is implemented by the specific processing unit 290 of the data processing device 12 and notifies the relevant parties. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the progress analysis unit, adjustment unit, management unit, data analysis unit, risk detection unit, adjustment proposal generation unit, visualization unit, and notification unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the progress analysis unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of each task in the project. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts resources and timelines. The management unit is implemented by the control unit 46A of the headset terminal 314 and manages the progress of the project. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past project data. The risk detection unit is implemented by the control unit 46A of the headset terminal 314 and detects risks early. The adjustment proposal generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal adjustment proposals. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visualizes the progress status. The notification function is implemented by the specific processing unit 290 of the data processing device 12 and notifies the relevant parties. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the progress analysis unit, adjustment unit, management unit, data analysis unit, risk detection unit, adjustment proposal generation unit, visualization unit, and notification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the progress analysis unit is implemented by the control unit 46A of the robot 414 and monitors the progress of each task in the project. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts resources and timelines. The management unit is implemented by the control unit 46A of the robot 414 and manages the progress of the project. The data analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes past project data. The risk detection unit is implemented by the control unit 46A of the robot 414 and detects risks early. The adjustment proposal generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates optimal adjustment proposals. The visualization unit is implemented by the control unit 46A of the robot 414 and visualizes the progress status. The notification function is implemented by the specific processing unit 290 of the data processing device 12 and notifies the relevant parties. The correspondence between each part and the device or control unit is not limited to the example described above and can be modified in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The progress analysis department analyzes project progress in real time, An adjustment unit adjusts resources and timelines based on the progress status analyzed by the aforementioned progress analysis unit, The system includes a management unit that manages the progress of the project based on the resources and timeline adjusted by the adjustment unit. A system characterized by the following features. (Note 2) It has a data analysis department that analyzes past project data. The system described in Appendix 1, characterized by the features described herein. (Note 3) It is equipped with a risk detection unit that executes a risk detection algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a unit that generates adjustment proposals. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a visualization unit that visualizes the progress status. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a notification unit to inform relevant parties. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned progress analysis unit, It estimates the user's emotions and adjusts the frequency of progress analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned progress analysis unit, When analyzing project progress, adjust the level of detail in the analysis based on the importance of each task. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned progress analysis unit, When analyzing project progress, referencing data from similar past projects improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned progress analysis unit, It estimates the user's emotions and adjusts how the progress analysis results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned progress analysis unit, When analyzing project progress, the skill level of each task's assignee should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned progress analysis unit, When analyzing project progress, the analysis should take into account the resource consumption of each task. The system described in Appendix 1, characterized by the features described herein. (Note 13) The adjustment unit is, It estimates the user's emotions and selects how to adjust resources and timelines based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The adjustment unit is, When adjusting resources and timelines, consider the dependencies between each task. The system described in Appendix 1, characterized by the features described herein. (Note 15) The adjustment unit is, When adjusting resources and timelines, refer to past adjustment history to select the optimal adjustment method. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned management department, Estimate user sentiment and adjust project management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned management department, When managing project progress, monitor and manage the progress of each task in real time. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned management department, When managing project progress, referencing past project management data improves the accuracy of management. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data analysis unit, We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned data analysis unit, When analyzing past project data, we conduct a detailed analysis of the success and failure factors of each project. The system described in Appendix 2, characterized by the features described herein. (Note 21) The aforementioned data analysis unit, We estimate user sentiment and prioritize data analysis based on the estimated user sentiment. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned data analysis unit, When analyzing past project data, the analysis should take into account the resource consumption of each project. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned risk detection unit It estimates the user's emotions and adjusts the frequency of risk detection based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 24) The aforementioned risk detection unit When detecting risks, the progress of each task is monitored in real time to detect risks early. The system described in Appendix 3, characterized by the features described herein. (Note 25) The aforementioned risk detection unit The system estimates user sentiment and prioritizes risk detection based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned risk detection unit When detecting risks, the resource consumption of each task is monitored in real time to detect risks early. The system described in Appendix 3, characterized by the features described herein. (Note 27) The adjustment plan generation unit, We estimate the user's emotions and adjust the method for generating adjustment proposals based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 28) The adjustment plan generation unit, When generating adjustment proposals, the progress of each task is reflected in real time during the generation process. The system described in Appendix 4, characterized by the features described herein. (Note 29) The adjustment plan generation unit, The system estimates the user's emotions and prioritizes adjustment proposals based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 30) The adjustment plan generation unit, When generating adjustment proposals, the resource consumption of each task is reflected in real time during the process. The system described in Appendix 4, characterized by the features described herein. (Note 31) The visualization unit, It estimates the user's emotions and adjusts the visualization method based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 32) The visualization unit, When visualizing progress, the progress of each task is reflected and visualized in real time. The system described in Appendix 5, characterized by the features described herein. (Note 33) The visualization unit, It estimates the user's emotions and determines the priority of visualizations based on the estimated user emotions. The system described in Appendix 5, characterized by the features described herein. (Note 34) The visualization unit, When visualizing progress, the resource consumption of each task is reflected and visualized in real time. The system described in Appendix 5, characterized by the features described herein. (Note 35) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 36) The aforementioned notification unit, When notifying stakeholders, the notification should reflect the progress of each task in real time. The system described in Appendix 6, characterized by the features described herein. (Note 37) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 6, characterized by the features described herein. (Note 38) The aforementioned notification unit, When notifying stakeholders, the notification should reflect the resource consumption of each task in real time. The system described in Appendix 6, characterized by the features described herein. [Explanation of Symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The progress analysis department analyzes project progress in real time, An adjustment unit adjusts resources and timelines based on the progress status analyzed by the aforementioned progress analysis unit, The system includes a management unit that manages the progress of the project based on the resources and timeline adjusted by the adjustment unit. A system characterized by the following features.

2. It has a data analysis department that analyzes past project data. The system according to feature 1.

3. It is equipped with a risk detection unit that executes a risk detection algorithm. The system according to feature 1.

4. It includes a unit that generates adjustment proposals. The system according to feature 1.

5. It includes a visualization unit that visualizes the progress status. The system according to feature 1.

6. It includes a notification unit to inform relevant parties. The system according to feature 1.

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

8. The aforementioned progress analysis unit, When analyzing project progress, adjust the level of detail in the analysis based on the importance of each task. The system according to feature 1.

9. The aforementioned progress analysis unit, When analyzing project progress, referencing data from similar past projects improves the accuracy of the analysis. The system according to feature 1.

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