system

Abstract

The system according to this embodiment aims to monitor the progress of a project in real time and to respond quickly when delays occur. [Solution] The system according to the embodiment comprises a collection unit, a notification unit, and an evaluation unit. The collection unit monitors the task progress of each member. The notification unit automatically notifies of any delays in progress monitored by the collection unit. The evaluation unit re-evaluates the task priorities based on the delays notified by the notification unit and makes suggestions for optimizing resources.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to grasp the progress of a project in real time and to respond promptly when a delay occurs.

[0005] The system according to the embodiment aims to grasp the progress of a project in real time and to respond promptly when a delay occurs.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a notification unit, and an evaluation unit. The collection unit monitors the task progress of each member. The notification unit automatically notifies of any delays in progress monitored by the collection unit. The evaluation unit re-evaluates the task priorities based on the delays notified by the notification unit and makes suggestions for optimizing resources. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the progress of a project in real time and respond quickly when delays occur. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management system according to an embodiment of the present invention is a system that monitors the progress of a project, re-evaluates task priorities, and automatically notifies of delays in progress. This system monitors the task progress of each member, and if a delay occurs, an agent automatically sends a notification. Furthermore, the AI ​​re-evaluates task priorities and proposes resource optimization. This helps to ensure the smooth progress of the project. For example, the task progress of each member can be grasped in real time. By automatically visualizing the progress of tasks, the progress of each member can be checked at a glance. If a delay occurs, the agent immediately sends a notification to inform the team of the delay. This allows for a quick response to the delay. The AI ​​dynamically optimizes task priorities and proposes efficient resource allocation. This reduces wasted resources and allows the entire project to proceed smoothly. Project managers and team leaders can avoid the effort of keeping track of task progress. Because a quick response is possible even if a delay occurs, resource allocation and changes in priorities will not be delayed. This reduces the burden of project management and improves the overall performance of the team. For example, a project manager can maximize the project's success rate by monitoring each member's task progress in real time and responding immediately if delays occur. AI can ensure the smooth progress of the entire project by re-evaluating task priorities and suggesting efficient resource allocation. Thus, an agent that monitors project progress, re-evaluates task priorities, and automatically notifies of delays is an effective means of reducing the burden of project management and improving overall team performance. In this way, project management systems can ensure the smooth progress of projects by efficiently monitoring project progress, notifying of delays, and optimizing resources.

[0029] The project management system according to this embodiment comprises a data collection unit, a notification unit, and an evaluation unit. The data collection unit monitors the task progress of each member. The data collection unit can, for example, grasp the task progress of each member in real time. The data collection unit can also automatically visualize the progress of tasks. For example, the data collection unit can display the task progress as a graph or chart, allowing each member's progress to be seen at a glance. The notification unit automatically notifies of any delays in progress monitored by the data collection unit. The notification unit, for example, immediately notifies when a delay in progress occurs. The notification unit can also adjust the level of detail of the notification based on the importance of the delay in progress. For example, the notification unit provides a detailed notification if the delay in progress is significant, and a concise notification if it is minor. The evaluation unit re-evaluates the task priorities based on the delays notified by the notification unit and makes suggestions for optimizing resources. For example, the evaluation unit dynamically optimizes task priorities when a delay in progress occurs and proposes efficient resource allocation. The evaluation unit can also make suggestions to reduce wasted resources and ensure the smooth progress of the entire project. For example, the evaluation unit makes suggestions to reduce resource waste based on resource allocation methods and optimization evaluation criteria. As a result, the project management system according to this embodiment can efficiently monitor the progress of the project, notify of delays, and optimize resources, thereby enabling the project to proceed smoothly.

[0030] The data collection unit monitors the task progress of each member. Specifically, it automatically collects the work content and progress of each member to understand the progress of the tasks they are responsible for in real time. The data collection unit works in conjunction with project management tools and task management software to acquire progress data entered by each member. For example, every time a member updates the progress of a task, that information is sent to the data collection unit and stored in a central database. Based on this data, the data collection unit visualizes the progress of tasks as graphs and charts. This allows project managers and team leaders to see the progress of each member at a glance. Furthermore, the data collection unit reflects changes in progress in real time, ensuring that the latest status is always known. For example, it uses Gantt charts and burn-down charts to visually display the progress of tasks and the amount of work remaining. This allows for efficient management of the overall progress of the project. The data collection unit can also record each member's working time and work content in detail, which is useful for analyzing and evaluating progress. This provides basic data for accurately understanding the progress of the project and taking appropriate action.

[0031] The notification unit automatically notifies stakeholders of delays in progress monitored by the data collection unit. Specifically, it analyzes progress data provided by the data collection unit and immediately notifies stakeholders if a task is behind schedule. The notification unit has a function to adjust the level of detail of notifications based on the severity of the delay. For example, if the delay is significant, it provides a detailed notification including information such as the cause of the delay, the scope of impact, and proposed countermeasures. On the other hand, if the delay is minor, it provides a concise notification with information sufficient to prompt necessary action. The notification unit can send notifications using multiple communication methods, such as email, chat tools, and push notifications. This allows stakeholders to quickly receive information about delays and take appropriate action. Furthermore, the notification unit records the notification history, allowing users to refer to past notification content and response status. This allows project managers and team leaders to review the progress management history and use it to improve future project management. The notification unit can notify not only of delays but also of important information regarding project progress, such as task completion and achievement of key milestones. This allows stakeholders to share the overall project progress and strengthen collaboration within the team.

[0032] The evaluation department re-evaluates task priorities based on delays notified by the notification department and proposes resource optimization. Specifically, when delays occur, it dynamically reviews task priorities and proposes efficient resource allocation. The evaluation department comprehensively evaluates the importance, dependencies, and resource utilization of each task and proposes optimal resource allocation. For example, for tasks with significant delays, resources are allocated preferentially to minimize the impact of the delay. For tasks with minor delays, resources are reallocated to concentrate on other important tasks. The evaluation department can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, it proposes ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria. This improves project efficiency and promotes effective resource utilization. Furthermore, the evaluation department can also optimize resource allocation by utilizing historical data and statistical information. For example, it analyzes resource utilization trends and efficiency based on past project data to help optimize future resource allocation. This allows the evaluation department to efficiently manage project progress, minimize delays, and optimize resources, thereby ensuring the project runs smoothly.

[0033] The data collection unit can monitor each member's task progress in real time. For example, the data collection unit can grasp each member's task progress in real time. The data collection unit can also automatically visualize task progress. For example, the data collection unit can display task progress as graphs or charts, allowing each member's progress to be seen at a glance. This enables quick response by allowing real-time understanding of each member's task progress. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to collect and analyze data in order to monitor each member's task progress in real time.

[0034] The notification unit can immediately notify if a delay in progress occurs. For example, the notification unit will immediately notify if a delay in progress occurs. The notification unit can also adjust the level of detail of the notification based on the severity of the delay. For example, the notification unit will provide a detailed notification if the delay is significant, and a concise notification if it is minor. This allows for a quick response by immediately notifying of delays in progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze data and generate notifications in order to detect delays in progress and immediately notify.

[0035] The evaluation unit can dynamically optimize task priorities and propose efficient resource allocation when delays occur. For example, the evaluation unit can dynamically optimize task priorities and propose efficient resource allocation when delays occur. The evaluation unit can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation unit can propose ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria. This enables efficient resource allocation by dynamically optimizing task priorities. Some or all of the above processes in the evaluation unit may be performed using generative AI, or not. For example, when delays occur, the evaluation unit can use generative AI to re-evaluate task priorities and propose optimization of resource allocation.

[0036] The data collection unit can automatically visualize the progress of tasks. For example, the data collection unit can display the progress of tasks as graphs or charts, allowing users to see the progress of each member at a glance. The data collection unit can also grasp the progress of tasks in real time. For example, the data collection unit can update the progress of tasks in real time, providing the latest information. This allows users to see the progress of each member at a glance by automatically visualizing the progress of tasks. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to automatically visualize the progress of tasks and update it in real time.

[0037] The evaluation department can make suggestions to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation department can make suggestions to reduce resource waste based on resource allocation methods and optimization evaluation criteria. The evaluation department can also dynamically optimize task priorities and propose efficient resource allocation when delays occur. For example, when delays occur, the evaluation department can make suggestions to reduce resource waste and ensure the smooth progress of the entire project. This reduces resource waste and ensures the smooth progress of the entire project. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can use AI to make suggestions to reduce resource waste and ensure the smooth progress of the entire project.

[0038] The data collection unit can analyze each member's past task progress history and select the optimal data collection method. For example, the data collection unit can customize the method of collecting progress based on each member's past task progress history. Based on past history, the data collection unit can also perform weekly collection for certain members and daily collection for others. The data collection unit can also analyze each member's progress history and optimize the timing of progress collection. This allows the optimal data collection method to be selected by analyzing past task progress history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze each member's past task progress history and select the optimal data collection method.

[0039] The data collection unit can filter task progress data based on each member's current projects and areas of interest. For example, the data collection unit can collect only tasks related to each member's current project. The data collection unit can also prioritize the collection of relevant task progress based on each member's areas of interest. The data collection unit can also adjust the scope of tasks to collect according to the progress of each member's project. This allows for the priority collection of relevant task progress by filtering based on current projects and areas of interest. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can use AI to analyze and filter each member's current projects and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each member when collecting task progress information. For example, the data collection unit can prioritize the collection of progress information for nearby projects based on the geographical location of each member. When collecting progress information for geographically distant members, the data collection unit can also adjust the collection frequency to account for communication delays. The data collection unit can also prioritize the collection of relevant task progress information based on the location of each member. This enables efficient information collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the geographical location of each member and prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze each member's social media activity and collect relevant information when collecting task progress. For example, the data collection unit can analyze each member's social media activity and collect information related to the progress. The data collection unit can also adjust the frequency of progress collection based on the frequency of activity on social media. The data collection unit can also analyze the content of social media posts and collect information related to the progress. This allows for efficient collection of information related to the progress by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze each member's social media activity and collect relevant information.

[0042] The notification unit can adjust the level of detail in notifications based on the severity of the delay in progress. For example, if the delay in progress is significant, the notification unit will provide a detailed notification. If the delay in progress is minor, the notification unit may provide a concise notification. The notification unit can also dynamically adjust the level of detail in notifications according to the severity of the delay in progress. This allows for appropriate notifications by adjusting the level of detail according to the severity of the delay in progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to evaluate the severity of the delay in progress and adjust the level of detail in notifications.

[0043] The notification unit can apply different notification algorithms depending on the cause of the delay when sending a notification. For example, if the cause is a technical problem, the notification unit will send a notification that includes technical details. If the cause is a human error, the notification unit can also send a notification that includes countermeasures. The notification unit can also apply the most suitable notification algorithm depending on the cause of the delay. This allows for appropriate notifications by applying the most suitable notification algorithm depending on the cause of the delay. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze the cause of the delay and apply the most suitable notification algorithm.

[0044] The notification unit can determine the priority of notifications based on when the delay occurred. For example, the notification unit can send a notification immediately after the delay occurs. The notification unit can also prioritize notifications if the delay has been ongoing for a long period. The notification unit can also dynamically adjust the priority of notifications according to when the delay occurred. This enables appropriate notifications by determining the priority of notifications according to when the delay occurred. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze when the delay occurred and determine the priority of notifications.

[0045] The notification unit can adjust the order of notifications based on the relevance of the delays. For example, the notification unit may prioritize notifications for delays in important tasks. It may also postpone notifications for delays in less relevant tasks. The notification unit can also dynamically adjust the order of notifications according to the relevance of the delays. This allows for appropriate notifications by adjusting the order of notifications according to the relevance of the delays. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze the relevance of delays and adjust the order of notifications.

[0046] The evaluation unit can consider each member's past performance when re-evaluating task priorities. For example, the evaluation unit can re-evaluate task priorities based on each member's past performance. The evaluation unit can also prioritize assigning important tasks to members with high past performance. The evaluation unit can also analyze each member's performance history to determine the optimal task priorities. This allows for the determination of optimal task priorities by considering each member's past performance. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze each member's past performance and re-evaluate task priorities.

[0047] The evaluation unit can dynamically optimize task priorities in accordance with the project's progress when re-evaluating them. For example, the evaluation unit can grasp the project's progress in real time and dynamically optimize task priorities. The evaluation unit can also raise the priority of important tasks according to the project's progress. The evaluation unit can also analyze the project's progress and determine the optimal task priorities. This enables efficient task management by dynamically optimizing task priorities according to the project's progress. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze the project's progress and dynamically optimize task priorities.

[0048] The evaluation unit can propose the optimal resource allocation by considering each member's geographical location when re-evaluating task priorities. For example, the evaluation unit optimizes resource allocation based on each member's geographical location. The evaluation unit can also prioritize resource allocation to members who are geographically closer. The evaluation unit can also propose the optimal resource allocation by considering each member's location. This enables efficient resource management by optimizing resource allocation while considering each member's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze each member's geographical location and propose the optimal resource allocation.

[0049] The evaluation unit can improve the accuracy of resource allocation by referring to data from related projects when re-evaluating task priorities. For example, the evaluation unit can improve the accuracy of resource allocation based on data from related projects. The evaluation unit can also propose the optimal resource allocation by referring to past project data. The evaluation unit can also improve the accuracy of resource allocation by analyzing the progress of related projects. In this way, the accuracy of resource allocation can be improved by referring to data from related projects. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze data from related projects and improve the accuracy of resource allocation.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] A project management system can also include a feedback department. This department can collect feedback from each member and incorporate it into the project's progress and task priorities. For example, the feedback department can collect problems and areas for improvement that members feel regarding task progress and provide this information to the evaluation department. Based on the collected feedback, the evaluation department can re-evaluate task priorities and propose optimized resource allocation. This enables flexible project management that reflects the opinions of the members. Furthermore, the feedback department can conduct regular surveys of members to understand their satisfaction levels and stress levels regarding project progress. This allows the project management system to leverage member feedback to achieve more effective project management.

[0052] Project management systems can also include a forecasting unit. This unit can analyze historical project data to predict future delays and resource shortages. For example, it can analyze patterns of delays in past projects to predict potential delays in the current project. It can also analyze resource usage to predict future resource shortages. This allows the project management system to proactively address predicted delays and resource shortages. Furthermore, the forecasting unit can suggest resource reallocations and changes in task priorities based on the project's progress. By predicting and proactively addressing future problems, the project management system can improve the project's success rate.

[0053] A project management system can also include a learning unit. This unit can improve the accuracy of project management by continuously learning from and collecting project progress and member performance data. For example, the learning unit collects each member's task completion time and quality data and provides it to the evaluation unit. Based on the collected data, the evaluation unit can re-evaluate task priorities and propose optimized resource allocation. This allows the project management system to leverage historical data for more effective project management. Furthermore, the learning unit can improve task assignment methods and resource allocation optimization algorithms according to the project's progress. This allows the project management system to continuously learn and evolve, thereby increasing the project's success rate.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The data collection unit monitors the task progress of each member. The data collection unit can, for example, grasp the task progress of each member in real time and automatically visualize the task progress. For example, the data collection unit can display the task progress as graphs or charts, allowing each member's progress to be seen at a glance. Step 2: The notification unit automatically notifies of any delays in progress monitored by the collection unit. For example, the notification unit immediately notifies if a delay occurs. The notification unit can also adjust the level of detail of the notification based on the severity of the delay. For example, the notification unit can provide a detailed notification if the delay is significant and a concise notification if it is minor. Step 3: The evaluation department re-evaluates task priorities based on delays notified by the notification department and proposes resource optimization. For example, the evaluation department dynamically optimizes task priorities and proposes efficient resource allocation when delays occur. The evaluation department can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation department proposes ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria.

[0056] (Example of form 2) The project management system according to an embodiment of the present invention is a system that monitors the progress of a project, re-evaluates task priorities, and automatically notifies of delays in progress. This system monitors the task progress of each member, and if a delay occurs, an agent automatically sends a notification. Furthermore, the AI ​​re-evaluates task priorities and proposes resource optimization. This helps to ensure the smooth progress of the project. For example, the task progress of each member can be grasped in real time. By automatically visualizing the progress of tasks, the progress of each member can be checked at a glance. If a delay occurs, the agent immediately sends a notification to inform the team of the delay. This allows for a quick response to the delay. The AI ​​dynamically optimizes task priorities and proposes efficient resource allocation. This reduces wasted resources and allows the entire project to proceed smoothly. Project managers and team leaders can avoid the effort of keeping track of task progress. Because a quick response is possible even if a delay occurs, resource allocation and changes in priorities will not be delayed. This reduces the burden of project management and improves the overall performance of the team. For example, a project manager can maximize the project's success rate by monitoring each member's task progress in real time and responding immediately if delays occur. AI can ensure the smooth progress of the entire project by re-evaluating task priorities and suggesting efficient resource allocation. Thus, an agent that monitors project progress, re-evaluates task priorities, and automatically notifies of delays is an effective means of reducing the burden of project management and improving overall team performance. In this way, project management systems can ensure the smooth progress of projects by efficiently monitoring project progress, notifying of delays, and optimizing resources.

[0057] The project management system according to this embodiment comprises a data collection unit, a notification unit, and an evaluation unit. The data collection unit monitors the task progress of each member. The data collection unit can, for example, grasp the task progress of each member in real time. The data collection unit can also automatically visualize the progress of tasks. For example, the data collection unit can display the task progress as a graph or chart, allowing each member's progress to be seen at a glance. The notification unit automatically notifies of any delays in progress monitored by the data collection unit. The notification unit, for example, immediately notifies when a delay in progress occurs. The notification unit can also adjust the level of detail of the notification based on the importance of the delay in progress. For example, the notification unit provides a detailed notification if the delay in progress is significant, and a concise notification if it is minor. The evaluation unit re-evaluates the task priorities based on the delays notified by the notification unit and makes suggestions for optimizing resources. For example, the evaluation unit dynamically optimizes task priorities when a delay in progress occurs and proposes efficient resource allocation. The evaluation unit can also make suggestions to reduce wasted resources and ensure the smooth progress of the entire project. For example, the evaluation unit makes suggestions to reduce resource waste based on resource allocation methods and optimization evaluation criteria. As a result, the project management system according to this embodiment can efficiently monitor the progress of the project, notify of delays, and optimize resources, thereby enabling the project to proceed smoothly.

[0058] The data collection unit monitors the task progress of each member. Specifically, it automatically collects the work content and progress of each member to understand the progress of the tasks they are responsible for in real time. The data collection unit works in conjunction with project management tools and task management software to acquire progress data entered by each member. For example, every time a member updates the progress of a task, that information is sent to the data collection unit and stored in a central database. Based on this data, the data collection unit visualizes the progress of tasks as graphs and charts. This allows project managers and team leaders to see the progress of each member at a glance. Furthermore, the data collection unit reflects changes in progress in real time, ensuring that the latest status is always known. For example, it uses Gantt charts and burn-down charts to visually display the progress of tasks and the amount of work remaining. This allows for efficient management of the overall progress of the project. The data collection unit can also record each member's working time and work content in detail, which is useful for analyzing and evaluating progress. This provides basic data for accurately understanding the progress of the project and taking appropriate action.

[0059] The notification unit automatically notifies stakeholders of delays in progress monitored by the data collection unit. Specifically, it analyzes progress data provided by the data collection unit and immediately notifies stakeholders if a task is behind schedule. The notification unit has a function to adjust the level of detail of notifications based on the severity of the delay. For example, if the delay is significant, it provides a detailed notification including information such as the cause of the delay, the scope of impact, and proposed countermeasures. On the other hand, if the delay is minor, it provides a concise notification with information sufficient to prompt necessary action. The notification unit can send notifications using multiple communication methods, such as email, chat tools, and push notifications. This allows stakeholders to quickly receive information about delays and take appropriate action. Furthermore, the notification unit records the notification history, allowing users to refer to past notification content and response status. This allows project managers and team leaders to review the progress management history and use it to improve future project management. The notification unit can notify not only of delays but also of important information regarding project progress, such as task completion and achievement of key milestones. This allows stakeholders to share the overall project progress and strengthen collaboration within the team.

[0060] The evaluation department re-evaluates task priorities based on delays notified by the notification department and proposes resource optimization. Specifically, when delays occur, it dynamically reviews task priorities and proposes efficient resource allocation. The evaluation department comprehensively evaluates the importance, dependencies, and resource utilization of each task and proposes optimal resource allocation. For example, for tasks with significant delays, resources are allocated preferentially to minimize the impact of the delay. For tasks with minor delays, resources are reallocated to concentrate on other important tasks. The evaluation department can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, it proposes ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria. This improves project efficiency and promotes effective resource utilization. Furthermore, the evaluation department can also optimize resource allocation by utilizing historical data and statistical information. For example, it analyzes resource utilization trends and efficiency based on past project data to help optimize future resource allocation. This allows the evaluation department to efficiently manage project progress, minimize delays, and optimize resources, thereby ensuring the project runs smoothly.

[0061] The data collection unit can monitor each member's task progress in real time. For example, the data collection unit can grasp each member's task progress in real time. The data collection unit can also automatically visualize task progress. For example, the data collection unit can display task progress as graphs or charts, allowing each member's progress to be seen at a glance. This enables quick response by allowing real-time understanding of each member's task progress. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to collect and analyze data in order to monitor each member's task progress in real time.

[0062] The notification unit can immediately notify if a delay in progress occurs. For example, the notification unit will immediately notify if a delay in progress occurs. The notification unit can also adjust the level of detail of the notification based on the severity of the delay. For example, the notification unit will provide a detailed notification if the delay is significant, and a concise notification if it is minor. This allows for a quick response by immediately notifying of delays in progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze data and generate notifications in order to detect delays in progress and immediately notify.

[0063] The evaluation unit can dynamically optimize task priorities and propose efficient resource allocation when delays occur. For example, the evaluation unit can dynamically optimize task priorities and propose efficient resource allocation when delays occur. The evaluation unit can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation unit can propose ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria. This enables efficient resource allocation by dynamically optimizing task priorities. Some or all of the above processes in the evaluation unit may be performed using generative AI, or not. For example, when delays occur, the evaluation unit can use generative AI to re-evaluate task priorities and propose optimization of resource allocation.

[0064] The data collection unit can automatically visualize the progress of tasks. For example, the data collection unit can display the progress of tasks as graphs or charts, allowing users to see the progress of each member at a glance. The data collection unit can also grasp the progress of tasks in real time. For example, the data collection unit can update the progress of tasks in real time, providing the latest information. This allows users to see the progress of each member at a glance by automatically visualizing the progress of tasks. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to automatically visualize the progress of tasks and update it in real time.

[0065] The evaluation department can make suggestions to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation department can make suggestions to reduce resource waste based on resource allocation methods and optimization evaluation criteria. The evaluation department can also dynamically optimize task priorities and propose efficient resource allocation when delays occur. For example, when delays occur, the evaluation department can make suggestions to reduce resource waste and ensure the smooth progress of the entire project. This reduces resource waste and ensures the smooth progress of the entire project. Some or all of the above processes in the evaluation department may be performed using AI or not. For example, the evaluation department can use AI to make suggestions to reduce resource waste and ensure the smooth progress of the entire project.

[0066] The data collection unit can estimate the user's emotions and adjust the frequency of task progress data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the collection frequency to alleviate the burden. If the user is relaxed, the data collection unit can increase the collection frequency to get a more detailed understanding of the progress. If the user is in a hurry, the data collection unit can increase the collection frequency to get a real-time understanding of the progress. This reduces the user's burden by adjusting the collection frequency according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The data collection unit can analyze each member's past task progress history and select the optimal data collection method. For example, the data collection unit can customize the method of collecting progress based on each member's past task progress history. Based on past history, the data collection unit can also perform weekly collection for certain members and daily collection for others. The data collection unit can also analyze each member's progress history and optimize the timing of progress collection. This allows the optimal data collection method to be selected by analyzing past task progress history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze each member's past task progress history and select the optimal data collection method.

[0068] The data collection unit can filter task progress data based on each member's current projects and areas of interest. For example, the data collection unit can collect only tasks related to each member's current project. The data collection unit can also prioritize the collection of relevant task progress based on each member's areas of interest. The data collection unit can also adjust the scope of tasks to collect according to the progress of each member's project. This allows for the priority collection of relevant task progress by filtering based on current projects and areas of interest. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can use AI to analyze and filter each member's current projects and areas of interest.

[0069] The data collection unit can estimate the user's emotions and determine the priority of task progress to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone the collection of less important tasks. If the user is relaxed, the data collection unit may collect all task progress equally. If the user is in a hurry, the data collection unit may prioritize the collection of high-priority task progress. This allows for the priority collection of important tasks by determining the priority of task progress according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of each member when collecting task progress information. For example, the data collection unit can prioritize the collection of progress information for nearby projects based on the geographical location of each member. When collecting progress information for geographically distant members, the data collection unit can also adjust the collection frequency to account for communication delays. The data collection unit can also prioritize the collection of relevant task progress information based on the location of each member. This enables efficient information collection by prioritizing the collection of highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the geographical location of each member and prioritize the collection of highly relevant information.

[0071] The data collection unit can analyze each member's social media activity and collect relevant information when collecting task progress. For example, the data collection unit can analyze each member's social media activity and collect information related to the progress. The data collection unit can also adjust the frequency of progress collection based on the frequency of activity on social media. The data collection unit can also analyze the content of social media posts and collect information related to the progress. This allows for efficient collection of information related to the progress by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze each member's social media activity and collect relevant information.

[0072] The notification unit can estimate the user's emotions and adjust the way notifications are expressed based on the estimated emotions. For example, if the user is tense, the notification unit can use a calm expression. If the user is relaxed, the notification unit can use a cheerful expression. If the user is in a hurry, the notification unit can use a concise and quick expression. This allows for appropriate notifications by adjusting the way notifications are expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the way notifications are expressed.

[0073] The notification unit can adjust the level of detail in notifications based on the severity of the delay in progress. For example, if the delay in progress is significant, the notification unit will provide a detailed notification. If the delay in progress is minor, the notification unit may provide a concise notification. The notification unit can also dynamically adjust the level of detail in notifications according to the severity of the delay in progress. This allows for appropriate notifications by adjusting the level of detail according to the severity of the delay in progress. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to evaluate the severity of the delay in progress and adjust the level of detail in notifications.

[0074] The notification unit can apply different notification algorithms depending on the cause of the delay when sending a notification. For example, if the cause is a technical problem, the notification unit will send a notification that includes technical details. If the cause is a human error, the notification unit can also send a notification that includes countermeasures. The notification unit can also apply the most suitable notification algorithm depending on the cause of the delay. This allows for appropriate notifications by applying the most suitable notification algorithm depending on the cause of the delay. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze the cause of the delay and apply the most suitable notification algorithm.

[0075] The notification unit can estimate the user's emotions and adjust the timing of notifications based on the estimated emotions. For example, if the user is stressed, the notification unit may delay the notification. If the user is relaxed, the notification unit may send an immediate notification. If the user is in a hurry, the notification unit may send a rapid notification. This allows notifications to be sent at the appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI and have the generative AI adjust the timing of notifications.

[0076] The notification unit can determine the priority of notifications based on when the delay occurred. For example, the notification unit can send a notification immediately after the delay occurs. The notification unit can also prioritize notifications if the delay has been ongoing for a long period. The notification unit can also dynamically adjust the priority of notifications according to when the delay occurred. This enables appropriate notifications by determining the priority of notifications according to when the delay occurred. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze when the delay occurred and determine the priority of notifications.

[0077] The notification unit can adjust the order of notifications based on the relevance of the delays. For example, the notification unit may prioritize notifications for delays in important tasks. It may also postpone notifications for delays in less relevant tasks. The notification unit can also dynamically adjust the order of notifications according to the relevance of the delays. This allows for appropriate notifications by adjusting the order of notifications according to the relevance of the delays. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can use AI to analyze the relevance of delays and adjust the order of notifications.

[0078] The evaluation unit can estimate the user's emotions and re-evaluate task priorities based on the estimated emotions. For example, if the user is stressed, the evaluation unit can lower the priority of low-priority tasks. If the user is relaxed, the evaluation unit can also re-evaluate all tasks equally. If the user is in a hurry, the evaluation unit can also raise the priority of high-priority tasks. This allows for appropriate task management by re-evaluating task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI re-evaluate task priorities.

[0079] The evaluation unit can consider each member's past performance when re-evaluating task priorities. For example, the evaluation unit can re-evaluate task priorities based on each member's past performance. The evaluation unit can also prioritize assigning important tasks to members with high past performance. The evaluation unit can also analyze each member's performance history to determine the optimal task priorities. This allows for the determination of optimal task priorities by considering each member's past performance. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze each member's past performance and re-evaluate task priorities.

[0080] The evaluation unit can dynamically optimize task priorities in accordance with the project's progress when re-evaluating them. For example, the evaluation unit can grasp the project's progress in real time and dynamically optimize task priorities. The evaluation unit can also raise the priority of important tasks according to the project's progress. The evaluation unit can also analyze the project's progress and determine the optimal task priorities. This enables efficient task management by dynamically optimizing task priorities according to the project's progress. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze the project's progress and dynamically optimize task priorities.

[0081] The evaluation unit can estimate the user's emotions and adjust resource allocation suggestions based on the estimated emotions. For example, if the user is stressed, the evaluation unit can adjust resource allocation to reduce the burden. If the user is relaxed, the evaluation unit can also allocate resources evenly. If the user is in a hurry, the evaluation unit can concentrate resources on important tasks. This allows for appropriate resource management by adjusting resource allocation suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI adjust resource allocation suggestions.

[0082] The evaluation unit can propose the optimal resource allocation by considering each member's geographical location when re-evaluating task priorities. For example, the evaluation unit optimizes resource allocation based on each member's geographical location. The evaluation unit can also prioritize resource allocation to members who are geographically closer. The evaluation unit can also propose the optimal resource allocation by considering each member's location. This enables efficient resource management by optimizing resource allocation while considering each member's geographical location. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze each member's geographical location and propose the optimal resource allocation.

[0083] The evaluation unit can improve the accuracy of resource allocation by referring to data from related projects when re-evaluating task priorities. For example, the evaluation unit can improve the accuracy of resource allocation based on data from related projects. The evaluation unit can also propose the optimal resource allocation by referring to past project data. The evaluation unit can also improve the accuracy of resource allocation by analyzing the progress of related projects. In this way, the accuracy of resource allocation can be improved by referring to data from related projects. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can use AI to analyze data from related projects and improve the accuracy of resource allocation.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] A project management system can also include a feedback department. This department can collect feedback from each member and incorporate it into the project's progress and task priorities. For example, the feedback department can collect problems and areas for improvement that members feel regarding task progress and provide this information to the evaluation department. Based on the collected feedback, the evaluation department can re-evaluate task priorities and propose optimized resource allocation. This enables flexible project management that reflects the opinions of the members. Furthermore, the feedback department can conduct regular surveys of members to understand their satisfaction levels and stress levels regarding project progress. This allows the project management system to leverage member feedback to achieve more effective project management.

[0086] Project management systems can also include a forecasting unit. This unit can analyze historical project data to predict future delays and resource shortages. For example, it can analyze patterns of delays in past projects to predict potential delays in the current project. It can also analyze resource usage to predict future resource shortages. This allows the project management system to proactively address predicted delays and resource shortages. Furthermore, the forecasting unit can suggest resource reallocations and changes in task priorities based on the project's progress. By predicting and proactively addressing future problems, the project management system can improve the project's success rate.

[0087] A project management system can also include a learning unit. This unit can improve the accuracy of project management by continuously learning from and collecting project progress and member performance data. For example, the learning unit collects each member's task completion time and quality data and provides it to the evaluation unit. Based on the collected data, the evaluation unit can re-evaluate task priorities and propose optimized resource allocation. This allows the project management system to leverage historical data for more effective project management. Furthermore, the learning unit can improve task assignment methods and resource allocation optimization algorithms according to the project's progress. This allows the project management system to continuously learn and evolve, thereby increasing the project's success rate.

[0088] A project management system can also include a communications department. This department provides functions to facilitate communication among team members. For example, it can offer chat and video conferencing features to enable real-time information sharing. The communications department can also collect discussions and feedback on tasks and provide them to the evaluation department. This allows the project management system to strengthen communication among members and ensure smooth project progress. Furthermore, the communications department can estimate members' emotions and suggest appropriate communication methods. For example, if a member is feeling stressed, it can suggest communication methods that promote relaxation. This enables the project management system to achieve emotionally sensitive communication and improve team performance.

[0089] Project management systems can also include a motivation section. This section provides functions to improve member motivation. For example, it can offer rewards and praise for tasks completed by members. It can also estimate members' emotions and suggest motivational measures accordingly. For instance, it might suggest a break if a member is tired, or offer additional challenges if a member is feeling motivated. This allows the project management system to maintain member motivation and ensure smooth project progress. Furthermore, the motivation section can introduce gamification elements to encourage competition and collaboration among members. This further enhances member motivation and improves overall team performance.

[0090] A project management system can also include a health management department. This department can monitor the health status of team members and make suggestions for maintaining their health. For example, it can monitor members' stress levels and fatigue levels and suggest appropriate rest and exercise. It can also estimate members' emotions and suggest health management measures tailored to those emotions. For instance, if a member is stressed, it can suggest relaxing activities. If a member is relaxed, it can suggest light exercise. This allows the project management system to maintain the health of its members and ensure the smooth progress of the project. Furthermore, the health management department can collect members' health data and develop long-term health management plans. This enables the project management system to support member health and improve overall team performance.

[0091] A project management system can also include a scheduling unit. This unit can automatically adjust each member's schedule and efficiently assign tasks. For example, it can identify each member's free time and optimize task assignments. It can also estimate members' emotions and adjust schedules accordingly. For instance, if a member is stressed, it can reduce their workload. If a member is relaxed, it can add tasks. This allows the project management system to efficiently adjust members' schedules and ensure smooth project progress. Furthermore, the scheduling unit can suggest schedule changes based on the project's progress. This enables flexible scheduling and improves the project's success rate.

[0092] A project management system can also include a risk management department. This department can assess risks associated with project progress and propose risk mitigation measures. For example, it can analyze past project data to identify potential risks. It can also evaluate the probability and impact of risks and propose risk mitigation strategies. This allows the project management system to identify risks in advance and take appropriate measures. Furthermore, the risk management department can continuously conduct risk assessments and update risk mitigation strategies according to the project's progress. This enables dynamic risk management and improves the project's success rate. Additionally, the risk management department can estimate the emotions of team members and propose risk mitigation strategies tailored to those emotions. For example, if a member is experiencing stress, risk mitigation strategies can be strengthened. This allows the project management system to implement risk management that considers the emotions of team members, ensuring smooth project progress.

[0093] A project management system can also include a knowledge sharing section. This section provides functions for sharing project-related knowledge and information among members. For example, it can centrally manage project-related documents and materials, making them accessible to members. It can also provide a platform for members to share their expertise and experience. This allows the project management system to facilitate knowledge sharing among members and ensure smooth project progress. Furthermore, the knowledge sharing section can estimate members' emotions and suggest appropriate methods of knowledge sharing. For example, if a member is stressed, it can provide concise and easy-to-understand information. If a member is relaxed, it can provide detailed information. This enables the project management system to achieve emotionally sensitive knowledge sharing, improving team performance.

[0094] Project management systems can also include an incentives section. This section provides rewards and incentives based on member performance. For example, it can offer bonuses and perks to members based on task completion and quality. The incentives section can also estimate members' emotions and suggest incentives accordingly. For instance, if a member is stressed, it can offer relaxing perks. If a member is motivated, it can offer additional challenges. This allows the project management system to maintain member motivation and ensure smooth project progress. Furthermore, the incentives section can implement incentive programs to promote competition and collaboration among members. This allows the project management system to boost member motivation and improve overall team performance.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The data collection unit monitors the task progress of each member. The data collection unit can, for example, grasp the task progress of each member in real time and automatically visualize the task progress. For example, the data collection unit can display the task progress as graphs or charts, allowing each member's progress to be seen at a glance. Step 2: The notification unit automatically notifies of any delays in progress monitored by the collection unit. For example, the notification unit immediately notifies if a delay occurs. The notification unit can also adjust the level of detail of the notification based on the severity of the delay. For example, the notification unit can provide a detailed notification if the delay is significant and a concise notification if it is minor. Step 3: The evaluation department re-evaluates task priorities based on delays notified by the notification department and proposes resource optimization. For example, the evaluation department dynamically optimizes task priorities and proposes efficient resource allocation when delays occur. The evaluation department can also propose ways to reduce resource waste and ensure the smooth progress of the entire project. For example, the evaluation department proposes ways to reduce resource waste based on resource allocation methods and optimization evaluation criteria.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the collection unit, notification unit, and evaluation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and monitors the task progress of each member in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately notifies if a delay in progress occurs. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and re-evaluates the task priorities and makes suggestions for optimizing resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the data collection unit, notification unit, and evaluation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and monitors the task progress of each member in real time. The notification unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and immediately notifies if a delay in progress occurs. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and re-evaluates the task priorities and makes suggestions for optimizing resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, notification unit, and evaluation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and monitors the task progress of each member in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately notifies if a delay in progress occurs. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and re-evaluates the task priorities and makes suggestions for optimizing resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the data collection unit, notification unit, and evaluation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and monitors the task progress of each member in real time. The notification unit is implemented by the specific processing unit 290 of the data processing unit 12 and immediately notifies if a delay in progress occurs. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and re-evaluates the task priorities and makes suggestions for optimizing resources. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A data collection unit monitors the task progress of each member, A notification unit that automatically notifies of any delays in progress monitored by the aforementioned collection unit, The system includes an evaluation unit that re-evaluates task priorities based on delays notified by the notification unit and makes suggestions for optimizing resources. A system characterized by the following features. (Note 2) The aforementioned collection unit is Monitor each member's task progress in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned notification unit, We will immediately notify you if there are any delays in progress. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, It dynamically optimizes task priorities and proposes efficient resource allocation when delays occur. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Automatically visualize the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The evaluation unit, We propose ways to reduce wasted resources and ensure the smooth progress of the entire project. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of task progress updates based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze each member's past task progress history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting task progress updates, filter them based on each member's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of task progress to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting task progress information, prioritize collecting highly relevant information by considering the geographical location of each member. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting task progress updates, analyze each member's social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned notification unit, It estimates the user's emotions and adjusts the way notifications are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the severity of the delay in progress. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned notification unit, When sending a notification, a different notification algorithm is applied depending on the cause of the delay. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned notification unit, It estimates the user's emotions and adjusts the timing of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned notification unit, When sending notifications, the priority of notifications is determined based on when the delay occurred. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the delay. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, Estimate user emotions and re-evaluate task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When re-evaluating task priorities, consider each member's past performance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When re-evaluating task priorities, dynamically optimize them according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, It estimates the user's emotions and adjusts resource allocation suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When re-evaluating task priorities, we propose the optimal resource allocation by considering each member's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When re-evaluating task priorities, referencing data from related projects improves the accuracy of resource allocation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit monitors the task progress of each member, A notification unit that automatically notifies of any delays in progress monitored by the aforementioned collection unit, The system includes an evaluation unit that re-evaluates task priorities based on delays notified by the notification unit and makes suggestions for optimizing resources. A system characterized by the following features.

2. The aforementioned notification unit, We will immediately notify you if there are any delays in progress. The system according to feature 1.

3. The evaluation unit, It dynamically optimizes task priorities and proposes efficient resource allocation when delays occur. The system according to feature 1.

4. The aforementioned collection unit is Automatically visualize the progress of tasks. The system according to feature 1.

5. The evaluation unit, We propose ways to reduce wasted resources and ensure the smooth progress of the entire project. The system according to feature 1.

6. The aforementioned collection unit is The system estimates the user's emotions and adjusts the frequency of task progress updates based on those emotions. The system according to feature 1.

7. The aforementioned collection unit is Analyze each member's past task progress history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting task progress updates, filter them based on each member's current projects and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of task progress to collect based on the estimated user emotions. The system according to feature 1.

Images