System

Abstract

An object of a system according to an embodiment is to visualize the progress status of a project and appropriately assign tasks.SOLUTION: A system includes a progress management part, a task allocation part, and a member management part. The progress management unit visualizes the progress status of the project. The task allocation unit allocates a task on the basis of the progress status visualized by the progress management unit. The member management unit evaluates the performance of the task assigned by the task assignment unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques make it difficult to efficiently manage the progress of projects and appropriately assign tasks, and there is room for improvement.

[0005] The system according to the embodiment aims to visualize the progress of a project and appropriately allocate tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress management unit, a task allocation unit, and a member management unit. The progress management unit visualizes the progress of a project. The task allocation unit assigns tasks based on the progress visualized by the progress management unit. The member management unit evaluates the performance of the tasks assigned by the task allocation unit. [Effects of the Invention]

[0007] The system according to the embodiment can visualize the progress of a project and appropriately assign tasks. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A project management system according to an embodiment of the present invention is a system that efficiently manages projects and tasks and aims to improve productivity. This system has a wide range of functions, including progress management, task allocation and priority setting, member management, communication support, and resource management, and promotes improved team productivity and task efficiency. As a result, the project management system can improve the efficiency of projects and tasks and improve team productivity.

[0029] A project management system according to an embodiment includes a progress management unit, a task assignment unit, and a member management unit. The progress management unit visualizes the progress of a project. For example, in the progress management unit, a generation AI analyzes project progress data and generates Gantt charts and burndown charts. The progress management unit also updates the project progress in real time, allowing team members and managers to grasp the current situation at a glance. The task assignment unit assigns tasks based on the progress visualized by the progress management unit. For example, the generation AI analyzes each member's skills and current workload and automatically proposes optimal task assignments. The task assignment unit also has a function to set task priorities and sort tasks according to importance and urgency. The member management unit evaluates the performance of tasks assigned by the task assignment unit. For example, the generation AI analyzes each member's performance data and provides appropriate feedback and evaluations. The member management unit also provides advice to promote communication between members. This allows the project management system to visualize the progress of a project and efficiently assign tasks and evaluate member performance.

[0030] The progress management department can analyze progress data, predict project risk factors, and propose risk avoidance measures. For example, in the progress management department, the generation AI analyzes progress data and compares it with past project data to identify risk factors. For example, if a specific task is delayed, the generation AI predicts the impact that task will have on the entire project and proposes risk avoidance measures. The progress management department also monitors risk factors in real time based on progress data and issues alerts before risks occur. For example, if a resource shortage or task delay occurs, the generation AI immediately notifies the manager. After the generation AI analyzes progress data and identifies risk factors, the progress management department proposes risk avoidance measures based on past success stories. For example, the generation AI lists effective countermeasures for specific risks and proposes them to the manager. This predicts project risk factors and proposes risk avoidance measures, thereby increasing the project's success rate.

[0031] The progress management department can calculate the probability of project success based on progress data and present specific action plans to increase the success probability. For example, the generation AI in the progress management department analyzes progress data and develops an algorithm to calculate the project success probability. For example, the success probability is calculated based on the task completion rate and resource usage. The progress management department then presents a specific action plan to increase the success probability. For example, the generation AI makes suggestions such as changing the priority of specific tasks or allocating additional resources. The progress management department also updates the success probability in real time based on the progress data and dynamically adjusts the action plan. For example, the action plan is reevaluated each time the progress status changes and optimal measures are proposed. This allows the generation AI to calculate the project success probability and present specific action plans to support project success.

[0032] The progress management department can analyze progress data, perform comparative analysis with other projects, and extract best practices. In the progress management department, for example, the generation AI analyzes progress data and performs comparative analysis with other projects. For example, it evaluates progress by comparing with projects in the same industry or of the same scale. The progress management department also extracts best practices based on the results of the comparative analysis. For example, it refers to the progress management methods of successful projects and applies them to the current project. In addition, the generation AI in the progress management department performs comparative analysis with other projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. This makes it possible to optimize project progress management by performing comparative analysis with other projects and extracting best practices.

[0033] The progress management department can predict project progress based on progress data and perform simulations based on the prediction results. For example, the generation AI in the progress management department develops an algorithm to predict project progress based on progress data. For example, it predicts future progress based on past data. The progress management department also performs simulations based on the prediction results. For example, it simulates the impact of a specific task being delayed and considers countermeasures. The generation AI in the progress management department also updates the progress forecast in real time and dynamically adjusts the simulation results. For example, it re-runs the simulation every time the progress status changes and proposes optimal countermeasures. In this way, by predicting project progress and performing simulations, the probability of project success can be increased.

[0034] When allocating tasks, the task allocation unit can analyze members' past performance data and make optimal task allocations. For example, the task allocation unit uses a generation AI to analyze members' past performance data and make optimal task allocations. For example, it prioritizes allocating tasks where members have demonstrated high performance in the past. The task allocation unit also identifies members' areas of expertise and skill sets based on performance data and assigns tasks based on that. For example, it assigns tasks that require specific skills to members who have those skills. The task allocation unit also uses a generation AI to update past performance data in real time and dynamically adjust optimal task allocations. For example, it reevaluates task allocations every time new data is added and makes optimal allocations. In this way, task efficiency can be improved by analyzing members' past performance data and making optimal task allocations.

[0035] The task allocation unit can consider task dependencies and propose the optimal task order when setting task priorities. For example, the task allocation unit's generation AI analyzes task dependencies and proposes the optimal task order. For example, if the next task cannot start until a specific task is completed, the task allocation unit sets the priority by taking that dependency into consideration. The task allocation unit also optimizes the overall project schedule based on task dependencies. For example, project delays can be prevented by prioritizing the completion of tasks with many dependencies. The task allocation unit's generation AI also updates task dependencies in real time and dynamically adjusts the optimal task order. For example, when a new task is added, the dependency relationships are reevaluated and the priorities are reset. This makes it possible to consider task dependencies and propose the optimal task order, preventing project delays.

[0036] When allocating tasks, the task allocation unit can refer to task allocation data from other projects and learn the optimal allocation method. For example, the generation AI analyzes task allocation data from other projects to learn the optimal allocation method. For example, it refers to the task allocation methods of successful projects. The task allocation unit also proposes a task allocation method that can be applied to the current project based on data from other projects. For example, it refers to data from projects in the same industry or of the same scale. The generation AI also updates the task allocation data of other projects in real time and dynamically adjusts the optimal allocation method. For example, it reevaluates the allocation method each time new data is added and proposes the optimal method. In this way, efficient task allocation can be achieved by referring to task allocation data from other projects and learning the optimal allocation method.

[0037] The task allocation unit can consider not only the urgency of a task but also its long-term impact when setting task priorities. For example, the generation AI analyzes the urgency and long-term impact of a task and sets the optimal priority. For example, it prioritizes tasks that are not urgent in the short term but important in the long term. The task allocation unit also optimizes the overall project schedule based on the urgency and long-term impact of a task. For example, completing tasks that are important in the long term early increases the probability of project success. The generation AI also updates the urgency and long-term impact of a task in real time and dynamically adjusts priorities. For example, when a new task is added, it reevaluates the urgency and long-term impact and resets the priority. This increases the probability of project success by considering not only the urgency of a task but also its long-term impact.

[0038] The member management department can analyze members' performance data and propose customized training plans for individual members. In the member management department, for example, the generative AI analyzes members' performance data and proposes the optimal training plan for each member. For example, for a member who is lacking in a particular skill, it proposes training to strengthen that skill. The member management department also identifies members' strengths and weaknesses based on the performance data and creates customized training plans based on that. For example, it proposes training to further develop strengths and training to overcome weaknesses. In addition, the generative AI updates members' performance data in real time and dynamically adjusts the training plan. For example, it reevaluates the training plan every time new data is added and proposes the optimal plan. In this way, it is possible to analyze members' performance data and propose customized training plans to help members improve their skills.

[0039] The member management section can analyze members' communication patterns and provide advice to improve communication efficiency within the team. For example, the generation AI in the member management section analyzes members' communication patterns and suggests efficient communication methods. For example, it can suggest regular meetings for members who frequently lose communication. The member management section also optimizes information sharing within the team based on communication patterns. For example, it can suggest channels for quickly sharing important information. The generation AI in the member management section also updates members' communication patterns in real time and dynamically adjusts efficient communication methods. For example, it reevaluates communication methods every time new data is added and suggests the optimal method. This makes it possible to analyze members' communication patterns and provide advice to improve communication efficiency within the team, thereby strengthening team cooperation.

[0040] The Member Management Department can analyze member performance data and perform comparative analysis with other teams and projects to extract best practices. In the Member Management Department, for example, the generation AI analyzes member performance data and performs comparative analysis with other teams and projects. For example, performance is evaluated by comparing it with projects in the same industry or of the same scale. The Member Management Department also extracts best practices based on the results of the comparative analysis. For example, performance management techniques from successful projects can be referenced and applied to the current project. The Member Management Department also uses the generation AI to perform comparative analysis with other teams and projects in real time and dynamically update best practices. For example, a comparative analysis is performed every time new data is added and the latest best practices are proposed. This makes it possible to optimize member performance by performing comparative analysis with other teams and projects and extracting best practices.

[0041] The member management department can analyze members' skill sets, identify areas where skills are expected to improve, and propose training plans. For example, the member management department uses generative AI to analyze members' skill sets and identify areas where skills are expected to improve. For example, for members who lack specific skills, the department proposes training to strengthen those skills. The member management department also optimizes members' career paths based on their skill sets. For example, it identifies skills that will be needed in the future and creates training plans based on those skills. The member management department also uses generative AI to update members' skill sets in real time and dynamically adjust training plans. For example, it reevaluates skill sets every time new data is added and proposes an optimal training plan. This makes it possible to support members' skill improvement by analyzing members' skill sets, identifying areas where skills are expected to improve, and proposing training plans.

[0042] The communication support unit can analyze communication data within a team, identify communication bottlenecks, and propose improvement measures. For example, the generation AI in the communication support unit analyzes communication data within a team and identifies bottlenecks. For example, if communication has broken down between specific members, it identifies the cause. The communication support unit also proposes communication improvement measures based on the bottlenecks. For example, it proposes specific measures such as setting up regular meetings or changing communication tools. The generation AI in the communication support unit also updates communication data in real time and dynamically identifies bottlenecks. For example, each time new data is added, it reevaluates the communication situation and proposes improvement measures. In this way, by analyzing communication data within a team, identifying communication bottlenecks, and proposing improvement measures, it is possible to improve team communication efficiency.

[0043] The communication support unit can analyze communication patterns within a team and suggest the optimal communication channel. For example, the generation AI in the communication support unit analyzes communication patterns within a team and suggests the optimal communication channel. For example, it suggests the optimal chat tool or video conferencing tool for a specific task. The communication support unit also optimizes information sharing within a team based on communication patterns. For example, it suggests a channel for quickly sharing important information. The generation AI in the communication support unit also updates communication patterns in real time and dynamically adjusts the optimal communication channel. For example, it reevaluates communication channels every time new data is added and suggests the optimal channel. In this way, by analyzing communication patterns within a team and suggesting the optimal communication channel, it is possible to optimize information sharing within the team.

[0044] The communication support unit can analyze communication data within a team and perform comparative analysis with other teams and projects to extract best practices. For example, the generation AI in the communication support unit analyzes communication data within a team and performs comparative analysis with other teams and projects. For example, it evaluates communication efficiency by comparing with projects in the same industry or of the same scale. The communication support unit also extracts best practices based on the results of the comparative analysis. For example, it references communication methods from successful projects and applies them to the current project. The generation AI in the communication support unit also performs comparative analysis with other teams and projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. This allows for comparative analysis with other teams and projects and extraction of best practices, thereby improving team communication efficiency.

[0045] The resource management unit can analyze resource usage data, identify resource waste, and propose optimal resource allocation. For example, the resource management unit uses a generation AI to analyze resource usage data and identify resource waste. For example, if a specific resource is being overused, it identifies the cause. The resource management unit also proposes optimal resource allocation based on the waste. For example, it proposes specific measures such as reallocating overused resources to other tasks. The resource management unit also uses a generation AI to update resource usage data in real time and dynamically adjust optimal resource allocation. For example, it reevaluates resource allocation every time new data is added and proposes optimal allocation. In this way, efficient resource use can be achieved by analyzing resource usage data, identifying resource waste, and proposing optimal resource allocation.

[0046] The resource management unit monitors resource usage in real time, predicts resource shortages or surpluses, and makes appropriate adjustments. For example, the resource management unit uses a generation AI to monitor resource usage in real time and predict resource shortages or surpluses. For example, if a specific resource is in short supply, it predicts the impact. The resource management unit also makes appropriate adjustments based on the shortage or surplus. For example, it proposes specific measures such as adding scarce resources or reallocating excess resources to other tasks. The resource management unit also uses a generation AI to update resource usage in real time and dynamically make appropriate adjustments. For example, it reevaluates resource usage every time new data is added and makes optimal adjustments. This allows resource usage to be monitored in real time, predicts resource shortages or surpluses, and makes appropriate adjustments, thereby achieving efficient resource use.

[0047] The resource management unit can analyze resource usage data, perform comparative analysis with other projects, and extract best practices. In the resource management unit, for example, the generation AI analyzes resource usage data and performs comparative analysis with other projects. For example, it evaluates resource usage efficiency by comparing with projects in the same industry or of the same scale. The resource management unit also extracts best practices based on the results of the comparative analysis. For example, it refers to the resource management techniques of successful projects and applies them to the current project. In addition, the resource management unit's generation AI performs comparative analysis with other projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. In this way, it is possible to achieve efficient resource use by performing comparative analysis with other projects and extracting best practices.

[0048] The resource management unit can predict future resource demand based on resource usage and propose a resource plan based on the prediction results. In the resource management unit, for example, the generation AI analyzes resource usage and predicts future resource demand. For example, future resource demand is predicted based on past data. The resource management unit also proposes a resource plan based on the prediction results. For example, specific measures such as adding resources according to future resource demand or improving resource usage efficiency are proposed. In addition, the resource management unit updates the resource usage status in real time with the generation AI, dynamically adjusting the resource plan. For example, the resource demand is reevaluated each time new data is added and an optimal plan is proposed. In this way, efficient resource use can be achieved by predicting future resource demand based on resource usage and proposing a resource plan.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] Project management systems can also provide dashboards to visualize project progress. For example, dashboards can visually display project progress using graphs and charts so that project progress can be understood at a glance. Dashboards can also highlight important project milestones and deadlines, allowing team members to focus on important tasks. Furthermore, dashboards can provide real-time updates on project progress, providing the latest information. This makes project progress visible and allows team members to efficiently manage tasks.

[0051] The project management system may further include a risk assessment unit for predicting project risk factors. For example, the risk assessment unit may analyze project progress data and identify potential risk factors. The risk assessment unit may also calculate the probability of risk occurrence and evaluate the impact of risks based on past project data. The risk assessment unit may also propose risk avoidance measures and provide a specific action plan to increase the probability of project success. In this way, by predicting project risk factors and proposing risk avoidance measures, the probability of project success can be increased.

[0052] The project management system may further include a success prediction unit for calculating the success probability of the project. For example, the success prediction unit may analyze project progress data and develop an algorithm for calculating the success probability. The success prediction unit may also present a specific action plan for increasing the success probability of the project. Furthermore, the success prediction unit may reevaluate the action plan each time the project progress status changes and propose optimal measures. In this way, the system can support the success of the project by calculating the success probability of the project and presenting a specific action plan.

[0053] The project management system may further include a comparative analysis unit that performs comparative analysis with other projects and extracts best practices. For example, the comparative analysis unit may analyze project progress data and evaluate progress by comparing it with projects in the same industry or of the same scale. The comparative analysis unit may also refer to progress management methods from successful projects and apply them to the current project. Furthermore, the comparative analysis unit may perform comparative analysis each time new data is added and propose the latest best practices. This allows project progress management to be optimized by performing comparative analysis with other projects and extracting best practices.

[0054] The project management system can further include a simulation unit for predicting project progress and performing simulations based on the prediction results. For example, the simulation unit develops an algorithm for predicting future progress based on project progress data. The simulation unit can also simulate the impact of a specific task being delayed and consider countermeasures. Furthermore, the simulation unit can re-run the simulation every time the progress status changes and propose optimal countermeasures. In this way, by predicting project progress and performing simulations, the probability of project success can be increased.

[0055] The project management system can further include a performance analysis unit that analyzes members' past performance data and optimally assigns tasks when allocating tasks. For example, the performance analysis unit analyzes members' past performance data and optimally assigns tasks. It can also identify members' areas of expertise and skill sets based on the performance data and assign tasks based on that. Furthermore, the performance analysis unit can reevaluate task allocations every time new data is added and optimally assign tasks. In this way, analyzing members' past performance data and optimally assigning tasks can improve task efficiency.

[0056] The project management system may further include a dependency analysis unit that considers task dependencies when setting task priorities and proposes an optimal task order. For example, the dependency analysis unit analyzes task dependencies and proposes an optimal task order. The dependency analysis unit can also optimize the overall project schedule based on the task dependencies. Furthermore, the dependency analysis unit can reevaluate dependencies and reset priorities when new tasks are added. This allows the system to prevent project delays by considering task dependencies and proposing an optimal task order.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The progress management department visualizes the project's progress. For example, the generation AI analyzes the project's progress data and generates Gantt charts and burndown charts. The progress management department also updates the project's progress in real time, allowing team members and managers to grasp the current situation at a glance. Step 2: The task allocation unit assigns tasks based on the progress visualized by the progress management unit. For example, the generation AI analyzes each member's skills and current workload and automatically proposes optimal task allocations. The task allocation unit also has the ability to set task priorities and sort tasks according to importance and urgency. Step 3: The Member Management Department evaluates the performance of the tasks assigned by the Task Assignment Department. For example, the Generative AI analyzes each member's performance data and provides appropriate feedback and evaluation. The Member Management Department also provides advice to promote communication between members.

[0059] (Example 2) A project management system according to an embodiment of the present invention is a system that efficiently manages projects and tasks and aims to improve productivity. This system has a wide range of functions, including progress management, task allocation and priority setting, member management, communication support, and resource management, and promotes improved team productivity and task efficiency. As a result, the project management system can improve the efficiency of projects and tasks and improve team productivity.

[0060] A project management system according to an embodiment includes a progress management unit, a task assignment unit, and a member management unit. The progress management unit visualizes the progress of a project. For example, in the progress management unit, a generation AI analyzes project progress data and generates Gantt charts and burndown charts. The progress management unit also updates the project progress in real time, allowing team members and managers to grasp the current situation at a glance. The task assignment unit assigns tasks based on the progress visualized by the progress management unit. For example, the generation AI analyzes each member's skills and current workload and automatically proposes optimal task assignments. The task assignment unit also has a function to set task priorities and sort tasks according to importance and urgency. The member management unit evaluates the performance of tasks assigned by the task assignment unit. For example, the generation AI analyzes each member's performance data and provides appropriate feedback and evaluations. The member management unit also provides advice to promote communication between members. This allows the project management system to visualize the progress of a project and efficiently assign tasks and evaluate member performance.

[0061] The progress management department can analyze progress data, predict project risk factors, and propose risk avoidance measures. For example, in the progress management department, the generation AI analyzes progress data and compares it with past project data to identify risk factors. For example, if a specific task is delayed, the generation AI predicts the impact that task will have on the entire project and proposes risk avoidance measures. The progress management department also monitors risk factors in real time based on progress data and issues alerts before risks occur. For example, if a resource shortage or task delay occurs, the generation AI immediately notifies the manager. After the generation AI analyzes progress data and identifies risk factors, the progress management department proposes risk avoidance measures based on past success stories. For example, the generation AI lists effective countermeasures for specific risks and proposes them to the manager. This predicts project risk factors and proposes risk avoidance measures, thereby increasing the project's success rate.

[0062] The progress management department can calculate the probability of project success based on progress data and present specific action plans to increase the success probability. For example, the generation AI in the progress management department analyzes progress data and develops an algorithm to calculate the project success probability. For example, the success probability is calculated based on the task completion rate and resource usage. The progress management department then presents a specific action plan to increase the success probability. For example, the generation AI makes suggestions such as changing the priority of specific tasks or allocating additional resources. The progress management department also updates the success probability in real time based on the progress data and dynamically adjusts the action plan. For example, the action plan is reevaluated each time the progress status changes and optimal measures are proposed. This allows the generation AI to calculate the project success probability and present specific action plans to support project success.

[0063] The progress management unit can use the emotion estimation function to analyze the motivation of team members and propose appropriate support for members whose motivation is declining. The progress management unit, for example, uses the emotion estimation function to analyze the motivation of team members in real time. For example, it analyzes members' facial expressions and voices to detect a decline in motivation. The progress management unit also proposes appropriate support for members whose motivation is declining. For example, it proposes specific measures such as taking a break or reducing task load. The progress management unit also predicts the impact that a decline in motivation will have on the entire project based on the emotion estimation data and takes measures early. For example, it provides support such as sending encouraging messages to members whose motivation is declining. In this way, by analyzing team members' motivation and proposing appropriate support, it is possible to improve team productivity.

[0064] The progress management department can analyze progress data, perform comparative analysis with other projects, and extract best practices. In the progress management department, for example, the generation AI analyzes progress data and performs comparative analysis with other projects. For example, it evaluates progress by comparing with projects in the same industry or of the same scale. The progress management department also extracts best practices based on the results of the comparative analysis. For example, it refers to the progress management methods of successful projects and applies them to the current project. In addition, the generation AI in the progress management department performs comparative analysis with other projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. This makes it possible to optimize project progress management by performing comparative analysis with other projects and extracting best practices.

[0065] The progress management department can predict project progress based on progress data and perform simulations based on the prediction results. For example, the generation AI in the progress management department develops an algorithm to predict project progress based on progress data. For example, it predicts future progress based on past data. The progress management department also performs simulations based on the prediction results. For example, it simulates the impact of a specific task being delayed and considers countermeasures. The generation AI in the progress management department also updates the progress forecast in real time and dynamically adjusts the simulation results. For example, it re-runs the simulation every time the progress status changes and proposes optimal countermeasures. In this way, by predicting project progress and performing simulations, the probability of project success can be increased.

[0066] The progress management unit can use the emotion estimation function to analyze the emotional reactions of team members to the progress status and provide feedback to elicit positive emotions. The progress management unit, for example, uses the emotion estimation function to analyze the emotional reactions of team members to the progress status in real time. For example, if progress is delayed, the progress management unit detects the member's stress level. The progress management unit also provides feedback to elicit positive emotions based on the emotional reaction data. For example, if progress is going well, the progress management unit sends a message praising the member. The progress management unit also dynamically adjusts the feedback on the progress status based on the emotion estimation data. For example, if progress is delayed, the progress management unit provides feedback according to the situation, such as sending an encouraging message. In this way, by analyzing the emotional reactions of team members to the progress status and providing feedback to elicit positive emotions, it is possible to improve the motivation of the team.

[0067] When allocating tasks, the task allocation unit can analyze members' past performance data and make optimal task allocations. For example, the task allocation unit uses a generation AI to analyze members' past performance data and make optimal task allocations. For example, it prioritizes allocating tasks where members have demonstrated high performance in the past. The task allocation unit also identifies members' areas of expertise and skill sets based on performance data and assigns tasks based on that. For example, it assigns tasks that require specific skills to members who have those skills. The task allocation unit also uses a generation AI to update past performance data in real time and dynamically adjust optimal task allocations. For example, it reevaluates task allocations every time new data is added and makes optimal allocations. In this way, task efficiency can be improved by analyzing members' past performance data and making optimal task allocations.

[0068] The task allocation unit can consider task dependencies and propose the optimal task order when setting task priorities. For example, the task allocation unit's generation AI analyzes task dependencies and proposes the optimal task order. For example, if the next task cannot start until a specific task is completed, the task allocation unit sets the priority by taking that dependency into consideration. The task allocation unit also optimizes the overall project schedule based on task dependencies. For example, project delays can be prevented by prioritizing the completion of tasks with many dependencies. The task allocation unit's generation AI also updates task dependencies in real time and dynamically adjusts the optimal task order. For example, when a new task is added, the dependency relationships are reevaluated and the priorities are reset. This makes it possible to consider task dependencies and propose the optimal task order, preventing project delays.

[0069] The task allocator can use the emotion estimation function to analyze the emotions of members when tasks are assigned and allocate tasks to reduce stress. The task allocator, for example, uses the emotion estimation function to analyze the emotions of members when tasks are assigned in real time. For example, it detects the stress level of members when the task load is high. The task allocator also allocates tasks to reduce stress. For example, it suggests specific measures such as dividing high-load tasks among multiple members or taking breaks. The task allocator also dynamically adjusts task allocation based on the emotion estimation data. For example, it allocates tasks according to the situation, such as changing the priority of tasks when a member's stress level is high. In this way, by analyzing the emotions of members when tasks are assigned and allocating tasks to reduce stress, it is possible to reduce the burden on members and achieve efficient task management.

[0070] When allocating tasks, the task allocation unit can refer to task allocation data from other projects and learn the optimal allocation method. For example, the generation AI analyzes task allocation data from other projects to learn the optimal allocation method. For example, it refers to the task allocation methods of successful projects. The task allocation unit also proposes a task allocation method that can be applied to the current project based on data from other projects. For example, it refers to data from projects in the same industry or of the same scale. The generation AI also updates the task allocation data of other projects in real time and dynamically adjusts the optimal allocation method. For example, it reevaluates the allocation method each time new data is added and proposes the optimal method. In this way, efficient task allocation can be achieved by referring to task allocation data from other projects and learning the optimal allocation method.

[0071] The task allocation unit can consider not only the urgency of a task but also its long-term impact when setting task priorities. For example, the generation AI analyzes the urgency and long-term impact of a task and sets the optimal priority. For example, it prioritizes tasks that are not urgent in the short term but important in the long term. The task allocation unit also optimizes the overall project schedule based on the urgency and long-term impact of a task. For example, completing tasks that are important in the long term early increases the probability of project success. The generation AI also updates the urgency and long-term impact of a task in real time and dynamically adjusts priorities. For example, when a new task is added, it reevaluates the urgency and long-term impact and resets the priority. This increases the probability of project success by considering not only the urgency of a task but also its long-term impact.

[0072] The task allocation unit can use the emotion estimation function to analyze members' emotional reactions to task priority setting and set priorities that elicit positive reactions. The task allocation unit, for example, uses the emotion estimation function to analyze members' emotional reactions to task priority setting in real time. For example, it detects stress levels for high-priority tasks. The task allocation unit also sets priorities that elicit positive reactions. For example, it suggests specific measures such as preferentially allocating tasks that members are good at or distributing tasks that are heavy loads. The task allocation unit also dynamically adjusts task priority setting based on the emotion estimation data. For example, it sets priorities according to the situation, such as changing the priorities if a member's emotional reaction is not positive. In this way, by analyzing members' emotional reactions to task priority setting and setting priorities that elicit positive reactions, it is possible to improve members' motivation.

[0073] The member management department can analyze members' performance data and propose customized training plans for individual members. In the member management department, for example, the generative AI analyzes members' performance data and proposes the optimal training plan for each member. For example, for a member who is lacking in a particular skill, it proposes training to strengthen that skill. The member management department also identifies members' strengths and weaknesses based on the performance data and creates customized training plans based on that. For example, it proposes training to further develop strengths and training to overcome weaknesses. In addition, the generative AI updates members' performance data in real time and dynamically adjusts the training plan. For example, it reevaluates the training plan every time new data is added and proposes the optimal plan. In this way, it is possible to analyze members' performance data and propose customized training plans to help members improve their skills.

[0074] The member management section can analyze members' communication patterns and provide advice to improve communication efficiency within the team. For example, the generation AI in the member management section analyzes members' communication patterns and suggests efficient communication methods. For example, it can suggest regular meetings for members who frequently lose communication. The member management section also optimizes information sharing within the team based on communication patterns. For example, it can suggest channels for quickly sharing important information. The generation AI in the member management section also updates members' communication patterns in real time and dynamically adjusts efficient communication methods. For example, it reevaluates communication methods every time new data is added and suggests the optimal method. This makes it possible to analyze members' communication patterns and provide advice to improve communication efficiency within the team, thereby strengthening team cooperation.

[0075] The member management unit can use the emotion estimation function to analyze the emotional state of members and provide appropriate feedback to members who need emotional support. The member management unit, for example, uses the emotion estimation function to analyze the emotional state of members in real time. For example, it identifies members who are feeling stressed or anxious. The member management unit also provides appropriate feedback based on the emotional state. For example, it provides advice on how to relax to a member who is feeling stressed. The member management unit also dynamically adjusts feedback according to the situation for members who need emotional support based on the emotion estimation data. For example, it updates the feedback every time the emotional state changes. In this way, by analyzing the emotional state of members and providing appropriate feedback to members who need emotional support, it is possible to reduce the psychological burden on members.

[0076] The Member Management Department can analyze member performance data and perform comparative analysis with other teams and projects to extract best practices. In the Member Management Department, for example, the generation AI analyzes member performance data and performs comparative analysis with other teams and projects. For example, performance is evaluated by comparing it with projects in the same industry or of the same scale. The Member Management Department also extracts best practices based on the results of the comparative analysis. For example, performance management techniques from successful projects can be referenced and applied to the current project. The Member Management Department also uses the generation AI to perform comparative analysis with other teams and projects in real time and dynamically update best practices. For example, a comparative analysis is performed every time new data is added and the latest best practices are proposed. This makes it possible to optimize member performance by performing comparative analysis with other teams and projects and extracting best practices.

[0077] The member management department can analyze members' skill sets, identify areas where skills are expected to improve, and propose training plans. For example, the member management department uses generative AI to analyze members' skill sets and identify areas where skills are expected to improve. For example, for members who lack specific skills, the department proposes training to strengthen those skills. The member management department also optimizes members' career paths based on their skill sets. For example, it identifies skills that will be needed in the future and creates training plans based on those skills. The member management department also uses generative AI to update members' skill sets in real time and dynamically adjust training plans. For example, it reevaluates skill sets every time new data is added and proposes an optimal training plan. This makes it possible to support members' skill improvement by analyzing members' skill sets, identifying areas where skills are expected to improve, and proposing training plans.

[0078] The member management unit can use the emotion estimation function to monitor the emotional state of members in real time and identify when emotional support is needed. The member management unit, for example, uses the emotion estimation function to monitor the emotional state of members in real time. For example, it identifies members who are feeling stressed or anxious. The member management unit also identifies when emotional support is needed based on the emotional state. For example, it provides support when stress reaches its peak. The member management unit also dynamically adjusts the timing when emotional support is needed based on the emotion estimation data. For example, it updates the timing of support every time the emotional state changes. In this way, by monitoring the emotional state of members in real time and identifying when emotional support is needed, it is possible to reduce the psychological burden on members.

[0079] The communication support unit can analyze communication data within a team, identify communication bottlenecks, and propose improvement measures. For example, the generation AI in the communication support unit analyzes communication data within a team and identifies bottlenecks. For example, if communication has broken down between specific members, it identifies the cause. The communication support unit also proposes communication improvement measures based on the bottlenecks. For example, it proposes specific measures such as setting up regular meetings or changing communication tools. The generation AI in the communication support unit also updates communication data in real time and dynamically identifies bottlenecks. For example, each time new data is added, it reevaluates the communication situation and proposes improvement measures. In this way, by analyzing communication data within a team, identifying communication bottlenecks, and proposing improvement measures, it is possible to improve team communication efficiency.

[0080] The communication support unit can analyze communication patterns within a team and suggest the optimal communication channel. For example, the generation AI in the communication support unit analyzes communication patterns within a team and suggests the optimal communication channel. For example, it suggests the optimal chat tool or video conferencing tool for a specific task. The communication support unit also optimizes information sharing within a team based on communication patterns. For example, it suggests a channel for quickly sharing important information. The generation AI in the communication support unit also updates communication patterns in real time and dynamically adjusts the optimal communication channel. For example, it reevaluates communication channels every time new data is added and suggests the optimal channel. In this way, by analyzing communication patterns within a team and suggesting the optimal communication channel, it is possible to optimize information sharing within the team.

[0081] The communication support unit can use the emotion estimation function to analyze the emotions of members during communication and provide advice to promote positive communication. The communication support unit, for example, uses the emotion estimation function to analyze the emotions of members during communication in real time. For example, it identifies members who are feeling stressed or anxious. The communication support unit also provides advice to promote positive communication based on the emotional state. For example, it provides advice to relax to a member who is feeling stressed. The communication support unit also dynamically adjusts advice during communication based on the emotion estimation data. For example, it updates the advice every time the emotional state changes. In this way, it is possible to analyze the emotions of members during communication and provide advice to promote positive communication, thereby strengthening team cooperation.

[0082] The communication support unit can analyze communication data within a team and perform comparative analysis with other teams and projects to extract best practices. For example, the generation AI in the communication support unit analyzes communication data within a team and performs comparative analysis with other teams and projects. For example, it evaluates communication efficiency by comparing with projects in the same industry or of the same scale. The communication support unit also extracts best practices based on the results of the comparative analysis. For example, it references communication methods from successful projects and applies them to the current project. The generation AI in the communication support unit also performs comparative analysis with other teams and projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. This allows for comparative analysis with other teams and projects and extraction of best practices, thereby improving team communication efficiency.

[0083] The communication support unit can use the emotion estimation function to monitor the emotional reactions of members during communication in real time and identify the timing when emotional support is needed. The communication support unit, for example, uses the emotion estimation function to monitor the emotional reactions of members during communication in real time. For example, it identifies members who are feeling stressed or anxious. The communication support unit also identifies the timing when emotional support is needed based on the emotional reactions. For example, it provides support when stress reaches its peak. The communication support unit also dynamically adjusts the timing when emotional support is needed based on the emotion estimation data. For example, it updates the timing of support every time the emotional state changes. In this way, by monitoring the emotional reactions of members during communication in real time and identifying the timing when emotional support is needed, it is possible to reduce the psychological burden on members.

[0084] The resource management unit can analyze resource usage data, identify resource waste, and propose optimal resource allocation. For example, the resource management unit uses a generation AI to analyze resource usage data and identify resource waste. For example, if a specific resource is being overused, it identifies the cause. The resource management unit also proposes optimal resource allocation based on the waste. For example, it proposes specific measures such as reallocating overused resources to other tasks. The resource management unit also uses a generation AI to update resource usage data in real time and dynamically adjust optimal resource allocation. For example, it reevaluates resource allocation every time new data is added and proposes optimal allocation. In this way, efficient resource use can be achieved by analyzing resource usage data, identifying resource waste, and proposing optimal resource allocation.

[0085] The resource management unit monitors resource usage in real time, predicts resource shortages or surpluses, and makes appropriate adjustments. For example, the resource management unit uses a generation AI to monitor resource usage in real time and predict resource shortages or surpluses. For example, if a specific resource is in short supply, it predicts the impact. The resource management unit also makes appropriate adjustments based on the shortage or surplus. For example, it proposes specific measures such as adding scarce resources or reallocating excess resources to other tasks. The resource management unit also uses a generation AI to update resource usage in real time and dynamically make appropriate adjustments. For example, it reevaluates resource usage every time new data is added and makes optimal adjustments. This allows resource usage to be monitored in real time, predicts resource shortages or surpluses, and makes appropriate adjustments, thereby achieving efficient resource use.

[0086] The resource management unit can use the emotion estimation function to analyze members' emotional reactions to resource management and allocate resources to elicit positive emotions. The resource management unit, for example, uses the emotion estimation function to analyze members' emotional reactions to resource management in real time. For example, it detects stress levels due to resource shortages or excesses. The resource management unit also allocates resources to elicit positive emotions. For example, it proposes specific measures such as allocating additional resources to resolve resource shortages or reallocating excess resources to other tasks. The resource management unit also dynamically adjusts resource allocation based on the emotion estimation data. For example, it allocates resources according to the situation, such as changing the resource allocation if a member's emotional reaction is not positive. In this way, by analyzing members' emotional reactions to resource management and allocating resources to elicit positive emotions, it is possible to improve members' motivation.

[0087] The resource management unit can analyze resource usage data, perform comparative analysis with other projects, and extract best practices. In the resource management unit, for example, the generation AI analyzes resource usage data and performs comparative analysis with other projects. For example, it evaluates resource usage efficiency by comparing with projects in the same industry or of the same scale. The resource management unit also extracts best practices based on the results of the comparative analysis. For example, it refers to the resource management techniques of successful projects and applies them to the current project. In addition, the resource management unit's generation AI performs comparative analysis with other projects in real time and dynamically updates best practices. For example, it performs comparative analysis every time new data is added and proposes the latest best practices. In this way, it is possible to achieve efficient resource use by performing comparative analysis with other projects and extracting best practices.

[0088] The resource management unit can predict future resource demand based on resource usage and propose a resource plan based on the prediction results. In the resource management unit, for example, the generation AI analyzes resource usage and predicts future resource demand. For example, future resource demand is predicted based on past data. The resource management unit also proposes a resource plan based on the prediction results. For example, specific measures such as adding resources according to future resource demand or improving resource usage efficiency are proposed. In addition, the resource management unit updates the resource usage status in real time with the generation AI, dynamically adjusting the resource plan. For example, the resource demand is reevaluated each time new data is added and an optimal plan is proposed. In this way, efficient resource use can be achieved by predicting future resource demand based on resource usage and proposing a resource plan.

[0089] The resource management unit can use the emotion estimation function to monitor the emotional reactions of members to resource management in real time and identify the timing when emotional support is needed. The resource management unit, for example, uses the emotion estimation function to monitor the emotional reactions of members to resource management in real time. For example, it detects stress levels in response to resource shortages or excesses. The resource management unit also identifies the timing when emotional support is needed based on the emotional reactions. For example, it provides support when stress reaches its peak. The resource management unit also dynamically adjusts the timing when emotional support is needed based on the emotion estimation data. For example, it updates the timing of support every time the emotional state changes. In this way, by monitoring the emotional reactions of members to resource management in real time and identifying the timing when emotional support is needed, it is possible to reduce the psychological burden on members.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] Project management systems can also provide dashboards to visualize project progress. For example, dashboards can visually display project progress using graphs and charts so that project progress can be understood at a glance. Dashboards can also highlight important project milestones and deadlines, allowing team members to focus on important tasks. Furthermore, dashboards can provide real-time updates on project progress, providing the latest information. This makes project progress visible and allows team members to efficiently manage tasks.

[0092] The project management system may further include a risk assessment unit for predicting project risk factors. For example, the risk assessment unit may analyze project progress data and identify potential risk factors. The risk assessment unit may also calculate the probability of risk occurrence and evaluate the impact of risks based on past project data. The risk assessment unit may also propose risk avoidance measures and provide a specific action plan to increase the probability of project success. In this way, by predicting project risk factors and proposing risk avoidance measures, the probability of project success can be increased.

[0093] The project management system may further include a success prediction unit for calculating the success probability of the project. For example, the success prediction unit may analyze project progress data and develop an algorithm for calculating the success probability. The success prediction unit may also present a specific action plan for increasing the success probability of the project. Furthermore, the success prediction unit may reevaluate the action plan each time the project progress status changes and propose optimal measures. In this way, the system can support the success of the project by calculating the success probability of the project and presenting a specific action plan.

[0094] The project management system can also use emotion estimation to analyze the motivation of team members and propose appropriate support for members who are experiencing low motivation. For example, the emotion estimation function can be used to analyze members' facial expressions and voices to detect a decline in motivation. Specific measures, such as taking a break or reducing task load, can also be proposed to members who are experiencing low motivation. Furthermore, based on the emotion estimation data, the impact of a decline in motivation on the entire project can be predicted and countermeasures can be taken early. This allows for the team's productivity to be improved by analyzing the motivation of team members and proposing appropriate support.

[0095] The project management system may further include a comparative analysis unit that performs comparative analysis with other projects and extracts best practices. For example, the comparative analysis unit may analyze project progress data and evaluate progress by comparing it with projects in the same industry or of the same scale. The comparative analysis unit may also refer to progress management methods from successful projects and apply them to the current project. Furthermore, the comparative analysis unit may perform comparative analysis each time new data is added and propose the latest best practices. This allows project progress management to be optimized by performing comparative analysis with other projects and extracting best practices.

[0096] The project management system can further include a simulation unit for predicting project progress and performing simulations based on the prediction results. For example, the simulation unit develops an algorithm for predicting future progress based on project progress data. The simulation unit can also simulate the impact of a specific task being delayed and consider countermeasures. Furthermore, the simulation unit can re-run the simulation every time the progress status changes and propose optimal countermeasures. In this way, by predicting project progress and performing simulations, the probability of project success can be increased.

[0097] The project management system can further use an emotion estimation function to analyze the emotional reactions of team members to the progress status and provide feedback to elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotional reactions of team members to the progress status in real time. Feedback to elicit positive emotions can be provided based on the emotional reaction data. Furthermore, feedback on the progress status can be dynamically adjusted based on the emotion estimation data. In this way, by analyzing the emotional reactions of team members to the progress status and providing feedback to elicit positive emotions, it is possible to improve team motivation.

[0098] The project management system can further include a performance analysis unit that analyzes members' past performance data and optimally assigns tasks when allocating tasks. For example, the performance analysis unit analyzes members' past performance data and optimally assigns tasks. It can also identify members' areas of expertise and skill sets based on the performance data and assign tasks based on that. Furthermore, the performance analysis unit can reevaluate task allocations every time new data is added and optimally assign tasks. In this way, analyzing members' past performance data and optimally assigning tasks can improve task efficiency.

[0099] The project management system may further include a dependency analysis unit that considers task dependencies when setting task priorities and proposes an optimal task order. For example, the dependency analysis unit analyzes task dependencies and proposes an optimal task order. The dependency analysis unit can also optimize the overall project schedule based on the task dependencies. Furthermore, the dependency analysis unit can reevaluate dependencies and reset priorities when new tasks are added. This allows the system to prevent project delays by considering task dependencies and proposing an optimal task order.

[0100] The project management system can further use an emotion estimation function to analyze the emotions of members when tasks are assigned and allocate tasks to reduce stress. For example, the emotion estimation function can be used to analyze the emotions of members when tasks are assigned in real time. Task allocation can also be performed to reduce stress. Furthermore, task allocation can be dynamically adjusted based on the emotion estimation data. This allows for analyzing the emotions of members when tasks are assigned and allocating tasks to reduce stress, thereby reducing the burden on members and achieving efficient task management.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The progress management department visualizes the project's progress. For example, the generation AI analyzes the project's progress data and generates Gantt charts and burndown charts. The progress management department also updates the project's progress in real time, allowing team members and managers to grasp the current situation at a glance. Step 2: The task allocation unit assigns tasks based on the progress visualized by the progress management unit. For example, the generation AI analyzes each member's skills and current workload and automatically proposes optimal task allocations. The task allocation unit also has the ability to set task priorities and sort tasks according to importance and urgency. Step 3: The Member Management Department evaluates the performance of the tasks assigned by the Task Assignment Department. For example, the Generative AI analyzes each member's performance data and provides appropriate feedback and evaluation. The Member Management Department also provides advice to promote communication between members.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A progress management department that visualizes the progress of projects, a task allocation unit that allocates tasks based on the progress status visualized by the progress management unit; a member management unit that evaluates the performance of the tasks assigned by the task assignment unit; A system characterized by:

2. The progress management unit Analyze progress data, predict risk factors for the project, and propose risk avoidance measures 2. The system of claim 1.

3. The progress management unit Based on the progress data, calculate the success probability of the project and present a specific action plan to increase the success probability.

2. The system of claim 1.

4. The progress management unit To analyze the motivation of team members and propose appropriate support for the team members whose motivation is declining.

2. The system of claim 1.

5. The progress management unit Analyze progress data, conduct comparative analysis with other projects, and extract best practices 2. The system of claim 1.

6. The progress management unit Based on the progress data, the progress of the project is predicted and a simulation is performed based on the predicted results.

2. The system of claim 1.

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