System

Abstract

An object of a system according to an embodiment is to automate progress management of a project and reduce management costs.SOLUTION: A system according to an embodiment includes a progress monitoring unit, a task allocation unit, and a resource proposal unit. The progress monitoring unit monitors the progress status of the project in real time. The task allocation unit automatically allocates a task based on the information acquired by the progress monitoring unit. The resource proposal unit proposes an optimal distribution of resources based on the tasks allocated by the task allocation 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] In conventional techniques, project progress management is often done manually, which can increase management costs.

[0005] The system according to the embodiment aims to automate project progress management and reduce management costs. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress monitoring unit, a task allocation unit, and a resource proposal unit. The progress monitoring unit monitors the progress of a project in real time. The task allocation unit automatically assigns tasks based on information acquired by the progress monitoring unit. The resource proposal unit proposes an optimal allocation of resources based on the tasks assigned by the task allocation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate project progress management and reduce management costs. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 progress management system according to an embodiment of the present invention uses generative AI to automate project progress management and reduce management costs. This allows project teams to focus their resources on resolving issues that should be their primary focus, improving the probability of project success.

[0029] A progress management system according to an embodiment includes a progress monitoring unit, a task allocation unit, and a resource proposal unit. The progress monitoring unit monitors the progress of a project in real time. For example, when a project member reports the completion of a task, the progress monitoring unit updates the progress based on that information. The progress monitoring unit also monitors project progress indicators and can grasp the progress in real time. The task allocation unit automatically assigns tasks based on the information acquired by the progress monitoring unit. For example, the task allocation unit analyzes the skill sets and past performance data of project members to assign optimal tasks. The task allocation unit also prioritizes tasks according to the progress of the project and can perform efficient task allocation. The resource proposal unit proposes optimal resource allocation based on the tasks assigned by the task allocation unit. For example, if a specific task is behind schedule, the resource proposal unit proposes allocating additional resources to that task. The resource proposal unit also optimizes resource allocation for the entire project and achieves efficient resource utilization. As a result, the progress management system according to an embodiment automates project progress management and reduces management costs, thereby enabling project teams to focus resources on resolving issues that should be their primary focus and improving the probability of project success.

[0030] The task allocation unit can analyze the past performance data of project members and make optimal task allocations. For example, the task allocation unit uses a generation AI to collect past performance data of project members and analyze each member's areas of expertise and work speed. For example, it assigns optimal tasks based on past task completion times and quality evaluations. The task allocation unit can also assign appropriate tasks by taking into account the skill sets of project members. For example, it can assign specialized tasks to members with specific skills. This makes it possible to improve project efficiency by making optimal task allocations based on project members' past performance data.

[0031] The progress monitoring unit can automatically detect risk factors in a project and propose risk avoidance measures. For example, the progress monitoring unit uses generative AI to monitor the progress of a project in real time and automatically detect risk factors. For example, it can detect risks such as task delays and resource shortages early. In addition, when a risk factor is detected, the progress monitoring unit can propose appropriate risk avoidance measures. For example, it can propose avoidance measures such as resource reallocation or changing task priorities. In this way, by detecting risk factors early and proposing appropriate risk avoidance measures, the probability of project success can be improved.

[0032] Generative AI can automate not only progress management but also budget management or schedule management. For example, generative AI can manage the budget at the same time as project progress management. For example, it can track the cost of each task in real time and detect the risk of going over budget early. Generative AI can also automate schedule management. For example, it can automatically adjust the schedule according to the progress of tasks to prevent delays. This makes it possible to automate not only progress management but also budget management and schedule management, further reducing project management costs and improving efficiency.

[0033] Generative AI can optimize resource sharing between different projects. For example, generative AI monitors the progress of multiple projects and optimizes resource sharing. For example, it reallocates resources from projects with surplus resources to projects with a shortage of resources. Generative AI can also propose optimal resource allocation to ensure efficient resource use between projects. In this way, optimizing resource sharing between different projects can achieve efficient resource use and improve the probability of project success.

[0034] Generative AI can monitor project progress in real time and use an anomaly detection algorithm to detect problems early. Generative AI can, for example, monitor project progress in real time and use an anomaly detection algorithm to detect problems early. For example, it can detect anomalies such as task delays and resource shortages. Generative AI can also use an anomaly detection algorithm to detect problems that affect project progress early and propose appropriate countermeasures. As a result, early detection of problems using an anomaly detection algorithm can optimize project progress and increase the probability of success.

[0035] Generative AI can take external factors into account when monitoring project progress. For example, generative AI can take external factors such as weather and economic conditions into account when monitoring project progress. For example, it can adjust outdoor work schedules based on weather data. Generative AI can also predict factors that will affect project progress by taking economic conditions into account and suggest appropriate countermeasures. By taking external factors into account, it is possible to optimize project progress and increase the probability of success.

[0036] When monitoring the progress of a project, the generation AI can provide a dashboard that visualizes the progress. For example, the generation AI provides a dashboard that monitors and visualizes the progress of a project in real time. For example, it displays the progress of tasks and resource usage in graphs and charts. The generation AI can also customize the dashboard to make it easier to grasp the progress of a project at a glance. In this way, by providing a dashboard that visualizes the progress, it becomes easier to grasp the progress of a project at a glance and improves efficiency.

[0037] Generative AI can compare the progress of different projects and extract best practices. For example, generative AI can monitor and compare the progress of multiple projects in real time. For example, it can analyze the differences between projects that are progressing smoothly and projects that are experiencing delays. Generative AI can also extract best practices and apply them to other projects. For example, it can apply the methods and approaches of successful projects to other projects. In this way, by comparing the progress of different projects and extracting best practices, it is possible to improve the probability of project success.

[0038] When recording task progress, the generative AI maintains a detailed history that can be used for future project planning. For example, the generative AI records task progress and maintains a detailed history. For example, it saves data such as the start date, end date, person in charge, and progress of each task. The generative AI can also use the detailed history for future project planning. For example, it can refer to past project data to optimize schedules and resource allocation. In this way, project efficiency can be improved by maintaining a detailed task history and utilizing it for future project planning.

[0039] When recording task progress, generative AI can automatically analyze task dependencies and optimize the schedule. For example, generative AI records task progress and automatically analyzes task dependencies. For example, if the next task cannot start until a specific task is completed, the schedule is adjusted taking that dependency into account. Generative AI can also optimize schedules based on task dependencies. For example, it can set task priorities taking dependencies into account and create an efficient schedule. In this way, by automatically analyzing task dependencies and optimizing the schedule, projects can progress smoothly.

[0040] When recording task progress, generative AI can collect information from a wider variety of data sources using voice input and image recognition. For example, when recording task progress, generative AI can collect information using voice input. For example, project members can report task progress by voice, and the data can be automatically recorded. Generative AI can also collect information using image recognition. For example, the task progress can be recorded as an image, and the data can be analyzed using image recognition technology. This allows for a more accurate understanding of task progress by collecting information from a wider variety of data sources using voice input and image recognition.

[0041] Generative AI can strengthen collaboration with different project management tools when recording task progress. For example, Generative AI can collaborate with different project management tools to automatically record task progress. For example, it can collaborate with tools such as Trello and Asana to synchronize data. Generative AI can also strengthen collaboration with project management tools to make it easier to centrally manage task progress. By strengthening collaboration with different project management tools, this makes it easier to centrally manage task progress and improves project efficiency.

[0042] When assigning tasks, generative AI can make optimal assignments based on members' skill sets and past performance. For example, generative AI can analyze project members' skill sets and past performance data to assign optimal tasks. For example, it can assign specialized tasks to members with specific skills. Generative AI can also assign tasks taking into account project members' areas of expertise and work speed. This allows for improved project efficiency by assigning tasks taking into account members' skill sets and past performance.

[0043] When assigning tasks, the generation AI can automatically evaluate the urgency and importance of the tasks and set priorities. For example, the generation AI can prioritize tasks with approaching deadlines or important tasks that directly contribute to the success of the project. The generation AI can also efficiently assign tasks based on task priorities. This allows the automatic evaluation of task urgency and importance and prioritization to optimize project progress.

[0044] When allocating tasks, the generative AI can optimize task sharing among different projects. For example, the generative AI monitors tasks across multiple projects and optimizes task sharing. For example, it reallocates tasks from projects with ample resources to projects with a shortage of resources. The generative AI can also propose optimal task allocation to achieve efficient task sharing among projects. By optimizing task sharing among different projects, this makes it possible to achieve efficient resource utilization and improve the probability of project success.

[0045] The generation AI can monitor the progress of tasks in real time and reallocate tasks as needed. The generation AI can, for example, monitor the progress of tasks in real time and reallocate tasks as needed. For example, it can reallocate tasks that are experiencing delays to other members. The generation AI can also perform efficient reallocations based on the progress of tasks. This makes it possible to optimize project progress by monitoring the progress of tasks in real time and reallocating tasks as needed.

[0046] When performing progress management, the generation AI can quantitatively evaluate the effect of reducing management costs and optimize cost reductions. For example, when performing progress management, the generation AI can quantitatively evaluate the effect of reducing management costs. For example, it can calculate the time and labor costs required for progress management and quantify the cost reduction effect. The generation AI can also propose ways to improve the efficiency of management work in order to optimize cost reductions. This makes it possible to quantitatively evaluate and optimize the effect of reducing management costs, thereby optimizing the progress of the project.

[0047] Generative AI can automate not only progress management but also other management tasks. For example, generative AI can automate budget management at the same time as progress management. For example, it can track the cost of each task in real time and detect the risk of going over budget early. Generative AI can also automate risk management. For example, it can monitor the progress of a project, automatically detect risk factors, and propose appropriate risk avoidance measures. This allows for the automation of not only progress management but also budget management and risk management, further reducing project management costs and improving efficiency.

[0048] Generative AI can share best practices between different projects to reduce overall costs. For example, generative AI monitors the progress of multiple projects and extracts best practices. For example, it applies the methods and approaches of successful projects to other projects. Generative AI can also reduce overall costs by sharing best practices. By sharing best practices between different projects, it is possible to reduce overall costs and improve project efficiency.

[0049] When allocating resources, the generation AI can make optimal allocations by taking into account the progress of the project and the importance of the tasks. For example, the generation AI can monitor the progress of the project in real time and make optimal resource allocations. For example, it can allocate additional resources to tasks where progress is behind schedule. The generation AI can also allocate resources by taking into account the importance of the tasks. For example, it can prioritize resources to important tasks that are directly linked to the success of the project. In this way, by allocating resources by taking into account the progress of the project and the importance of the tasks, it is possible to optimize the progress of the project.

[0050] When allocating resources, the generation AI can analyze past project data and extract the optimal resource allocation pattern. The generation AI, for example, analyzes past project data and extracts the optimal resource allocation pattern. For example, it refers to the resource allocation of past successful projects. The generation AI can also optimize resource allocation based on project data. In this way, by analyzing past project data and extracting the optimal resource allocation pattern, it is possible to optimize the progress of a project.

[0051] When allocating resources, generative AI can optimize resource sharing among different projects. For example, generative AI monitors the resources of multiple projects and optimizes resource sharing. For example, it reallocates resources from projects with surplus resources to projects with a shortage of resources. Generative AI can also propose optimal resource allocation to ensure efficient resource sharing among projects. In this way, optimizing resource sharing among different projects can achieve efficient resource utilization and improve the probability of project success.

[0052] The generation AI can monitor resource utilization efficiency in real time and reallocate resources as needed. The generation AI can, for example, monitor resource utilization efficiency in real time and reallocate resources as needed. For example, if resource utilization is low, it will reallocate resources to other tasks. The generation AI can also efficiently reallocate resources based on resource utilization efficiency. This makes it possible to optimize project progress by monitoring resource utilization efficiency in real time and reallocating resources as needed.

[0053] When concentrating resources on problem-solving, generative AI can automatically evaluate the importance and urgency of issues and allocate resources optimally. For example, generative AI can prioritize resources by allocating them to important issues that directly affect the success of a project. Generative AI can also allocate resources taking into account the urgency of issues. This allows the progress of a project to be optimized by automatically evaluating the importance and urgency of issues and allocating resources optimally.

[0054] When concentrating resources on problem-solving, generative AI can analyze past problem-solving data and propose optimal solutions. For example, generative AI can analyze past problem-solving data and propose optimal solutions. For example, it can refer to solutions that have been successful in the past. Generative AI can also propose optimal solutions based on problem-solving data. In this way, by analyzing past problem-solving data and proposing optimal solutions, it is possible to optimize the progress of a project.

[0055] When concentrating resources on problem-solving, generative AI can share problem-solving methods between different projects, improving overall efficiency. For example, generative AI can monitor problem-solving methods across multiple projects and extract best practices. For example, it can apply the methods and approaches of successful projects to other projects. Generative AI can also improve overall efficiency by sharing problem-solving methods. In this way, sharing problem-solving methods between different projects can improve overall efficiency and increase the probability of project success.

[0056] The generative AI can monitor the progress of problem-solving in real time and adjust resource allocation as needed. The generative AI can, for example, monitor the progress of problem-solving in real time and adjust resource allocation as needed. For example, it can allocate additional resources to issues that are experiencing delays. The generative AI can also allocate resources efficiently based on the progress of problem-solving. This makes it possible to optimize project progress by monitoring the progress of problem-solving in real time and adjusting resource allocation as needed.

[0057] Generative AI can analyze data on past successful projects and extract success factors. For example, generative AI can analyze data on past successful projects and extract success factors. For example, it can identify commonalities and methods between successful projects. Generative AI can also optimize project progress based on success factors. In this way, by analyzing data on past successful projects and extracting success factors, it is possible to improve the probability of project success.

[0058] Generative AI can monitor project progress in real time, automatically detect risk factors, and propose risk avoidance measures. Generative AI, for example, monitors project progress in real time and automatically detects risk factors. For example, it can detect risks such as task delays and resource shortages early. Furthermore, when a risk factor is detected, generative AI can propose appropriate risk avoidance measures. For example, it can propose avoidance measures such as resource reallocation or change in task priorities. In this way, by detecting risk factors early and proposing appropriate risk avoidance measures, the probability of project success can be improved.

[0059] Generative AI can share best practices between different projects and improve the overall probability of success. For example, generative AI monitors the progress of multiple projects and extracts best practices. For example, it applies the methods and approaches of successful projects to other projects. Generative AI can also improve the overall probability of success by sharing best practices. By sharing best practices between different projects, it can improve the overall probability of success.

[0060] The generative AI can monitor the progress of a project in real time and adjust resource allocation as needed. The generative AI can, for example, monitor the progress of a project in real time and adjust resource allocation as needed. For example, it can allocate additional resources to tasks that are experiencing delays. The generative AI can also make efficient resource allocations based on the progress of the project. This makes it possible to optimize project progress by monitoring the progress of a project in real time and adjusting resource allocation as needed.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] Progress management systems can monitor project progress in real time and use anomaly detection algorithms to detect problems early. For example, they can detect anomalies such as task delays and resource shortages. Furthermore, they can use anomaly detection algorithms to quickly detect problems that could affect project progress and propose appropriate countermeasures. By using anomaly detection algorithms to detect problems early, project progress can be optimized and the probability of success increased.

[0063] Progress management systems can take external factors into account when monitoring project progress. For example, they can take into account external factors such as weather and economic conditions. They can adjust outdoor work schedules based on weather data. They can also take economic conditions into account to predict factors that will affect project progress and propose appropriate countermeasures. By taking external factors into account, project progress can be optimized and the probability of success can be improved.

[0064] Progress management systems can monitor project progress in real time and use anomaly detection algorithms to detect problems early. For example, they can detect anomalies such as task delays and resource shortages. Furthermore, they can use anomaly detection algorithms to quickly detect problems that could affect project progress and propose appropriate countermeasures. By using anomaly detection algorithms to detect problems early, project progress can be optimized and the probability of success increased.

[0065] When monitoring the progress of a project, a progress management system can provide a dashboard that visualizes the progress. For example, it can display the progress of tasks and resource usage in graphs and charts. In addition, the dashboard can be customized to make it easier to grasp the progress of a project at a glance. By providing a dashboard that visualizes the progress, it becomes easier to grasp the progress of a project at a glance, thereby improving efficiency.

[0066] A progress management system can optimize resource sharing between different projects. For example, it can monitor the progress of multiple projects and optimize resource sharing. It can reallocate resources from projects with surplus resources to projects with resource shortages. It can also propose optimal resource allocation to ensure efficient resource use between projects. By optimizing resource sharing between different projects, it can achieve efficient resource use and improve the probability of project success.

[0067] A progress management system can monitor the progress of a project in real time and adjust resource allocation as needed. For example, it can allocate additional resources to tasks that are experiencing delays. It can also allocate resources efficiently based on the progress of the project. This allows you to optimize the progress of a project by monitoring the progress of the project in real time and adjusting resource allocation as needed.

[0068] The processing flow of the first embodiment will be briefly explained below.

[0069] Step 1: The progress monitoring unit monitors the progress of the project in real time. For example, when a project member reports the completion of a task, the progress status is updated based on that information. The progress monitoring unit also monitors the project's progress indicators, allowing the progress status to be grasped in real time. Step 2: The task allocation unit automatically allocates tasks based on the information obtained by the progress monitoring unit. For example, the task allocation unit analyzes the skill sets and past performance data of project members and allocates the most appropriate tasks. The task allocation unit can also set task priorities according to the progress of the project, allowing for efficient task allocation. Step 3: The resource suggestion unit proposes optimal resource allocation based on the tasks assigned by the task allocation unit. For example, if a specific task is delayed, it will propose allocating additional resources to that task. The resource suggestion unit can also optimize resource allocation for the entire project to achieve efficient resource utilization.

[0070] (Example 2) A progress management system according to an embodiment of the present invention uses generative AI to automate project progress management and reduce management costs. This allows project teams to focus their resources on resolving issues that should be their primary focus, improving the probability of project success.

[0071] A progress management system according to an embodiment includes a progress monitoring unit, a task allocation unit, and a resource proposal unit. The progress monitoring unit monitors the progress of a project in real time. For example, when a project member reports the completion of a task, the progress monitoring unit updates the progress based on that information. The progress monitoring unit also monitors project progress indicators and can grasp the progress in real time. The task allocation unit automatically assigns tasks based on the information acquired by the progress monitoring unit. For example, the task allocation unit analyzes the skill sets and past performance data of project members to assign optimal tasks. The task allocation unit also prioritizes tasks according to the progress of the project and can perform efficient task allocation. The resource proposal unit proposes optimal resource allocation based on the tasks assigned by the task allocation unit. For example, if a specific task is behind schedule, the resource proposal unit proposes allocating additional resources to that task. The resource proposal unit also optimizes resource allocation for the entire project and achieves efficient resource utilization. As a result, the progress management system according to an embodiment automates project progress management and reduces management costs, thereby enabling project teams to focus resources on resolving issues that should be their primary focus and improving the probability of project success.

[0072] The task allocation unit can analyze the past performance data of project members and make optimal task allocations. For example, the task allocation unit uses a generation AI to collect past performance data of project members and analyze each member's areas of expertise and work speed. For example, it assigns optimal tasks based on past task completion times and quality evaluations. The task allocation unit can also assign appropriate tasks by taking into account the skill sets of project members. For example, it can assign specialized tasks to members with specific skills. This makes it possible to improve project efficiency by making optimal task allocations based on project members' past performance data.

[0073] The progress monitoring unit can automatically detect risk factors in a project and propose risk avoidance measures. For example, the progress monitoring unit uses generative AI to monitor the progress of a project in real time and automatically detect risk factors. For example, it can detect risks such as task delays and resource shortages early. In addition, when a risk factor is detected, the progress monitoring unit can propose appropriate risk avoidance measures. For example, it can propose avoidance measures such as resource reallocation or changing task priorities. In this way, by detecting risk factors early and proposing appropriate risk avoidance measures, the probability of project success can be improved.

[0074] The resource suggestion unit can analyze the emotional state of project members and adjust the task load for members with high stress. For example, the resource suggestion unit uses a generation AI to analyze the emotional state of project members in real time and identify members with high stress. For example, it calculates an emotion score by analyzing facial expressions and voice tone. The resource suggestion unit can also adjust the task load for members with high stress. For example, it can assign lighter tasks to members with high stress. In this way, adjusting the task load taking into account the emotional state of project members can reduce member stress and improve project efficiency.

[0075] Generative AI can automate not only progress management but also budget management or schedule management. For example, generative AI can manage the budget at the same time as project progress management. For example, it can track the cost of each task in real time and detect the risk of going over budget early. Generative AI can also automate schedule management. For example, it can automatically adjust the schedule according to the progress of tasks to prevent delays. This makes it possible to automate not only progress management but also budget management and schedule management, further reducing project management costs and improving efficiency.

[0076] Generative AI can optimize resource sharing between different projects. For example, generative AI monitors the progress of multiple projects and optimizes resource sharing. For example, it reallocates resources from projects with surplus resources to projects with a shortage of resources. Generative AI can also propose optimal resource allocation to ensure efficient resource use between projects. In this way, optimizing resource sharing between different projects can achieve efficient resource use and improve the probability of project success.

[0077] The generation AI can automatically generate feedback to boost motivation based on the emotional state of project members. For example, the generation AI can analyze the emotional state of project members and automatically generate feedback to boost motivation, such as providing positive messages or words of encouragement. The generation AI can also use its emotion estimation function to monitor the emotional state of project members in real time and provide appropriate feedback. This allows for automatic generation of feedback based on the emotional state of project members, thereby maintaining their motivation and improving the probability of project success.

[0078] Generative AI can monitor project progress in real time and use an anomaly detection algorithm to detect problems early. Generative AI can, for example, monitor project progress in real time and use an anomaly detection algorithm to detect problems early. For example, it can detect anomalies such as task delays and resource shortages. Generative AI can also use an anomaly detection algorithm to detect problems that affect project progress early and propose appropriate countermeasures. As a result, early detection of problems using an anomaly detection algorithm can optimize project progress and increase the probability of success.

[0079] Generative AI can take external factors into account when monitoring project progress. For example, generative AI can take external factors such as weather and economic conditions into account when monitoring project progress. For example, it can adjust outdoor work schedules based on weather data. Generative AI can also predict factors that will affect project progress by taking economic conditions into account and suggest appropriate countermeasures. By taking external factors into account, it is possible to optimize project progress and increase the probability of success.

[0080] The generative AI can monitor the emotional states of project members in real time and suggest actions to maintain team morale. The generative AI can, for example, monitor the emotional states of project members in real time and suggest actions to maintain team morale. For example, it can suggest a refreshing break for members who are highly stressed. The generative AI can also use its emotion estimation function to analyze the emotional states of project members and suggest appropriate actions. In this way, by monitoring the emotional states of project members in real time and suggesting appropriate actions, it is possible to maintain team morale and improve the probability of project success.

[0081] When monitoring the progress of a project, the generation AI can provide a dashboard that visualizes the progress. For example, the generation AI provides a dashboard that monitors and visualizes the progress of a project in real time. For example, it displays the progress of tasks and resource usage in graphs and charts. The generation AI can also customize the dashboard to make it easier to grasp the progress of a project at a glance. In this way, by providing a dashboard that visualizes the progress, it becomes easier to grasp the progress of a project at a glance and improves efficiency.

[0082] Generative AI can compare the progress of different projects and extract best practices. For example, generative AI can monitor and compare the progress of multiple projects in real time. For example, it can analyze the differences between projects that are progressing smoothly and projects that are experiencing delays. Generative AI can also extract best practices and apply them to other projects. For example, it can apply the methods and approaches of successful projects to other projects. In this way, by comparing the progress of different projects and extracting best practices, it is possible to improve the probability of project success.

[0083] When recording task progress, the generative AI maintains a detailed history that can be used for future project planning. For example, the generative AI records task progress and maintains a detailed history. For example, it saves data such as the start date, end date, person in charge, and progress of each task. The generative AI can also use the detailed history for future project planning. For example, it can refer to past project data to optimize schedules and resource allocation. In this way, project efficiency can be improved by maintaining a detailed task history and utilizing it for future project planning.

[0084] When recording task progress, generative AI can automatically analyze task dependencies and optimize the schedule. For example, generative AI records task progress and automatically analyzes task dependencies. For example, if the next task cannot start until a specific task is completed, the schedule is adjusted taking that dependency into account. Generative AI can also optimize schedules based on task dependencies. For example, it can set task priorities taking dependencies into account and create an efficient schedule. In this way, by automatically analyzing task dependencies and optimizing the schedule, projects can progress smoothly.

[0085] When recording task progress, the generative AI records the emotional responses of members and can use this information in project reviews. For example, when recording task progress, the generative AI simultaneously records the emotional responses of members. For example, the emotional score at the time of task completion can be saved and used in project reviews. The generative AI can also use its emotion estimation function to record members' emotional responses to task progress in real time. This allows members' emotional responses to be recorded and used in project reviews, which can be used to improve the next project.

[0086] When recording task progress, generative AI can collect information from a wider variety of data sources using voice input and image recognition. For example, when recording task progress, generative AI can collect information using voice input. For example, project members can report task progress by voice, and the data can be automatically recorded. Generative AI can also collect information using image recognition. For example, the task progress can be recorded as an image, and the data can be analyzed using image recognition technology. This allows for a more accurate understanding of task progress by collecting information from a wider variety of data sources using voice input and image recognition.

[0087] Generative AI can strengthen collaboration with different project management tools when recording task progress. For example, Generative AI can collaborate with different project management tools to automatically record task progress. For example, it can collaborate with tools such as Trello and Asana to synchronize data. Generative AI can also strengthen collaboration with project management tools to make it easier to centrally manage task progress. By strengthening collaboration with different project management tools, this makes it easier to centrally manage task progress and improves project efficiency.

[0088] When recording task progress, the generation AI can adjust task priorities based on members' emotional reactions. For example, when recording task progress, the generation AI analyzes members' emotional reactions and adjusts task priorities. For example, it can prioritize completing tasks that cause high stress. The generation AI can also use its emotion estimation function to record members' emotional reactions to task progress in real time and adjust priorities. This makes it possible to optimize project progress by adjusting task priorities based on members' emotional reactions.

[0089] When assigning tasks, generative AI can make optimal assignments based on members' skill sets and past performance. For example, generative AI can analyze project members' skill sets and past performance data to assign optimal tasks. For example, it can assign specialized tasks to members with specific skills. Generative AI can also assign tasks taking into account project members' areas of expertise and work speed. This allows for improved project efficiency by assigning tasks taking into account members' skill sets and past performance.

[0090] When assigning tasks, the generation AI can automatically evaluate the urgency and importance of the tasks and set priorities. For example, the generation AI can prioritize tasks with approaching deadlines or important tasks that directly contribute to the success of the project. The generation AI can also efficiently assign tasks based on task priorities. This allows the automatic evaluation of task urgency and importance and prioritization to optimize project progress.

[0091] When assigning tasks, the generative AI takes into account the emotional state of members and can assign tasks that cause less stress. For example, the generative AI can analyze the emotional state of members and assign tasks that cause less stress. For example, it can assign lighter tasks to members who are highly stressed. The generative AI can also use its emotion estimation function to monitor the emotional state of members in real time and assign appropriate tasks. This allows tasks to be assigned taking into account the emotional state of members, reducing member stress and improving project efficiency.

[0092] When allocating tasks, the generative AI can optimize task sharing among different projects. For example, the generative AI monitors tasks across multiple projects and optimizes task sharing. For example, it reallocates tasks from projects with ample resources to projects with a shortage of resources. The generative AI can also propose optimal task allocation to achieve efficient task sharing among projects. By optimizing task sharing among different projects, this makes it possible to achieve efficient resource utilization and improve the probability of project success.

[0093] The generation AI can monitor the progress of tasks in real time and reallocate tasks as needed. The generation AI can, for example, monitor the progress of tasks in real time and reallocate tasks as needed. For example, it can reallocate tasks that are experiencing delays to other members. The generation AI can also perform efficient reallocations based on the progress of tasks. This makes it possible to optimize project progress by monitoring the progress of tasks in real time and reallocating tasks as needed.

[0094] Generative AI can adjust task assignments based on members' emotional states to maintain team morale. For example, generative AI can analyze members' emotional states and adjust task assignments. For example, it can assign lighter tasks to members who are highly stressed. Generative AI can also use emotion estimation functions to monitor members' emotional states in real time and assign appropriate tasks. This allows adjusting task assignments based on members' emotional states to maintain team morale and improve the chances of project success.

[0095] When performing progress management, the generation AI can quantitatively evaluate the effect of reducing management costs and optimize cost reductions. For example, when performing progress management, the generation AI can quantitatively evaluate the effect of reducing management costs. For example, it can calculate the time and labor costs required for progress management and quantify the cost reduction effect. The generation AI can also propose ways to improve the efficiency of management work in order to optimize cost reductions. This makes it possible to quantitatively evaluate and optimize the effect of reducing management costs, thereby optimizing the progress of the project.

[0096] Generative AI can indirectly reduce management costs by analyzing members' emotional states and providing a less stressful environment. For example, generative AI can analyze members' emotional states and provide a less stressful environment. For example, it can suggest a break to refresh members who are highly stressed. Generative AI can also use its emotion estimation function to monitor members' emotional states in real time and provide an appropriate environment. This can indirectly reduce management costs and improve project efficiency by analyzing members' emotional states and providing a less stressful environment.

[0097] Generative AI can automate not only progress management but also other management tasks. For example, generative AI can automate budget management at the same time as progress management. For example, it can track the cost of each task in real time and detect the risk of going over budget early. Generative AI can also automate risk management. For example, it can monitor the progress of a project, automatically detect risk factors, and propose appropriate risk avoidance measures. This allows for the automation of not only progress management but also budget management and risk management, further reducing project management costs and improving efficiency.

[0098] Generative AI can share best practices between different projects to reduce overall costs. For example, generative AI monitors the progress of multiple projects and extracts best practices. For example, it applies the methods and approaches of successful projects to other projects. Generative AI can also reduce overall costs by sharing best practices. By sharing best practices between different projects, it is possible to reduce overall costs and improve project efficiency.

[0099] The generative AI can suggest efficient management methods based on the emotional state of members. For example, the generative AI can analyze the emotional state of members and suggest efficient management methods. For example, it can suggest a break to refresh a member who is highly stressed. The generative AI can also use its emotion estimation function to monitor the emotional state of members in real time and suggest appropriate management methods. This makes it possible to improve project efficiency by suggesting efficient management methods based on the emotional state of members.

[0100] When allocating resources, the generation AI can make optimal allocations by taking into account the progress of the project and the importance of the tasks. For example, the generation AI can monitor the progress of the project in real time and make optimal resource allocations. For example, it can allocate additional resources to tasks where progress is behind schedule. The generation AI can also allocate resources by taking into account the importance of the tasks. For example, it can prioritize resources to important tasks that are directly linked to the success of the project. In this way, by allocating resources by taking into account the progress of the project and the importance of the tasks, it is possible to optimize the progress of the project.

[0101] When allocating resources, the generation AI can analyze past project data and extract the optimal resource allocation pattern. The generation AI, for example, analyzes past project data and extracts the optimal resource allocation pattern. For example, it refers to the resource allocation of past successful projects. The generation AI can also optimize resource allocation based on project data. In this way, by analyzing past project data and extracting the optimal resource allocation pattern, it is possible to optimize the progress of a project.

[0102] When allocating resources, the generative AI can take into account the emotional state of members and allocate resources in a way that causes less stress. For example, the generative AI can analyze the emotional state of members and allocate resources in a way that causes less stress. For example, it can assign lighter tasks to members who are highly stressed. The generative AI can also use its emotion estimation function to monitor the emotional state of members in real time and allocate resources appropriately. This allows resource allocation that takes into account the emotional state of members, thereby reducing member stress and improving project efficiency.

[0103] When allocating resources, generative AI can optimize resource sharing among different projects. For example, generative AI monitors the resources of multiple projects and optimizes resource sharing. For example, it reallocates resources from projects with surplus resources to projects with a shortage of resources. Generative AI can also propose optimal resource allocation to ensure efficient resource sharing among projects. In this way, optimizing resource sharing among different projects can achieve efficient resource utilization and improve the probability of project success.

[0104] The generation AI can monitor resource utilization efficiency in real time and reallocate resources as needed. The generation AI can, for example, monitor resource utilization efficiency in real time and reallocate resources as needed. For example, if resource utilization is low, it will reallocate resources to other tasks. The generation AI can also efficiently reallocate resources based on resource utilization efficiency. This makes it possible to optimize project progress by monitoring resource utilization efficiency in real time and reallocating resources as needed.

[0105] The generative AI can adjust resource allocation based on the emotional state of members and maintain team morale. For example, the generative AI can analyze members' emotional states and adjust resource allocation. For example, it can assign lighter tasks to members who are highly stressed. The generative AI can also use its emotion estimation function to monitor members' emotional states in real time and allocate resources appropriately. This allows for adjusting resource allocation based on members' emotional states to maintain team morale and improve the probability of project success.

[0106] When concentrating resources on problem-solving, generative AI can automatically evaluate the importance and urgency of issues and allocate resources optimally. For example, generative AI can prioritize resources by allocating them to important issues that directly affect the success of a project. Generative AI can also allocate resources taking into account the urgency of issues. This allows the progress of a project to be optimized by automatically evaluating the importance and urgency of issues and allocating resources optimally.

[0107] When concentrating resources on problem-solving, generative AI can analyze past problem-solving data and propose optimal solutions. For example, generative AI can analyze past problem-solving data and propose optimal solutions. For example, it can refer to solutions that have been successful in the past. Generative AI can also propose optimal solutions based on problem-solving data. In this way, by analyzing past problem-solving data and proposing optimal solutions, it is possible to optimize the progress of a project.

[0108] Generative AI can propose less stressful problem-solving methods based on the emotional state of members. For example, generative AI can analyze members' emotional states and propose less stressful problem-solving methods. For example, it can assign lighter tasks to members with high stress levels. Generative AI can also use its emotion estimation function to monitor members' emotional states in real time and propose appropriate problem-solving methods. This can reduce members' stress and improve project efficiency by proposing less stressful problem-solving methods based on members' emotional states.

[0109] When concentrating resources on problem-solving, generative AI can share problem-solving methods between different projects, improving overall efficiency. For example, generative AI can monitor problem-solving methods across multiple projects and extract best practices. For example, it can apply the methods and approaches of successful projects to other projects. Generative AI can also improve overall efficiency by sharing problem-solving methods. In this way, sharing problem-solving methods between different projects can improve overall efficiency and increase the probability of project success.

[0110] The generative AI can monitor the progress of problem-solving in real time and adjust resource allocation as needed. The generative AI can, for example, monitor the progress of problem-solving in real time and adjust resource allocation as needed. For example, it can allocate additional resources to issues that are experiencing delays. The generative AI can also allocate resources efficiently based on the progress of problem-solving. This makes it possible to optimize project progress by monitoring the progress of problem-solving in real time and adjusting resource allocation as needed.

[0111] Generative AI can analyze data on past successful projects and extract success factors. For example, generative AI can analyze data on past successful projects and extract success factors. For example, it can identify commonalities and methods between successful projects. Generative AI can also optimize project progress based on success factors. In this way, by analyzing data on past successful projects and extracting success factors, it is possible to improve the probability of project success.

[0112] Generative AI can monitor project progress in real time, automatically detect risk factors, and propose risk avoidance measures. Generative AI, for example, monitors project progress in real time and automatically detects risk factors. For example, it can detect risks such as task delays and resource shortages early. Furthermore, when a risk factor is detected, generative AI can propose appropriate risk avoidance measures. For example, it can propose avoidance measures such as resource reallocation or change in task priorities. In this way, by detecting risk factors early and proposing appropriate risk avoidance measures, the probability of project success can be improved.

[0113] The generative AI can analyze the emotional state of members and suggest actions to boost team morale. For example, the generative AI can analyze the emotional state of members and suggest actions to boost team morale. For example, it can suggest a refreshing break for members who are under a lot of stress. The generative AI can also use its emotion estimation function to monitor the emotional state of members in real time and suggest appropriate actions. This allows the probability of project success to be improved by analyzing the emotional state of members and suggesting actions to boost team morale.

[0114] Generative AI can share best practices between different projects and improve the overall probability of success. For example, generative AI monitors the progress of multiple projects and extracts best practices. For example, it applies the methods and approaches of successful projects to other projects. Generative AI can also improve the overall probability of success by sharing best practices. By sharing best practices between different projects, it can improve the overall probability of success.

[0115] The generative AI can monitor the progress of a project in real time and adjust resource allocation as needed. The generative AI can, for example, monitor the progress of a project in real time and adjust resource allocation as needed. For example, it can allocate additional resources to tasks that are experiencing delays. The generative AI can also make efficient resource allocations based on the progress of the project. This makes it possible to optimize project progress by monitoring the progress of a project in real time and adjusting resource allocation as needed.

[0116] Generative AI can optimize project progress based on the emotional state of members. For example, generative AI can analyze members' emotional states and optimize project progress. For example, it can assign lighter tasks to members who are highly stressed. Generative AI can also use its emotion estimation function to monitor members' emotional states in real time and assign appropriate tasks. This can improve the chances of project success by optimizing project progress based on members' emotional states.

[0117] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0118] Progress management systems can monitor project progress in real time and use anomaly detection algorithms to detect problems early. For example, they can detect anomalies such as task delays and resource shortages. Furthermore, they can use anomaly detection algorithms to quickly detect problems that could affect project progress and propose appropriate countermeasures. By using anomaly detection algorithms to detect problems early, project progress can be optimized and the probability of success increased.

[0119] Progress management systems can take external factors into account when monitoring project progress. For example, they can take into account external factors such as weather and economic conditions. They can adjust outdoor work schedules based on weather data. They can also take economic conditions into account to predict factors that will affect project progress and propose appropriate countermeasures. By taking external factors into account, project progress can be optimized and the probability of success can be improved.

[0120] The progress management system can automatically generate feedback to boost motivation based on the emotional state of project members. For example, it can analyze the emotional state of project members and provide positive messages and encouraging words. It can also use emotion estimation functionality to monitor the emotional state of project members in real time and provide appropriate feedback. This allows for automatic generation of feedback based on the emotional state of project members, thereby maintaining their motivation and improving the probability of project success.

[0121] Progress management systems can monitor project progress in real time and use anomaly detection algorithms to detect problems early. For example, they can detect anomalies such as task delays and resource shortages. Furthermore, they can use anomaly detection algorithms to quickly detect problems that could affect project progress and propose appropriate countermeasures. By using anomaly detection algorithms to detect problems early, project progress can be optimized and the probability of success increased.

[0122] The progress management system can monitor the emotional state of project members in real time and suggest actions to maintain team morale. For example, it can suggest a break to refresh members who are under a lot of stress. It can also use emotion estimation to analyze the emotional state of project members and suggest appropriate actions. This allows the system to maintain team morale and improve the chances of project success by monitoring the emotional state of project members in real time and suggesting appropriate actions.

[0123] When monitoring the progress of a project, a progress management system can provide a dashboard that visualizes the progress. For example, it can display the progress of tasks and resource usage in graphs and charts. In addition, the dashboard can be customized to make it easier to grasp the progress of a project at a glance. By providing a dashboard that visualizes the progress, it becomes easier to grasp the progress of a project at a glance, thereby improving efficiency.

[0124] A progress management system can optimize resource sharing between different projects. For example, it can monitor the progress of multiple projects and optimize resource sharing. It can reallocate resources from projects with surplus resources to projects with resource shortages. It can also propose optimal resource allocation to ensure efficient resource use between projects. By optimizing resource sharing between different projects, it can achieve efficient resource use and improve the probability of project success.

[0125] The progress management system can suggest less stressful problem-solving methods based on the emotional state of project members. For example, it can analyze members' emotional states and suggest less stressful problem-solving methods. It can assign lighter tasks to members with high stress levels. It can also use emotion estimation functions to monitor members' emotional states in real time and suggest appropriate problem-solving methods. This reduces members' stress and improves project efficiency by suggesting less stressful problem-solving methods based on members' emotional states.

[0126] A progress management system can monitor the progress of a project in real time and adjust resource allocation as needed. For example, it can allocate additional resources to tasks that are experiencing delays. It can also allocate resources efficiently based on the progress of the project. This allows you to optimize the progress of a project by monitoring the progress of the project in real time and adjusting resource allocation as needed.

[0127] The progress management system can propose efficient management methods based on the emotional state of project members. For example, it can analyze members' emotional states and propose efficient management methods. For members who are under high stress, it can suggest a break to refresh themselves. It can also use an emotion estimation function to monitor members' emotional states in real time and propose appropriate management methods. This makes it possible to improve project efficiency by proposing efficient management methods based on members' emotional states.

[0128] The processing flow of the second embodiment will be briefly explained below.

[0129] Step 1: The progress monitoring unit monitors the progress of the project in real time. For example, when a project member reports the completion of a task, the progress status is updated based on that information. The progress monitoring unit also monitors the project's progress indicators, allowing the progress status to be grasped in real time. Step 2: The task allocation unit automatically allocates tasks based on the information obtained by the progress monitoring unit. For example, the task allocation unit analyzes the skill sets and past performance data of project members and allocates the most appropriate tasks. The task allocation unit can also set task priorities according to the progress of the project, allowing for efficient task allocation. Step 3: The resource suggestion unit proposes optimal resource allocation based on the tasks assigned by the task allocation unit. For example, if a specific task is delayed, it will propose allocating additional resources to that task. The resource suggestion unit can also optimize resource allocation for the entire project to achieve efficient resource utilization.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0132] 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.

[0133] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0143] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0156] 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.

[0157] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0158] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0174] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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]

[0197] 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 monitoring department that monitors the progress of projects in real time; a task allocation unit that automatically allocates tasks based on the information acquired by the progress monitoring unit; a resource suggestion unit that suggests an optimal allocation of resources based on the tasks assigned by the task allocation unit. A system characterized by:

2. The progress monitoring unit Automatically detects project risk factors and proposes risk avoidance measures 2. The system of claim 1.

3. The generating AI is Automate not only progress management but also budget and schedule management 2. The system of claim 1.

4. The generating AI is Keep a detailed history of the progress of said tasks to help plan future projects.

2. The system of claim 1.

5. The generating AI is When assigning tasks, consider the emotional state of the members and assign tasks with less stress.

2. The system of claim 1.

6. The generating AI is Analyzing members' emotional states and providing a less stressful environment indirectly reduces management costs 2. The system of claim 1.

7. The generating AI is When allocating resources, consider the emotional state of the team members and allocate resources in a way that minimizes stress.

2. The system of claim 1.

8. The generating AI is Propose less stressful problem-solving methods based on the emotional state of members 2. The system of claim 1.

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