System

Abstract

An object of the system according to the embodiment is to automate project management and to efficiently perform risk prediction and document management.SOLUTION: A system according to an embodiment includes a progress management unit, a status confirmation unit, a risk prediction unit, and a document management unit. The progress management unit automates progress management. The status confirmation unit confirms the status of each member based on the data collected by the progress management unit. The risk prediction unit predicts a project risk based on the situation confirmed by the situation confirmation unit. The document management unit manages and creates each document based on the risk predicted by the risk prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that project management requires a significant amount of man-hours, and risk prediction and document management are not carried out efficiently.

[0005] The system according to the embodiment aims to automate project management and efficiently perform risk prediction and document management. [Means for solving the problem]

[0006] The system according to the embodiment includes a progress management unit, a status confirmation unit, a risk prediction unit, and a document management unit. The progress management unit automates progress management. The status confirmation unit checks the status of each member based on data collected by the progress management unit. The risk prediction unit predicts project risks based on the status confirmed by the status confirmation unit. The document management unit manages and creates each document based on the risks predicted by the risk prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment automates project management and can efficiently perform risk prediction and document management. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A project management system according to an embodiment of the present invention is a system for reducing the man-hours and improving efficiency in project management. This system uses a generative AI to automate progress management, check the status of each member, predict project risks, and manage and create each document. For example, the project management system uses a generative AI to monitor progress in real time and predict the progress and expected completion date of each task. Next, the project management system grasps the work status of each member in real time and predicts risks before they occur. Furthermore, the project management system uses a generative AI to automatically create and manage explanatory materials for stakeholders and various documents. Furthermore, if a security concern arises during system design or development, the generative AI automatically proposes a solution. This significantly reduces the man-hours required for project management and enables efficient project operation. This allows the project management system to grasp the project progress and risks in real time and respond quickly. For example, this can improve the project success rate and minimize the need for additional budgets.

[0029] A project management system according to an embodiment includes a progress management unit, a status confirmation unit, a risk prediction unit, and a document management unit. The progress management unit automates project progress. For example, the progress management unit monitors the project progress in real time using a generation AI and predicts the progress and expected completion date of each task. For example, the generation AI can monitor the project progress and issue an alert if a delay occurs. The progress management unit can also automatically update the task progress using the generation AI. The status confirmation unit checks the status of each member based on data collected by the progress management unit. For example, the status confirmation unit grasps the work status of each member in real time using the generation AI. For example, if a specific member is delayed, the impact can be predicted and countermeasures can be taken in advance. The risk prediction unit predicts project risks based on the status confirmed by the status confirmation unit. For example, the risk prediction unit can predict project risks using the generation AI and take countermeasures before the risk occurs. For example, if a specific member is delayed, the generation AI can predict the impact and take countermeasures in advance. The document management unit manages and creates each document based on the risks predicted by the risk prediction unit. The document management unit automatically creates and manages explanatory materials for stakeholders and various documents, for example, using a generation AI. For example, the generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required to create documents. As a result, the project management system according to the embodiment significantly reduces the man-hours required for project management, enabling efficient project management.

[0030] The progress management unit can monitor the progress of the project in real time and predict the progress and expected completion date of each task. The progress management unit, for example, uses a generation AI to monitor the progress of the project in real time. For example, the generation AI can monitor the progress of the project and issue an alert if a delay occurs. The progress management unit can also predict the progress and expected completion date of each task using the generation AI. For example, the generation AI can automatically update the progress of tasks and predict the expected completion date. This allows the progress of the project to be understood in real time and enables prompt response. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for monitoring the progress of the project into the generation AI and have the generation AI monitor the progress.

[0031] The status confirmation unit can grasp the work status of each member in real time. The status confirmation unit grasps the work status of each member in real time, for example, using a generation AI. For example, the generation AI can monitor the work status of each member and issue an alert if a delay occurs. The status confirmation unit can also automatically update the work status of each member using the generation AI. For example, the generation AI can grasp the work status of each member in real time and take measures before a delay occurs. This makes it possible to grasp the work status of each member in real time and take appropriate action. Some or all of the above-mentioned processing in the status confirmation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for monitoring the work status of each member into the generation AI and have the generation AI monitor the work status.

[0032] The risk prediction unit can predict the impact when a specific member is delayed. The risk prediction unit, for example, uses a generation AI to predict the impact when a specific member is delayed. For example, the generation AI can predict the impact when a delay occurs and take measures in advance. The risk prediction unit can also automatically update the impact of a delay using the generation AI. For example, the generation AI can predict the impact of a delay in real time and take appropriate measures. This makes it possible to predict the impact of a delay in advance and take appropriate measures. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for predicting the impact of a delay into the generation AI and have the generation AI execute an impact prediction.

[0033] The document management unit can automatically create and manage explanatory materials and various documents for stakeholders. The document management unit can automatically create and manage explanatory materials and various documents for stakeholders, for example, using a generation AI. For example, the generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required for document creation. The document management unit can also automatically manage various documents using a generation AI. For example, the generation AI can automate document management and reduce the burden on project managers. This can significantly reduce the time required for document creation and reduce the burden on project managers. Some or all of the above-mentioned processing in the document management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the document management unit can input data for creating explanatory materials and documents into a generation AI and have the generation AI create the documents.

[0034] If a security concern arises during system design or development, the document management unit can propose specific measures to mitigate the risk. For example, if a security concern arises during system design or development, the document management unit can use a generation AI to propose specific measures to mitigate the risk. For example, if a security risk arises, the generation AI can propose specific measures to mitigate the risk and respond quickly. The document management unit can also use the generation AI to automatically update security risk countermeasures. For example, the generation AI can propose security risk countermeasures in real time and take appropriate measures. This allows for quick proposal of countermeasures and risk mitigation when a security risk arises. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for proposing security risk countermeasures into the generation AI and have the generation AI execute the proposed countermeasures.

[0035] When monitoring the progress of a project, the progress management unit can improve the accuracy of the progress prediction by referring to past project data. For example, when monitoring the progress of a project using a generation AI, the progress management unit improves the accuracy of the progress prediction by referring to past project data. For example, the generation AI analyzes past project data and predicts current progress based on progress patterns of similar projects. The generation AI can also identify the cause of delays from past project data and predict that similar delays will not occur in the current project. Furthermore, the generation AI can adjust algorithms to improve the accuracy of the progress prediction based on past project data. Thus, by referring to past project data, the accuracy of the progress prediction is improved. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for referencing past project data into the generation AI and have the generation AI execute a progress prediction.

[0036] When predicting the progress of each task, the progress management unit can improve prediction accuracy by taking task dependency relationships into account. For example, when predicting the progress of each task using a generation AI, the progress management unit improves prediction accuracy by taking task dependency relationships into account. For example, the generation AI analyzes task dependency relationships and predicts the progress of a current task based on the progress of dependent tasks. The generation AI can also predict the impact of a delay by taking task dependency relationships into account. Furthermore, the generation AI can adjust an algorithm for improving progress prediction accuracy based on task dependency relationships. In this way, taking task dependency relationships into account improves progress prediction accuracy. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for taking task dependency relationships into account into the generation AI and have the generation AI execute progress prediction.

[0037] When collecting progress management data, the progress management unit can improve the accuracy of the data by taking into account each member's work environment and tools used. For example, when collecting progress management data using a generation AI, the progress management unit improves the accuracy of the data by taking into account each member's work environment and tools used. For example, the generation AI analyzes the tools used by each member and collects progress data based on the tool usage status. The generation AI can also adjust the progress data collection method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the progress data based on each member's work environment and tools used. This improves the accuracy of the data by taking into account each member's work environment and tools used. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data that takes into account each member's work environment and tools used into the generation AI and have the generation AI collect data.

[0038] When collecting progress management data, the progress management unit can optimize the data collection method by taking into account the geographical location information of each member. For example, when collecting progress management data using a generation AI, the progress management unit optimizes the data collection method by taking into account the geographical location information of each member. For example, the generation AI proposes an optimal data collection method based on the geographical location information of each member. The generation AI can also adjust the timing of data collection by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of data collection based on the geographical location information. In this way, the data collection method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to collect data.

[0039] When collecting progress management data, the progress management unit can analyze each member's social media activity to collect related data. When collecting progress management data using, for example, a generation AI, the progress management unit analyzes each member's social media activity to collect related data. For example, the generation AI analyzes each member's social media activity to collect data related to progress. The generation AI can also understand the member's work status based on the social media activity. Furthermore, the generation AI can adjust the progress data collection method based on the social media activity. This makes it easier to collect related data by analyzing social media activity. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0040] When collecting progress management data, the progress management unit can customize the data collection method by reflecting each member's past feedback. When collecting progress management data using, for example, a generation AI, the progress management unit customizes the data collection method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes an optimal data collection method. The generation AI can also adjust the timing of data collection based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of data collection based on the past feedback. In this way, the data collection method is customized by reflecting the past feedback. Some or all of the above-described processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for reflecting the past feedback into the generation AI and have the generation AI collect data.

[0041] The status confirmation unit can improve the accuracy of the status confirmation by referring to past work history when grasping the work status of each member. For example, when grasping the work status of each member using a generation AI, the status confirmation unit improves the accuracy of the status confirmation by referring to past work history. For example, the generation AI analyzes the past work history of each member to grasp the current work status. The generation AI can also adjust the method of confirming the work status based on the past work history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the status confirmation based on the past work history. In this way, the accuracy of the status confirmation is improved by referring to the past work history. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for referencing the past work history into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0042] When grasping the work status of each member, the status confirmation unit can improve the accuracy of the status confirmation by taking into account the communication history between members. For example, when grasping the work status of each member using a generation AI, the status confirmation unit improves the accuracy of the status confirmation by taking into account the communication history between members. For example, the generation AI analyzes the communication history between members to grasp the work status. The generation AI can also adjust the method of checking the work status based on the communication history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the status confirmation based on the communication history. In this way, the accuracy of the situation confirmation is improved by taking the communication history into account. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the situation confirmation unit can input data for taking the communication history into account into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0043] When grasping the work status of each member, the situation confirmation unit can improve the accuracy of the situation confirmation by taking into account the tools used by the member and the work environment. For example, when grasping the work status of each member using a generation AI, the situation confirmation unit improves the accuracy of the situation confirmation by taking into account the tools used by the member and the work environment. For example, the generation AI analyzes the tools used by each member to grasp the work status. The generation AI can also adjust the method of checking the work status based on the tools used and the work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the situation confirmation based on the tools used and the work environment. In this way, the accuracy of the situation confirmation is improved by taking into account the tools used and the work environment. Some or all of the above-described processing in the situation confirmation unit may be performed using, or without, the generation AI. For example, the situation confirmation unit can input data for taking into account the tools used and the work environment into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0044] When grasping the work status of each member, the status confirmation unit can optimize the status confirmation method by taking into account the geographical location information of the member. For example, when grasping the work status of each member using a generation AI, the status confirmation unit optimizes the status confirmation method by taking into account the geographical location information of the member. For example, the generation AI proposes an optimal status confirmation method based on the geographical location information of each member. The generation AI can also adjust the timing of the status confirmation by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the status confirmation based on the geographical location information. In this way, the status confirmation method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the status confirmation unit may be performed using, or without, the generation AI. For example, the situation confirmation unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to execute the status confirmation method.

[0045] When grasping the work status of each member, the status confirmation unit can analyze the social media activities of the members to collect related data. When grasping the work status of each member using, for example, a generation AI, the status confirmation unit analyzes the social media activities of the members to collect related data. For example, the generation AI analyzes the social media activities of each member and collects data related to the work status. The generation AI can also grasp the work status of the members based on the social media activities. Furthermore, the generation AI can adjust an algorithm to improve the accuracy of the status confirmation based on the social media activities. This makes it easier to collect related data by analyzing the social media activities. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for analyzing social media activities into the generation AI and have the generation AI collect the data.

[0046] When grasping the work status of each member, the status confirmation unit can customize the status confirmation method by reflecting the member's past feedback. When grasping the work status of each member using, for example, a generation AI, the status confirmation unit customizes the status confirmation method by reflecting the member's past feedback. For example, the generation AI analyzes the member's past feedback and proposes an optimal status confirmation method. The generation AI can also adjust the timing of the status confirmation based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the status confirmation based on the past feedback. In this way, the status confirmation method is customized by reflecting the past feedback. Some or all of the above-described processing in the status confirmation unit may be performed using, or without, the generation AI. For example, the status confirmation unit can input data for reflecting the past feedback into the generation AI and cause the generation AI to execute the status confirmation method.

[0047] When performing risk prediction, the risk prediction unit can improve the accuracy of the risk prediction by referring to past project data. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of the risk prediction by referring to past project data. For example, the generation AI analyzes past project data and predicts current risks based on risk patterns of similar projects. The generation AI can also identify risk causes from past project data and predict that similar risks will not occur in the current project. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the risk prediction based on past project data. In this way, the accuracy of the risk prediction is improved by referring to past project data. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for referencing past project data into the generation AI and cause the generation AI to execute processing for improving the accuracy of the risk prediction.

[0048] The risk prediction unit can improve the accuracy of risk prediction by taking task dependency relationships into account when performing risk prediction. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of risk prediction by taking task dependency relationships into account. For example, the generation AI analyzes task dependency relationships and predicts current risks based on the progress of dependent tasks. The generation AI can also predict the impact of delays by taking task dependency relationships into account. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on task dependency relationships. In this way, the accuracy of risk prediction is improved by taking task dependency relationships into account. Some or all of the above-described processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking task dependency relationships into account into the generation AI and cause the generation AI to execute processing for improving the accuracy of risk prediction.

[0049] When performing risk prediction, the risk prediction unit can improve the accuracy of the risk prediction by taking into account each member's work environment and tools used. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of the risk prediction by taking into account each member's work environment and tools used. For example, the generation AI analyzes the tools used by each member and predicts risks based on the tool usage status. The generation AI can also adjust the risk prediction method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the risk prediction based on each member's work environment and tools used. This improves the accuracy of the risk prediction by taking into account the work environment and tools used. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking into account each member's work environment and tools used into the generation AI and cause the generation AI to execute processing for improving the accuracy of the risk prediction.

[0050] When performing risk prediction, the risk prediction unit can optimize the risk prediction method by taking into account the geographical location information of each member. For example, when performing risk prediction using a generation AI, the risk prediction unit optimizes the risk prediction method by taking into account the geographical location information of each member. For example, the generation AI proposes an optimal risk prediction method based on the geographical location information of each member. The generation AI can also adjust the timing of risk prediction by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on the geographical location information. In this way, the risk prediction method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to execute the risk prediction method.

[0051] When performing risk prediction, the risk prediction unit can analyze each member's social media activity to collect relevant data. When performing risk prediction using, for example, a generation AI, the risk prediction unit analyzes each member's social media activity to collect relevant data. For example, the generation AI analyzes each member's social media activity to collect risk-related data. The generation AI can also understand the member's work status based on the social media activity. Furthermore, the generation AI can adjust an algorithm to improve the accuracy of risk prediction based on the social media activity. This makes it easier to collect relevant data by analyzing social media activity. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0052] When performing risk prediction, the risk prediction unit can customize the risk prediction method by reflecting each member's past feedback. For example, when performing risk prediction using a generation AI, the risk prediction unit customizes the risk prediction method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes an optimal risk prediction method. The generation AI can also adjust the timing of risk prediction based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on the past feedback. In this way, the risk prediction method is customized by reflecting the past feedback. Some or all of the above-described processing in the risk prediction unit may be performed using, or without, the generation AI. For example, the risk prediction unit can input data for reflecting the past feedback into the generation AI and cause the generation AI to execute the risk prediction method.

[0053] The document management unit can improve the accuracy of document creation and management by referring to past document data when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit can improve the accuracy of document creation and management by referring to past document data. For example, the generation AI analyzes past document data and creates current documents based on the creation patterns of similar documents. The generation AI can also identify the cause of errors from past document data and create current documents so that similar errors do not occur. Furthermore, the generation AI can adjust algorithms to improve the accuracy of document creation and management based on past document data. Thus, the accuracy of document creation and management is improved by referring to past document data. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for referencing past document data into the generation AI and have the generation AI execute processing to improve the accuracy of document creation and management.

[0054] The document management unit can improve the accuracy of document creation and management by taking into account the work history of each member when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit can improve the accuracy of document creation and management by taking into account the work history of each member. For example, the generation AI analyzes the work history of each member and creates the current document. The generation AI can also adjust the document creation and management method based on the work history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of document creation and management based on the work history. This improves the accuracy of document creation and management by taking the work history into account. Some or all of the above-described processing in the document management unit can be performed using, or without, the generation AI. For example, the document management unit can input data for taking the work history into account into the generation AI and have the generation AI perform processing to improve the accuracy of document creation and management.

[0055] The document management unit can improve the accuracy of document creation and management by taking into account the tools and work environment of each member when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit improves the accuracy of document creation and management by taking into account the tools and work environment of each member. For example, the generation AI analyzes the tools used by each member and creates documents based on the tool usage status. The generation AI can also adjust the document creation and management method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of document creation and management based on each member's work environment and tools used. This improves the accuracy of document creation and management by taking into account the tools and work environment used. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data to the generation AI to take into account the tools and work environment used, and have the generation AI execute processing to improve the accuracy of document creation and management.

[0056] The document management unit can optimize the document creation and management method by taking into account each member's geographic location information when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit optimizes the creation and management method by taking into account each member's geographic location information. For example, the generation AI proposes an optimal document creation and management method based on each member's geographic location information. The generation AI can also adjust the timing of document creation and management by taking into account the geographic location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of document creation and management based on the geographic location information. In this way, the creation and management method is optimized by taking into account the geographic location information. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for taking into account the geographic location information into the generation AI and have the generation AI execute the creation and management method.

[0057] The document management unit can analyze each member's social media activity to collect relevant data when creating and managing documents. For example, when creating and managing documents using a generation AI, the document management unit analyzes each member's social media activity to collect relevant data. For example, the generation AI analyzes each member's social media activity to collect data related to document creation. The generation AI can also understand the member's work status based on social media activity. Furthermore, the generation AI can adjust algorithms to improve the accuracy of document creation and management based on social media activity. This makes it easier to collect relevant data by analyzing social media activity. Some or all of the above-mentioned processing in the document management unit can be performed using, or without, the generation AI. For example, the document management unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0058] When creating and managing documents, the document management unit can customize the creation and management method by reflecting each member's past feedback. For example, when creating and managing documents using a generation AI, the document management unit customizes the creation and management method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes the optimal document creation and management method. The generation AI can also adjust the timing of document creation and management based on the past feedback. Furthermore, the generation AI can adjust the algorithm to improve the accuracy of document creation and management based on the past feedback. In this way, the creation and management method is customized by reflecting the past feedback. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit may input data to reflect past feedback into the generation AI and have the generation AI execute the creation and management method.

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

[0060] Project management systems can also be equipped with a skills management section that manages team members' skill sets. The skills management section collects skill data for each team member and automatically assigns the most suitable team members to the project. For example, it can prioritize the assignment of members with the skills required for a specific task. The skills management section can also identify team member skill gaps and suggest necessary training. Furthermore, the skills management section can update team members' skill sets as the project progresses, maintaining optimal team composition. This improves the project success rate and enables efficient resource management.

[0061] The project management system can also be equipped with a communication support unit to facilitate smooth communication between members. The communication support unit analyzes each member's communication history and suggests appropriate communication methods. For example, if communication between certain members is lacking, it can suggest meetings or encourage chats. The communication support unit can also provide the optimal communication method depending on the communication style between members. Furthermore, the communication support unit can adjust the frequency of communication depending on the progress of the project. This maintains smooth communication and improves the success rate of the project.

[0062] The project management system can also include an environment management unit to optimize each member's work environment. The environment management unit collects data on each member's work environment and proposes the optimal work environment. For example, it can adjust lighting, temperature, sound environment, etc. to provide a comfortable working environment for each member. The environment management unit can also adjust the workload according to each member's work environment. Furthermore, the environment management unit can also propose break times based on each member's work environment. This improves the work efficiency of each member and increases the success rate of the project.

[0063] A project management system can also be equipped with a learning management section that manages the learning status of members. The learning management section collects learning data from each member and provides the necessary skills and knowledge. For example, it can suggest training on new technologies and tools to help members improve their skills. The learning management section can also customize the content of training according to each member's learning status. Furthermore, the learning management section can adjust learning priorities according to the progress of the project. This improves members' skills and increases the success rate of the project.

[0064] The project management system may further include a performance evaluation unit that evaluates the work performance of members. The performance evaluation unit collects work data from each member and evaluates their performance. For example, it can evaluate and provide feedback based on task completion time and quality. The performance evaluation unit can also propose rewards and incentives according to the members' performance. Furthermore, the performance evaluation unit can adjust the evaluation criteria according to the progress of the project. This improves member performance and increases the success rate of the project.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The progress management unit automates the project progress. The progress management unit uses the generation AI to monitor the project progress in real time and predict the progress and expected completion date of each task. For example, the generation AI can monitor the project progress and issue an alert if a delay occurs. The progress management unit can also use the generation AI to automatically update the task progress. Step 2: The Status Confirmation Department checks the status of each member based on the data collected by the Progress Management Department. The Status Confirmation Department uses the Generative AI to grasp the work status of each member in real time. For example, if a specific member is delayed, the department can predict the impact and take measures in advance. Step 3: The Risk Prediction Unit predicts project risks based on the situation confirmed by the Situation Confirmation Unit. The Risk Prediction Unit uses generative AI to predict project risks and can take measures before the risks occur. For example, if a specific member is delayed, the impact can be predicted and measures can be taken in advance. Step 4: The Document Management Department manages and creates each document based on the risks predicted by the Risk Prediction Department. The Document Management Department uses generation AI to automatically create and manage explanatory materials for stakeholders and various documents. For example, generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required to create documents.

[0067] (Example 2) A project management system according to an embodiment of the present invention is a system for reducing the man-hours and improving efficiency in project management. This system uses a generative AI to automate progress management, check the status of each member, predict project risks, and manage and create each document. For example, the project management system uses a generative AI to monitor progress in real time and predict the progress and expected completion date of each task. Next, the project management system grasps the work status of each member in real time and predicts risks before they occur. Furthermore, the project management system uses a generative AI to automatically create and manage explanatory materials for stakeholders and various documents. Furthermore, if a security concern arises during system design or development, the generative AI automatically proposes a solution. This significantly reduces the man-hours required for project management and enables efficient project operation. This allows the project management system to grasp the project progress and risks in real time and respond quickly. For example, this can improve the project success rate and minimize the need for additional budgets.

[0068] A project management system according to an embodiment includes a progress management unit, a status confirmation unit, a risk prediction unit, and a document management unit. The progress management unit automates project progress. For example, the progress management unit monitors the project progress in real time using a generation AI and predicts the progress and expected completion date of each task. For example, the generation AI can monitor the project progress and issue an alert if a delay occurs. The progress management unit can also automatically update the task progress using the generation AI. The status confirmation unit checks the status of each member based on data collected by the progress management unit. For example, the status confirmation unit grasps the work status of each member in real time using the generation AI. For example, if a specific member is delayed, the impact can be predicted and countermeasures can be taken in advance. The risk prediction unit predicts project risks based on the status confirmed by the status confirmation unit. For example, the risk prediction unit can predict project risks using the generation AI and take countermeasures before the risk occurs. For example, if a specific member is delayed, the generation AI can predict the impact and take countermeasures in advance. The document management unit manages and creates each document based on the risks predicted by the risk prediction unit. The document management unit automatically creates and manages explanatory materials for stakeholders and various documents, for example, using a generation AI. For example, the generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required to create documents. As a result, the project management system according to the embodiment significantly reduces the man-hours required for project management, enabling efficient project management.

[0069] The progress management unit can monitor the progress of the project in real time and predict the progress and expected completion date of each task. The progress management unit, for example, uses a generation AI to monitor the progress of the project in real time. For example, the generation AI can monitor the progress of the project and issue an alert if a delay occurs. The progress management unit can also predict the progress and expected completion date of each task using the generation AI. For example, the generation AI can automatically update the progress of tasks and predict the expected completion date. This allows the progress of the project to be understood in real time and enables prompt response. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for monitoring the progress of the project into the generation AI and have the generation AI monitor the progress.

[0070] The status confirmation unit can grasp the work status of each member in real time. The status confirmation unit grasps the work status of each member in real time, for example, using a generation AI. For example, the generation AI can monitor the work status of each member and issue an alert if a delay occurs. The status confirmation unit can also automatically update the work status of each member using the generation AI. For example, the generation AI can grasp the work status of each member in real time and take measures before a delay occurs. This makes it possible to grasp the work status of each member in real time and take appropriate action. Some or all of the above-mentioned processing in the status confirmation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for monitoring the work status of each member into the generation AI and have the generation AI monitor the work status.

[0071] The risk prediction unit can predict the impact when a specific member is delayed. The risk prediction unit, for example, uses a generation AI to predict the impact when a specific member is delayed. For example, the generation AI can predict the impact when a delay occurs and take measures in advance. The risk prediction unit can also automatically update the impact of a delay using the generation AI. For example, the generation AI can predict the impact of a delay in real time and take appropriate measures. This makes it possible to predict the impact of a delay in advance and take appropriate measures. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for predicting the impact of a delay into the generation AI and have the generation AI execute an impact prediction.

[0072] The document management unit can automatically create and manage explanatory materials and various documents for stakeholders. The document management unit can automatically create and manage explanatory materials and various documents for stakeholders, for example, using a generation AI. For example, the generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required for document creation. The document management unit can also automatically manage various documents using a generation AI. For example, the generation AI can automate document management and reduce the burden on project managers. This can significantly reduce the time required for document creation and reduce the burden on project managers. Some or all of the above-mentioned processing in the document management unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the document management unit can input data for creating explanatory materials and documents into a generation AI and have the generation AI create the documents.

[0073] If a security concern arises during system design or development, the document management unit can propose specific measures to mitigate the risk. For example, if a security concern arises during system design or development, the document management unit can use a generation AI to propose specific measures to mitigate the risk. For example, if a security risk arises, the generation AI can propose specific measures to mitigate the risk and respond quickly. The document management unit can also use the generation AI to automatically update security risk countermeasures. For example, the generation AI can propose security risk countermeasures in real time and take appropriate measures. This allows for quick proposal of countermeasures and risk mitigation when a security risk arises. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for proposing security risk countermeasures into the generation AI and have the generation AI execute the proposed countermeasures.

[0074] The progress management unit can estimate the user's emotions and adjust the urgency of the progress management alert based on the estimated user emotions. The progress management unit, for example, uses a generation AI to estimate the user's emotions and adjust the urgency of the progress management alert based on the estimated user emotions. For example, if the user is stressed, the generation AI can set the urgency of the alert low to avoid placing an excessive burden on the user. Alternatively, if the user is relaxed, the generation AI can set the urgency of the alert high to encourage a prompt response. Alternatively, if the user is in a hurry, the generation AI can set the urgency of the alert to the maximum to request an immediate response. This allows the urgency of the alert to be adjusted according to the user's emotions, enabling an appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the progress management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the progress management unit can input data for estimating a user's emotions into the generation AI and have the generation AI perform emotion estimation.

[0075] When monitoring the progress of a project, the progress management unit can improve the accuracy of the progress prediction by referring to past project data. For example, when monitoring the progress of a project using a generation AI, the progress management unit improves the accuracy of the progress prediction by referring to past project data. For example, the generation AI analyzes past project data and predicts current progress based on progress patterns of similar projects. The generation AI can also identify the cause of delays from past project data and predict that similar delays will not occur in the current project. Furthermore, the generation AI can adjust algorithms to improve the accuracy of the progress prediction based on past project data. Thus, by referring to past project data, the accuracy of the progress prediction is improved. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for referencing past project data into the generation AI and have the generation AI execute a progress prediction.

[0076] When predicting the progress of each task, the progress management unit can improve prediction accuracy by taking task dependency relationships into account. For example, when predicting the progress of each task using a generation AI, the progress management unit improves prediction accuracy by taking task dependency relationships into account. For example, the generation AI analyzes task dependency relationships and predicts the progress of a current task based on the progress of dependent tasks. The generation AI can also predict the impact of a delay by taking task dependency relationships into account. Furthermore, the generation AI can adjust an algorithm for improving progress prediction accuracy based on task dependency relationships. In this way, taking task dependency relationships into account improves progress prediction accuracy. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for taking task dependency relationships into account into the generation AI and have the generation AI execute progress prediction.

[0077] When collecting progress management data, the progress management unit can improve the accuracy of the data by taking into account each member's work environment and tools used. For example, when collecting progress management data using a generation AI, the progress management unit improves the accuracy of the data by taking into account each member's work environment and tools used. For example, the generation AI analyzes the tools used by each member and collects progress data based on the tool usage status. The generation AI can also adjust the progress data collection method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the progress data based on each member's work environment and tools used. This improves the accuracy of the data by taking into account each member's work environment and tools used. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data that takes into account each member's work environment and tools used into the generation AI and have the generation AI collect data.

[0078] The progress management unit can estimate the user's emotions and customize the progress management display method based on the estimated user emotions. The progress management unit, for example, uses a generation AI to estimate the user's emotions and customize the progress management display method based on the estimated user emotions. For example, if the user is stressed, the generation AI can provide a simple display method to reduce visual burden. Alternatively, if the user is relaxed, the generation AI can provide a display method that includes detailed information. Alternatively, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This customizes the display method according to the user's emotions and reduces visual burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for estimating the user's emotions into the generation AI and have the generation AI execute emotion estimation.

[0079] When collecting progress management data, the progress management unit can optimize the data collection method by taking into account the geographical location information of each member. For example, when collecting progress management data using a generation AI, the progress management unit optimizes the data collection method by taking into account the geographical location information of each member. For example, the generation AI proposes an optimal data collection method based on the geographical location information of each member. The generation AI can also adjust the timing of data collection by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of data collection based on the geographical location information. In this way, the data collection method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the progress management unit may be performed using, or without, the generation AI. For example, the progress management unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to collect data.

[0080] When collecting progress management data, the progress management unit can analyze each member's social media activity to collect related data. When collecting progress management data using, for example, a generation AI, the progress management unit analyzes each member's social media activity to collect related data. For example, the generation AI analyzes each member's social media activity to collect data related to progress. The generation AI can also understand the member's work status based on the social media activity. Furthermore, the generation AI can adjust the progress data collection method based on the social media activity. This makes it easier to collect related data by analyzing social media activity. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0081] When collecting progress management data, the progress management unit can customize the data collection method by reflecting each member's past feedback. When collecting progress management data using, for example, a generation AI, the progress management unit customizes the data collection method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes an optimal data collection method. The generation AI can also adjust the timing of data collection based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of data collection based on the past feedback. In this way, the data collection method is customized by reflecting the past feedback. Some or all of the above-described processing in the progress management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the progress management unit can input data for reflecting the past feedback into the generation AI and have the generation AI collect data.

[0082] The situation confirmation unit can estimate the user's emotions and adjust the frequency of status confirmation for each member based on the estimated user emotions. The situation confirmation unit, for example, uses a generation AI to estimate the user's emotions and adjust the frequency of status confirmation for each member based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can set the frequency of status confirmation low to avoid placing an excessive burden on the user. Also, if the user is relaxed, the generation AI can set the frequency of status confirmation high to encourage a quick response. Also, if the user is in a hurry, the generation AI can set the frequency of status confirmation to the maximum to request an immediate response. This allows the frequency of status confirmation to be adjusted according to the user's emotions, enabling an appropriate response. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the situation confirmation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the situation confirmation unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0083] The status confirmation unit can improve the accuracy of the status confirmation by referring to past work history when grasping the work status of each member. For example, when grasping the work status of each member using a generation AI, the status confirmation unit improves the accuracy of the status confirmation by referring to past work history. For example, the generation AI analyzes the past work history of each member to grasp the current work status. The generation AI can also adjust the method of confirming the work status based on the past work history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the status confirmation based on the past work history. In this way, the accuracy of the status confirmation is improved by referring to the past work history. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for referencing the past work history into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0084] When grasping the work status of each member, the status confirmation unit can improve the accuracy of the status confirmation by taking into account the communication history between members. For example, when grasping the work status of each member using a generation AI, the status confirmation unit improves the accuracy of the status confirmation by taking into account the communication history between members. For example, the generation AI analyzes the communication history between members to grasp the work status. The generation AI can also adjust the method of checking the work status based on the communication history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the status confirmation based on the communication history. In this way, the accuracy of the situation confirmation is improved by taking the communication history into account. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the situation confirmation unit can input data for taking the communication history into account into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0085] When grasping the work status of each member, the situation confirmation unit can improve the accuracy of the situation confirmation by taking into account the tools used by the member and the work environment. For example, when grasping the work status of each member using a generation AI, the situation confirmation unit improves the accuracy of the situation confirmation by taking into account the tools used by the member and the work environment. For example, the generation AI analyzes the tools used by each member to grasp the work status. The generation AI can also adjust the method of checking the work status based on the tools used and the work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the situation confirmation based on the tools used and the work environment. In this way, the accuracy of the situation confirmation is improved by taking into account the tools used and the work environment. Some or all of the above-described processing in the situation confirmation unit may be performed using, or without, the generation AI. For example, the situation confirmation unit can input data for taking into account the tools used and the work environment into the generation AI and cause the generation AI to execute processing for improving the accuracy of the situation confirmation.

[0086] The situation confirmation unit can estimate the user's emotions and customize the display method of each member's status confirmation based on the estimated user's emotions. The situation confirmation unit, for example, uses a generation AI to estimate the user's emotions and customize the display method of each member's status confirmation based on the estimated user's emotions. For example, if the user is feeling stressed, the generation AI can provide a simple display method to reduce visual burden. Alternatively, if the user is relaxed, the generation AI can provide a display method including detailed information. Alternatively, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This customizes the display method according to the user's emotions and reduces visual burden. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the situation confirmation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the situation confirmation unit can input data for estimating the user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0087] When grasping the work status of each member, the status confirmation unit can optimize the status confirmation method by taking into account the geographical location information of the member. For example, when grasping the work status of each member using a generation AI, the status confirmation unit optimizes the status confirmation method by taking into account the geographical location information of the member. For example, the generation AI proposes an optimal status confirmation method based on the geographical location information of each member. The generation AI can also adjust the timing of the status confirmation by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the status confirmation based on the geographical location information. In this way, the status confirmation method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the status confirmation unit may be performed using, or without, the generation AI. For example, the situation confirmation unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to execute the status confirmation method.

[0088] When grasping the work status of each member, the status confirmation unit can analyze the social media activities of the members to collect related data. When grasping the work status of each member using, for example, a generation AI, the status confirmation unit analyzes the social media activities of the members to collect related data. For example, the generation AI analyzes the social media activities of each member and collects data related to the work status. The generation AI can also grasp the work status of the members based on the social media activities. Furthermore, the generation AI can adjust an algorithm to improve the accuracy of the status confirmation based on the social media activities. This makes it easier to collect related data by analyzing the social media activities. Some or all of the above-described processing in the status confirmation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the status confirmation unit can input data for analyzing social media activities into the generation AI and have the generation AI collect the data.

[0089] When grasping the work status of each member, the status confirmation unit can customize the status confirmation method by reflecting the member's past feedback. When grasping the work status of each member using, for example, a generation AI, the status confirmation unit customizes the status confirmation method by reflecting the member's past feedback. For example, the generation AI analyzes the member's past feedback and proposes an optimal status confirmation method. The generation AI can also adjust the timing of the status confirmation based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the status confirmation based on the past feedback. In this way, the status confirmation method is customized by reflecting the past feedback. Some or all of the above-described processing in the status confirmation unit may be performed using, or without, the generation AI. For example, the status confirmation unit can input data for reflecting the past feedback into the generation AI and cause the generation AI to execute the status confirmation method.

[0090] The risk prediction unit can estimate the user's emotions and adjust the urgency of the risk prediction alert based on the estimated user emotions. The risk prediction unit, for example, uses a generation AI to estimate the user's emotions and adjust the urgency of the risk prediction alert based on the estimated user emotions. For example, if the user is stressed, the generation AI can set the urgency of the alert low to avoid placing an excessive burden on the user. Also, if the user is relaxed, the generation AI can set the urgency of the alert high to encourage a prompt response. Also, if the user is in a hurry, the generation AI can set the urgency of the alert to the maximum to request an immediate response. This allows the urgency of the alert to be adjusted according to the user's emotions, enabling an appropriate response. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the risk prediction unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for estimating a user's emotions into the generation AI and cause the generation AI to estimate the emotions.

[0091] When performing risk prediction, the risk prediction unit can improve the accuracy of the risk prediction by referring to past project data. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of the risk prediction by referring to past project data. For example, the generation AI analyzes past project data and predicts current risks based on risk patterns of similar projects. The generation AI can also identify risk causes from past project data and predict that similar risks will not occur in the current project. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of the risk prediction based on past project data. In this way, the accuracy of the risk prediction is improved by referring to past project data. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for referencing past project data into the generation AI and cause the generation AI to execute processing for improving the accuracy of the risk prediction.

[0092] The risk prediction unit can improve the accuracy of risk prediction by taking task dependency relationships into account when performing risk prediction. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of risk prediction by taking task dependency relationships into account. For example, the generation AI analyzes task dependency relationships and predicts current risks based on the progress of dependent tasks. The generation AI can also predict the impact of delays by taking task dependency relationships into account. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on task dependency relationships. In this way, the accuracy of risk prediction is improved by taking task dependency relationships into account. Some or all of the above-described processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking task dependency relationships into account into the generation AI and cause the generation AI to execute processing for improving the accuracy of risk prediction.

[0093] When performing risk prediction, the risk prediction unit can improve the accuracy of the risk prediction by taking into account each member's work environment and tools used. For example, when performing risk prediction using a generation AI, the risk prediction unit improves the accuracy of the risk prediction by taking into account each member's work environment and tools used. For example, the generation AI analyzes the tools used by each member and predicts risks based on the tool usage status. The generation AI can also adjust the risk prediction method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of the risk prediction based on each member's work environment and tools used. This improves the accuracy of the risk prediction by taking into account the work environment and tools used. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking into account each member's work environment and tools used into the generation AI and cause the generation AI to execute processing for improving the accuracy of the risk prediction.

[0094] The risk prediction unit can estimate the user's emotions and customize the display method of the risk prediction based on the estimated user emotions. The risk prediction unit, for example, uses a generation AI to estimate the user's emotions and customize the display method of the risk prediction based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple display method to reduce visual burden. Alternatively, if the user is relaxed, the generation AI can provide a display method including detailed information. Alternatively, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This customizes the display method according to the user's emotions and reduces visual burden. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for estimating the user's emotions into the generation AI and have the generation AI execute emotion estimation.

[0095] When performing risk prediction, the risk prediction unit can optimize the risk prediction method by taking into account the geographical location information of each member. For example, when performing risk prediction using a generation AI, the risk prediction unit optimizes the risk prediction method by taking into account the geographical location information of each member. For example, the generation AI proposes an optimal risk prediction method based on the geographical location information of each member. The generation AI can also adjust the timing of risk prediction by taking into account the geographical location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on the geographical location information. In this way, the risk prediction method is optimized by taking into account the geographical location information. Some or all of the above-described processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for taking into account the geographical location information into the generation AI and cause the generation AI to execute the risk prediction method.

[0096] When performing risk prediction, the risk prediction unit can analyze each member's social media activity to collect relevant data. When performing risk prediction using, for example, a generation AI, the risk prediction unit analyzes each member's social media activity to collect relevant data. For example, the generation AI analyzes each member's social media activity to collect risk-related data. The generation AI can also understand the member's work status based on the social media activity. Furthermore, the generation AI can adjust an algorithm to improve the accuracy of risk prediction based on the social media activity. This makes it easier to collect relevant data by analyzing social media activity. Some or all of the above-mentioned processing in the risk prediction unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the risk prediction unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0097] When performing risk prediction, the risk prediction unit can customize the risk prediction method by reflecting each member's past feedback. For example, when performing risk prediction using a generation AI, the risk prediction unit customizes the risk prediction method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes an optimal risk prediction method. The generation AI can also adjust the timing of risk prediction based on the past feedback. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of risk prediction based on the past feedback. In this way, the risk prediction method is customized by reflecting the past feedback. Some or all of the above-described processing in the risk prediction unit may be performed using, or without, the generation AI. For example, the risk prediction unit can input data for reflecting the past feedback into the generation AI and cause the generation AI to execute the risk prediction method.

[0098] The document management unit can estimate a user's emotions and adjust the priority of document creation and management based on the estimated user emotions. The document management unit, for example, uses a generation AI to estimate a user's emotions and adjust the priority of document creation and management based on the estimated user emotions. For example, if a user is stressed, the generation AI can set a low priority for document creation and management to avoid placing an excessive burden on the user. Also, if the user is relaxed, the generation AI can set a high priority for document creation and management to encourage a quick response. Also, if the user is in a hurry, the generation AI can set the highest priority for document creation and management to encourage an immediate response. This allows the document creation and management priority to be adjusted according to the user's emotions, enabling appropriate responses. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the document management unit may be performed, for example, using a generation AI, or without a generation AI. For example, the document management unit can input data for estimating a user's emotions into the generation AI and have the generation AI perform emotion estimation.

[0099] The document management unit can improve the accuracy of document creation and management by referring to past document data when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit can improve the accuracy of document creation and management by referring to past document data. For example, the generation AI analyzes past document data and creates current documents based on the creation patterns of similar documents. The generation AI can also identify the cause of errors from past document data and create current documents so that similar errors do not occur. Furthermore, the generation AI can adjust algorithms to improve the accuracy of document creation and management based on past document data. Thus, the accuracy of document creation and management is improved by referring to past document data. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for referencing past document data into the generation AI and have the generation AI execute processing to improve the accuracy of document creation and management.

[0100] The document management unit can improve the accuracy of document creation and management by taking into account the work history of each member when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit can improve the accuracy of document creation and management by taking into account the work history of each member. For example, the generation AI analyzes the work history of each member and creates the current document. The generation AI can also adjust the document creation and management method based on the work history. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of document creation and management based on the work history. This improves the accuracy of document creation and management by taking the work history into account. Some or all of the above-described processing in the document management unit can be performed using, or without, the generation AI. For example, the document management unit can input data for taking the work history into account into the generation AI and have the generation AI perform processing to improve the accuracy of document creation and management.

[0101] The document management unit can improve the accuracy of document creation and management by taking into account the tools and work environment of each member when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit improves the accuracy of document creation and management by taking into account the tools and work environment of each member. For example, the generation AI analyzes the tools used by each member and creates documents based on the tool usage status. The generation AI can also adjust the document creation and management method by taking into account each member's work environment. Furthermore, the generation AI can adjust the algorithm for improving the accuracy of document creation and management based on each member's work environment and tools used. This improves the accuracy of document creation and management by taking into account the tools and work environment used. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data to the generation AI to take into account the tools and work environment used, and have the generation AI execute processing to improve the accuracy of document creation and management.

[0102] The document management unit can estimate a user's emotions and customize the document display method based on the estimated user emotions. The document management unit, for example, uses a generation AI to estimate a user's emotions and customizes the document display method based on the estimated user emotions. For example, if the user is stressed, the generation AI can provide a simple display method to reduce visual burden. Alternatively, if the user is relaxed, the generation AI can provide a display method that includes detailed information. Alternatively, if the user is in a hurry, the generation AI can provide a display method that focuses on the main points. This customizes the display method according to the user's emotions and reduces visual burden. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the document management unit can be performed using, for example, a generation AI. For example, the document management unit can input data for estimating a user's emotions into the generation AI and have the generation AI perform emotion estimation.

[0103] The document management unit can optimize the document creation and management method by taking into account each member's geographic location information when creating and managing documents. For example, when using a generation AI to create and manage documents, the document management unit optimizes the creation and management method by taking into account each member's geographic location information. For example, the generation AI proposes an optimal document creation and management method based on each member's geographic location information. The generation AI can also adjust the timing of document creation and management by taking into account the geographic location information. Furthermore, the generation AI can adjust an algorithm for improving the accuracy of document creation and management based on the geographic location information. In this way, the creation and management method is optimized by taking into account the geographic location information. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit can input data for taking into account the geographic location information into the generation AI and have the generation AI execute the creation and management method.

[0104] The document management unit can analyze each member's social media activity to collect relevant data when creating and managing documents. For example, when creating and managing documents using a generation AI, the document management unit analyzes each member's social media activity to collect relevant data. For example, the generation AI analyzes each member's social media activity to collect data related to document creation. The generation AI can also understand the member's work status based on social media activity. Furthermore, the generation AI can adjust algorithms to improve the accuracy of document creation and management based on social media activity. This makes it easier to collect relevant data by analyzing social media activity. Some or all of the above-mentioned processing in the document management unit can be performed using, or without, the generation AI. For example, the document management unit can input data for analyzing social media activity into the generation AI and have the generation AI collect the data.

[0105] When creating and managing documents, the document management unit can customize the creation and management method by reflecting each member's past feedback. For example, when creating and managing documents using a generation AI, the document management unit customizes the creation and management method by reflecting each member's past feedback. For example, the generation AI analyzes each member's past feedback and proposes the optimal document creation and management method. The generation AI can also adjust the timing of document creation and management based on the past feedback. Furthermore, the generation AI can adjust the algorithm to improve the accuracy of document creation and management based on the past feedback. In this way, the creation and management method is customized by reflecting the past feedback. Some or all of the above-described processing in the document management unit may be performed using, or without, the generation AI. For example, the document management unit may input data to reflect past feedback into the generation AI and have the generation AI execute the creation and management method. === Hard Collateral 1-1 === Each of the multiple elements, including the progress management unit, status confirmation unit, risk prediction unit, and document management unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the progress management unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of the project in real time, predicting the progress and expected completion date of each task. The status confirmation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and grasps the work status of each member in real time. The risk prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and predicts project risks and takes measures before the risks occur. The document management unit is implemented, for example, by the control unit 46A of the smart device 14 and automatically creates and manages explanatory materials for stakeholders and various documents. === Hard Collateral 1-2 === Each of the multiple elements, including the progress management unit, status confirmation unit, risk prediction unit, and document management unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the progress management unit is realized by the control unit 46A of the smart glasses 214, and monitors the progress status of the project in real time and predicts the progress and expected completion date of each task. The status confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and grasps the work status of each member in real time. The risk prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts project risks and takes measures before the risks occur. The document management unit is realized, for example, by the control unit 46A of the smart glasses 214, and automatically creates and manages explanatory materials for stakeholders and various documents. === Hard Collateral 1-3 === Each of the multiple elements, including the progress management unit, status confirmation unit, risk prediction unit, and document management unit, described above, is implemented, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the progress management unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of the project in real time, predicting the progress and expected completion date of each task. The status confirmation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and grasps the work status of each member in real time. The risk prediction unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and predicts project risks and takes measures before the risks occur. The document management unit is implemented, for example, by the control unit 46A of the headset terminal 314 and automatically creates and manages explanatory materials for stakeholders and various documents. === Hard Collateral 1-4 === Each of the multiple elements, including the progress management unit, status confirmation unit, risk prediction unit, and document management unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the progress management unit is realized by the control unit 46A of the robot 414, and monitors the progress of the project in real time and predicts the progress and expected completion date of each task. The status confirmation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and grasps the work status of each member in real time. The risk prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and predicts project risks and takes measures before the risks occur. The document management unit is realized, for example, by the control unit 46A of the robot 414, and automatically creates and manages explanatory materials for stakeholders and various documents.

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

[0107] A project management system can also be equipped with a health management module that monitors the health of members. The health management module collects each member's health data and monitors their stress levels and fatigue in real time. For example, it can analyze a member's heart rate and sleep data and issue an alert if it detects signs of overwork. The health management module can also adjust the workload based on the member's health status. For example, it can reduce tasks for members who are accumulating fatigue and increase tasks for members who are in good health. The health management module can also suggest break times based on the member's health status. This enables efficient project management while maintaining the health of members.

[0108] Project management systems can also be equipped with a skills management section that manages team members' skill sets. The skills management section collects skill data for each team member and automatically assigns the most suitable team members to the project. For example, it can prioritize the assignment of members with the skills required for a specific task. The skills management section can also identify team member skill gaps and suggest necessary training. Furthermore, the skills management section can update team members' skill sets as the project progresses, maintaining optimal team composition. This improves the project success rate and enables efficient resource management.

[0109] The project management system can also include a gamification section to improve member motivation. The gamification section increases motivation by awarding points and badges based on each member's work status. For example, a member can earn points for completing a task and earn a badge when they reach a certain number of points. The gamification section can also promote competition among members to improve the overall performance of the team. Furthermore, the gamification section can provide rewards based on the member's motivation. This enables efficient project management while maintaining member motivation.

[0110] The project management system can also be equipped with a communication support unit to facilitate smooth communication between members. The communication support unit analyzes each member's communication history and suggests appropriate communication methods. For example, if communication between certain members is lacking, it can suggest meetings or encourage chats. The communication support unit can also provide the optimal communication method depending on the communication style between members. Furthermore, the communication support unit can adjust the frequency of communication depending on the progress of the project. This maintains smooth communication and improves the success rate of the project.

[0111] The project management system may further include an emotion management unit that estimates the emotions of members and adjusts the progress of the project based on the estimated emotions. The emotion management unit collects emotion data from each member and identifies factors that affect the progress of the project. For example, if a member is feeling stressed, it can adjust task priorities and reduce the burden. The emotion management unit can also adjust communication methods according to the member's emotions. Furthermore, the emotion management unit can adjust the progress of the project in real time based on the member's emotions. This enables efficient project management that takes members' emotions into consideration.

[0112] The project management system can also include an environment management unit to optimize each member's work environment. The environment management unit collects data on each member's work environment and proposes the optimal work environment. For example, it can adjust lighting, temperature, sound environment, etc. to provide a comfortable working environment for each member. The environment management unit can also adjust the workload according to each member's work environment. Furthermore, the environment management unit can also propose break times based on each member's work environment. This improves the work efficiency of each member and increases the success rate of the project.

[0113] A project management system can also be equipped with a learning management section that manages the learning status of members. The learning management section collects learning data from each member and provides the necessary skills and knowledge. For example, it can suggest training on new technologies and tools to help members improve their skills. The learning management section can also customize the content of training according to each member's learning status. Furthermore, the learning management section can adjust learning priorities according to the progress of the project. This improves members' skills and increases the success rate of the project.

[0114] The project management system may further include an emotional risk assessment unit that estimates the emotions of members and assesses project risks based on the estimated emotions. The emotional risk assessment unit collects emotional data from each member and identifies risk factors for the project. For example, if a member is feeling stressed, the impact can be assessed and risk countermeasures can be implemented. The emotional risk assessment unit can also adjust the risk assessment method according to the emotions of the members. Furthermore, the emotional risk assessment unit can update the risk assessment results in real time based on the emotions of the members. This enables risk assessment that takes members' emotions into consideration, improving the success rate of projects.

[0115] The project management system may further include a performance evaluation unit that evaluates the work performance of members. The performance evaluation unit collects work data from each member and evaluates their performance. For example, it can evaluate and provide feedback based on task completion time and quality. The performance evaluation unit can also propose rewards and incentives according to the members' performance. Furthermore, the performance evaluation unit can adjust the evaluation criteria according to the progress of the project. This improves member performance and increases the success rate of the project.

[0116] The project management system may further include an emotion communication unit that estimates the emotions of members and adjusts project communication based on the estimated emotions. The emotion communication unit collects emotion data from each member and optimizes the communication method. For example, if a member is feeling stressed, the frequency of communication can be reduced to ease the burden. The emotion communication unit can also adjust the content of communication according to the member's emotions. Furthermore, the emotion communication unit can adjust the timing of communication based on the member's emotions. This enables smooth communication that takes members' emotions into consideration, improving the success rate of the project.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The progress management unit automates the project progress. The progress management unit uses the generation AI to monitor the project progress in real time and predict the progress and expected completion date of each task. For example, the generation AI can monitor the project progress and issue an alert if a delay occurs. The progress management unit can also use the generation AI to automatically update the task progress. Step 2: The Status Confirmation Department checks the status of each member based on the data collected by the Progress Management Department. The Status Confirmation Department uses the Generative AI to grasp the work status of each member in real time. For example, if a specific member is delayed, the department can predict the impact and take measures in advance. Step 3: The Risk Prediction Unit predicts project risks based on the situation confirmed by the Situation Confirmation Unit. The Risk Prediction Unit uses generative AI to predict project risks and can take measures before the risks occur. For example, if a specific member is delayed, the impact can be predicted and measures can be taken in advance. Step 4: The Document Management Department manages and creates each document based on the risks predicted by the Risk Prediction Department. The Document Management Department uses generation AI to automatically create and manage explanatory materials for stakeholders and various documents. For example, generation AI can automatically create explanatory materials for stakeholders, significantly reducing the time required to create documents.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0136] 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.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] The data processing device 12 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0169] 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.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

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

Claims

1. A progress management department that automates progress management, a status confirmation unit that confirms the status of each member based on the data collected by the progress management unit; a risk prediction unit that predicts project risks based on the situation confirmed by the situation confirmation unit; a document management unit that manages and creates each document based on the risk predicted by the risk prediction unit. A system characterized by:

2. The progress management unit Monitor project progress in real time and predict progress and completion dates for each task 2. The system of claim 1.

3. The status confirmation unit Understand the work status of each member in real time 2. The system of claim 1.

4. The risk prediction unit If a specific member is late, predict the impact 2. The system of claim 1.

5. The document management unit Automatically create and manage explanatory materials and various documents for stakeholders 2. The system of claim 1.

6. The document management unit If a security concern arises during system design or development, we will propose specific measures to mitigate the risk.

2. The system of claim 1.

7. The progress management unit Estimate user emotions and adjust the urgency of progress management alerts based on the estimated user emotions 2. The system of claim 1.

8. The progress management unit When monitoring project progress, refer to past project data to improve progress forecast accuracy.

2. The system of claim 1.

Images