System

Abstract

An object of a system according to an embodiment is to centrally manage information related to a project and support efficient communication.SOLUTION: A system includes an information acquisition unit, a classification unit, a cooperation unit, and a notification unit. The information acquisition unit acquires information related to a project. The classification unit organizes and classifies the information acquired by the information acquisition unit. The cooperation unit manages the information organized and classified by the classification unit in cooperation with a communication tool or Teams. The notification part notifies the project member of the information managed by the cooperation part.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 technology has the problem that project-related information is scattered, making efficient information management and communication difficult.

[0005] The system according to the embodiment aims to centrally manage project-related information and support efficient communication. [Means for solving the problem]

[0006] The system according to the embodiment includes an information acquisition unit, a classification unit, a collaboration unit, and a notification unit. The information acquisition unit acquires information related to a project. The classification unit organizes and classifies the information acquired by the information acquisition unit. The collaboration unit manages the information organized and classified by the classification unit in collaboration with a communication tool or Teams. The notification unit notifies project members of the information managed by the collaboration unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage project-related information and support efficient communication. [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) The information management system according to an embodiment of the present invention is a system that centrally manages information on a project-by-project basis and automatically manages the information using a generation AI. This information management system supports efficient communication and information organization on a project-by-project basis, thereby saving time and effort in the progress of the project.

[0029] An information management system according to an embodiment includes an information acquisition unit, a classification unit, a collaboration unit, and a notification unit. The information acquisition unit acquires project-related information, such as emails, chats, documents, and tasks. The information acquisition unit can also analyze the progress of a project in real time and automatically prioritize necessary information. The classification unit organizes and classifies the information acquired by the information acquisition unit, such as by classifying important information into general information and providing information that is individually customized based on the roles and skill sets of project members. The collaboration unit manages the information organized and classified by the classification unit in collaboration with a communication tool or Teams. For example, it automatically acquires conversation content and shared files in Teams and organizes them by project. The collaboration unit can also monitor the progress of a project in real time and provide necessary information at the appropriate time. The notification unit notifies project members of the information managed by the collaboration unit, such as by automatically creating meeting minutes and distributing them to relevant parties. The notification unit can also automatically track the progress of tasks and issue alerts if delays occur. As a result, the information management system according to the embodiment supports efficient communication and information organization on a project-by-project basis, making it possible to save time and effort in the progress of a project.

[0030] The information acquisition unit can analyze the progress of a project in real time and automatically prioritize necessary information according to progress. For example, the information acquisition unit allows the generation AI to monitor the progress of a project in real time and automatically change the priority of tasks according to progress. For example, if an important milestone is approaching, that task will be set as the highest priority. The information acquisition unit also allows the generation AI to automatically organize related documents and emails based on the progress of the project and prioritize displaying necessary information. For example, progress reports and meeting minutes will be prioritized. The information acquisition unit also allows the generation AI to analyze the progress of a project and issue an alert if a delay occurs. For example, if progress on a task is behind schedule, that task will be displayed as a priority and relevant parties will be notified. This allows necessary information to be provided prioritized according to the progress of the project.

[0031] The classification unit can provide individually customized information based on the role and skill set of each project member. For example, the classification unit allows the generation AI to analyze the role and skill set of a project member and provide the most appropriate information to each member. For example, the classification unit may prioritize displaying technical documents to engineers and progress reports to managers. The classification unit also allows the generation AI to provide relevant training materials and resources based on the skill set of a project member. For example, the classification unit may provide materials for learning new technologies to engineers. The classification unit also allows the generation AI to automatically adjust task assignments according to the role of a project member. For example, the classification unit may prioritize assigning tasks that are suitable for members with specific skills. This allows the generation AI to provide the most appropriate information to project members.

[0032] The collaboration unit monitors the progress of a project in real time and can provide necessary information at the appropriate time. For example, in the collaboration unit, the generation AI monitors the progress of a project in real time and provides necessary information at the appropriate time. For example, when an important milestone approaches, that task is set as the highest priority. In addition, the collaboration unit has the generation AI automatically organize related documents and emails based on the progress of the project and display necessary information preferentially. For example, progress reports and meeting minutes are displayed preferentially. In addition, the collaboration unit has the generation AI analyze the progress of the project and issue an alert if a delay occurs. For example, if the progress of a task is behind schedule, that task is displayed preferentially and relevant parties are notified. This allows necessary information to be provided at the appropriate time according to the progress of the project.

[0033] The collaboration unit can analyze project progress data and propose efficient methods of progress. In the collaboration unit, for example, the generation AI analyzes project progress data and proposes efficient methods of progress. For example, it proposes optimal task order and resource allocation based on past project data. In addition, the collaboration unit has the generation AI analyze project progress data and propose efficient methods of progress and notify project members. For example, it proposes adjustments to task priorities and schedules. In addition, the collaboration unit has the generation AI analyze project progress data in real time and proposes efficient methods of progress. For example, it optimizes resource reallocation and task allocation. This makes it possible to propose efficient methods of progress based on project progress data.

[0034] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0035] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0036] The information acquisition unit analyzes the content of business chats and can automatically summarize and notify important information. In the information acquisition unit, for example, the generation AI analyzes the content of business chats and automatically summarizes and notifies important information. For example, it summarizes meeting minutes and important decisions and sends them to relevant parties. In addition, the generation AI analyzes the chat content and summarizes important information to display on a dashboard. For example, it displays the progress of a project or an overview of important tasks. In addition, the information acquisition unit analyzes the chat content in real time and summarizes and notifies important information. For example, it immediately notifies relevant parties of urgent notices and important changes. This allows important information in business chats to be automatically summarized and notified.

[0037] The information acquisition unit classifies chat content according to the progress of the project and can prioritize displaying highly relevant information. For example, the generation AI in the information acquisition unit classifies chat content according to the progress of the project and prioritizes displaying highly relevant information. For example, progress reports and task updates are prioritized. The generation AI in the information acquisition unit analyzes the chat content and classifies information based on the progress of the project and prioritizes displaying important information. For example, minutes of important meetings and decisions are prioritized. The generation AI in the information acquisition unit analyzes chat content in real time and classifies information according to the progress of the project. For example, information related to progress is prioritized and casual conversations and general communications are postponed. This allows the chat content to be classified according to the progress of the project and highly relevant information to be prioritized.

[0038] The collaboration unit can analyze the content of Teams conversations and automatically extract and organize information necessary for the progress of a project. For example, the generation AI in the collaboration unit analyzes the content of Teams conversations and automatically extracts and organizes information necessary for the progress of a project. For example, it automatically extracts meeting minutes and important decisions and notifies relevant parties. The collaboration unit also has the generation AI analyze the content of Teams conversations and automatically organizes information necessary for the progress of a project and displays it on a dashboard. For example, it displays progress status and task overviews. The collaboration unit also has the generation AI analyze the content of Teams conversations in real time and automatically extracts and organizes information necessary for the progress of a project. For example, it immediately communicates urgent notices and important changes to relevant parties. This makes it possible to analyze the content of Teams conversations and automatically extract and organize information necessary for the progress of a project.

[0039] The collaboration unit updates the content of Teams conversations in real time according to the progress of the project, allowing the team to provide the latest information. For example, the generation AI in the collaboration unit updates the content of Teams conversations in real time according to the progress of the project, providing the latest information. For example, progress reports and task updates are displayed in real time. The collaboration unit also has a generation AI that analyzes the content of Teams conversations and updates information in real time based on the progress of the project, prioritizing the display of important information. For example, important meeting minutes and decisions are displayed in real time. The collaboration unit also has a generation AI that analyzes the content of Teams conversations in real time and updates information according to the progress of the project. For example, information related to progress is displayed in real time, while casual conversations and general communications are postponed. This allows the content of Teams conversations to be updated in real time according to the progress of the project, allowing the team to provide the latest information.

[0040] The collaboration unit can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, the generation AI in the collaboration unit can collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, it can automatically acquire and organize Slack messages and Zoom meeting records. In addition, the collaboration unit has the generation AI analyze the content of communication tools other than Teams to centrally manage information and organize it by project. For example, it can categorize Slack messages and Zoom meeting records by project. In addition, the collaboration unit has the generation AI collaborate with communication tools other than Teams to achieve centralized information management. For example, it can automatically acquire Slack messages and Zoom meeting records and display them on a dashboard. This allows collaboration with communication tools other than Teams to achieve centralized information management.

[0041] The collaboration unit can automatically summarize Teams conversation content and notify project members. For example, the collaboration unit's generation AI automatically summarizes Teams conversation content and notifies project members. For example, it summarizes meeting minutes and important decisions and sends them to relevant parties. The collaboration unit also has the generation AI analyze Teams conversation content and summarizes important information and displays it on a dashboard. For example, it displays the project progress and an overview of important tasks. The collaboration unit's generation AI also analyzes Teams conversation content in real time and summarizes and notifies important information. For example, it immediately communicates urgent notices and important changes to relevant parties. This allows Teams conversation content to be automatically summarized and notified to project members.

[0042] The information acquisition unit can also apply the project progress to household task management and event planning. For example, the information acquisition unit applies generative AI to household task management, automatically assigning tasks based on the roles and schedules of family members. For example, it optimally allocates tasks such as housework and child pick-up and drop-off. The information acquisition unit also applies generative AI to household event planning, centrally managing the schedules of all family members. For example, it automatically coordinates plans for family trips and birthday parties. The information acquisition unit also centrally manages information about household tasks and events, providing necessary information to family members. For example, it shares shopping lists and event details. This allows the project progress to be applied to household task management and event planning.

[0043] The information acquisition unit can automatically detect risk factors in a project and make proposals for risk management. In the information acquisition unit, for example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI, having detected risk factors, to make specific proposals for risk management. For example, it proposes resource reallocation or schedule adjustments. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make proposals for risk management.

[0044] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0045] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0046] The information acquisition unit can also apply the project progress to household task management and event planning. For example, the information acquisition unit applies generative AI to household task management, automatically assigning tasks based on the roles and schedules of family members. For example, it optimally allocates tasks such as housework and child pick-up and drop-off. The information acquisition unit also applies generative AI to household event planning, centrally managing the schedules of all family members. For example, it automatically coordinates plans for family trips and birthday parties. The information acquisition unit also centrally manages information about household tasks and events, providing necessary information to family members. For example, it shares shopping lists and event details. This allows the project progress to be applied to household task management and event planning.

[0047] The information acquisition unit can automatically detect risk factors in a project and make proposals for risk management. In the information acquisition unit, for example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI, having detected risk factors, to make specific proposals for risk management. For example, it proposes resource reallocation or schedule adjustments. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make proposals for risk management.

[0048] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0049] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

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

[0051] The information acquisition unit can also apply project progress to household task management and event planning. For example, generative AI can be applied to household task management to automatically assign tasks based on the roles and schedules of family members. For example, it can optimally allocate tasks such as housework and child pick-up and drop-off. The information acquisition unit can also apply generative AI to household event planning to centrally manage the schedules of all family members. For example, it can automatically coordinate plans for family trips and birthday parties. The information acquisition unit can also centrally manage information about household tasks and events and provide necessary information to family members. For example, it can share shopping lists and event details. This allows project progress to be applied to household task management and event planning.

[0052] The collaboration unit can analyze project progress data and propose efficient methods of progress. For example, the generation AI analyzes project progress data and proposes efficient methods of progress. For example, it proposes optimal task order and resource allocation based on past project data. The collaboration unit also has the generation AI analyze project progress data and propose efficient methods of progress and notify project members. For example, it proposes adjustments to task priorities and schedules. The collaboration unit also has the generation AI analyze project progress data in real time and proposes efficient methods of progress. For example, it optimizes resource reallocation and task allocation. This makes it possible to propose efficient methods of progress based on project progress data.

[0053] The information acquisition unit can automatically detect risk factors in a project and make suggestions for risk management. For example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI to detect risk factors and make specific suggestions for risk management. For example, it suggests reallocating resources or adjusting the schedule. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make suggestions for risk management.

[0054] The collaboration unit can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, the generation AI can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, it can automatically retrieve and organize Slack messages and Zoom meeting records. The collaboration unit also uses the generation AI to analyze the content of communication tools other than Teams and centrally manage information and organize it by project. For example, it can classify Slack messages and Zoom meeting records by project. The collaboration unit also uses the generation AI to collaborate with communication tools other than Teams to achieve centralized information management. For example, it can automatically retrieve Slack messages and Zoom meeting records and display them on a dashboard. This allows collaboration with communication tools other than Teams to achieve centralized information management.

[0055] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting, and the generation AI automatically creates minutes and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0056] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI can monitor the progress of tasks in real time and issue alerts if delays occur. For example, it can notify relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it can propose resource reallocation or schedule adjustments. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it can notify the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

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

[0058] Step 1: The information acquisition unit acquires information related to the project, such as emails, chats, documents, and tasks. The information acquisition unit can also analyze the progress of the project in real time and automatically prioritize the necessary information. Step 2: The classification unit organizes and classifies the information acquired by the information acquisition unit. For example, it can categorize important information from general information. The classification unit can also provide customized information based on the roles and skill sets of project members. Step 3: The Collaboration Unit manages the information organized and categorized by the Classification Unit in collaboration with communication tools or Teams. For example, it can automatically retrieve conversations and shared files in Teams and organize them by project. The Collaboration Unit can also monitor project progress in real time and provide necessary information at the appropriate time. Step 4: The Notification Department notifies project members of the information managed by the Collaboration Department. For example, it can automatically create meeting minutes and distribute them to relevant parties. The Notification Department can also automatically track the progress of tasks and issue alerts if delays occur.

[0059] (Example 2) The information management system according to an embodiment of the present invention is a system that centrally manages information on a project-by-project basis and automatically manages the information using a generation AI. This information management system supports efficient communication and information organization on a project-by-project basis, thereby saving time and effort in the progress of the project.

[0060] An information management system according to an embodiment includes an information acquisition unit, a classification unit, a collaboration unit, and a notification unit. The information acquisition unit acquires project-related information, such as emails, chats, documents, and tasks. The information acquisition unit can also analyze the progress of a project in real time and automatically prioritize necessary information. The classification unit organizes and classifies the information acquired by the information acquisition unit, such as by classifying important information into general information and providing information that is individually customized based on the roles and skill sets of project members. The collaboration unit manages the information organized and classified by the classification unit in collaboration with a communication tool or Teams. For example, it automatically acquires conversation content and shared files in Teams and organizes them by project. The collaboration unit can also monitor the progress of a project in real time and provide necessary information at the appropriate time. The notification unit notifies project members of the information managed by the collaboration unit, such as by automatically creating meeting minutes and distributing them to relevant parties. The notification unit can also automatically track the progress of tasks and issue alerts if delays occur. As a result, the information management system according to the embodiment supports efficient communication and information organization on a project-by-project basis, making it possible to save time and effort in the progress of a project.

[0061] The information acquisition unit can analyze the progress of a project in real time and automatically prioritize necessary information according to progress. For example, the information acquisition unit allows the generation AI to monitor the progress of a project in real time and automatically change the priority of tasks according to progress. For example, if an important milestone is approaching, that task will be set as the highest priority. The information acquisition unit also allows the generation AI to automatically organize related documents and emails based on the progress of the project and prioritize displaying necessary information. For example, progress reports and meeting minutes will be prioritized. The information acquisition unit also allows the generation AI to analyze the progress of a project and issue an alert if a delay occurs. For example, if progress on a task is behind schedule, that task will be displayed as a priority and relevant parties will be notified. This allows necessary information to be provided prioritized according to the progress of the project.

[0062] The classification unit can provide individually customized information based on the role and skill set of each project member. For example, the classification unit allows the generation AI to analyze the role and skill set of a project member and provide the most appropriate information to each member. For example, the classification unit may prioritize displaying technical documents to engineers and progress reports to managers. The classification unit also allows the generation AI to provide relevant training materials and resources based on the skill set of a project member. For example, the classification unit may provide materials for learning new technologies to engineers. The classification unit also allows the generation AI to automatically adjust task assignments according to the role of a project member. For example, the classification unit may prioritize assigning tasks that are suitable for members with specific skills. This allows the generation AI to provide the most appropriate information to project members.

[0063] The classification unit can use the emotion estimation function to analyze the emotional state of project members and provide information to reduce stress or fatigue. For example, the classification unit can use the emotion estimation function to analyze the emotional state of project members in real time and suggest relaxation methods or breaks to members who are highly stressed. The classification unit also uses a generation AI based on the emotional data of project members to provide resources and support for stress reduction. For example, it can introduce mental health materials and counseling services. The classification unit can also use the emotion estimation function to analyze the fatigue state of project members and suggest appropriate timing for breaks. For example, it can send a notification encouraging members who have been working for long periods of time to take a break. This can reduce stress and fatigue among project members.

[0064] The collaboration unit monitors the progress of a project in real time and can provide necessary information at the appropriate time. For example, in the collaboration unit, the generation AI monitors the progress of a project in real time and provides necessary information at the appropriate time. For example, when an important milestone approaches, that task is set as the highest priority. In addition, the collaboration unit has the generation AI automatically organize related documents and emails based on the progress of the project and display necessary information preferentially. For example, progress reports and meeting minutes are displayed preferentially. In addition, the collaboration unit has the generation AI analyze the progress of the project and issue an alert if a delay occurs. For example, if the progress of a task is behind schedule, that task is displayed preferentially and relevant parties are notified. This allows necessary information to be provided at the appropriate time according to the progress of the project.

[0065] The collaboration unit can analyze project progress data and propose efficient methods of progress. In the collaboration unit, for example, the generation AI analyzes project progress data and proposes efficient methods of progress. For example, it proposes optimal task order and resource allocation based on past project data. In addition, the collaboration unit has the generation AI analyze project progress data and propose efficient methods of progress and notify project members. For example, it proposes adjustments to task priorities and schedules. In addition, the collaboration unit has the generation AI analyze project progress data in real time and proposes efficient methods of progress. For example, it optimizes resource reallocation and task allocation. This makes it possible to propose efficient methods of progress based on project progress data.

[0066] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0067] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0068] The notification unit can use the emotion estimation function to analyze the emotional state of project members and provide information to reduce stress or fatigue. For example, the notification unit can use the emotion estimation function to analyze the emotional state of project members in real time and suggest relaxation methods or breaks to members who are highly stressed. The notification unit also uses generation AI based on the project members' emotional data to provide resources and support for stress reduction. For example, it can introduce mental health materials and counseling services. The notification unit can also use the emotion estimation function to analyze the fatigue state of project members and suggest appropriate times to take a break. For example, it can send a notification encouraging members who have been working for long periods of time to take a break. This can reduce the stress and fatigue of project members.

[0069] The information acquisition unit analyzes the content of business chats and can automatically summarize and notify important information. In the information acquisition unit, for example, the generation AI analyzes the content of business chats and automatically summarizes and notifies important information. For example, it summarizes meeting minutes and important decisions and sends them to relevant parties. In addition, the generation AI analyzes the chat content and summarizes important information to display on a dashboard. For example, it displays the progress of a project or an overview of important tasks. In addition, the information acquisition unit analyzes the chat content in real time and summarizes and notifies important information. For example, it immediately notifies relevant parties of urgent notices and important changes. This allows important information in business chats to be automatically summarized and notified.

[0070] The information acquisition unit classifies chat content according to the progress of the project and can prioritize displaying highly relevant information. For example, the generation AI in the information acquisition unit classifies chat content according to the progress of the project and prioritizes displaying highly relevant information. For example, progress reports and task updates are prioritized. The generation AI in the information acquisition unit analyzes the chat content and classifies information based on the progress of the project and prioritizes displaying important information. For example, minutes of important meetings and decisions are prioritized. The generation AI in the information acquisition unit analyzes chat content in real time and classifies information according to the progress of the project. For example, information related to progress is prioritized and casual conversations and general communications are postponed. This allows the chat content to be classified according to the progress of the project and highly relevant information to be prioritized.

[0071] The information acquisition unit can use the emotion estimation function to analyze the tone and emotions of chat messages and make suggestions to improve negative communication. For example, the information acquisition unit can use the emotion estimation function to analyze the tone and emotions of chat messages in real time and make suggestions for improvement if it detects negative communication. For example, it can suggest a gentler expression for a message with an aggressive tone. The information acquisition unit also makes specific suggestions to improve negative communication using the generation AI based on the emotion data of the chat content. For example, it can send positive feedback or encouraging messages. The information acquisition unit also uses the emotion estimation function to analyze the tone and emotions of chat messages and provide training materials and resources to improve negative communication. For example, it can provide materials for improving communication skills. This makes it possible to analyze the tone and emotions of chat messages and make suggestions to improve negative communication.

[0072] The collaboration unit can analyze the content of Teams conversations and automatically extract and organize information necessary for the progress of a project. For example, the generation AI in the collaboration unit analyzes the content of Teams conversations and automatically extracts and organizes information necessary for the progress of a project. For example, it automatically extracts meeting minutes and important decisions and notifies relevant parties. The collaboration unit also has the generation AI analyze the content of Teams conversations and automatically organizes information necessary for the progress of a project and displays it on a dashboard. For example, it displays progress status and task overviews. The collaboration unit also has the generation AI analyze the content of Teams conversations in real time and automatically extracts and organizes information necessary for the progress of a project. For example, it immediately communicates urgent notices and important changes to relevant parties. This makes it possible to analyze the content of Teams conversations and automatically extract and organize information necessary for the progress of a project.

[0073] The collaboration unit updates the content of Teams conversations in real time according to the progress of the project, allowing the team to provide the latest information. For example, the generation AI in the collaboration unit updates the content of Teams conversations in real time according to the progress of the project, providing the latest information. For example, progress reports and task updates are displayed in real time. The collaboration unit also has a generation AI that analyzes the content of Teams conversations and updates information in real time based on the progress of the project, prioritizing the display of important information. For example, important meeting minutes and decisions are displayed in real time. The collaboration unit also has a generation AI that analyzes the content of Teams conversations in real time and updates information according to the progress of the project. For example, information related to progress is displayed in real time, while casual conversations and general communications are postponed. This allows the content of Teams conversations to be updated in real time according to the progress of the project, allowing the team to provide the latest information.

[0074] The collaboration unit can use the emotion estimation function to analyze the emotional state of members in Teams conversations and improve the quality of communication. For example, the collaboration unit can use the emotion estimation function to analyze the emotional state of members in Teams conversations in real time and make suggestions to improve the quality of communication. For example, it can suggest a gentler expression for a message with an aggressive tone. The collaboration unit can also use the emotion estimation function to make specific suggestions to improve the quality of communication using the generation AI based on the emotional data of the Teams conversation content. For example, it can send positive feedback or encouraging messages. The collaboration unit can also use the emotion estimation function to analyze the emotional state of members in Teams conversations and provide training materials and resources to improve the quality of communication. For example, it can provide materials for improving communication skills. This makes it possible to analyze the emotional state of members in Teams conversations and improve the quality of communication.

[0075] The collaboration unit can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, the generation AI in the collaboration unit can collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, it can automatically acquire and organize Slack messages and Zoom meeting records. In addition, the collaboration unit has the generation AI analyze the content of communication tools other than Teams to centrally manage information and organize it by project. For example, it can categorize Slack messages and Zoom meeting records by project. In addition, the collaboration unit has the generation AI collaborate with communication tools other than Teams to achieve centralized information management. For example, it can automatically acquire Slack messages and Zoom meeting records and display them on a dashboard. This allows collaboration with communication tools other than Teams to achieve centralized information management.

[0076] The collaboration unit can automatically summarize Teams conversation content and notify project members. For example, the collaboration unit's generation AI automatically summarizes Teams conversation content and notifies project members. For example, it summarizes meeting minutes and important decisions and sends them to relevant parties. The collaboration unit also has the generation AI analyze Teams conversation content and summarizes important information and displays it on a dashboard. For example, it displays the project progress and an overview of important tasks. The collaboration unit's generation AI also analyzes Teams conversation content in real time and summarizes and notifies important information. For example, it immediately communicates urgent notices and important changes to relevant parties. This allows Teams conversation content to be automatically summarized and notified to project members.

[0077] The collaboration unit can use the emotion estimation function to analyze members' emotional reactions in Teams conversations and provide positive feedback. For example, the collaboration unit can use the emotion estimation function to analyze members' emotional reactions in Teams conversations in real time and provide positive feedback. For example, it can send positive feedback or encouraging messages. The collaboration unit also makes specific suggestions for the generation AI to provide positive feedback based on the emotional data of the Teams conversation content. For example, it can send messages of gratitude or praise. The collaboration unit also uses the emotion estimation function to analyze members' emotional reactions in Teams conversations and provide training materials and resources for providing positive feedback. For example, it can provide materials for improving communication skills. This makes it possible to analyze members' emotional reactions in Teams conversations and provide positive feedback.

[0078] The information acquisition unit can also apply the project progress to household task management and event planning. For example, the information acquisition unit applies generative AI to household task management, automatically assigning tasks based on the roles and schedules of family members. For example, it optimally allocates tasks such as housework and child pick-up and drop-off. The information acquisition unit also applies generative AI to household event planning, centrally managing the schedules of all family members. For example, it automatically coordinates plans for family trips and birthday parties. The information acquisition unit also centrally manages information about household tasks and events, providing necessary information to family members. For example, it shares shopping lists and event details. This allows the project progress to be applied to household task management and event planning.

[0079] The information acquisition unit can automatically detect risk factors in a project and make proposals for risk management. In the information acquisition unit, for example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI, having detected risk factors, to make specific proposals for risk management. For example, it proposes resource reallocation or schedule adjustments. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make proposals for risk management.

[0080] The classification unit can use the emotion estimation function to analyze members' motivation regarding the progress of the project and provide feedback to improve their motivation. For example, the classification unit can use the emotion estimation function to analyze the motivation of project members in real time and send encouraging messages to members whose motivation is declining. The classification unit also uses a generation AI based on the project members' emotional data to provide specific feedback to improve their motivation. For example, it can assign tasks that will give them a sense of accomplishment or share success stories. The classification unit can also use the emotion estimation function to analyze the motivation of project members and suggest events and activities to boost the morale of the entire team. For example, it can plan team building activities or refreshing events. This can improve the motivation of project members.

[0081] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0082] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0083] The notification unit can use the emotion estimation function to analyze the emotional state of project members and provide information to reduce stress or fatigue. For example, the notification unit can use the emotion estimation function to analyze the emotional state of project members in real time and suggest relaxation methods or breaks to members who are highly stressed. The notification unit also uses generation AI based on the project members' emotional data to provide resources and support for stress reduction. For example, it can introduce mental health materials and counseling services. The notification unit can also use the emotion estimation function to analyze the fatigue state of project members and suggest appropriate times to take a break. For example, it can send a notification encouraging members who have been working for long periods of time to take a break. This can reduce the stress and fatigue of project members.

[0084] The information acquisition unit can also apply the project progress to household task management and event planning. For example, the information acquisition unit applies generative AI to household task management, automatically assigning tasks based on the roles and schedules of family members. For example, it optimally allocates tasks such as housework and child pick-up and drop-off. The information acquisition unit also applies generative AI to household event planning, centrally managing the schedules of all family members. For example, it automatically coordinates plans for family trips and birthday parties. The information acquisition unit also centrally manages information about household tasks and events, providing necessary information to family members. For example, it shares shopping lists and event details. This allows the project progress to be applied to household task management and event planning.

[0085] The information acquisition unit can automatically detect risk factors in a project and make proposals for risk management. In the information acquisition unit, for example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI, having detected risk factors, to make specific proposals for risk management. For example, it proposes resource reallocation or schedule adjustments. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make proposals for risk management.

[0086] The classification unit can use the emotion estimation function to analyze members' motivation regarding the progress of the project and provide feedback to improve their motivation. For example, the classification unit can use the emotion estimation function to analyze the motivation of project members in real time and send encouraging messages to members whose motivation is declining. The classification unit also uses a generation AI based on the project members' emotional data to provide specific feedback to improve their motivation. For example, it can assign tasks that will give them a sense of accomplishment or share success stories. The classification unit can also use the emotion estimation function to analyze the motivation of project members and suggest events and activities to boost the morale of the entire team. For example, it can plan team building activities or refreshing events. This can improve the motivation of project members.

[0087] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI in the notification unit analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting using the generation AI, automatically creates minutes, and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0088] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI monitors the progress of tasks in real time and issues alerts if delays occur. For example, it notifies relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it suggests reallocating resources or adjusting the schedule. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it notifies the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The information acquisition unit can also apply project progress to household task management and event planning. For example, generative AI can be applied to household task management to automatically assign tasks based on the roles and schedules of family members. For example, it can optimally allocate tasks such as housework and child pick-up and drop-off. The information acquisition unit can also apply generative AI to household event planning to centrally manage the schedules of all family members. For example, it can automatically coordinate plans for family trips and birthday parties. The information acquisition unit can also centrally manage information about household tasks and events and provide necessary information to family members. For example, it can share shopping lists and event details. This allows project progress to be applied to household task management and event planning.

[0091] The classification unit can use the emotion estimation function to analyze the emotional state of project members and provide information to reduce stress or fatigue. For example, the emotion estimation function can be used to analyze the emotional state of project members in real time and suggest relaxation methods or breaks to members who are highly stressed. The classification unit also uses a generation AI based on the project members' emotional data to provide resources and support for stress reduction. For example, it can introduce mental health materials and counseling services. The classification unit can also use the emotion estimation function to analyze the fatigue state of project members and suggest appropriate times to take a break. For example, it can send a notification encouraging members who have been working for long periods of time to take a break. This can reduce stress and fatigue among project members.

[0092] The collaboration unit can analyze project progress data and propose efficient methods of progress. For example, the generation AI analyzes project progress data and proposes efficient methods of progress. For example, it proposes optimal task order and resource allocation based on past project data. The collaboration unit also has the generation AI analyze project progress data and propose efficient methods of progress and notify project members. For example, it proposes adjustments to task priorities and schedules. The collaboration unit also has the generation AI analyze project progress data in real time and proposes efficient methods of progress. For example, it optimizes resource reallocation and task allocation. This makes it possible to propose efficient methods of progress based on project progress data.

[0093] The notification unit can use the emotion estimation function to analyze the emotional state of project members and provide information to reduce stress or fatigue. For example, it can use the emotion estimation function to analyze the emotional state of project members in real time and suggest relaxation methods or breaks to members who are highly stressed. The notification unit also uses generation AI based on the project members' emotional data to provide resources and support for stress reduction. For example, it can introduce mental health materials and counseling services. The notification unit can also use the emotion estimation function to analyze the fatigue state of project members and suggest appropriate times to take a break. For example, it can send a notification encouraging members who have been working for long periods of time to take a break. This can reduce stress and fatigue among project members.

[0094] The information acquisition unit can automatically detect risk factors in a project and make suggestions for risk management. For example, the generation AI analyzes the progress of a project and automatically detects risk factors. For example, it identifies risks such as task delays and resource shortages. In addition, the information acquisition unit allows the generation AI to detect risk factors and make specific suggestions for risk management. For example, it suggests reallocating resources or adjusting the schedule. In addition, the information acquisition unit allows the generation AI to monitor project risk factors in real time and issue an alert if a risk occurs. For example, it notifies relevant parties if an important task is delayed. This makes it possible to automatically detect risk factors in a project and make suggestions for risk management.

[0095] The classification unit can use the emotion estimation function to analyze members' motivation regarding the progress of the project and provide feedback to improve motivation. For example, the emotion estimation function can be used to analyze the motivation of project members in real time and send encouraging messages to members whose motivation is declining. The classification unit also uses a generation AI based on the project members' emotional data to provide specific feedback to improve motivation. For example, it can assign tasks that will give members a sense of accomplishment or share success stories. The classification unit also uses the emotion estimation function to analyze the motivation of project members and suggest events and activities to boost the morale of the entire team. For example, it can plan team building activities or refreshing events. This can improve the motivation of project members.

[0096] The collaboration unit can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, the generation AI can also collaborate with communication tools other than Teams (for example, Slack or Zoom) to achieve centralized information management. For example, it can automatically retrieve and organize Slack messages and Zoom meeting records. The collaboration unit also uses the generation AI to analyze the content of communication tools other than Teams and centrally manage information and organize it by project. For example, it can classify Slack messages and Zoom meeting records by project. The collaboration unit also uses the generation AI to collaborate with communication tools other than Teams to achieve centralized information management. For example, it can automatically retrieve Slack messages and Zoom meeting records and display them on a dashboard. This allows collaboration with communication tools other than Teams to achieve centralized information management.

[0097] The notification unit can automatically create meeting minutes and distribute them to relevant parties. For example, the generation AI analyzes the audio data of a meeting and automatically creates minutes. For example, it summarizes the important points and decisions of the meeting, converts them into text, and distributes them to relevant parties. The notification unit also analyzes the audio data of a meeting in real time, and the generation AI automatically creates minutes and distributes them to relevant parties. For example, it sends the minutes by email immediately after the meeting ends. The notification unit also analyzes the audio data of a meeting, and the generation AI automatically creates minutes and uploads them to a project management tool. For example, it reflects the content of the meeting in the progress of the project. This allows meeting minutes to be automatically created and distributed to relevant parties.

[0098] The notification unit can automatically track the progress of tasks and issue alerts if delays occur. For example, the generation AI can monitor the progress of tasks in real time and issue alerts if delays occur. For example, it can notify relevant parties if task progress is behind schedule. The notification unit also allows the generation AI to analyze task progress data and propose specific countermeasures if delays occur. For example, it can propose resource reallocation or schedule adjustments. The notification unit also allows the generation AI to automatically track the progress of tasks and issue alerts if delays occur. For example, it can notify the project manager if an important task is delayed. This makes it possible to automatically track the progress of tasks and issue alerts if delays occur.

[0099] The information acquisition unit can use the emotion estimation function to analyze the tone and emotions of chat messages and make suggestions to improve negative communication. For example, the emotion estimation function can be used to analyze the tone and emotions of chat messages in real time, and if negative communication is detected, suggestions for improvement can be made. For example, a gentler expression can be suggested for a message with an aggressive tone. The information acquisition unit also makes specific suggestions to improve negative communication using the generation AI based on the emotional data of the chat content. For example, sending positive feedback or encouraging messages. The information acquisition unit also uses the emotion estimation function to analyze the tone and emotions of chat messages and provide training materials and resources to improve negative communication. For example, it can provide materials for improving communication skills. This makes it possible to analyze the tone and emotions of chat messages and make suggestions to improve negative communication.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The information acquisition unit acquires information related to the project, such as emails, chats, documents, and tasks. The information acquisition unit can also analyze the progress of the project in real time and automatically prioritize the necessary information. Step 2: The classification unit organizes and classifies the information acquired by the information acquisition unit. For example, it can categorize important information from general information. The classification unit can also provide customized information based on the roles and skill sets of project members. Step 3: The Collaboration Unit manages the information organized and categorized by the Classification Unit in collaboration with communication tools or Teams. For example, it can automatically retrieve conversations and shared files in Teams and organize them by project. The Collaboration Unit can also monitor project progress in real time and provide necessary information at the appropriate time. Step 4: The Notification Department notifies project members of the information managed by the Collaboration Department. For example, it can automatically create meeting minutes and distribute them to relevant parties. The Notification Department can also automatically track the progress of tasks and issue alerts if delays occur.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 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. an information acquisition unit that acquires information related to the project; a classification unit that organizes and classifies the information acquired by the information acquisition unit; a linking unit that manages the information organized and classified by the classification unit in cooperation with a communication tool or Teams; a notification unit that notifies project members of the information managed by the collaboration unit. A system characterized by:

2. The information acquisition unit Analyze the progress of the project in real time and automatically prioritize the necessary information according to progress. The system of claim 1 .

3. The classification unit Customize and provide this information based on the roles and skill sets of the project members The system of claim 1 .

4. The classification unit Analyzing the emotional state of the project members and providing information to reduce stress or fatigue The system of claim 1 .

5. The linking unit is Monitor the progress of said projects in real time and provide necessary information at the right time The system of claim 1 .

6. The linking unit is Analyze the progress data of the project and propose an efficient way to proceed The system of claim 1 .

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