system

Abstract

The system according to this embodiment aims to grasp the progress of a project in real time and provide appropriate advice to the project manager. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a visualization unit, an advice unit, and a support unit. The collection unit collects the contents of conversations and chats of project members. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the progress of the project based on the data analyzed by the analysis unit. The advice unit provides advice to the project manager based on the information visualized by the visualization unit. The support unit assists in improving the skills of the project manager based on the advice provided by the advice unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to grasp the progress and problems of a project in real time and provide appropriate advice.

[0005] The system according to the embodiment aims to grasp the progress of a project in real time and provide appropriate advice to the project manager.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, an advice unit, and a support unit. The data collection unit collects the content of conversations and chats of project members. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the progress of the project based on the data analyzed by the analysis unit. The advice unit provides advice to the project manager based on the information visualized by the visualization unit. The support unit assists in improving the skills of the project manager based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to this embodiment can monitor the progress of a project in real time and provide appropriate advice to the project manager. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The project management support system according to an embodiment of the present invention is a system that automatically collects the content of conversations and chats of project members and grasps the progress and challenges of the project in real time. This project management support system automatically collects the content of conversations and chats of project members and grasps the progress and challenges of the project in real time. In particular, when a new project manager does not know what to do or how to proceed, they can use this AI as a reference to smoothly advance the project. For example, the project management support system automatically collects the content of conversations and chats of project members. At this time, the AI ​​extracts important information related to the project and stores it in a database. For example, it collects information such as the progress of the project, challenges, and risks. Next, the project management support system analyzes the collected data and grasps the progress and challenges of the project in real time. The AI ​​visualizes the progress of the project and provides it to the project manager. For example, it displays the progress of the project and the progress of challenges in graphs and charts. Furthermore, the project management support system provides the project manager with advice on project progress. The AI ​​learns from past project data and proposes the optimal actions to the project manager. For example, it proposes methods for risk management, task prioritization, and methods for improving communication. This AI system allows new project managers to understand project progress and challenges in real time and take appropriate action. This enables smoother project execution and contributes to improving project managers' skills. Thus, the project management support system can understand project progress and challenges in real time and support the skill development of project managers.

[0029] The project management support system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, an advice unit, and a support unit. The data collection unit collects the content of conversations and chats of project members. The data collection unit can collect, for example, text chats, voice conversations, and video conversations of project members. The data collection unit can collect the content of conversations and chats in real time. The data collection unit can also collect the content of conversations and chats periodically. Furthermore, the data collection unit can collect the content of conversations and chats based on specific keywords. For example, the data collection unit sets keywords related to the project and collects the content of conversations and chats based on those keywords. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data using, for example, text mining technology. The analysis unit can also analyze the emotions of project members using sentiment analysis technology. Furthermore, the analysis unit can analyze the progress and issues of the project using topic modeling technology. For example, the analysis unit extracts the progress and issues of the project from the collected data and grasps them in real time. The visualization unit visualizes the progress of the project based on the data analyzed by the analysis unit. The visualization unit can, for example, display the project's progress using graph display technology. The visualization unit can also display the project's progress using dashboard display technology. Furthermore, the visualization unit can display the project's progress using heatmap display technology. For example, the visualization unit displays the project's progress and the progress of issues using graphs and charts. The advice unit provides advice to the project manager based on the information visualized by the visualization unit. The advice unit learns from past project data and proposes optimal actions to the project manager. For example, the advice unit can suggest methods for risk management, task prioritization, and improving communication. The support unit assists the project manager in improving their skills based on the advice provided by the advice unit.The support department can, for example, provide training programs to help improve the skills of project managers. The support department can also provide feedback. As a result, the project management support system according to the embodiment can grasp the progress and challenges of the project in real time and support the improvement of the skills of project managers.

[0030] The data collection unit collects the content of conversations and chats among project members. For example, it can collect text chats, voice conversations, and video conversations among project members. Specifically, for text chats, it acquires messages sent and received through project management tools and messaging applications in real time. For voice conversations, it collects audio data from conference systems and teleconferencing and converts it to text using speech recognition technology. For video conversations, it acquires video and audio from video conferencing systems and extracts important information using video analysis technology. The data collection unit can collect this data not only in real time but also periodically. For example, it can collect the content of conversations and chats for the day at the end of each day. Furthermore, the data collection unit can collect conversations and chats based on specific keywords. For example, it can set project-related keywords such as "deadline," "risk," and "progress," and prioritize the collection of conversations and chats containing those keywords. This allows the data collection unit to efficiently collect important information regarding project progress and issues. In addition, the data collection unit centrally manages the collected data, making it accessible to the analysis and visualization units. This allows the data collection unit to grasp the project's progress in real time and provide a foundation for rapid response.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze collected data using text mining techniques. Specifically, it can analyze text data using natural language processing techniques to extract important keywords and phrases. This allows for obtaining information to understand the project's progress and challenges. The analysis unit can also analyze the emotions of project members using sentiment analysis techniques. For example, it can detect positive and negative emotions from text data to evaluate the motivation and stress levels of project members. Furthermore, the analysis unit can analyze project progress and challenges using topic modeling techniques. For example, it can extract topics from collected data to understand project progress and challenges in real time. In addition, the analysis unit can analyze data using machine learning algorithms to predict project progress and challenges. For example, it can predict project progress based on past data and detect risks in advance. This allows the analysis unit to quickly and accurately analyze collected data and understand project progress and challenges in real time. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The visualization unit visualizes the project's progress based on data analyzed by the analysis unit. For example, the visualization unit can display project progress using graphing technology. Specifically, it can display project progress using line graphs or bar graphs, allowing for a quick overview of task completion and delays. The visualization unit can also display project progress using dashboard display technology. For example, it can provide a dashboard that centrally displays project progress and issue progress, enabling project managers to understand the situation in real time. Furthermore, the visualization unit can display project progress using heatmap display technology. For example, it can display project progress and issue progress using a heatmap, visually highlighting problematic areas. This allows the visualization unit to intuitively grasp project progress and issues, supporting rapid response. In addition, the visualization unit provides customizable display options, enabling project managers to efficiently obtain the necessary information. For example, it provides options to display detailed progress for specific tasks or members, allowing project managers to quickly access the information they need. This allows the visualization unit to effectively visualize project progress and issues, supporting project managers' decision-making.

[0033] The advisory department provides advice to project managers based on information visualized by the visualization department. The advisory department learns from past project data and proposes optimal actions to project managers. Specifically, it analyzes past project data using machine learning algorithms to identify patterns in successful projects and the causes of failures. This allows the advisory department to suggest risk management methods, task prioritization, and communication improvement methods to project managers. For example, it may advise prioritizing high-risk tasks depending on the project's progress. If there is a lack of communication among project members, it may suggest holding regular meetings and promoting information sharing. Furthermore, the advisory department can provide project managers with concrete action plans based on the project's progress. For example, if the project is behind schedule, it may suggest allocating additional resources or reassigning tasks. This allows the advisory department to help project managers take appropriate actions quickly. Additionally, the advisory department can provide feedback on project progress and challenges to support project managers in improving their skills. This enables the advisory department to provide support for project managers to effectively manage and successfully lead projects.

[0034] The Support Department assists project managers in improving their skills based on advice provided by the Advice Department. For example, the Support Department can offer training programs to help project managers improve their skills. Specifically, it can offer a wide range of training programs, from basic project management skills to advanced leadership skills. This allows project managers to continuously improve their skills. The Support Department can also provide feedback. For example, it can provide feedback on project progress and challenges, enabling project managers to evaluate their own performance and identify areas for improvement. Furthermore, the Support Department can offer mentorship programs, allowing experienced project managers to support new project managers. This allows new project managers to improve their skills with practical advice and support. The Support Department also provides support for specific challenges faced by project managers. For example, it can provide specific advice on risk management and task prioritization in particular projects, helping project managers effectively resolve challenges. In this way, the Support Department can support project managers in improving their skills and leading projects to success.

[0035] The data collection unit can analyze the past conversation history of project members and prioritize the collection of important information. For example, the data collection unit can extract important keywords related to project progress from past conversation history and prioritize the collection of related conversations. The data collection unit can also analyze past conversation history and prioritize the collection of information on frequently discussed issues and risks. Based on past conversation history, the data collection unit can also prioritize the collection of information that may affect project progress. In this way, by analyzing past conversation history, important information related to project progress can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past conversation history data into a generating AI and have the generating AI perform the extraction of important information.

[0036] The data collection unit can adjust its collection method according to the project's stage when collecting conversation and chat content. For example, in the early stages of a project, the collection unit may prioritize collecting conversations about the overall direction and goals. In the middle stages of a project, it may also prioritize collecting conversations about progress and challenges. In the final stages of a project, it may also prioritize collecting conversations about confirming deliverables and making final adjustments. By adjusting the collection method according to the project's stage, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input project progress data into a generating AI and have the generating AI adjust the collection method.

[0037] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of project members when collecting conversation and chat content. For example, if project members are in different regions, the data collection unit can prioritize the collection of information specific to each region's challenges and risks. If project members are concentrated in a particular region, the data collection unit can also prioritize the collection of information related to that region. If project members are on the move, the data collection unit can also prioritize the collection of information related to their destination region. This allows for the priority collection of information related to region-specific challenges and risks by considering the geographical location of project members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the geographical location data of project members into a generating AI and have the generating AI collect highly relevant information.

[0038] The data collection unit can collect relevant information by analyzing the social media activities of project members when collecting the content of conversations and chats. For example, the data collection unit can extract and collect project-related information from the social media activities of project members. The data collection unit can also analyze the social media activities of project members and collect information that may affect the progress of the project. Based on the social media activities of project members, the data collection unit can also identify project risk factors and collect relevant information. In this way, project-related information can be collected by analyzing the social media activities of project members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input project members' social media data into a generating AI and have the generating AI perform the collection of relevant information.

[0039] The analysis unit can change its analysis algorithm according to the project's progress when analyzing the collected data. For example, in the early stages of the project, the analysis unit may prioritize analyzing data related to the overall direction and goals. In the middle stages of the project, the analysis unit may also prioritize analyzing data related to progress and challenges. In the final stages of the project, the analysis unit may also prioritize analyzing data related to the verification and final adjustment of deliverables. By changing the analysis algorithm according to the project's progress, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project progress data into a generating AI and have the generating AI execute the changes to the analysis algorithm.

[0040] The analysis unit can identify project risk factors and extract information for risk management when analyzing collected data. For example, the analysis unit can identify risk factors that may affect the progress of the project from the collected data. The analysis unit can also prioritize the analysis of data related to risk factors and extract information for risk management. After identifying risk factors, the analysis unit can also propose specific action plans for risk management. This improves project risk management by identifying project risk factors and extracting information for risk management. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the identification of risk factors and the extraction of risk management information.

[0041] The analysis unit can perform analysis of collected data while taking into account the geographical location information of project members. For example, if project members are in different regions, the analysis unit can prioritize the analysis of data related to the specific challenges and risks of each region. If project members are concentrated in a particular region, the analysis unit can also prioritize the analysis of data related to that region. If project members are on the move, the analysis unit can also prioritize the analysis of data related to their destination region. This allows for the prioritization of data related to region-specific challenges and risks by considering the geographical location information of project members. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical location data of project members into a generating AI and have the generating AI perform the analysis.

[0042] The analysis unit can improve the accuracy of its analysis by referencing relevant external data when analyzing collected data. For example, the analysis unit can reference external market data to identify factors that may affect the progress of the project. The analysis unit can also reference external competitor data to extract information useful for the progress of the project. The analysis unit can also reference external technical data to identify technical challenges related to the progress of the project. This improves the accuracy of the analysis by referencing relevant external data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0043] The visualization unit can change the displayed content according to the project's progress when visualizing the analyzed data. For example, in the initial stages of a project, the visualization unit can highlight information related to the overall direction and goals. In the middle stages of a project, the visualization unit can also highlight information related to progress and challenges. In the final stages of a project, the visualization unit can also highlight information related to the confirmation and final adjustment of deliverables. By changing the displayed content according to the project's progress, more appropriate information can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input project progress data into a generating AI and have the generating AI perform the changes to the displayed content.

[0044] The visualization unit can highlight project risk factors when visualizing the analyzed data. For example, the visualization unit can highlight risk factors that may affect the progress of the project based on the analyzed data. The visualization unit can also prioritize the display of data related to risk factors and provide information for risk management. After identifying risk factors, the visualization unit can also display specific action plans for risk management. This improves risk management by highlighting project risk factors. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input risk factor data into a generating AI and have the generating AI perform the highlighting of risk factors.

[0045] The visualization unit can adjust the displayed content when visualizing the analyzed data, taking into account the geographical location information of project members. For example, if project members are in different regions, the visualization unit can highlight and display information related to the specific challenges and risks of each region. If project members are concentrated in a particular region, the visualization unit can also highlight and display information related to that region. If project members are on the move, the visualization unit can also highlight and display information related to the destination region. In this way, by taking into account the geographical location information of project members, information related to region-specific challenges and risks can be highlighted and displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input the geographical location data of project members into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0046] The visualization unit can supplement the displayed content by referencing relevant external data when visualizing the analyzed data. For example, the visualization unit can reference external market data to display factors that may affect the progress of the project. The visualization unit can also reference external competitor data to display information useful for the progress of the project. The visualization unit can also reference external technical data to display technical challenges related to the progress of the project. In this way, by referencing relevant external data, the displayed content can be supplemented and more accurate information can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input external data into a generating AI and have the generating AI perform the supplementation of the displayed content.

[0047] The advisory unit can learn from past project data and propose optimal actions according to the stage of the project. For example, in the initial stages of a project, the advisory unit can propose actions related to the overall direction and goals. In the middle stages of a project, the advisory unit can also propose actions related to progress and challenges. In the final stages of a project, the advisory unit can also propose actions related to verifying deliverables and making final adjustments. In this way, by learning from past project data, it can propose optimal actions according to the stage of the project. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input past project data into a generating AI and have the generating AI execute the proposal of optimal actions.

[0048] The advisory unit can identify risk factors for a project and provide advice for risk management. For example, the advisory unit can identify risk factors that may affect the progress of the project from the collected data. The advisory unit can also prioritize the analysis of data related to risk factors and provide advice for risk management. After identifying risk factors, the advisory unit can also propose specific action plans for risk management. This improves project risk management by identifying project risk factors and providing advice for risk management. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input risk factor data into a generating AI and have the generating AI execute risk management advice.

[0049] The advisory unit can learn from past project data and provide advice based on the geographical location of project members. For example, if project members are in different regions, the advisory unit can provide advice on the specific challenges and risks of each region. If project members are concentrated in a particular region, the advisory unit can also provide advice relevant to that region. If project members are on the move, the advisory unit can also provide advice relevant to their destination region. This allows the advisory unit to provide advice on region-specific challenges and risks by considering the geographical location of project members. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input the geographical location data of project members into a generating AI and have the generating AI perform the provision of advice.

[0050] The advisory unit can improve the accuracy of its advice by learning from past project data and referencing relevant external data. For example, the advisory unit can refer to external market data to identify factors that may affect the progress of the project. The advisory unit can also refer to external competitor data to provide advice that will help the project progress. The advisory unit can also refer to external technical data to provide advice on technical challenges related to the progress of the project. This improves the accuracy of the advice by referencing relevant external data. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input external data into a generating AI and have the generating AI perform the improvement of the accuracy of the advice.

[0051] The support department can analyze the project manager's past skill history and provide an optimal training program. For example, the support department can analyze the project manager's past skill history and identify skill gaps. Based on these skill gaps, the support department can also provide an optimal training program. Based on the project manager's past skill history, the support department can propose specific training programs for skill improvement. In this way, by analyzing the project manager's past skill history, skill gaps can be identified and an optimal training program can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's skill history data into a generating AI and have the generating AI provide a training program.

[0052] The support department can modify the content of the training program according to the stage of the project. For example, in the initial stages of the project, the support department may provide a training program on the overall direction and objectives. In the middle stages of the project, the support department may also provide a training program on progress and challenges. In the final stages of the project, the support department may also provide a training program on verifying deliverables and making final adjustments. By modifying the content of the training program according to the stage of the project, more appropriate training can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input project progress data into a generating AI and have the generating AI execute the modification of the training program content.

[0053] The support department can provide optimal training programs by taking into account the project manager's geographical location. For example, if the project manager is in a different region, the support department can provide training programs that address the specific challenges and risks of each region. If the project manager is concentrated in a particular region, the support department can also provide training programs relevant to that region. If the project manager is traveling, the support department can also provide training programs relevant to the destination region. In this way, by taking into account the project manager's geographical location, training programs that address region-specific challenges and risks can be provided. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's geographical location data into a generating AI and have the generating AI perform the task of providing training programs.

[0054] The support department can analyze the project manager's social media activity and propose relevant training programs. For example, the support department can identify project-related skills and knowledge from the project manager's social media activity and provide training programs. The support department can also analyze the project manager's social media activity and propose training programs for skill improvement. The support department can also provide training programs that are useful for project progress based on the project manager's social media activity. In this way, by analyzing the project manager's social media activity, project-related skills and knowledge can be identified and training programs can be provided. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's social media data into a generating AI and have the generating AI propose training programs.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] A project management support system can also include motivation management functions to improve the productivity of project members. For example, the data collection unit can monitor project members' work hours and break times and encourage breaks at appropriate times. The analysis unit can analyze project members' work patterns and propose an optimal work schedule. The visualization unit can display project members' productivity data in graphs and charts and provide feedback to maintain motivation. The advice unit can propose specific actions to boost project members' motivation. This can improve the productivity of project members and contribute to the success of the project.

[0057] A project management support system can also include functions to monitor the health status of project members and support their health management. For example, the data collection unit can collect health data of project members (heart rate, sleep duration, exercise level, etc.). The analysis unit can analyze the collected health data and evaluate the health status of the project members. The visualization unit can display the health data of project members in graphs and charts, making changes in their health status visible. The advice unit can suggest specific actions to maintain the health of project members (recommend exercise, improve diet, etc.). This can support the health of project members and improve project performance.

[0058] A project management support system can also include functions to evaluate the skill sets of project members and provide training programs for skill improvement. For example, the data collection unit can collect data on project members' skills (qualifications, experience, past projects, etc.). The analysis unit can analyze the collected skill data and identify skill gaps among project members. The visualization unit can display the project members' skill data in graphs and charts and provide feedback for skill improvement. The advice unit can propose specific training programs to improve the skills of project members. This can improve the skills of project members and contribute to the success of the project.

[0059] A project management support system can also include functions to predict project progress and identify future risks in advance. For example, the data collection unit can collect data related to project progress (task completion status, resource usage, etc.). The analysis unit can analyze the collected data and predict project progress. The visualization unit can display the project progress prediction data in graphs and charts, visualizing future risks. The advice unit can propose specific actions to avoid future risks. In this way, by predicting project progress and identifying risks in advance, the success rate of the project can be increased.

[0060] A project management support system can also include a function to compare the progress of a project with other projects and provide benchmarks. For example, the data collection unit can collect data on the progress of other projects. The analysis unit can analyze the collected data and compare the progress of the current project with that of other projects. The visualization unit can display the comparison results in graphs and charts and provide benchmarks. The advice unit can propose specific actions to improve the current project's progress based on the benchmarks. This allows for evaluation of the current project's progress and identification of areas for improvement by comparing it with other projects.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects the content of conversations and chats among project members. The collection unit can collect, for example, text chats, voice conversations, and video conversations among project members. The collection unit can collect conversations and chats in real time. It can also collect conversations and chats periodically. Furthermore, the collection unit can collect conversations and chats based on specific keywords. For example, the collection unit can set project-related keywords and collect conversations and chats based on those keywords. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, text mining techniques. The analysis unit can also analyze the emotions of project members using sentiment analysis techniques. Furthermore, the analysis unit can analyze the progress and challenges of the project using topic modeling techniques. For example, the analysis unit can extract the project progress and challenges from the collected data and understand them in real time. Step 3: The visualization unit visualizes the project progress based on the data analyzed by the analysis unit. The visualization unit can display the project progress using, for example, graph display technology. The visualization unit can also display the project progress using dashboard display technology. Furthermore, the visualization unit can display the project progress using heatmap display technology. For example, the visualization unit displays the project progress and the progress of issues using graphs and charts. Step 4: The advisory team provides advice to the project manager based on the information visualized by the visualization team. The advisory team learns from past project data and proposes optimal actions to the project manager. For example, the advisory team can suggest methods for risk management, task prioritization, and improving communication. Step 5: The support department assists in improving the project manager's skills based on the advice provided by the advisory department. For example, the support department can provide training programs to help improve the project manager's skills. The support department can also provide feedback.

[0063] (Example of form 2) The project management support system according to an embodiment of the present invention is a system that automatically collects the content of conversations and chats of project members and grasps the progress and challenges of the project in real time. This project management support system automatically collects the content of conversations and chats of project members and grasps the progress and challenges of the project in real time. In particular, when a new project manager does not know what to do or how to proceed, they can use this AI as a reference to smoothly advance the project. For example, the project management support system automatically collects the content of conversations and chats of project members. At this time, the AI ​​extracts important information related to the project and stores it in a database. For example, it collects information such as the progress of the project, challenges, and risks. Next, the project management support system analyzes the collected data and grasps the progress and challenges of the project in real time. The AI ​​visualizes the progress of the project and provides it to the project manager. For example, it displays the progress of the project and the progress of challenges in graphs and charts. Furthermore, the project management support system provides the project manager with advice on project progress. The AI ​​learns from past project data and proposes the optimal actions to the project manager. For example, it proposes methods for risk management, task prioritization, and methods for improving communication. This AI system allows new project managers to understand project progress and challenges in real time and take appropriate action. This enables smoother project execution and contributes to improving project managers' skills. Thus, the project management support system can understand project progress and challenges in real time and support the skill development of project managers.

[0064] The project management support system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, an advice unit, and a support unit. The data collection unit collects the content of conversations and chats of project members. The data collection unit can collect, for example, text chats, voice conversations, and video conversations of project members. The data collection unit can collect the content of conversations and chats in real time. The data collection unit can also collect the content of conversations and chats periodically. Furthermore, the data collection unit can collect the content of conversations and chats based on specific keywords. For example, the data collection unit sets keywords related to the project and collects the content of conversations and chats based on those keywords. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the collected data using, for example, text mining technology. The analysis unit can also analyze the emotions of project members using sentiment analysis technology. Furthermore, the analysis unit can analyze the progress and issues of the project using topic modeling technology. For example, the analysis unit extracts the progress and issues of the project from the collected data and grasps them in real time. The visualization unit visualizes the progress of the project based on the data analyzed by the analysis unit. The visualization unit can, for example, display the project's progress using graph display technology. The visualization unit can also display the project's progress using dashboard display technology. Furthermore, the visualization unit can display the project's progress using heatmap display technology. For example, the visualization unit displays the project's progress and the progress of issues using graphs and charts. The advice unit provides advice to the project manager based on the information visualized by the visualization unit. The advice unit learns from past project data and proposes optimal actions to the project manager. For example, the advice unit can suggest methods for risk management, task prioritization, and improving communication. The support unit assists the project manager in improving their skills based on the advice provided by the advice unit.The support department can, for example, provide training programs to help improve the skills of project managers. The support department can also provide feedback. As a result, the project management support system according to the embodiment can grasp the progress and challenges of the project in real time and support the improvement of the skills of project managers.

[0065] The data collection unit collects the content of conversations and chats among project members. For example, it can collect text chats, voice conversations, and video conversations among project members. Specifically, for text chats, it acquires messages sent and received through project management tools and messaging applications in real time. For voice conversations, it collects audio data from conference systems and teleconferencing and converts it to text using speech recognition technology. For video conversations, it acquires video and audio from video conferencing systems and extracts important information using video analysis technology. The data collection unit can collect this data not only in real time but also periodically. For example, it can collect the content of conversations and chats for the day at the end of each day. Furthermore, the data collection unit can collect conversations and chats based on specific keywords. For example, it can set project-related keywords such as "deadline," "risk," and "progress," and prioritize the collection of conversations and chats containing those keywords. This allows the data collection unit to efficiently collect important information regarding project progress and issues. In addition, the data collection unit centrally manages the collected data, making it accessible to the analysis and visualization units. This allows the data collection unit to grasp the project's progress in real time and provide a foundation for rapid response.

[0066] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit can analyze collected data using text mining techniques. Specifically, it can analyze text data using natural language processing techniques to extract important keywords and phrases. This allows for obtaining information to understand the project's progress and challenges. The analysis unit can also analyze the emotions of project members using sentiment analysis techniques. For example, it can detect positive and negative emotions from text data to evaluate the motivation and stress levels of project members. Furthermore, the analysis unit can analyze project progress and challenges using topic modeling techniques. For example, it can extract topics from collected data to understand project progress and challenges in real time. In addition, the analysis unit can analyze data using machine learning algorithms to predict project progress and challenges. For example, it can predict project progress based on past data and detect risks in advance. This allows the analysis unit to quickly and accurately analyze collected data and understand project progress and challenges in real time. Furthermore, the analysis unit can utilize past data and statistical information to conduct long-term risk assessments and trend analyses. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The visualization unit visualizes the project's progress based on data analyzed by the analysis unit. For example, the visualization unit can display project progress using graphing technology. Specifically, it can display project progress using line graphs or bar graphs, allowing for a quick overview of task completion and delays. The visualization unit can also display project progress using dashboard display technology. For example, it can provide a dashboard that centrally displays project progress and issue progress, enabling project managers to understand the situation in real time. Furthermore, the visualization unit can display project progress using heatmap display technology. For example, it can display project progress and issue progress using a heatmap, visually highlighting problematic areas. This allows the visualization unit to intuitively grasp project progress and issues, supporting rapid response. In addition, the visualization unit provides customizable display options, enabling project managers to efficiently obtain the necessary information. For example, it provides options to display detailed progress for specific tasks or members, allowing project managers to quickly access the information they need. This allows the visualization unit to effectively visualize project progress and issues, supporting project managers' decision-making.

[0068] The advisory department provides advice to project managers based on information visualized by the visualization department. The advisory department learns from past project data and proposes optimal actions to project managers. Specifically, it analyzes past project data using machine learning algorithms to identify patterns in successful projects and the causes of failures. This allows the advisory department to suggest risk management methods, task prioritization, and communication improvement methods to project managers. For example, it may advise prioritizing high-risk tasks depending on the project's progress. If there is a lack of communication among project members, it may suggest holding regular meetings and promoting information sharing. Furthermore, the advisory department can provide project managers with concrete action plans based on the project's progress. For example, if the project is behind schedule, it may suggest allocating additional resources or reassigning tasks. This allows the advisory department to help project managers take appropriate actions quickly. Additionally, the advisory department can provide feedback on project progress and challenges to support project managers in improving their skills. This enables the advisory department to provide support for project managers to effectively manage and successfully lead projects.

[0069] The Support Department assists project managers in improving their skills based on advice provided by the Advice Department. For example, the Support Department can offer training programs to help project managers improve their skills. Specifically, it can offer a wide range of training programs, from basic project management skills to advanced leadership skills. This allows project managers to continuously improve their skills. The Support Department can also provide feedback. For example, it can provide feedback on project progress and challenges, enabling project managers to evaluate their own performance and identify areas for improvement. Furthermore, the Support Department can offer mentorship programs, allowing experienced project managers to support new project managers. This allows new project managers to improve their skills with practical advice and support. The Support Department also provides support for specific challenges faced by project managers. For example, it can provide specific advice on risk management and task prioritization in particular projects, helping project managers effectively resolve challenges. In this way, the Support Department can support project managers in improving their skills and leading projects to success.

[0070] The data collection unit can estimate the emotions of project members and filter and collect conversations and chat content based on the estimated emotions. For example, if a project member is stressed, the data collection unit can use an emotion engine to prioritize collecting conversations related to stress. If a project member is relaxed, the data collection unit can also use an emotion engine to collect conversations in a relaxed state and extract information useful for project progress. If a project member is tense, the data collection unit can use an emotion engine to collect conversations related to tension and identify risk factors for the project. This allows for the collection of more relevant information by filtering conversations and chat content based on the emotions of project members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input project member emotion data into a generative AI and have the generative AI perform emotion-based filtering.

[0071] The data collection unit can analyze the past conversation history of project members and prioritize the collection of important information. For example, the data collection unit can extract important keywords related to project progress from past conversation history and prioritize the collection of related conversations. The data collection unit can also analyze past conversation history and prioritize the collection of information on frequently discussed issues and risks. Based on past conversation history, the data collection unit can also prioritize the collection of information that may affect project progress. In this way, by analyzing past conversation history, important information related to project progress can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input past conversation history data into a generating AI and have the generating AI perform the extraction of important information.

[0072] The data collection unit can adjust its collection method according to the project's stage when collecting conversation and chat content. For example, in the early stages of a project, the collection unit may prioritize collecting conversations about the overall direction and goals. In the middle stages of a project, it may also prioritize collecting conversations about progress and challenges. In the final stages of a project, it may also prioritize collecting conversations about confirming deliverables and making final adjustments. By adjusting the collection method according to the project's stage, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input project progress data into a generating AI and have the generating AI adjust the collection method.

[0073] The data collection unit can estimate the emotions of project members and determine the priority of information to collect based on the estimated emotions. For example, if a project member is stressed, the data collection unit can use an emotion engine to prioritize the collection of stress-related information. If a project member is relaxed, the data collection unit can also use an emotion engine to prioritize the collection of information related to that relaxed state. If a project member is tense, the data collection unit can also use an emotion engine to prioritize the collection of tension-related information. This allows for the priority collection of more important information by determining the priority of information based on the emotions of project members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input project member emotion data into a generative AI and have the generative AI determine the priority of information.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of project members when collecting conversation and chat content. For example, if project members are in different regions, the data collection unit can prioritize the collection of information specific to each region's challenges and risks. If project members are concentrated in a particular region, the data collection unit can also prioritize the collection of information related to that region. If project members are on the move, the data collection unit can also prioritize the collection of information related to their destination region. This allows for the priority collection of information related to region-specific challenges and risks by considering the geographical location of project members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the geographical location data of project members into a generating AI and have the generating AI collect highly relevant information.

[0075] The data collection unit can collect relevant information by analyzing the social media activities of project members when collecting the content of conversations and chats. For example, the data collection unit can extract and collect project-related information from the social media activities of project members. The data collection unit can also analyze the social media activities of project members and collect information that may affect the progress of the project. Based on the social media activities of project members, the data collection unit can also identify project risk factors and collect relevant information. In this way, project-related information can be collected by analyzing the social media activities of project members. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input project members' social media data into a generating AI and have the generating AI perform the collection of relevant information.

[0076] The analysis unit can estimate the emotions of project members and adjust the data analysis method based on the estimated emotions. For example, if a project member is stressed, the analysis unit can use an emotion engine to prioritize the analysis of stress-related data. If a project member is relaxed, the analysis unit can also use an emotion engine to analyze data in a relaxed state and extract information useful for project progress. If a project member is tense, the analysis unit can use an emotion engine to analyze data related to tension and identify risk factors for the project. By adjusting the data analysis method based on the emotions of project members, more appropriate analysis results can be obtained. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input project member emotion data into a generative AI and have the generative AI perform the adjustment of the analysis method.

[0077] The analysis unit can change its analysis algorithm according to the project's progress when analyzing the collected data. For example, in the early stages of the project, the analysis unit may prioritize analyzing data related to the overall direction and goals. In the middle stages of the project, the analysis unit may also prioritize analyzing data related to progress and challenges. In the final stages of the project, the analysis unit may also prioritize analyzing data related to the verification and final adjustment of deliverables. By changing the analysis algorithm according to the project's progress, more appropriate analysis results can be obtained. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project progress data into a generating AI and have the generating AI execute the changes to the analysis algorithm.

[0078] The analysis unit can identify project risk factors and extract information for risk management when analyzing collected data. For example, the analysis unit can identify risk factors that may affect the progress of the project from the collected data. The analysis unit can also prioritize the analysis of data related to risk factors and extract information for risk management. After identifying risk factors, the analysis unit can also propose specific action plans for risk management. This improves project risk management by identifying project risk factors and extracting information for risk management. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform the identification of risk factors and the extraction of risk management information.

[0079] The analysis unit can estimate the emotions of project members and adjust the display method of the analysis results based on the estimated emotions. For example, if a project member is feeling stressed, the analysis unit can use the emotion engine to highlight and display analysis results related to stress. If a project member is relaxed, the analysis unit can also use the emotion engine to display analysis results in a relaxed state, providing information useful for the progress of the project. If a project member is tense, the analysis unit can use the emotion engine to display analysis results related to tension, highlighting project risk factors. This allows for the provision of more appropriate information by adjusting the display method of the analysis results based on the emotions of project members. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input project member emotion data into a generative AI and have the generative AI adjust the display method of the analysis results.

[0080] The analysis unit can perform analysis of collected data while taking into account the geographical location information of project members. For example, if project members are in different regions, the analysis unit can prioritize the analysis of data related to the specific challenges and risks of each region. If project members are concentrated in a particular region, the analysis unit can also prioritize the analysis of data related to that region. If project members are on the move, the analysis unit can also prioritize the analysis of data related to their destination region. This allows for the prioritization of data related to region-specific challenges and risks by considering the geographical location information of project members. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the geographical location data of project members into a generating AI and have the generating AI perform the analysis.

[0081] The analysis unit can improve the accuracy of its analysis by referencing relevant external data when analyzing collected data. For example, the analysis unit can reference external market data to identify factors that may affect the progress of the project. The analysis unit can also reference external competitor data to extract information useful for the progress of the project. The analysis unit can also reference external technical data to identify technical challenges related to the progress of the project. This improves the accuracy of the analysis by referencing relevant external data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input external data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0082] The visualization unit can estimate the emotions of project members and adjust the display method of the visualization based on the estimated emotions. For example, if a project member is feeling stressed, the visualization unit can use an emotion engine to highlight and display information related to stress. If a project member is relaxed, the visualization unit can also use an emotion engine to display information related to a relaxed state, providing information useful for the progress of the project. If a project member is tense, the visualization unit can also use an emotion engine to highlight and display information related to tension. In this way, by adjusting the display method of the visualization based on the emotions of project members, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input project member emotion data into a generative AI and have the generative AI perform the adjustment of the display method.

[0083] The visualization unit can change the displayed content according to the project's progress when visualizing the analyzed data. For example, in the initial stages of a project, the visualization unit can highlight information related to the overall direction and goals. In the middle stages of a project, the visualization unit can also highlight information related to progress and challenges. In the final stages of a project, the visualization unit can also highlight information related to the confirmation and final adjustment of deliverables. By changing the displayed content according to the project's progress, more appropriate information can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input project progress data into a generating AI and have the generating AI perform the changes to the displayed content.

[0084] The visualization unit can highlight project risk factors when visualizing the analyzed data. For example, the visualization unit can highlight risk factors that may affect the progress of the project based on the analyzed data. The visualization unit can also prioritize the display of data related to risk factors and provide information for risk management. After identifying risk factors, the visualization unit can also display specific action plans for risk management. This improves risk management by highlighting project risk factors. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input risk factor data into a generating AI and have the generating AI perform the highlighting of risk factors.

[0085] The visualization unit can estimate the emotions of project members and determine visualization priorities based on the estimated emotions. For example, if a project member is stressed, the visualization unit can use an emotion engine to prioritize displaying stress-related information. If a project member is relaxed, the visualization unit can also use an emotion engine to prioritize displaying information related to a relaxed state. If a project member is tense, the visualization unit can also use an emotion engine to prioritize displaying information related to tension. This allows for the prioritization of visualization priorities based on the emotions of project members, thereby prioritizing the display of more important information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, or not using AI. For example, the visualization unit can input project member emotion data into a generative AI and have the generative AI perform the priority determination.

[0086] The visualization unit can adjust the displayed content when visualizing the analyzed data, taking into account the geographical location information of project members. For example, if project members are in different regions, the visualization unit can highlight and display information related to the specific challenges and risks of each region. If project members are concentrated in a particular region, the visualization unit can also highlight and display information related to that region. If project members are on the move, the visualization unit can also highlight and display information related to the destination region. In this way, by taking into account the geographical location information of project members, information related to region-specific challenges and risks can be highlighted and displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input the geographical location data of project members into a generating AI and have the generating AI perform the adjustment of the displayed content.

[0087] The visualization unit can supplement the displayed content by referencing relevant external data when visualizing the analyzed data. For example, the visualization unit can reference external market data to display factors that may affect the progress of the project. The visualization unit can also reference external competitor data to display information useful for the progress of the project. The visualization unit can also reference external technical data to display technical challenges related to the progress of the project. In this way, by referencing relevant external data, the displayed content can be supplemented and more accurate information can be provided. Some or all of the above processing in the visualization unit may be performed using AI, for example, or not using AI. For example, the visualization unit can input external data into a generating AI and have the generating AI perform the supplementation of the displayed content.

[0088] The advice unit can estimate the emotions of project members and adjust the way advice is expressed based on the estimated emotions. For example, if a project member is stressed, the advice unit can use an emotion engine to provide advice to reduce stress. If a project member is relaxed, the advice unit can also use an emotion engine to provide advice in a relaxed state. If a project member is tense, the advice unit can also use an emotion engine to provide advice to alleviate tension. This allows for the provision of more appropriate advice by adjusting the way advice is expressed based on the emotions of the project members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input project member emotion data into a generative AI and have the generative AI adjust the way the advice is expressed.

[0089] The advisory unit can learn from past project data and propose optimal actions according to the stage of the project. For example, in the initial stages of a project, the advisory unit can propose actions related to the overall direction and goals. In the middle stages of a project, the advisory unit can also propose actions related to progress and challenges. In the final stages of a project, the advisory unit can also propose actions related to verifying deliverables and making final adjustments. In this way, by learning from past project data, it can propose optimal actions according to the stage of the project. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input past project data into a generating AI and have the generating AI execute the proposal of optimal actions.

[0090] The advisory unit can identify risk factors for a project and provide advice for risk management. For example, the advisory unit can identify risk factors that may affect the progress of the project from the collected data. The advisory unit can also prioritize the analysis of data related to risk factors and provide advice for risk management. After identifying risk factors, the advisory unit can also propose specific action plans for risk management. This improves project risk management by identifying project risk factors and providing advice for risk management. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input risk factor data into a generating AI and have the generating AI execute risk management advice.

[0091] The advice unit can estimate the emotions of project members and prioritize advice based on those estimated emotions. For example, if a project member is stressed, the advice unit can use an emotion engine to prioritize advice to reduce stress. If a project member is relaxed, the advice unit can also use an emotion engine to prioritize advice that promotes relaxation. If a project member is tense, the advice unit can also use an emotion engine to prioritize advice to alleviate tension. This allows for the prioritization of more important advice based on the emotions of project members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI, or not. For example, the advice unit can input project member emotion data into a generative AI and have the generative AI determine the priority of advice.

[0092] The advisory unit can learn from past project data and provide advice based on the geographical location of project members. For example, if project members are in different regions, the advisory unit can provide advice on the specific challenges and risks of each region. If project members are concentrated in a particular region, the advisory unit can also provide advice relevant to that region. If project members are on the move, the advisory unit can also provide advice relevant to their destination region. This allows the advisory unit to provide advice on region-specific challenges and risks by considering the geographical location of project members. Some or all of the above processing in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input the geographical location data of project members into a generating AI and have the generating AI perform the provision of advice.

[0093] The advisory unit can improve the accuracy of its advice by learning from past project data and referencing relevant external data. For example, the advisory unit can refer to external market data to identify factors that may affect the progress of the project. The advisory unit can also refer to external competitor data to provide advice that will help the project progress. The advisory unit can also refer to external technical data to provide advice on technical challenges related to the progress of the project. This improves the accuracy of the advice by referencing relevant external data. Some or all of the above processes in the advisory unit may be performed using AI, for example, or not using AI. For example, the advisory unit can input external data into a generating AI and have the generating AI perform the improvement of the accuracy of the advice.

[0094] The support unit can estimate the emotions of project members and adjust the content of the training program based on the estimated emotions. For example, if a project member is feeling stressed, the support unit can use an emotion engine to provide a training program to reduce stress. If a project member is relaxed, the support unit can also use an emotion engine to provide a training program designed for a relaxed state. If a project member is tense, the support unit can also use an emotion engine to provide a training program to alleviate tension. This allows for more appropriate training by adjusting the content of the training program based on the emotions of the project members. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, for example, or without AI. For example, the support unit can input project member emotion data into a generative AI and have the generative AI adjust the content of the training program.

[0095] The support department can analyze the project manager's past skill history and provide an optimal training program. For example, the support department can analyze the project manager's past skill history and identify skill gaps. Based on these skill gaps, the support department can also provide an optimal training program. Based on the project manager's past skill history, the support department can propose specific training programs for skill improvement. In this way, by analyzing the project manager's past skill history, skill gaps can be identified and an optimal training program can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's skill history data into a generating AI and have the generating AI provide a training program.

[0096] The support department can modify the content of the training program according to the stage of the project. For example, in the initial stages of the project, the support department may provide a training program on the overall direction and objectives. In the middle stages of the project, the support department may also provide a training program on progress and challenges. In the final stages of the project, the support department may also provide a training program on verifying deliverables and making final adjustments. By modifying the content of the training program according to the stage of the project, more appropriate training can be provided. Some or all of the above processes in the support department may be performed using AI, for example, or not using AI. For example, the support department can input project progress data into a generating AI and have the generating AI execute the modification of the training program content.

[0097] The support unit can estimate the emotions of project members and prioritize training programs based on those estimated emotions. For example, if a project member is stressed, the support unit can use the emotion engine to prioritize training programs designed to reduce stress. If a project member is relaxed, the support unit can also use the emotion engine to prioritize training programs designed for relaxation. If a project member is tense, the support unit can also use the emotion engine to prioritize training programs designed to alleviate tension. This allows for prioritizing more important training by determining the training program priorities based on the emotions of project members. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the support unit may be performed using AI, or not. For example, the support unit can input project member emotion data into a generative AI and have the generative AI determine the priority of training programs.

[0098] The support department can provide optimal training programs by taking into account the project manager's geographical location. For example, if the project manager is in a different region, the support department can provide training programs that address the specific challenges and risks of each region. If the project manager is concentrated in a particular region, the support department can also provide training programs relevant to that region. If the project manager is traveling, the support department can also provide training programs relevant to the destination region. In this way, by taking into account the project manager's geographical location, training programs that address region-specific challenges and risks can be provided. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's geographical location data into a generating AI and have the generating AI perform the task of providing training programs.

[0099] The support department can analyze the project manager's social media activity and propose relevant training programs. For example, the support department can identify project-related skills and knowledge from the project manager's social media activity and provide training programs. The support department can also analyze the project manager's social media activity and propose training programs for skill improvement. The support department can also provide training programs that are useful for project progress based on the project manager's social media activity. In this way, by analyzing the project manager's social media activity, project-related skills and knowledge can be identified and training programs can be provided. Some or all of the above processing in the support department may be performed using AI, for example, or not using AI. For example, the support department can input the project manager's social media data into a generating AI and have the generating AI propose training programs.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] A project management support system can also include motivation management functions to improve the productivity of project members. For example, the data collection unit can monitor project members' work hours and break times and encourage breaks at appropriate times. The analysis unit can analyze project members' work patterns and propose an optimal work schedule. The visualization unit can display project members' productivity data in graphs and charts and provide feedback to maintain motivation. The advice unit can propose specific actions to boost project members' motivation. This can improve the productivity of project members and contribute to the success of the project.

[0102] A project management support system can also include functions to monitor the health status of project members and support their health management. For example, the data collection unit can collect health data of project members (heart rate, sleep duration, exercise level, etc.). The analysis unit can analyze the collected health data and evaluate the health status of the project members. The visualization unit can display the health data of project members in graphs and charts, making changes in their health status visible. The advice unit can suggest specific actions to maintain the health of project members (recommend exercise, improve diet, etc.). This can support the health of project members and improve project performance.

[0103] A project management support system can also include functions to evaluate the skill sets of project members and provide training programs for skill improvement. For example, the data collection unit can collect data on project members' skills (qualifications, experience, past projects, etc.). The analysis unit can analyze the collected skill data and identify skill gaps among project members. The visualization unit can display the project members' skill data in graphs and charts and provide feedback for skill improvement. The advice unit can propose specific training programs to improve the skills of project members. This can improve the skills of project members and contribute to the success of the project.

[0104] A project management support system can also include functions to predict project progress and identify future risks in advance. For example, the data collection unit can collect data related to project progress (task completion status, resource usage, etc.). The analysis unit can analyze the collected data and predict project progress. The visualization unit can display the project progress prediction data in graphs and charts, visualizing future risks. The advice unit can propose specific actions to avoid future risks. In this way, by predicting project progress and identifying risks in advance, the success rate of the project can be increased.

[0105] A project management support system can also include a function to estimate the emotions of project members and adjust the project's progress based on those estimated emotions. For example, the data collection unit can collect emotional data from project members (facial expressions, tone of voice, text content, etc.). The analysis unit can analyze the collected emotional data and evaluate the emotional state of the project members. The visualization unit can display the emotional data of project members in graphs and charts, visualizing changes in their emotional state. The advice unit can propose specific actions to adjust the project's progress based on the emotional state of the project members. By adjusting the project's progress while considering the emotions of the project members, the project's success rate can be increased.

[0106] A project management support system can also include a function to estimate the emotions of project members and adjust communication methods based on those estimates. For example, the data collection unit can collect emotional data from project members (facial expressions, tone of voice, text content, etc.). The analysis unit can analyze the collected emotional data and evaluate the emotional state of the project members. The visualization unit can display the emotional data of project members in graphs and charts, visualizing changes in their emotional state. The advice unit can propose specific actions to adjust communication methods based on the emotional state of the project members. By adjusting communication methods while considering the emotions of project members, the success rate of the project can be increased.

[0107] A project management support system can also include a function to estimate the emotions of project members and adjust task assignments based on those estimates. For example, the data collection unit can collect emotional data from project members (facial expressions, tone of voice, text content, etc.). The analysis unit can analyze the collected emotional data and evaluate the emotional state of the project members. The visualization unit can display the emotional data of project members in graphs and charts, visualizing changes in their emotional state. The advice unit can suggest specific actions to adjust task assignments based on the emotional state of the project members. By adjusting task assignments while considering the emotions of project members, the success rate of the project can be increased.

[0108] A project management support system can also include a function to estimate the emotions of project members and adjust the content of feedback based on those estimated emotions. For example, the data collection unit can collect emotional data from project members (facial expressions, tone of voice, text content, etc.). The analysis unit can analyze the collected emotional data and evaluate the emotional state of the project members. The visualization unit can display the emotional data of project members in graphs and charts, visualizing changes in their emotional state. The advice unit can propose specific actions to adjust the content of feedback based on the emotional state of the project members. This allows for an increase in the success rate of the project by adjusting the content of feedback while considering the emotions of the project members.

[0109] A project management support system can also include a function to estimate the emotions of project members and adjust the project's progress based on those emotions. For example, the data collection unit can collect emotional data from project members (facial expressions, tone of voice, text content, etc.). The analysis unit can analyze the collected emotional data and evaluate the emotional state of the project members. The visualization unit can display the emotional data of project members in graphs and charts, visualizing changes in their emotional state. The advice unit can propose specific actions to adjust the project's progress based on the emotional state of the project members. By adjusting the project's progress while considering the emotions of the project members, the project's success rate can be increased.

[0110] A project management support system can also include a function to compare the progress of a project with other projects and provide benchmarks. For example, the data collection unit can collect data on the progress of other projects. The analysis unit can analyze the collected data and compare the progress of the current project with that of other projects. The visualization unit can display the comparison results in graphs and charts and provide benchmarks. The advice unit can propose specific actions to improve the current project's progress based on the benchmarks. This allows for evaluation of the current project's progress and identification of areas for improvement by comparing it with other projects.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The collection unit collects the content of conversations and chats among project members. The collection unit can collect, for example, text chats, voice conversations, and video conversations among project members. The collection unit can collect conversations and chats in real time. It can also collect conversations and chats periodically. Furthermore, the collection unit can collect conversations and chats based on specific keywords. For example, the collection unit can set project-related keywords and collect conversations and chats based on those keywords. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the collected data using, for example, text mining techniques. The analysis unit can also analyze the emotions of project members using sentiment analysis techniques. Furthermore, the analysis unit can analyze the progress and challenges of the project using topic modeling techniques. For example, the analysis unit can extract the project progress and challenges from the collected data and understand them in real time. Step 3: The visualization unit visualizes the project progress based on the data analyzed by the analysis unit. The visualization unit can display the project progress using, for example, graph display technology. The visualization unit can also display the project progress using dashboard display technology. Furthermore, the visualization unit can display the project progress using heatmap display technology. For example, the visualization unit displays the project progress and the progress of issues using graphs and charts. Step 4: The advisory team provides advice to the project manager based on the information visualized by the visualization team. The advisory team learns from past project data and proposes optimal actions to the project manager. For example, the advisory team can suggest methods for risk management, task prioritization, and improving communication. Step 5: The support department assists in improving the project manager's skills based on the advice provided by the advisory department. For example, the support department can provide training programs to help improve the project manager's skills. The support department can also provide feedback.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, advice unit, and support unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the content of conversations and chats of project members, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the progress of the project based on the analyzed data. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice to the project manager based on the visualized information. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports the improvement of the project manager's skills based on the advice. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

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

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, advice unit, and support unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect the content of conversations and chats of project members, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The visualization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and visualizes the progress of the project based on the analyzed data. The advice unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides advice to the project manager based on the visualized information. The support unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and supports the improvement of the project manager's skills based on the advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, advice unit, and support unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the content of conversations and chats of project members, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the progress of the project based on the analyzed data. The advice unit is implemented in the specific processing unit 290 of the data processing unit 12 and provides advice to the project manager based on the visualized information. The support unit is implemented in the specific processing unit 290 of the data processing unit 12 and supports the improvement of the project manager's skills based on the advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, advice unit, and support unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the content of conversations and chats of project members, and the control unit 46A transmits the collected data to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The visualization unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and visualizes the progress of the project based on the analyzed data. The advice unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides advice to the project manager based on the visualized information. The support unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and supports the improvement of the project manager's skills based on the advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) A collection unit that collects the content of conversations and chats among project members, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes the progress of the project based on the data analyzed by the aforementioned analysis unit, An advisory unit provides advice to the project manager based on the information visualized by the aforementioned visualization unit, The system includes a support unit that assists in improving the skills of project managers based on the advice provided by the aforementioned advisory unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system estimates the emotions of project members and filters and collects conversation and chat content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Analyze project members' past conversation history and prioritize collecting important information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The system according to Appendix 1, characterized in that the collection unit changes the collection method according to the stage of project progress when collecting the contents of conversations and chats. (Note 5) The aforementioned collection unit is Estimate the emotions of project members and prioritize the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The system described in Appendix 1 is characterized in that, when collecting the contents of conversations and chats, the collection unit prioritizes collecting highly relevant information based on the geographical location information of project members. (Note 7) The aforementioned collection unit is When collecting conversation and chat content, we analyze project members' social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We estimate the emotions of project members and adjust the data analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The system described in Appendix 1, characterized in that the analysis unit changes the analysis algorithm according to the stage of the project when analyzing the collected data. (Note 10) The aforementioned analysis unit, When analyzing the collected data, identify the project's risk factors and extract information for risk management. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the emotions of project members and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The system described in Appendix 1, wherein the analysis unit performs the analysis based on the geographical location information of the project members when analyzing the collected data. (Note 13) The aforementioned analysis unit, When analyzing collected data, we refer to relevant external data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned visualization unit, The system estimates the emotions of project members and adjusts the visualization display method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned visualization unit, When visualizing the analyzed data, the displayed content changes according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned visualization unit, When visualizing the analyzed data, highlight the project's risk factors. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned visualization unit, Estimate the emotions of project members and determine visualization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned visualization unit, When visualizing the analyzed data, the display content is adjusted to take into account the geographical location information of the project members. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, When visualizing analyzed data, the display content is supplemented by referencing related external data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, The system estimates the emotions of project members and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, It learns from past project data and proposes the optimal actions according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, Identify project risk factors and provide advice for risk management. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, Estimate the emotions of project members and prioritize advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The system described in Appendix 1 is characterized in that the advice unit learns from past project data and provides advice based on the geographical location information of project members. (Note 25) The aforementioned advice section, We improve the accuracy of our advice by learning from past project data and referencing relevant external data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, The program estimates the emotions of project members and adjusts the training program content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, We analyze the project manager's past skill history and provide the optimal training program. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, The content of the training program will be modified according to the stage of the project. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, Estimate the emotions of project members and prioritize training programs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, We provide the optimal training program, taking into account the geographical location of the project manager. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned support unit, Analyze project managers' social media activity and propose relevant training programs. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection unit that collects the content of conversations and chats among project members, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit visualizes the progress of the project based on the data analyzed by the aforementioned analysis unit, An advisory unit provides advice to the project manager based on the information visualized by the aforementioned visualization unit, The system includes a support unit that assists in improving the skills of project managers based on the advice provided by the aforementioned advisory unit. A system characterized by the following features.

2. The aforementioned collection unit is The system estimates the emotions of project members and filters and collects conversation and chat content based on those estimated emotions. The system according to feature 1.

3. The aforementioned collection unit is Analyze project members' past conversation history and prioritize collecting important information. The system according to feature 1.

4. The system according to claim 1, characterized in that the collection unit changes the collection method according to the stage of the project when collecting the contents of conversations and chats.

5. The aforementioned collection unit is Estimate the emotions of project members and prioritize the information to collect based on those estimated emotions. The system according to feature 1.

6. The system according to claim 1, characterized in that the collection unit, when collecting the contents of conversations and chats, prioritizes collecting information that is highly relevant based on the geographical location information of project members.

7. The aforementioned collection unit is When collecting conversation and chat content, we analyze project members' social media activity and gather relevant information. The system according to feature 1.

8. The aforementioned analysis unit, We estimate the emotions of project members and adjust the data analysis method based on the estimated emotions. The system according to feature 1.

9. The system according to claim 1, characterized in that the analysis unit changes the analysis algorithm according to the stage of the project when analyzing the collected data.

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