system

Abstract

The system according to this embodiment aims to automate progress management and task prioritization in project management. [Solution] The system according to the embodiment comprises an update unit, a creation unit, a detection unit, and a decision unit. The update unit updates the progress management table. The creation unit creates a document based on the progress status updated by the update unit. The detection unit detects dependencies between tasks. The decision unit determines the priority of tasks based on the dependencies detected by the detection unit.

Description

Technical Field

[0006] , , , ,

[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 the chatbot's 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 conventional technology, in project management, progress management and task priority determination are performed manually, so there are problems of low efficiency and high probability of errors.

[0005] The system according to the embodiment aims to automate progress management and task priority determination in project management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an update unit, a creation unit, a detection unit, and a decision unit. The update unit updates the progress management table. The creation unit creates a document based on the progress status updated by the update unit. The detection unit detects dependencies between tasks. The decision unit determines the priority of tasks based on the dependencies detected by the detection unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate progress management and task prioritization in project management. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple 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 comprehensively automates the tasks necessary for project management. This project management support system automates the creation of internal and upper-level documents (documents, diagrams), updates progress management tables, and determines task priorities. It also detects dependencies between tasks and suggests appropriate personnel based on employees' skill sets. Furthermore, it detects undecided or uncertain specifications, facilitates specification determination, and creates meeting agendas. In this way, it takes on all the complex tasks in project management and provides a solution to maximize efficiency. For example, the project management support system automatically generates reports summarizing project progress and deliverables, as well as diagrams showing the overall picture of the project. This reduces the time and effort required for document creation. Next, the project management support system grasps the progress of each task in the project in real time and automatically updates progress management tables. This ensures that the project progress is always kept up-to-date. The project management support system also evaluates the importance and urgency of each task in the project and automatically determines its priority. This allows the project to proceed smoothly. Furthermore, the project management support system analyzes the dependencies between each task in the project and automatically detects dependencies. This allows for the optimization of task order and scheduling. The project management support system automatically identifies employees with the necessary skills for the project and suggests the appropriate personnel. This enables the formation of the optimal team for the project. The project management support system analyzes project specifications and automatically detects undecided or uncertain specifications. This allows for rapid specification definition. Finally, the project management support system automatically creates meeting agendas based on project progress and issues. This supports efficient meeting management. In this way, the project management support system can take on all the complex tasks in project management and maximize efficiency.

[0029] The project management support system according to this embodiment comprises an update unit, a creation unit, a detection unit, and a decision unit. The update unit updates the progress management table. The update unit, for example, grasps the progress of each task in the project in real time and automatically updates the progress management table. The update unit, for example, periodically checks the progress of the project and keeps the progress management table up to date. The update unit, for example, displays the progress of the project in graphs and charts to make it easier to understand visually. The creation unit creates documents based on the progress updated by the update unit. The creation unit, for example, automatically generates a report summarizing the progress of the project and deliverables. The creation unit, for example, automatically generates a diagram showing the overall picture of the project. The creation unit, for example, creates a plan showing the next steps based on the progress of the project. The detection unit detects dependencies between tasks. The detection unit, for example, analyzes the dependencies of each task in the project and automatically detects dependencies. The detection unit, for example, detects dependencies in order to optimize the order and schedule of tasks. The detection unit visually displays task dependencies, for example, to make it easier to grasp the overall picture of the project. The decision unit determines the priority of tasks based on the dependencies detected by the detection unit. The decision unit automatically determines the priority by evaluating the importance and urgency of each task in the project, for example. The decision unit creates a schedule to ensure the smooth progress of the project based on the task priorities. The decision unit visually displays the task priorities, for example, to make it easier to grasp the overall picture of the project. As a result, the project management support system according to the embodiment can automate progress management, document creation, dependency detection, and task priority determination in project management.

[0030] The update unit updates the progress management table. For example, the update unit grasps the progress of each task in the project in real time and automatically updates the progress management table. Specifically, the update unit integrates with project management tools and task management systems to periodically obtain the progress status of each task. This includes information such as the task's start date, end date, progress rate, and comments from the person in charge. Based on this information, the update unit keeps the progress management table up to date. For example, when a task is completed, it changes the status of that task to "completed" and automatically triggers the start of the next task. In addition, the progress management table displays the overall progress of the project in graphs and charts, making it easy to understand visually. This allows project managers and team members to check the project's progress at a glance and make necessary adjustments quickly. Furthermore, the update unit also has a function to notify changes in progress in real time, so stakeholders can be immediately notified if there are any delays or changes in the progress of important tasks. This prevents project delays and supports smooth progress.

[0031] The creation unit creates documents based on the progress status updated by the update unit. For example, the creation unit automatically generates reports summarizing project progress and deliverables. Specifically, the creation unit generates a detailed report of project progress based on data from the progress management table. This report includes the progress status of each task, a list of completed tasks, a list of incomplete tasks, task dependencies, and risk assessments. The creation unit also automatically generates diagrams that show the overall picture of the project. For example, it generates Gantt charts and network diagrams to visually display the project schedule and task dependencies. Furthermore, based on the project progress, the creation unit creates a plan outlining the next steps. The plan includes the next tasks to be performed, the necessary resources, anticipated risks, and countermeasures. This allows project managers and team members to clearly understand what needs to be done next and to efficiently advance the project. By automatically generating these documents, the creation unit significantly reduces the time and effort required for document creation and improves the efficiency of project management.

[0032] The detection unit detects dependencies between tasks. For example, it analyzes the dependencies of each task in a project and automatically detects them. Specifically, the detection unit analyzes information such as the start date, end date, prerequisites, and resource allocation of tasks to identify dependencies between tasks. For example, if a task cannot start until a task is completed, the detection unit automatically detects that dependency and reflects it in the project management table. The detection unit also detects dependencies to optimize the order and schedule of tasks. This optimizes the project schedule and prevents wasted time and resources. Furthermore, the detection unit visually displays task dependencies, making it easier to grasp the overall picture of the project. For example, it generates network diagrams and Gantt charts showing task dependencies, allowing project managers and team members to see the progress and dependencies of tasks at a glance. This enables smooth project management and minimizes risks.

[0033] The decision unit determines task priorities based on dependencies detected by the detection unit. For example, the decision unit evaluates the importance and urgency of each task in a project and automatically determines its priority. Specifically, the decision unit comprehensively evaluates information such as task importance, urgency, resource utilization, and dependencies to determine the priority of each task. For example, tasks that are both important and urgent are given a high priority and are judged to require immediate attention. The decision unit also creates a schedule to ensure the smooth progress of the project based on the task priorities. This allows project managers and team members to clearly understand which tasks should be prioritized and to proceed with the project efficiently. Furthermore, the decision unit visually displays task priorities, making it easier to grasp the overall picture of the project. For example, by displaying task priorities in different colors, important and urgent tasks can be identified at a glance. This allows for smooth project management and minimizes risks.

[0034] The suggestion department suggests suitable personnel based on employees' skill sets. For example, the suggestion department automatically identifies employees with the skills required for a project and suggests the appropriate personnel. For example, the suggestion department retrieves employees' skill sets from a database and suggests personnel that match the project requirements. For example, the suggestion department analyzes employees' past project history and suggests the most suitable personnel. This allows the system to automatically suggest the most suitable personnel for a project.

[0035] The Facilitation Department detects undetermined or uncertain specifications and facilitates specification determination. For example, the Facilitation Department analyzes project specifications and automatically detects undetermined or uncertain specifications. For example, the Facilitation Department detects ambiguity or potential changes in specifications and facilitates specification determination. For example, the Facilitation Department evaluates the degree of certainty of specifications and follows up on the progress of specification determination. This enables the automatic detection of undetermined or uncertain specifications and facilitates specification determination.

[0036] The Agenda Creation Department is responsible for creating meeting agendas. For example, the Agenda Creation Department automatically creates meeting agendas based on project progress and issues. For example, the Agenda Creation Department automatically sets meeting topics, participants, and time allocations. For example, the Agenda Creation Department creates the optimal agenda according to the meeting's purpose and objectives. This allows for the automatic creation of meeting agendas.

[0037] The creation unit automatically generates reports summarizing project progress and deliverables, as well as diagrams illustrating the overall project structure. For example, the creation unit automatically generates reports detailing project progress. For example, the creation unit automatically generates reports listing project deliverables. For example, the creation unit automatically generates Gantt charts and flowcharts illustrating the overall project structure. This allows for the automatic generation of reports and diagrams detailing project progress and deliverables.

[0038] The update unit monitors the progress of each task in the project in real time and automatically updates the progress management sheet. For example, the update unit monitors the project's progress in real time and automatically updates the progress management sheet. For example, the update unit periodically checks the project's progress and keeps the progress management sheet up-to-date. For example, the update unit displays the project's progress using graphs and charts to make it easier to understand visually. This allows for real-time monitoring of the project's progress and automatic updates of the progress management sheet.

[0039] The update unit analyzes past project data and automatically determines the optimal update timing. For example, the update unit identifies times when progress is likely to stagnate from past project data and performs updates at those times. For example, the update unit predicts the completion date of a task based on past project data and performs updates just before that completion date. For example, the update unit analyzes past project data and sets update timings that match the team's work pace. This allows the optimal update timing to be determined based on past project data.

[0040] The update unit adds a function to monitor the progress of each task in real time and issue alerts if an anomaly is detected. For example, the update unit will issue an alert in real time if the progress of a task is behind schedule. For example, the update unit will issue an alert if the progress of a task is progressing too rapidly, as this is considered an anomaly. For example, the update unit will issue an alert immediately if the progress of a task has stopped. This allows for real-time monitoring of task progress and the issuance of alerts when an anomaly is detected.

[0041] The update unit automatically updates project progress in conjunction with other project management tools. For example, the update unit retrieves progress information from other project management tools and automatically updates the progress management table. For example, the update unit synchronizes progress information in real time by collaborating with other project management tools. For example, the update unit uses the API of other project management tools to automatically retrieve and update progress information. This allows for automatic updates of progress status in conjunction with other project management tools.

[0042] The update section adds a function to visualize the progress of each task and display it using graphs and charts. For example, the update section can display the progress of each task using a Gantt chart. For example, the update section can display the progress of each task using a pie chart. For example, the update section can display the progress of each task using a bar graph. This makes it possible to visualize and display the progress of each task.

[0043] The creation unit automatically generates optimal content by referring to documents from similar past projects when creating a document. For example, the creation unit can refer to reports from similar past projects and automatically generate optimal content. For example, the creation unit can refer to diagrams from similar past projects and automatically generate optimal diagrams. For example, the creation unit can analyze documents from similar past projects and automatically generate the optimal structure. This allows for the automatic generation of optimal content by referring to documents from similar past projects.

[0044] The creation department uses templates that are automatically updated according to the project's progress when creating documents. For example, the creation department automatically updates report templates according to the project's progress. For example, the creation department automatically updates diagram templates according to the project's progress. For example, the creation department automatically updates the document structure according to the project's progress. This allows templates to be automatically updated according to the project's progress.

[0045] The creation unit automatically imports and reflects data from other project management tools when creating documents. For example, the creation unit imports progress data from other project management tools and automatically reflects it in the document. For example, the creation unit imports task data from other project management tools and automatically reflects it in the document. For example, the creation unit imports resource data from other project management tools and automatically reflects it in the document. This makes it possible to import data from other project management tools and reflect it in the document.

[0046] The creation unit automatically generates diagrams and graphs to visualize project progress when creating documents. For example, the creation unit visualizes project progress using a Gantt chart and automatically generates it in the document. For example, the creation unit visualizes project progress using a pie chart and automatically generates it in the document. For example, the creation unit visualizes project progress using a bar graph and automatically generates it in the document. This makes it possible to visualize project progress and automatically generate diagrams and graphs.

[0047] The detection unit analyzes past project data and optimizes an algorithm to predict dependencies between tasks. For example, the detection unit optimizes an algorithm to predict dependencies between tasks from past project data. For example, the detection unit optimizes a dependency prediction algorithm that considers task priority based on past project data. For example, the detection unit analyzes past project data and optimizes a dependency prediction algorithm that considers task importance. This allows for the optimization of an algorithm to predict dependencies between tasks based on past project data.

[0048] The detection unit improves the detection accuracy by considering the priority and importance of tasks when detecting dependencies between tasks. For example, the detection unit improves the accuracy of dependency detection by considering the priority of tasks. For example, the detection unit improves the accuracy of dependency detection by considering the importance of tasks. For example, the detection unit improves the accuracy of dependency detection by considering the urgency of tasks. This makes it possible to improve the accuracy of dependency detection by considering the priority and importance of tasks.

[0049] The detection unit automatically detects dependencies between tasks in conjunction with other project management tools. For example, the detection unit retrieves task data from other project management tools and automatically detects dependencies. For example, the detection unit synchronizes task dependencies in real time in conjunction with other project management tools. For example, the detection unit uses the API of other project management tools to automatically detect task dependencies. This enables the automatic detection of dependencies between tasks in conjunction with other project management tools.

[0050] The detection unit adds a function to visualize the dependencies between tasks and display them in Gantt charts and network diagrams. For example, the detection unit can display the dependencies between tasks in a Gantt chart. For example, the detection unit can display the dependencies between tasks in a network diagram. For example, the detection unit can display the dependencies between tasks in a flowchart. This allows for the visualization and display of the dependencies between tasks.

[0051] The decision-making unit analyzes past project data and optimizes an algorithm for predicting task priorities. For example, the decision-making unit optimizes an algorithm for predicting task priorities from past project data. For example, the decision-making unit optimizes a priority prediction algorithm that considers the importance of tasks based on past project data. For example, the decision-making unit analyzes past project data and optimizes a priority prediction algorithm that considers the urgency of tasks. This allows for the optimization of an algorithm for predicting task priorities based on past project data.

[0052] The decision-making unit optimizes task priorities by considering task dependencies and resource utilization. For example, the decision-making unit optimizes priorities by considering task dependencies. For example, the decision-making unit optimizes priorities by considering resource utilization. For example, the decision-making unit optimizes priorities by comprehensively considering task importance and resource utilization. This makes it possible to optimize task priorities by considering task dependencies and resource utilization.

[0053] The decision-making unit automatically determines task priorities in conjunction with other project management tools. For example, the decision-making unit retrieves task data from other project management tools and automatically determines priorities. For example, the decision-making unit synchronizes task priorities in real time in conjunction with other project management tools. For example, the decision-making unit uses the API of other project management tools to automatically determine task priorities. This allows for the automatic determination of task priorities in conjunction with other project management tools.

[0054] The decision-making section adds a function to visualize task priorities and display them in graphs and charts. For example, the decision-making section can display task priorities in a Gantt chart. For example, the decision-making section can display task priorities in a pie chart. For example, the decision-making section can display task priorities in a bar graph. This allows for the visualization and display of task priorities.

[0055] The suggestion department analyzes employees' past project history and optimizes algorithms to predict the most suitable personnel. For example, the suggestion department optimizes algorithms to predict the most suitable personnel based on employees' past project history. For example, the suggestion department optimizes personnel prediction algorithms that consider skill sets based on employees' past project history. For example, the suggestion department analyzes employees' past project history and optimizes algorithms to predict the most suitable personnel for project requirements. This allows for the optimization of algorithms that predict the most suitable personnel based on employees' past project history.

[0056] The suggestion function automatically updates employee skill sets in conjunction with other talent management tools. For example, it retrieves skill set data from other talent management tools and updates it automatically. For example, it synchronizes skill sets in real time by integrating with other talent management tools. For example, it uses the APIs of other talent management tools to automatically update skill sets. This allows for the automatic updating of employee skill sets in conjunction with other talent management tools.

[0057] The acceleration unit analyzes past project data and optimizes algorithms to predict undetermined or uncertain specifications. For example, the acceleration unit optimizes algorithms to predict undetermined or uncertain specifications from past project data. For example, the acceleration unit optimizes prediction algorithms that take into account the degree of certainty of specifications based on past project data. For example, the acceleration unit analyzes past project data and optimizes prediction algorithms that take into account the importance of specifications. This makes it possible to optimize algorithms to predict undetermined or uncertain specifications based on past project data.

[0058] The acceleration unit automatically detects unconfirmed or uncertain specifications by coordinating with other project management tools. For example, the acceleration unit retrieves specification data from other project management tools and automatically detects unconfirmed or uncertain specifications. For example, the acceleration unit coordinates with other project management tools to synchronize the degree of specification confirmation in real time. For example, the acceleration unit uses the API of other project management tools to automatically detect unconfirmed or uncertain specifications. This enables the automatic detection of unconfirmed or uncertain specifications by coordinating with other project management tools.

[0059] The agenda creation department analyzes past meeting data and optimizes an algorithm to predict the optimal agenda. For example, the agenda creation department optimizes an algorithm to predict the optimal agenda from past meeting data. For example, the agenda creation department optimizes an agenda prediction algorithm that considers the importance of the meeting based on past meeting data. For example, the agenda creation department analyzes past meeting data and optimizes an algorithm to predict the agenda best suited to the meeting's purpose. This allows for the optimization of an algorithm that predicts the optimal agenda based on past meeting data.

[0060] The agenda creation unit automatically creates agendas in conjunction with other meeting management tools. For example, the agenda creation unit retrieves meeting data from other meeting management tools and automatically creates agendas. For example, the agenda creation unit synchronizes agendas in real time by integrating with other meeting management tools. For example, the agenda creation unit uses the API of other meeting management tools to automatically create agendas. This enables the automatic creation of agendas in conjunction with other meeting management tools.

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

[0062] The project management support system can also include a risk management unit. This unit automatically detects risks associated with project progress and assesses their impact. For example, it can monitor project progress and changes in the external environment to detect potential risks early. Furthermore, it can propose countermeasures for detected risks and notify the project team. Additionally, it can quantitatively evaluate the impact of risks and determine their priority. This automates project risk management and minimizes the impact of risks.

[0063] The project management support system can also include a communications section. This section provides functions to facilitate communication within the project team. For example, it can notify team members in real time about project progress and task changes. It can also support messaging among team members and promote the sharing of project-related information. Furthermore, it can centrally manage project-related communication, such as scheduling meetings and sharing meeting minutes. This streamlines communication within the project team and ensures smooth project progress.

[0064] The project management support system can also include a feedback unit. This unit automatically collects and analyzes feedback on project progress and deliverables. For example, it can collect feedback from project teams and stakeholders to identify areas for project improvement. It can also analyze the collected feedback and assess its impact on project progress. Furthermore, it can adjust project plans and tasks based on the feedback to improve project quality. This allows for efficient management of feedback on project progress and deliverables, thereby improving project quality.

[0065] The project management support system can also include a learning component. This component assists in improving the project team's skills based on project progress and deliverables. For example, the learning component can analyze project progress and identify the skills and knowledge required by team members. It can also provide appropriate training programs and learning resources based on the identified skills and knowledge. Furthermore, the learning component can monitor the learning progress of team members and evaluate the effectiveness of skill improvement. This helps support the skill development of the project team and increases the project's success rate.

[0066] The project management support system can also include a motivation department. This department provides functions to maintain and improve the motivation of the project team. For example, it can evaluate the contributions of team members to the project's progress and deliverables, and provide appropriate rewards and recognition. It can also plan and implement events and activities to boost team members' motivation. Furthermore, it can collect feedback from team members and propose measures to improve motivation. This helps maintain and improve the project team's motivation, contributing to the project's success.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The update unit updates the progress management sheet. The update unit monitors the progress of each task in the project in real time and automatically updates the progress management sheet. The update unit periodically checks the project progress and keeps the progress management sheet up to date. The update unit displays the project progress using graphs and charts to make it easier to understand visually. Step 2: The creation unit creates documents based on the progress updated by the update unit. The creation unit automatically generates a report summarizing the project's progress and deliverables. The creation unit automatically generates a diagram showing the overall project. Based on the project's progress, the creation unit creates a plan outlining the next steps. Step 3: The detection unit detects dependencies between tasks. The detection unit analyzes the dependencies of each task in the project and automatically detects them. The detection unit detects dependencies in order to optimize the order and schedule of tasks. The detection unit visually displays task dependencies to make it easier to grasp the overall picture of the project. Step 4: The decision unit determines task priorities based on dependencies detected by the detection unit. The decision unit evaluates the importance and urgency of each task in the project and automatically determines its priority. Based on the task priorities, the decision unit creates a schedule to ensure the smooth progress of the project. The decision unit visually displays the task priorities to make it easier to grasp the overall picture of the project.

[0069] (Example of form 2) The project management support system according to an embodiment of the present invention is a system that comprehensively automates the tasks necessary for project management. This project management support system automates the creation of internal and upper-level documents (documents, diagrams), updates progress management tables, and determines task priorities. It also detects dependencies between tasks and suggests appropriate personnel based on employees' skill sets. Furthermore, it detects undecided or uncertain specifications, facilitates specification determination, and creates meeting agendas. In this way, it takes on all the complex tasks in project management and provides a solution to maximize efficiency. For example, the project management support system automatically generates reports summarizing project progress and deliverables, as well as diagrams showing the overall picture of the project. This reduces the time and effort required for document creation. Next, the project management support system grasps the progress of each task in the project in real time and automatically updates progress management tables. This ensures that the project progress is always kept up-to-date. The project management support system also evaluates the importance and urgency of each task in the project and automatically determines its priority. This allows the project to proceed smoothly. Furthermore, the project management support system analyzes the dependencies between each task in the project and automatically detects dependencies. This allows for the optimization of task order and scheduling. The project management support system automatically identifies employees with the necessary skills for the project and suggests the appropriate personnel. This enables the formation of the optimal team for the project. The project management support system analyzes project specifications and automatically detects undecided or uncertain specifications. This allows for rapid specification definition. Finally, the project management support system automatically creates meeting agendas based on project progress and issues. This supports efficient meeting management. In this way, the project management support system can take on all the complex tasks in project management and maximize efficiency.

[0070] The project management support system according to this embodiment comprises an update unit, a creation unit, a detection unit, and a decision unit. The update unit updates the progress management table. The update unit, for example, grasps the progress of each task in the project in real time and automatically updates the progress management table. The update unit, for example, periodically checks the progress of the project and keeps the progress management table up to date. The update unit, for example, displays the progress of the project in graphs and charts to make it easier to understand visually. The creation unit creates documents based on the progress updated by the update unit. The creation unit, for example, automatically generates a report summarizing the progress of the project and deliverables. The creation unit, for example, automatically generates a diagram showing the overall picture of the project. The creation unit, for example, creates a plan showing the next steps based on the progress of the project. The detection unit detects dependencies between tasks. The detection unit, for example, analyzes the dependencies of each task in the project and automatically detects dependencies. The detection unit, for example, detects dependencies in order to optimize the order and schedule of tasks. The detection unit visually displays task dependencies, for example, to make it easier to grasp the overall picture of the project. The decision unit determines the priority of tasks based on the dependencies detected by the detection unit. The decision unit automatically determines the priority by evaluating the importance and urgency of each task in the project, for example. The decision unit creates a schedule to ensure the smooth progress of the project based on the task priorities. The decision unit visually displays the task priorities, for example, to make it easier to grasp the overall picture of the project. As a result, the project management support system according to the embodiment can automate progress management, document creation, dependency detection, and task priority determination in project management.

[0071] The update unit updates the progress management table. For example, the update unit grasps the progress of each task in the project in real time and automatically updates the progress management table. Specifically, the update unit integrates with project management tools and task management systems to periodically obtain the progress status of each task. This includes information such as the task's start date, end date, progress rate, and comments from the person in charge. Based on this information, the update unit keeps the progress management table up to date. For example, when a task is completed, it changes the status of that task to "completed" and automatically triggers the start of the next task. In addition, the progress management table displays the overall progress of the project in graphs and charts, making it easy to understand visually. This allows project managers and team members to check the project's progress at a glance and make necessary adjustments quickly. Furthermore, the update unit also has a function to notify changes in progress in real time, so stakeholders can be immediately notified if there are any delays or changes in the progress of important tasks. This prevents project delays and supports smooth progress.

[0072] The creation unit creates documents based on the progress status updated by the update unit. For example, the creation unit automatically generates reports summarizing project progress and deliverables. Specifically, the creation unit generates a detailed report of project progress based on data from the progress management table. This report includes the progress status of each task, a list of completed tasks, a list of incomplete tasks, task dependencies, and risk assessments. The creation unit also automatically generates diagrams that show the overall picture of the project. For example, it generates Gantt charts and network diagrams to visually display the project schedule and task dependencies. Furthermore, based on the project progress, the creation unit creates a plan outlining the next steps. The plan includes the next tasks to be performed, the necessary resources, anticipated risks, and countermeasures. This allows project managers and team members to clearly understand what needs to be done next and to efficiently advance the project. By automatically generating these documents, the creation unit significantly reduces the time and effort required for document creation and improves the efficiency of project management.

[0073] The detection unit detects dependencies between tasks. For example, it analyzes the dependencies of each task in a project and automatically detects them. Specifically, the detection unit analyzes information such as the start date, end date, prerequisites, and resource allocation of tasks to identify dependencies between tasks. For example, if a task cannot start until a task is completed, the detection unit automatically detects that dependency and reflects it in the project management table. The detection unit also detects dependencies to optimize the order and schedule of tasks. This optimizes the project schedule and prevents wasted time and resources. Furthermore, the detection unit visually displays task dependencies, making it easier to grasp the overall picture of the project. For example, it generates network diagrams and Gantt charts showing task dependencies, allowing project managers and team members to see the progress and dependencies of tasks at a glance. This enables smooth project management and minimizes risks.

[0074] The decision unit determines task priorities based on dependencies detected by the detection unit. For example, the decision unit evaluates the importance and urgency of each task in a project and automatically determines its priority. Specifically, the decision unit comprehensively evaluates information such as task importance, urgency, resource utilization, and dependencies to determine the priority of each task. For example, tasks that are both important and urgent are given a high priority and are judged to require immediate attention. The decision unit also creates a schedule to ensure the smooth progress of the project based on the task priorities. This allows project managers and team members to clearly understand which tasks should be prioritized and to proceed with the project efficiently. Furthermore, the decision unit visually displays task priorities, making it easier to grasp the overall picture of the project. For example, by displaying task priorities in different colors, important and urgent tasks can be identified at a glance. This allows for smooth project management and minimizes risks.

[0075] The suggestion department suggests suitable personnel based on employees' skill sets. For example, the suggestion department automatically identifies employees with the skills required for a project and suggests the appropriate personnel. For example, the suggestion department retrieves employees' skill sets from a database and suggests personnel that match the project requirements. For example, the suggestion department analyzes employees' past project history and suggests the most suitable personnel. This allows the system to automatically suggest the most suitable personnel for a project.

[0076] The Facilitation Department detects undetermined or uncertain specifications and facilitates specification determination. For example, the Facilitation Department analyzes project specifications and automatically detects undetermined or uncertain specifications. For example, the Facilitation Department detects ambiguity or potential changes in specifications and facilitates specification determination. For example, the Facilitation Department evaluates the degree of certainty of specifications and follows up on the progress of specification determination. This enables the automatic detection of undetermined or uncertain specifications and facilitates specification determination.

[0077] The Agenda Creation Department is responsible for creating meeting agendas. For example, the Agenda Creation Department automatically creates meeting agendas based on project progress and issues. For example, the Agenda Creation Department automatically sets meeting topics, participants, and time allocations. For example, the Agenda Creation Department creates the optimal agenda according to the meeting's purpose and objectives. This allows for the automatic creation of meeting agendas.

[0078] The creation unit automatically generates reports summarizing project progress and deliverables, as well as diagrams illustrating the overall project structure. For example, the creation unit automatically generates reports detailing project progress. For example, the creation unit automatically generates reports listing project deliverables. For example, the creation unit automatically generates Gantt charts and flowcharts illustrating the overall project structure. This allows for the automatic generation of reports and diagrams detailing project progress and deliverables.

[0079] The update unit monitors the progress of each task in the project in real time and automatically updates the progress management sheet. For example, the update unit monitors the project's progress in real time and automatically updates the progress management sheet. For example, the update unit periodically checks the project's progress and keeps the progress management sheet up-to-date. For example, the update unit displays the project's progress using graphs and charts to make it easier to understand visually. This allows for real-time monitoring of the project's progress and automatic updates of the progress management sheet.

[0080] The update unit estimates the user's emotions and adjusts the update frequency of the progress tracking sheet based on the estimated emotions. For example, if the user is stressed, the update unit reduces the update frequency and notifies only of important changes. For example, if the user is relaxed, the update unit frequently updates and provides detailed progress information. For example, if the user is in a hurry, the update unit updates progress information in real time and notifies immediately. This allows the update frequency of the progress tracking sheet to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The update unit analyzes past project data and automatically determines the optimal update timing. For example, the update unit identifies times when progress is likely to stagnate from past project data and performs updates at those times. For example, the update unit predicts the completion date of a task based on past project data and performs updates just before that completion date. For example, the update unit analyzes past project data and sets update timings that match the team's work pace. This allows the optimal update timing to be determined based on past project data.

[0082] The update unit adds a function to monitor the progress of each task in real time and issue alerts if an anomaly is detected. For example, the update unit will issue an alert in real time if the progress of a task is behind schedule. For example, the update unit will issue an alert if the progress of a task is progressing too rapidly, as this is considered an anomaly. For example, the update unit will issue an alert immediately if the progress of a task has stopped. This allows for real-time monitoring of task progress and the issuance of alerts when an anomaly is detected.

[0083] The update unit estimates the user's emotions and adjusts the display format of the progress management table based on the estimated emotions. For example, if the user is stressed, the update unit provides a simple and highly visible display format. For example, if the user is relaxed, the update unit provides a display format that includes detailed information. For example, if the user is in a hurry, the update unit provides a display format that gets straight to the point. This allows the display format of the progress management table to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The update unit automatically updates project progress in conjunction with other project management tools. For example, the update unit retrieves progress information from other project management tools and automatically updates the progress management table. For example, the update unit synchronizes progress information in real time by collaborating with other project management tools. For example, the update unit uses the API of other project management tools to automatically retrieve and update progress information. This allows for automatic updates of progress status in conjunction with other project management tools.

[0085] The update section adds a function to visualize the progress of each task and display it using graphs and charts. For example, the update section can display the progress of each task using a Gantt chart. For example, the update section can display the progress of each task using a pie chart. For example, the update section can display the progress of each task using a bar graph. This makes it possible to visualize and display the progress of each task.

[0086] The creation unit estimates the user's emotions and adjusts the document's presentation based on those emotions. For example, if the user is relaxed, the creation unit creates a document with detailed explanations. If the user is in a hurry, the creation unit creates a concise document that gets straight to the point. If the user is excited, the creation unit creates a document with a visually stimulating design. This allows the document's presentation to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The creation unit automatically generates optimal content by referring to documents from similar past projects when creating a document. For example, the creation unit can refer to reports from similar past projects and automatically generate optimal content. For example, the creation unit can refer to diagrams from similar past projects and automatically generate optimal diagrams. For example, the creation unit can analyze documents from similar past projects and automatically generate the optimal structure. This allows for the automatic generation of optimal content by referring to documents from similar past projects.

[0088] The creation department uses templates that are automatically updated according to the project's progress when creating documents. For example, the creation department automatically updates report templates according to the project's progress. For example, the creation department automatically updates diagram templates according to the project's progress. For example, the creation department automatically updates the document structure according to the project's progress. This allows templates to be automatically updated according to the project's progress.

[0089] The creation unit estimates the user's emotions and adjusts the document length based on the estimated emotions. For example, if the user is in a hurry, the creation unit will create a short, concise document. If the user is relaxed, the creation unit will create a longer document with detailed explanations. If the user is excited, the creation unit will create a document with a visually stimulating design. This allows the document length to be adjusted according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The creation unit automatically imports and reflects data from other project management tools when creating documents. For example, the creation unit imports progress data from other project management tools and automatically reflects it in the document. For example, the creation unit imports task data from other project management tools and automatically reflects it in the document. For example, the creation unit imports resource data from other project management tools and automatically reflects it in the document. This makes it possible to import data from other project management tools and reflect it in the document.

[0091] The creation unit automatically generates diagrams and graphs to visualize project progress when creating documents. For example, the creation unit visualizes project progress using a Gantt chart and automatically generates it in the document. For example, the creation unit visualizes project progress using a pie chart and automatically generates it in the document. For example, the creation unit visualizes project progress using a bar graph and automatically generates it in the document. This makes it possible to visualize project progress and automatically generate diagrams and graphs.

[0092] The detection unit estimates the user's emotions and adjusts the method for detecting dependencies between tasks based on the estimated user emotions. For example, if the user is tense, the detection unit provides a simple and highly visible method for detecting dependencies. For example, if the user is relaxed, the detection unit provides a method for detecting dependencies that includes detailed information. For example, if the user is in a hurry, the detection unit provides a method for detecting dependencies that gets straight to the point. This allows the method for detecting dependencies between tasks to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The detection unit analyzes past project data and optimizes an algorithm to predict dependencies between tasks. For example, the detection unit optimizes an algorithm to predict dependencies between tasks from past project data. For example, the detection unit optimizes a dependency prediction algorithm that considers task priority based on past project data. For example, the detection unit analyzes past project data and optimizes a dependency prediction algorithm that considers task importance. This allows for the optimization of an algorithm to predict dependencies between tasks based on past project data.

[0094] The detection unit improves the detection accuracy by considering the priority and importance of tasks when detecting dependencies between tasks. For example, the detection unit improves the accuracy of dependency detection by considering the priority of tasks. For example, the detection unit improves the accuracy of dependency detection by considering the importance of tasks. For example, the detection unit improves the accuracy of dependency detection by considering the urgency of tasks. This makes it possible to improve the accuracy of dependency detection by considering the priority and importance of tasks.

[0095] The detection unit estimates the user's emotions and adjusts the display method of dependencies based on the estimated user emotions. For example, if the user is tense, the detection unit provides a simple and highly visible display method. For example, if the user is relaxed, the detection unit provides a display method that includes detailed information. For example, if the user is in a hurry, the detection unit provides a display method that gets straight to the point. This allows the display method of dependencies to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The detection unit automatically detects dependencies between tasks in conjunction with other project management tools. For example, the detection unit retrieves task data from other project management tools and automatically detects dependencies. For example, the detection unit synchronizes task dependencies in real time in conjunction with other project management tools. For example, the detection unit uses the API of other project management tools to automatically detect task dependencies. This enables the automatic detection of dependencies between tasks in conjunction with other project management tools.

[0097] The detection unit adds a function to visualize the dependencies between tasks and display them in Gantt charts and network diagrams. For example, the detection unit can display the dependencies between tasks in a Gantt chart. For example, the detection unit can display the dependencies between tasks in a network diagram. For example, the detection unit can display the dependencies between tasks in a flowchart. This allows for the visualization and display of the dependencies between tasks.

[0098] The decision-making unit estimates the user's emotions and adjusts the task prioritization method based on the estimated emotions. For example, if the user is stressed, the decision-making unit prioritizes and displays high-priority tasks. For example, if the user is relaxed, the decision-making unit provides a priority determination method that includes detailed information. For example, if the user is in a hurry, the decision-making unit provides a priority determination method that gets straight to the point. This allows the task prioritization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The decision-making unit analyzes past project data and optimizes an algorithm for predicting task priorities. For example, the decision-making unit optimizes an algorithm for predicting task priorities from past project data. For example, the decision-making unit optimizes a priority prediction algorithm that considers the importance of tasks based on past project data. For example, the decision-making unit analyzes past project data and optimizes a priority prediction algorithm that considers the urgency of tasks. This allows for the optimization of an algorithm for predicting task priorities based on past project data.

[0100] The decision-making unit optimizes task priorities by considering task dependencies and resource utilization. For example, the decision-making unit optimizes priorities by considering task dependencies. For example, the decision-making unit optimizes priorities by considering resource utilization. For example, the decision-making unit optimizes priorities by comprehensively considering task importance and resource utilization. This makes it possible to optimize task priorities by considering task dependencies and resource utilization.

[0101] The decision-making unit estimates the user's emotions and adjusts the display method of task priorities based on the estimated emotions. For example, if the user is stressed, the decision-making unit provides a simple and highly visible display method. For example, if the user is relaxed, the decision-making unit provides a display method that includes detailed information. For example, if the user is in a hurry, the decision-making unit provides a display method that gets straight to the point. This allows the display method of task priorities to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The decision-making unit automatically determines task priorities in conjunction with other project management tools. For example, the decision-making unit retrieves task data from other project management tools and automatically determines priorities. For example, the decision-making unit synchronizes task priorities in real time in conjunction with other project management tools. For example, the decision-making unit uses the API of other project management tools to automatically determine task priorities. This allows for the automatic determination of task priorities in conjunction with other project management tools.

[0103] The decision-making section adds a function to visualize task priorities and display them in graphs and charts. For example, the decision-making section can display task priorities in a Gantt chart. For example, the decision-making section can display task priorities in a pie chart. For example, the decision-making section can display task priorities in a bar graph. This allows for the visualization and display of task priorities.

[0104] The suggestion unit estimates the user's emotions and adjusts the method of suggesting personnel based on the estimated emotions. For example, if the user is nervous, the suggestion unit provides a simple and highly visible suggestion method. For example, if the user is relaxed, the suggestion unit provides a suggestion method that includes detailed information. For example, if the user is in a hurry, the suggestion unit provides a suggestion method that gets straight to the point. This allows the method of suggesting personnel to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The suggestion department analyzes employees' past project history and optimizes algorithms to predict the most suitable personnel. For example, the suggestion department optimizes algorithms to predict the most suitable personnel based on employees' past project history. For example, the suggestion department optimizes personnel prediction algorithms that consider skill sets based on employees' past project history. For example, the suggestion department analyzes employees' past project history and optimizes algorithms to predict the most suitable personnel for project requirements. This allows for the optimization of algorithms that predict the most suitable personnel based on employees' past project history.

[0106] The suggestion section estimates the user's emotions and adjusts how talent suggestions are displayed based on the estimated emotions. For example, if the user is nervous, the suggestion section provides a simple and highly visible display method. For example, if the user is relaxed, the suggestion section provides a display method that includes detailed information. For example, if the user is in a hurry, the suggestion section provides a display method that gets straight to the point. This allows the display method of talent suggestions to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0107] The suggestion function automatically updates employee skill sets in conjunction with other talent management tools. For example, it retrieves skill set data from other talent management tools and updates it automatically. For example, it synchronizes skill sets in real time by integrating with other talent management tools. For example, it uses the APIs of other talent management tools to automatically update skill sets. This allows for the automatic updating of employee skill sets in conjunction with other talent management tools.

[0108] The facilitation unit estimates the user's emotions and adjusts the specification-making facilitation method based on the estimated user emotions. For example, if the user is nervous, the facilitation unit provides a simple and highly visible facilitation method. For example, if the user is relaxed, the facilitation unit provides a facilitation method that includes detailed information. For example, if the user is in a hurry, the facilitation unit provides a frank facilitation method. This allows the specification-making facilitation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0109] The acceleration unit analyzes past project data and optimizes algorithms to predict undetermined or uncertain specifications. For example, the acceleration unit optimizes algorithms to predict undetermined or uncertain specifications from past project data. For example, the acceleration unit optimizes prediction algorithms that take into account the degree of certainty of specifications based on past project data. For example, the acceleration unit analyzes past project data and optimizes prediction algorithms that take into account the importance of specifications. This makes it possible to optimize algorithms to predict undetermined or uncertain specifications based on past project data.

[0110] The facilitator estimates the user's emotions and adjusts the display method based on the estimated emotions. For example, if the user is nervous, the facilitator provides a simple and highly visible display method. For example, if the user is relaxed, the facilitator provides a display method that includes detailed information. For example, if the user is in a hurry, the facilitator provides a display method that gets straight to the point. This allows the display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The acceleration unit automatically detects unconfirmed or uncertain specifications by coordinating with other project management tools. For example, the acceleration unit retrieves specification data from other project management tools and automatically detects unconfirmed or uncertain specifications. For example, the acceleration unit coordinates with other project management tools to synchronize the degree of specification confirmation in real time. For example, the acceleration unit uses the API of other project management tools to automatically detect unconfirmed or uncertain specifications. This enables the automatic detection of unconfirmed or uncertain specifications by coordinating with other project management tools.

[0112] The agenda creation unit estimates the user's emotions and adjusts the agenda creation method based on the estimated emotions. For example, if the user is nervous, the agenda creation unit will create a simple and highly visible agenda. For example, if the user is relaxed, the agenda creation unit will create an agenda that includes detailed information. For example, if the user is in a hurry, the agenda creation unit will create a concise agenda. This allows the agenda creation method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0113] The agenda creation department analyzes past meeting data and optimizes an algorithm to predict the optimal agenda. For example, the agenda creation department optimizes an algorithm to predict the optimal agenda from past meeting data. For example, the agenda creation department optimizes an agenda prediction algorithm that considers the importance of the meeting based on past meeting data. For example, the agenda creation department analyzes past meeting data and optimizes an algorithm to predict the agenda best suited to the meeting's purpose. This allows for the optimization of an algorithm that predicts the optimal agenda based on past meeting data.

[0114] The agenda creation unit estimates the user's emotions and adjusts the agenda display method based on the estimated emotions. For example, if the user is nervous, the agenda creation unit provides a simple and highly visible display method. For example, if the user is relaxed, the agenda creation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the agenda creation unit provides a display method that gets straight to the point. This allows the agenda display method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0115] The agenda creation unit automatically creates agendas in conjunction with other meeting management tools. For example, the agenda creation unit retrieves meeting data from other meeting management tools and automatically creates agendas. For example, the agenda creation unit synchronizes agendas in real time by integrating with other meeting management tools. For example, the agenda creation unit uses the API of other meeting management tools to automatically create agendas. This enables the automatic creation of agendas in conjunction with other meeting management tools.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The project management support system can also include a risk management unit. This unit automatically detects risks associated with project progress and assesses their impact. For example, it can monitor project progress and changes in the external environment to detect potential risks early. Furthermore, it can propose countermeasures for detected risks and notify the project team. Additionally, it can quantitatively evaluate the impact of risks and determine their priority. This automates project risk management and minimizes the impact of risks.

[0118] The project management support system can also include a communications section. This section provides functions to facilitate communication within the project team. For example, it can notify team members in real time about project progress and task changes. It can also support messaging among team members and promote the sharing of project-related information. Furthermore, it can centrally manage project-related communication, such as scheduling meetings and sharing meeting minutes. This streamlines communication within the project team and ensures smooth project progress.

[0119] The project management support system can also include a feedback unit. This unit automatically collects and analyzes feedback on project progress and deliverables. For example, it can collect feedback from project teams and stakeholders to identify areas for project improvement. It can also analyze the collected feedback and assess its impact on project progress. Furthermore, it can adjust project plans and tasks based on the feedback to improve project quality. This allows for efficient management of feedback on project progress and deliverables, thereby improving project quality.

[0120] The project management support system can also include a learning component. This component assists in improving the project team's skills based on project progress and deliverables. For example, the learning component can analyze project progress and identify the skills and knowledge required by team members. It can also provide appropriate training programs and learning resources based on the identified skills and knowledge. Furthermore, the learning component can monitor the learning progress of team members and evaluate the effectiveness of skill improvement. This helps support the skill development of the project team and increases the project's success rate.

[0121] The project management support system can also include a motivation department. This department provides functions to maintain and improve the motivation of the project team. For example, it can evaluate the contributions of team members to the project's progress and deliverables, and provide appropriate rewards and recognition. It can also plan and implement events and activities to boost team members' motivation. Furthermore, it can collect feedback from team members and propose measures to improve motivation. This helps maintain and improve the project team's motivation, contributing to the project's success.

[0122] The project management support system can estimate the user's emotions and adjust feedback on project progress based on those emotions. For example, if the user is stressed, the feedback can be concise and highlight only the important points. If the user is relaxed, detailed feedback can be provided, explaining the project progress in more detail. Furthermore, if the user is in a hurry, feedback can be provided quickly, prioritizing matters that require immediate attention. This allows for smoother project progress by adjusting the content and method of feedback according to the user's emotions.

[0123] Project management support systems can estimate user emotions and adjust task assignments based on those emotions. For example, if a user is stressed, the system can reduce their task assignments to alleviate their burden. Conversely, if a user is relaxed, it can assign more complex tasks or new challenges. Furthermore, if a user is in a hurry, it can quickly assign high-priority tasks. This allows for the optimization of project progress by adjusting task assignments according to user emotions.

[0124] The project management support system can estimate the user's emotions and adjust the notification method regarding project progress based on those emotions. For example, if the user is stressed, the frequency of notifications can be reduced, and only important notifications can be sent. Conversely, if the user is relaxed, detailed notifications can be sent frequently to provide detailed information about the project's progress. Furthermore, if the user is in a hurry, notifications requiring immediate attention can be prioritized. This allows for smoother project progress by adjusting notification methods according to the user's emotions.

[0125] The project management support system can estimate the user's emotions and adjust the alerting method regarding project progress based on those emotions. For example, if the user is stressed, the frequency of alerts can be reduced, and only important alerts can be sent. Conversely, if the user is relaxed, detailed alerts can be sent frequently to provide more detailed information about project progress. Furthermore, if the user is in a hurry, alerts requiring immediate attention can be prioritized. This allows for smoother project progress by adjusting the alerting method according to the user's emotions.

[0126] The project management support system can estimate the user's emotions and adjust the reporting method for project progress based on those emotions. For example, if the user is stressed, the report can be concise and highlight only the important points. If the user is relaxed, a detailed report can be provided, explaining the project progress in detail. Furthermore, if the user is in a hurry, the report can be provided quickly, prioritizing matters that require immediate attention. This allows the reporting method to be adjusted according to the user's emotions, ensuring smooth project progress.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The update unit updates the progress management sheet. The update unit monitors the progress of each task in the project in real time and automatically updates the progress management sheet. The update unit periodically checks the project progress and keeps the progress management sheet up to date. The update unit displays the project progress using graphs and charts to make it easier to understand visually. Step 2: The creation unit creates documents based on the progress updated by the update unit. The creation unit automatically generates a report summarizing the project's progress and deliverables. The creation unit automatically generates a diagram showing the overall project. Based on the project's progress, the creation unit creates a plan outlining the next steps. Step 3: The detection unit detects dependencies between tasks. The detection unit analyzes the dependencies of each task in the project and automatically detects them. The detection unit detects dependencies in order to optimize the order and schedule of tasks. The detection unit visually displays task dependencies to make it easier to grasp the overall picture of the project. Step 4: The decision unit determines task priorities based on dependencies detected by the detection unit. The decision unit evaluates the importance and urgency of each task in the project and automatically determines its priority. Based on the task priorities, the decision unit creates a schedule to ensure the smooth progress of the project. The decision unit visually displays the task priorities to make it easier to grasp the overall picture of the project.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the update unit, creation unit, detection unit, decision unit, suggestion unit, promotion unit, and agenda creation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the update unit is implemented by the control unit 46A of the smart device 14, which grasps the project progress in real time and automatically updates the progress management table. The creation unit is implemented by the specific processing unit 290 of the data processing device 12, which automatically generates documents based on the progress status. The detection unit is implemented by the control unit 46A of the smart device 14, which detects dependencies between tasks. The decision unit is implemented by the specific processing unit 290 of the data processing device 12, which determines the priority of tasks. The suggestion unit is implemented by the control unit 46A of the smart device 14, which suggests appropriate personnel based on the skill sets of employees. The promotion unit is implemented by the specific processing unit 290 of the data processing device 12, which detects undetermined or uncertain specifications and facilitates specification determination. The agenda creation unit is implemented, for example, by the control unit 46A of the smart device 14, and automatically creates the MTG agenda. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

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

[0134] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0148] Each of the multiple elements described above, including the update unit, creation unit, detection unit, decision unit, suggestion unit, promotion unit, and agenda creation unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing device 12. For example, the update unit is implemented by the control unit 46A of the smart glasses 214, which grasps the project progress in real time and automatically updates the progress management table. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which automatically generates documents based on the progress status. The detection unit is implemented by, for example, the control unit 46A of the smart glasses 214, which detects dependencies between tasks. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which determines the priority of tasks. The suggestion unit is implemented by, for example, the control unit 46A of the smart glasses 214, which suggests appropriate personnel based on the skill sets of employees. The promotion unit is implemented by, for example, the specific processing unit 290 of the data processing device 12, which detects undetermined or uncertain specifications and promotes specification determination. The agenda creation unit is implemented, for example, by the control unit 46A of the smart glasses 214, and automatically creates the MTG agenda. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[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 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.

[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 (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).

[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] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the update unit, creation unit, detection unit, decision unit, suggestion unit, promotion unit, and agenda creation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the update unit is implemented by the control unit 46A of the headset terminal 314, which grasps the project progress in real time and automatically updates the progress management table. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates documents based on the progress status. The detection unit is implemented by, for example, the control unit 46A of the headset terminal 314, which detects dependencies between tasks. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the priority of tasks. The suggestion unit is implemented by, for example, the control unit 46A of the headset terminal 314, which suggests appropriate personnel based on the skill sets of employees. The promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which detects undetermined or uncertain specifications and promotes specification determination. The agenda creation unit is implemented, for example, by the control unit 46A of the headset terminal 314, and automatically creates the MTG agenda. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the update unit, creation unit, detection unit, decision unit, suggestion unit, promotion unit, and agenda creation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the update unit is implemented by the control unit 46A of the robot 414, which grasps the project progress in real time and automatically updates the progress management table. The creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates documents based on the progress status. The detection unit is implemented by, for example, the control unit 46A of the robot 414, which detects dependencies between tasks. The decision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which determines the priority of tasks. The suggestion unit is implemented by, for example, the control unit 46A of the robot 414, which suggests appropriate personnel based on the skill sets of employees. The promotion unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which detects undetermined or uncertain specifications and promotes specification determination. The agenda creation unit is implemented, for example, by the control unit 46A of the robot 414, and automatically creates the MTG agenda. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The update section updates the progress management sheet, A creation unit that creates a document based on the progress status updated by the update unit, A detection unit that detects dependencies between tasks, The system comprises a determination unit that determines the priority of a task based on the dependencies detected by the detection unit. A system characterized by the following features. (Note 2) We have a suggestion department that proposes suitable personnel based on the skill sets of our employees. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a feature that detects undetermined or uncertain specifications and facilitates specification determination. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an agenda creation unit for creating MTG agendas. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned creation unit, Automatically generates reports summarizing project progress and deliverables, as well as diagrams illustrating the overall project structure. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update unit is The system allows you to monitor the progress of each project task in real time and automatically updates the progress management sheet. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned update unit is The system estimates the user's emotions and adjusts the update frequency of the progress management table based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned update unit is By analyzing past project data, the system automatically determines the optimal update timing. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned update unit is We will add a feature that monitors the progress of each task in real time and issues an alert if an anomaly is detected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned update unit is It estimates the user's emotions and adjusts the display format of the progress management table based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned update unit is Automatically update project progress by integrating with other project management tools. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned update unit is Add a feature to visualize the progress of each task and display it using graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned creation unit, It estimates the user's emotions and adjusts the way the document is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned creation unit, When creating a document, the system automatically generates the most suitable content by referencing documents from similar past projects. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned creation unit, When creating documents, use templates that automatically update according to the project's progress. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned creation unit, It estimates the user's sentiment and adjusts the document length based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned creation unit, When creating a document, import data from other project management tools and automatically reflect it. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned creation unit, When creating documents, visualize project progress and automatically generate diagrams and graphs. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is We estimate the user's emotions and adjust the method of detecting dependencies between tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The detection unit is Optimize the algorithm that analyzes past project data and predicts dependencies between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The detection unit is When detecting dependencies between tasks, we improve detection accuracy by considering the priority and importance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The detection unit is It estimates the user's emotions and adjusts how dependencies are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The detection unit is Automatically detects dependencies between tasks by integrating with other project management tools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit is Add functionality to visualize dependencies between tasks and display them in Gantt charts and network diagrams. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, It estimates user emotions and adjusts task prioritization based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned determination unit, We optimize algorithms that analyze past project data to predict task priorities. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned determination unit, When determining task priorities, optimize by considering task dependencies and resource utilization. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned determination unit, It estimates the user's emotions and adjusts how task priorities are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned determination unit, Task priorities are automatically determined by integrating with other project management tools. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned determination unit, Add a feature to visualize task priorities and display them in graphs and charts. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned suggestion section is, It estimates the user's emotions and adjusts the method of suggesting talent based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned suggestion section is, We optimize an algorithm that analyzes employees' past project history to predict the most suitable personnel. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned suggestion section is, The system estimates the user's emotions and adjusts how talent suggestions are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned suggestion section is, Automatically update employee skill sets by integrating with other talent management tools. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned promotion unit is We estimate user emotions and adjust the specification development process based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned promotion unit is We analyze past project data and optimize algorithms to predict uncertain or undetermined specifications. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned promotion unit is The system estimates the user's emotions and adjusts the display method of the specifications based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned promotion unit is Automatically detects undetermined or uncertain specifications by integrating with other project management tools. The system described in Appendix 3, characterized by the features described herein. (Note 39) The agenda creation unit, We estimate user sentiment and adjust how the agenda is created based on that estimated sentiment. The system described in Appendix 4, characterized by the features described herein. (Note 40) The agenda creation unit, We optimize the algorithm to predict the optimal agenda by analyzing past meeting data. The system described in Appendix 4, characterized by the features described herein. (Note 41) The agenda creation unit, It estimates the user's emotions and adjusts how the agenda is displayed based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 42) The agenda creation unit, Automatically create agendas by integrating with other meeting management tools. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0201] 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. The update section updates the progress management sheet, A creation unit that creates a document based on the progress status updated by the update unit, A detection unit that detects dependencies between tasks, The system comprises a determination unit that determines the priority of a task based on the dependencies detected by the detection unit. A system characterized by the following features.

2. We have a suggestion department that proposes suitable personnel based on the skill sets of our employees. The system according to feature 1.

3. It includes a feature that detects undetermined or uncertain specifications and facilitates specification determination. The system according to feature 1.

4. It includes an agenda creation unit for creating MTG agendas. The system according to feature 1.

5. The aforementioned creation unit, Automatically generates reports summarizing project progress and deliverables, as well as diagrams illustrating the overall project structure. The system according to feature 1.

6. The aforementioned update unit is The system allows you to monitor the progress of each project task in real time and automatically updates the progress management sheet. The system according to feature 1.

7. The aforementioned update unit is The system estimates the user's emotions and adjusts the update frequency of the progress management table based on those estimated emotions. The system according to feature 1.

8. The aforementioned update unit is By analyzing past project data, the system automatically determines the optimal update timing. The system according to feature 1.

9. The aforementioned update unit is We will add a feature that monitors the progress of each task in real time and issues an alert if an anomaly is detected. The system according to feature 1.

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