System

Abstract

An object of a system according to an embodiment is to efficiently manage an operation task of an employee and set a priority by learning an operation task of a related person.SOLUTION: A system according to an embodiment includes an operation task collection unit, an operation task analysis unit, a related person task learning unit, a task management unit, and a priority setting unit. The operation task collection unit collects operation tasks of employees. The operation task analyzer analyzes the operation tasks collected by the operation task collector. The related-person task learning unit learns an operation task of a related person. The task management unit manages the operation task based on the information obtained by the operation task analysis unit and the concerned party task learning unit. The priority setting unit sets a priority of the operation task managed by the task management unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately manage employees' work tasks efficiently, learn the work tasks of stakeholders, and set priorities, leaving room for improvement.

[0005] The system according to the embodiment aims to efficiently manage the work tasks of employees, learn the work tasks of the relevant parties, and set priorities. [Means for solving the problem]

[0006] The system according to the embodiment includes a business task collection unit, a business task analysis unit, a related party task learning unit, a task management unit, and a priority setting unit. The business task collection unit collects business tasks of employees. The business task analysis unit analyzes the business tasks collected by the business task collection unit. The related party task learning unit learns the business tasks of related parties. The task management unit manages business tasks based on information obtained by the business task analysis unit and the related party task learning unit. The priority setting unit sets priorities for the business tasks managed by the task management unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage the work tasks of employees, learn the work tasks of the relevant parties, and set priorities. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The work task management system according to an embodiment of the present invention is a system that grasps the work tasks of all employees, learns the work tasks of related parties, and efficiently manages the work tasks of employees (TODO lists and priorities). As a result, the work task management system automatically collects and analyzes the work tasks of employees, and learns the associations with the work tasks of related parties, thereby enabling optimal management of each employee's work tasks.

[0029] A work task management system according to an embodiment includes a work task collection unit, a work task analysis unit, a participant task learning unit, a task management unit, and a priority setting unit. The work task collection unit collects work tasks of employees. For example, the work task collection unit automatically collects work tasks from tools and systems (e.g., email, chat, project management tools, etc.) that employees use on a daily basis. The work task collection unit can also analyze employees' voice conversations in real time and extract tasks from the conversation content. The work task collection unit can also analyze employees' digital calendars and automatically generate tasks from their schedules. The work task analysis unit analyzes the collected work tasks. For example, the work task analysis unit can analyze task patterns using data mining technology. The work task analysis unit can also analyze task content using text analysis technology. The work task analysis unit can also analyze task importance using pattern recognition technology. The participant task learning unit learns the work tasks of participants. For example, the participant task learning unit can learn the tasks and progress of other members of a project team. The stakeholder task learning unit can also analyze minutes of project meetings and automatically extract tasks for stakeholders. The stakeholder task learning unit can also analyze task progress reports from stakeholders and learn task dependencies. The task management unit manages work tasks. For example, the task management unit manages tasks based on the correlation between the collected and analyzed work tasks and the learned work tasks of stakeholders. The task management unit can also manage task progress and provide feedback to employees if progress is delayed. The task management unit can also analyze task dependencies and set priorities based on the dependencies. The priority setting unit sets task priorities. For example, the priority setting unit automatically sets task priorities based on importance and urgency. The priority setting unit can also analyze employees' emotional states using an emotion estimation function and present tasks in an order that will cause the least stress. The priority setting unit can also analyze the estimated completion time of tasks and adjust priorities based on the estimated time.As a result, the work task management system according to the embodiment can efficiently manage employees' work tasks and appropriately set priorities. For example, the work task management system can automatically collect and analyze employees' work tasks and learn the associations with the work tasks of related parties, thereby optimally managing each employee's work tasks. The work task management system can also manage the progress of tasks and provide feedback to employees if progress is delayed. Furthermore, the work task management system can automatically set task priorities and present tasks in an order that minimizes stress.

[0030] The work task collection unit can automatically collect work tasks from the tools and systems that employees use on a daily basis. For example, the work task collection unit uses a generation AI to analyze voice conversations during meetings in real time and automatically extract tasks from the content discussed. For example, it can automatically generate meeting minutes and list tasks from them. The work task collection unit can also analyze telephone conversations between employees and automatically extract tasks that come up during the conversation. For example, it can record instructions and requests made over the phone as tasks. The work task collection unit can also analyze the audio data when employees record voice memos and extract tasks from the memo content. For example, it can convert the contents of the voice memo into text and add it to a task list. This makes it possible to automatically collect work tasks from the tools and systems that employees use on a daily basis.

[0031] The stakeholder task learning unit learns the tasks and progress of other members in the project team and can adjust the priority of each employee's tasks based on that. For example, the generative AI in the stakeholder task learning unit analyzes employees' digital calendars and automatically generates tasks based on schedules. For example, it extracts preparation tasks from meeting schedules. The stakeholder task learning unit also analyzes events and deadlines registered in the calendar and automatically generates related tasks. For example, it lists tasks toward project deadlines. The stakeholder task learning unit also analyzes recurring events registered in employees' calendars and automatically generates recurring tasks. For example, it automatically adds tasks related to weekly regular meetings. This makes it possible to learn the tasks and progress of other members in the project team and adjust the priority of each employee's tasks.

[0032] The priority setting unit can automatically set task priorities according to importance and urgency. For example, the priority setting unit uses a generation AI to analyze the content of employees' emails and chats and evaluate their emotional tone. For example, it identifies tasks that are stressful and adjusts their priority. The priority setting unit also uses an emotion estimation function to automatically extract tasks that employees find stressful and report them to their superiors. For example, it lists tasks with high emotional scores. The priority setting unit also analyzes the emotional tone of employees' emails and chats and sends reminders for tasks that are stressful. For example, it sends early notifications for tasks with high emotional scores. This makes it possible to automatically set task priorities according to importance and urgency.

[0033] The task management unit can send notifications to employees about tasks with approaching deadlines or high-priority tasks. For example, the task management unit uses a generation AI to analyze employees' social media posts and automatically extract work-related tasks. For example, it can list tasks from posts about projects. The task management unit also analyzes employees' social media posts and collects information related to work. For example, it can record questions and suggestions about work as tasks. The task management unit also analyzes the content of social media posts and automatically generate work-related tasks. For example, it can extract tasks from posts about project progress. This makes it possible to send notifications to employees about tasks with approaching deadlines or high-priority tasks.

[0034] The task management unit manages the progress of tasks and can provide feedback to employees if progress is slow. For example, the task management unit uses a generation AI to analyze employees' browser history and automatically generate research tasks related to their work. For example, it extracts tasks from search history related to specific keywords. The task management unit also analyzes browser history and automatically generates information gathering tasks required for work. For example, it lists tasks to research information on competitors. The task management unit also automatically generates research tasks related to work based on employees' browser history. For example, it adds information gathering tasks on new technologies and trends. This makes it possible to manage the progress of tasks and provide feedback to employees if progress is slow.

[0035] The work task collection unit can analyze employees' voice conversations in real time and extract tasks from the content of the conversation. For example, the work task collection unit uses a generation AI to analyze voice conversations during meetings in real time and automatically extract tasks from the content discussed. For example, it can automatically generate meeting minutes and list tasks from them. The work task collection unit can also analyze telephone conversations between employees and automatically extract tasks that come up during the conversation. For example, it can record instructions and requests made over the phone as tasks. The work task collection unit can also analyze the voice data when employees record voice memos and extract tasks from the memo content. For example, it can convert the content of the voice memo into text and add it to a task list. This makes it possible to analyze employees' voice conversations in real time and extract tasks from the conversation content.

[0036] The work task collection unit can analyze employees' digital calendars and automatically generate tasks from schedules. For example, the generation AI in the work task collection unit analyzes employees' digital calendars and automatically generates tasks based on schedules. For example, it extracts preparation tasks from meeting schedules. The work task collection unit also analyzes events and deadlines registered in the calendar and automatically generates related tasks. For example, it lists tasks toward a project deadline. The work task collection unit also analyzes recurring schedules registered in employees' calendars and automatically generates periodic tasks. For example, it automatically adds tasks related to regular weekly meetings. This makes it possible to analyze employees' digital calendars and automatically generate tasks from schedules.

[0037] The work task collection unit can analyze employees' SNS posts and extract work-related tasks. For example, the work task collection unit uses a generation AI to analyze employees' SNS posts and automatically extract work-related tasks. For example, it lists tasks from posts related to projects. The work task collection unit also analyzes employees' SNS posts and collects information related to work. For example, it records questions and suggestions related to work as tasks. The work task collection unit also analyzes the content of SNS posts and automatically generates work-related tasks. For example, it extracts tasks from posts related to project progress. This makes it possible to analyze employees' SNS posts and extract work-related tasks.

[0038] The work task collection unit can analyze employees' browser history and automatically generate research tasks related to their work. For example, the work task collection unit uses a generation AI to analyze employees' browser history and automatically generate research tasks related to their work. For example, it extracts tasks from search history related to specific keywords. The work task collection unit also analyzes browser history and automatically generates information gathering tasks required for work. For example, it lists tasks to research information on competitors. The work task collection unit also automatically generates research tasks related to their work based on employees' browser history. For example, it adds information gathering tasks on new technologies and trends. This makes it possible to analyze employees' browser history and automatically generate research tasks related to their work.

[0039] The stakeholder task learning unit can analyze the minutes of project meetings and automatically extract tasks for stakeholders. For example, the generation AI in the stakeholder task learning unit analyzes the minutes of project meetings and automatically extracts tasks for stakeholders from the content discussed. For example, it automatically generates meeting minutes and lists tasks from them. The stakeholder task learning unit also analyzes the content of comments made during meetings and automatically extracts tasks for stakeholders. For example, it converts the content of comments into text and adds it to a task list. The stakeholder task learning unit also analyzes the minutes of project meetings and automatically generates tasks for stakeholders. For example, it records action items decided in meetings as tasks. This makes it possible to analyze the minutes of project meetings and automatically extract tasks for stakeholders.

[0040] The stakeholder task learning unit can analyze the task progress reports of stakeholders and learn task dependencies. For example, the stakeholder task learning unit uses a generation AI to analyze the task progress reports of stakeholders and automatically learn task dependencies. For example, task dependencies are extracted from the progress reports. The stakeholder task learning unit also analyzes the task progress reports and builds a system that learns task dependencies. For example, it lists task dependencies based on the content of the progress reports. The stakeholder task learning unit also analyzes the task progress reports of stakeholders and automatically generates task dependencies. For example, it extracts task dependencies from the progress reports and reflects them in a task list. In this way, task progress reports of stakeholders can be analyzed and task dependencies can be learned.

[0041] The stakeholder task learning unit can analyze the stakeholders' daily work reports and learn the progress status of tasks. In the stakeholder task learning unit, for example, a generation AI analyzes the stakeholders' daily work reports and automatically learns the progress status of tasks. For example, the task progress status is listed based on the content of the daily report. The stakeholder task learning unit also analyzes the stakeholders' daily work reports and builds a system that learns the progress status of tasks. For example, the task progress status is automatically generated based on the content of the daily report. The stakeholder task learning unit also analyzes the stakeholders' daily work reports and automatically generates the task progress status. For example, the task progress status is listed based on the content of the daily report and reflected in the task list. In this way, the stakeholder's daily work reports can be analyzed and the progress status of tasks can be learned.

[0042] The stakeholder task learning unit can analyze the schedules of stakeholders and adjust task priorities. In the stakeholder task learning unit, for example, a generation AI analyzes the schedules of stakeholders and automatically adjusts task priorities. For example, it lists task priorities based on the contents of the schedule. The stakeholder task learning unit also builds a system that analyzes schedules and adjusts task priorities. For example, it automatically generates task priorities based on the contents of the schedule. The stakeholder task learning unit also analyzes the schedules of stakeholders and automatically generates task priorities. For example, it lists task priorities based on the contents of the schedule and reflects them in the task list. In this way, the schedules of stakeholders can be analyzed and task priorities can be adjusted.

[0043] The task management unit can analyze task dependencies and set priorities based on the dependencies. In the task management unit, for example, a generation AI analyzes task dependencies and automatically sets priorities based on the dependencies. For example, it lists tasks with dependencies and adjusts priorities. The task management unit also builds a system that analyzes task dependencies and sets priorities. For example, it automatically generates tasks with dependencies and lists priorities. In the task management unit, a generation AI analyzes task dependencies and automatically generates priorities based on the dependencies. For example, it lists tasks with dependencies and reflects them in a task list. This makes it possible to analyze task dependencies and set priorities based on the dependencies.

[0044] The task management unit can analyze the predicted completion time of a task and adjust the priority based on the predicted time. In the task management unit, for example, a generation AI analyzes the predicted completion time of a task and automatically adjusts the priority based on the predicted time. For example, tasks with a short predicted completion time are listed first. The task management unit also builds a system that analyzes the predicted completion time of a task and adjusts the priority. For example, tasks with a long predicted completion time are postponed. In the task management unit, a generation AI analyzes the predicted completion time of a task and automatically generates a priority based on the predicted time. For example, tasks with a short predicted completion time are listed and reflected in the task list. This makes it possible to analyze the predicted completion time of a task and adjust the priority based on the predicted time.

[0045] The task management unit can analyze the importance of tasks and set priorities based on the importance. For example, the generation AI in the task management unit analyzes the importance of tasks and automatically sets priorities based on the importance. For example, it lists tasks with high importance first. The task management unit also builds a system that analyzes the importance of tasks and sets priorities. For example, it postpones tasks with low importance. The task management unit also builds a system that analyzes the importance of tasks and automatically generates priorities based on the importance. For example, it lists tasks with high importance and reflects this in the task list. This makes it possible to analyze the importance of tasks and set priorities based on the importance.

[0046] The task management unit can analyze the urgency of tasks and adjust priorities based on the urgency. For example, the generation AI in the task management unit analyzes the urgency of tasks and automatically adjusts priorities based on the urgency. For example, it lists tasks with high urgency first. The task management unit also builds a system that analyzes the urgency of tasks and adjusts priorities. For example, it postpones tasks with low urgency. The task management unit also builds a system that analyzes the urgency of tasks and automatically generates priorities based on the urgency. For example, it lists tasks with high urgency and reflects this in the task list. This makes it possible to analyze the urgency of tasks and adjust priorities based on the urgency.

[0047] The task management unit can analyze employees' location information and notify them of tasks at the appropriate time. For example, the task management unit uses a generation AI to analyze employees' location information and notify them of tasks at the appropriate time. For example, it can notify an employee of a task when they arrive at the office. The task management unit can also build a system that notifies employees of tasks when they are in a specific location based on location information. For example, it can notify them of meeting-related tasks when they are in a conference room. The task management unit can also analyze employees' location information and notify them of tasks at the appropriate time. For example, it can notify an employee of remote work-related tasks when they are at home. This makes it possible to analyze employees' location information and notify them of tasks at the appropriate time.

[0048] The task management unit can analyze employees' device usage and send notifications on the optimal device. For example, the task management unit uses a generation AI to analyze employees' device usage and send task notifications on the optimal device. For example, when an employee is using a PC, notifications are sent on the PC. The task management unit also builds a system that sends task notifications on the optimal device based on device usage. For example, when an employee is using a smartphone, notifications are sent on the smartphone. The task management unit also analyzes employees' device usage and sends task notifications on the optimal device. For example, when an employee is using a tablet, notifications are sent on the tablet. This makes it possible to analyze employees' device usage and send notifications on the optimal device.

[0049] The task management unit can analyze employees' calendars and set reminders based on their schedules. For example, the task management unit uses a generation AI to analyze employees' calendars and automatically set reminders based on their schedules. For example, sending a reminder before a meeting. The task management unit also builds a system that sets reminders based on the schedules, based on the contents of the calendar. For example, sending a reminder before a project deadline. The task management unit also analyzes employees' calendars and automatically generates reminders based on their schedules. For example, sending a reminder before an important event. This makes it possible to analyze employees' calendars and set reminders based on their schedules.

[0050] The task management unit analyzes the reply status of employees' emails and chats and can send reminders at the appropriate time. For example, the task management unit uses a generation AI to analyze the reply status of employees' emails and chats and send reminders at the appropriate time. For example, it sends a reminder if a reply is delayed. The task management unit also builds a system that sends reminders at the appropriate time based on the reply status of emails and chats. For example, it sends reminders for tasks that require a reply. The task management unit also analyzes the reply status of employees' emails and chats and can send reminders at the appropriate time. For example, it sends a reminder for tasks that are delayed in replying. This makes it possible to analyze the reply status of employees' emails and chats and send reminders at the appropriate time.

[0051] The task management unit can predict task completion and provide feedback based on the prediction. In the task management unit, for example, a generation AI predicts task completion and provides feedback based on the prediction. For example, early feedback is provided for tasks with a long predicted completion time. The task management unit also builds a system that predicts task completion and provides feedback based on the prediction. For example, feedback is provided for tasks with a short predicted completion time. In the task management unit, a generation AI predicts task completion and provides feedback based on the prediction. For example, early feedback is provided for tasks with a long predicted completion time. This makes it possible to predict task completion and provide feedback based on the prediction.

[0052] The task management unit can visualize the progress of tasks and display it in graphs and charts. For example, the generation AI in the task management unit visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a bar graph or pie chart. The task management unit also builds a system that visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a line graph. The task management unit also visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a heat map. This makes it possible to visualize the progress of tasks and display it in graphs and charts.

[0053] The task management unit can integrate the progress of tasks with other project management tools to manage them in an integrated manner. For example, the generation AI in the task management unit integrates the progress of tasks with other project management tools to manage them in an integrated manner. For example, the task progress is managed in collaboration with Trello or Asana. The task management unit also integrates the progress of tasks with other project management tools to build a system for integrated management. For example, the task progress is managed in collaboration with JIRA or Basecamp. The task management unit also integrates the progress of tasks with other project management tools to manage them in an integrated manner. For example, the task progress is managed in collaboration with Microsoft Project or Monday.com. This allows the progress of tasks to be integrated with other project management tools to manage them in an integrated manner.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The business task management system may further include a dependency display unit that visually displays task dependencies. For example, the dependency display unit may visually display the dependencies between tasks using graphs or charts, making it easier for employees to grasp the overall picture of the tasks. The dependency display unit may also dynamically update the dependencies according to the progress of the tasks, supporting real-time task management. Furthermore, the dependency display unit may automatically adjust priorities based on task dependencies, enabling efficient task management.

[0056] The business task management system may further include a progress prediction unit that predicts the progress of a task. For example, the progress prediction unit may analyze past task data and predict the current progress of the task. The progress prediction unit may also adjust the priority of the task based on the predicted progress, supporting efficient task management. Furthermore, the progress prediction unit may provide feedback according to the task progress to support employees in completing the task.

[0057] The business task management system may further include an importance evaluation unit that evaluates the importance of a task. For example, the importance evaluation unit may analyze the content of the task and the importance of the related project to evaluate the importance of the task. The importance evaluation unit may also automatically set the priority of the task based on the evaluated importance, thereby realizing efficient task management. Furthermore, the importance evaluation unit may send reminders according to the importance of the task to prevent employees from missing important tasks.

[0058] The business task management system may further include an urgency assessment unit that assesses the urgency of a task. For example, the urgency assessment unit may analyze the deadline for the task and the progress of related projects to assess the urgency of the task. The urgency assessment unit may also automatically set the priority of the task based on the assessed urgency, thereby achieving efficient task management. Furthermore, the urgency assessment unit may send reminders according to the urgency of the task to prevent employees from overlooking urgent tasks.

[0059] The business task management system may further include a completion prediction unit that evaluates the estimated completion time of a task. For example, the completion prediction unit may analyze past task data and evaluate the estimated completion time of a current task. The completion prediction unit may also automatically set task priorities based on the estimated estimated completion times, thereby achieving efficient task management. Furthermore, the completion prediction unit may send reminders according to the estimated completion times of the tasks to support employees in completing tasks efficiently.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The work task collection unit collects employees' work tasks. For example, the work task collection unit automatically collects work tasks from tools and systems that employees use on a daily basis (e.g., email, chat, project management tools, etc.). The work task collection unit can also analyze employees' voice conversations in real time and extract tasks from the conversation content. Furthermore, the work task collection unit can analyze employees' digital calendars and automatically generate tasks from schedules. Step 2: The work task analysis unit analyzes the collected work tasks. For example, the work task analysis unit analyzes task patterns using data mining technology. The work task analysis unit can also analyze task content using text analysis technology. Furthermore, the work task analysis unit can also analyze task importance using pattern recognition technology. Step 3: The stakeholder task learning unit learns the work tasks of stakeholders. For example, the stakeholder task learning unit learns the tasks and progress status of other members in the project team. The stakeholder task learning unit can also analyze the minutes of project meetings and automatically extract the tasks of stakeholders. Furthermore, the stakeholder task learning unit can analyze the task progress reports of stakeholders and learn the task dependencies. Step 4: The task management unit manages work tasks. For example, the task management unit manages tasks based on the collected and analyzed work tasks and the learned relevance of the work tasks of the relevant parties. The task management unit can also manage the progress of tasks and provide feedback to employees if progress is delayed. Furthermore, the task management unit can analyze task dependencies and set priorities based on the dependencies. Step 5: The priority setting unit sets the priority of the tasks. For example, the priority setting unit automatically sets the priority of the tasks according to the importance or urgency. The priority setting unit can also use an emotion estimation function to analyze the emotional state of employees and present tasks in an order that will cause the least stress. Furthermore, the priority setting unit can analyze the estimated completion time of the tasks and adjust the priority based on the estimated time.

[0062] (Example 2) The work task management system according to an embodiment of the present invention is a system that grasps the work tasks of all employees, learns the work tasks of related parties, and efficiently manages the work tasks of employees (TODO lists and priorities). As a result, the work task management system automatically collects and analyzes the work tasks of employees, and learns the associations with the work tasks of related parties, thereby enabling optimal management of each employee's work tasks.

[0063] A work task management system according to an embodiment includes a work task collection unit, a work task analysis unit, a participant task learning unit, a task management unit, and a priority setting unit. The work task collection unit collects work tasks of employees. For example, the work task collection unit automatically collects work tasks from tools and systems (e.g., email, chat, project management tools, etc.) that employees use on a daily basis. The work task collection unit can also analyze employees' voice conversations in real time and extract tasks from the conversation content. The work task collection unit can also analyze employees' digital calendars and automatically generate tasks from their schedules. The work task analysis unit analyzes the collected work tasks. For example, the work task analysis unit can analyze task patterns using data mining technology. The work task analysis unit can also analyze task content using text analysis technology. The work task analysis unit can also analyze task importance using pattern recognition technology. The participant task learning unit learns the work tasks of participants. For example, the participant task learning unit can learn the tasks and progress of other members of a project team. The stakeholder task learning unit can also analyze minutes of project meetings and automatically extract tasks for stakeholders. The stakeholder task learning unit can also analyze task progress reports from stakeholders and learn task dependencies. The task management unit manages work tasks. For example, the task management unit manages tasks based on the correlation between the collected and analyzed work tasks and the learned work tasks of stakeholders. The task management unit can also manage task progress and provide feedback to employees if progress is delayed. The task management unit can also analyze task dependencies and set priorities based on the dependencies. The priority setting unit sets task priorities. For example, the priority setting unit automatically sets task priorities based on importance and urgency. The priority setting unit can also analyze employees' emotional states using an emotion estimation function and present tasks in an order that will cause the least stress. The priority setting unit can also analyze the estimated completion time of tasks and adjust priorities based on the estimated time.As a result, the work task management system according to the embodiment can efficiently manage employees' work tasks and appropriately set priorities. For example, the work task management system can automatically collect and analyze employees' work tasks and learn the associations with the work tasks of related parties, thereby optimally managing each employee's work tasks. The work task management system can also manage the progress of tasks and provide feedback to employees if progress is delayed. Furthermore, the work task management system can automatically set task priorities and present tasks in an order that minimizes stress.

[0064] The work task collection unit can automatically collect work tasks from the tools and systems that employees use on a daily basis. For example, the work task collection unit uses a generation AI to analyze voice conversations during meetings in real time and automatically extract tasks from the content discussed. For example, it can automatically generate meeting minutes and list tasks from them. The work task collection unit can also analyze telephone conversations between employees and automatically extract tasks that come up during the conversation. For example, it can record instructions and requests made over the phone as tasks. The work task collection unit can also analyze the audio data when employees record voice memos and extract tasks from the memo content. For example, it can convert the contents of the voice memo into text and add it to a task list. This makes it possible to automatically collect work tasks from the tools and systems that employees use on a daily basis.

[0065] The stakeholder task learning unit learns the tasks and progress of other members in the project team and can adjust the priority of each employee's tasks based on that. For example, the generative AI in the stakeholder task learning unit analyzes employees' digital calendars and automatically generates tasks based on schedules. For example, it extracts preparation tasks from meeting schedules. The stakeholder task learning unit also analyzes events and deadlines registered in the calendar and automatically generates related tasks. For example, it lists tasks toward project deadlines. The stakeholder task learning unit also analyzes recurring events registered in employees' calendars and automatically generates recurring tasks. For example, it automatically adds tasks related to weekly regular meetings. This makes it possible to learn the tasks and progress of other members in the project team and adjust the priority of each employee's tasks.

[0066] The priority setting unit can automatically set task priorities according to importance and urgency. For example, the priority setting unit uses a generation AI to analyze the content of employees' emails and chats and evaluate their emotional tone. For example, it identifies tasks that are stressful and adjusts their priority. The priority setting unit also uses an emotion estimation function to automatically extract tasks that employees find stressful and report them to their superiors. For example, it lists tasks with high emotional scores. The priority setting unit also analyzes the emotional tone of employees' emails and chats and sends reminders for tasks that are stressful. For example, it sends early notifications for tasks with high emotional scores. This makes it possible to automatically set task priorities according to importance and urgency.

[0067] The task management unit can send notifications to employees about tasks with approaching deadlines or high-priority tasks. For example, the task management unit uses a generation AI to analyze employees' social media posts and automatically extract work-related tasks. For example, it can list tasks from posts about projects. The task management unit also analyzes employees' social media posts and collects information related to work. For example, it can record questions and suggestions about work as tasks. The task management unit also analyzes the content of social media posts and automatically generate work-related tasks. For example, it can extract tasks from posts about project progress. This makes it possible to send notifications to employees about tasks with approaching deadlines or high-priority tasks.

[0068] The task management unit manages the progress of tasks and can provide feedback to employees if progress is slow. For example, the task management unit uses a generation AI to analyze employees' browser history and automatically generate research tasks related to their work. For example, it extracts tasks from search history related to specific keywords. The task management unit also analyzes browser history and automatically generates information gathering tasks required for work. For example, it lists tasks to research information on competitors. The task management unit also automatically generates research tasks related to work based on employees' browser history. For example, it adds information gathering tasks on new technologies and trends. This makes it possible to manage the progress of tasks and provide feedback to employees if progress is slow.

[0069] The work task collection unit can analyze employees' voice conversations in real time and extract tasks from the content of the conversation. For example, the work task collection unit uses a generation AI to analyze voice conversations during meetings in real time and automatically extract tasks from the content discussed. For example, it can automatically generate meeting minutes and list tasks from them. The work task collection unit can also analyze telephone conversations between employees and automatically extract tasks that come up during the conversation. For example, it can record instructions and requests made over the phone as tasks. The work task collection unit can also analyze the voice data when employees record voice memos and extract tasks from the memo content. For example, it can convert the content of the voice memo into text and add it to a task list. This makes it possible to analyze employees' voice conversations in real time and extract tasks from the conversation content.

[0070] The work task collection unit can analyze employees' digital calendars and automatically generate tasks from schedules. For example, the generation AI in the work task collection unit analyzes employees' digital calendars and automatically generates tasks based on schedules. For example, it extracts preparation tasks from meeting schedules. The work task collection unit also analyzes events and deadlines registered in the calendar and automatically generates related tasks. For example, it lists tasks toward a project deadline. The work task collection unit also analyzes recurring schedules registered in employees' calendars and automatically generates periodic tasks. For example, it automatically adds tasks related to regular weekly meetings. This makes it possible to analyze employees' digital calendars and automatically generate tasks from schedules.

[0071] The work task collection unit can use the emotion estimation function to analyze the emotional tone of employees' emails and chats, identify high-stress tasks, and adjust their priorities. For example, the work task collection unit uses a generation AI to analyze the content of employees' emails and chats and evaluate the emotional tone. For example, it identifies high-stress tasks and adjusts their priorities. The work task collection unit also uses the emotion estimation function to automatically extract tasks that employees find stressful and report them to their superiors. For example, it lists tasks with high emotional scores. The work task collection unit also analyzes the emotional tone of employees' emails and chats and sends reminders for high-stress tasks. For example, it sends early notifications for tasks with high emotional scores. This makes it possible to use the emotion estimation function to analyze the emotional tone of employees' emails and chats, identify high-stress tasks, and adjust their priorities.

[0072] The work task collection unit can analyze employees' SNS posts and extract work-related tasks. For example, the work task collection unit uses a generation AI to analyze employees' SNS posts and automatically extract work-related tasks. For example, it lists tasks from posts related to projects. The work task collection unit also analyzes employees' SNS posts and collects information related to work. For example, it records questions and suggestions related to work as tasks. The work task collection unit also analyzes the content of SNS posts and automatically generates work-related tasks. For example, it extracts tasks from posts related to project progress. This makes it possible to analyze employees' SNS posts and extract work-related tasks.

[0073] The work task collection unit can analyze employees' browser history and automatically generate research tasks related to their work. For example, the work task collection unit uses a generation AI to analyze employees' browser history and automatically generate research tasks related to their work. For example, it extracts tasks from search history related to specific keywords. The work task collection unit also analyzes browser history and automatically generates information gathering tasks required for work. For example, it lists tasks to research information on competitors. The work task collection unit also automatically generates research tasks related to their work based on employees' browser history. For example, it adds information gathering tasks on new technologies and trends. This makes it possible to analyze employees' browser history and automatically generate research tasks related to their work.

[0074] The work task collection unit uses the emotion estimation function to estimate the emotions of employees when they enter tasks in real time and make suggestions that will elicit positive emotions. For example, the work task collection unit uses a generation AI to analyze the emotions of employees when they enter tasks in real time and make suggestions that will elicit positive emotions. For example, it displays encouraging messages. The work task collection unit also uses the emotion estimation function to analyze the emotions of employees when they enter tasks and provides an interface for eliciting positive emotions. For example, it presents success stories. The work task collection unit also analyzes the emotions of employees when they enter tasks in real time and makes suggestions that will elicit positive emotions. For example, it displays appropriate words of encouragement or praise based on the input content. This makes it possible to estimate the emotions of employees when they enter tasks in real time and make suggestions that will elicit positive emotions.

[0075] The stakeholder task learning unit can analyze the minutes of project meetings and automatically extract tasks for stakeholders. For example, the generation AI in the stakeholder task learning unit analyzes the minutes of project meetings and automatically extracts tasks for stakeholders from the content discussed. For example, it automatically generates meeting minutes and lists tasks from them. The stakeholder task learning unit also analyzes the content of comments made during meetings and automatically extracts tasks for stakeholders. For example, it converts the content of comments into text and adds it to a task list. The stakeholder task learning unit also analyzes the minutes of project meetings and automatically generates tasks for stakeholders. For example, it records action items decided in meetings as tasks. This makes it possible to analyze the minutes of project meetings and automatically extract tasks for stakeholders.

[0076] The stakeholder task learning unit can analyze the task progress reports of stakeholders and learn task dependencies. For example, the stakeholder task learning unit uses a generation AI to analyze the task progress reports of stakeholders and automatically learn task dependencies. For example, task dependencies are extracted from the progress reports. The stakeholder task learning unit also analyzes the task progress reports and builds a system that learns task dependencies. For example, it lists task dependencies based on the content of the progress reports. The stakeholder task learning unit also analyzes the task progress reports of stakeholders and automatically generates task dependencies. For example, it extracts task dependencies from the progress reports and reflects them in a task list. In this way, task progress reports of stakeholders can be analyzed and task dependencies can be learned.

[0077] The stakeholder task learning unit can use the emotion estimation function to analyze stakeholders' emotions regarding tasks and prioritize learning of emotionally important tasks. For example, the stakeholder task learning unit uses a generation AI to analyze stakeholders' emotions regarding tasks and prioritize learning of emotionally important tasks. For example, it lists tasks with high emotion scores. The stakeholder task learning unit also uses the emotion estimation function to analyze stakeholders' emotions regarding tasks and builds a system that prioritizes learning of emotionally important tasks. For example, it adjusts task priorities based on the emotion scores. The stakeholder task learning unit also analyzes stakeholders' emotions regarding tasks and prioritizes learning of emotionally important tasks. For example, it sends reminders for tasks with high emotion scores. This makes it possible to use the emotion estimation function to analyze stakeholders' emotions regarding tasks and prioritize learning of emotionally important tasks.

[0078] The stakeholder task learning unit can analyze the stakeholders' daily work reports and learn the progress status of tasks. In the stakeholder task learning unit, for example, a generation AI analyzes the stakeholders' daily work reports and automatically learns the progress status of tasks. For example, the task progress status is listed based on the content of the daily report. The stakeholder task learning unit also analyzes the stakeholders' daily work reports and builds a system that learns the progress status of tasks. For example, the task progress status is automatically generated based on the content of the daily report. The stakeholder task learning unit also analyzes the stakeholders' daily work reports and automatically generates the task progress status. For example, the task progress status is listed based on the content of the daily report and reflected in the task list. In this way, the stakeholder's daily work reports can be analyzed and the progress status of tasks can be learned.

[0079] The stakeholder task learning unit can analyze the schedules of stakeholders and adjust task priorities. In the stakeholder task learning unit, for example, a generation AI analyzes the schedules of stakeholders and automatically adjusts task priorities. For example, it lists task priorities based on the contents of the schedule. The stakeholder task learning unit also builds a system that analyzes schedules and adjusts task priorities. For example, it automatically generates task priorities based on the contents of the schedule. The stakeholder task learning unit also analyzes the schedules of stakeholders and automatically generates task priorities. For example, it lists task priorities based on the contents of the schedule and reflects them in the task list. In this way, the schedules of stakeholders can be analyzed and task priorities can be adjusted.

[0080] The stakeholder task learning unit can use the emotion estimation function to collect the emotional reactions of stakeholders to tasks and identify tasks that are likely to be emotionally empathetic. For example, the stakeholder task learning unit uses a generation AI to collect the emotional reactions of stakeholders to tasks and identify tasks that are likely to be emotionally empathetic. For example, it lists tasks with high emotion scores. The stakeholder task learning unit also uses the emotion estimation function to collect the emotional reactions of stakeholders to tasks and builds a system that identifies tasks that are likely to be emotionally empathetic. For example, it adjusts task priorities based on the emotion scores. The stakeholder task learning unit also collects the emotional reactions of stakeholders to tasks and identifies tasks that are likely to be emotionally empathetic. For example, it sends reminders for tasks with high emotion scores. In this way, the emotion estimation function can be used to collect the emotional reactions of stakeholders to tasks and identify tasks that are likely to be emotionally empathetic.

[0081] The task management unit can analyze task dependencies and set priorities based on the dependencies. In the task management unit, for example, a generation AI analyzes task dependencies and automatically sets priorities based on the dependencies. For example, it lists tasks with dependencies and adjusts priorities. The task management unit also builds a system that analyzes task dependencies and sets priorities. For example, it automatically generates tasks with dependencies and lists priorities. In the task management unit, a generation AI analyzes task dependencies and automatically generates priorities based on the dependencies. For example, it lists tasks with dependencies and reflects them in a task list. This makes it possible to analyze task dependencies and set priorities based on the dependencies.

[0082] The task management unit can analyze the predicted completion time of a task and adjust the priority based on the predicted time. In the task management unit, for example, a generation AI analyzes the predicted completion time of a task and automatically adjusts the priority based on the predicted time. For example, tasks with a short predicted completion time are listed first. The task management unit also builds a system that analyzes the predicted completion time of a task and adjusts the priority. For example, tasks with a long predicted completion time are postponed. In the task management unit, a generation AI analyzes the predicted completion time of a task and automatically generates a priority based on the predicted time. For example, tasks with a short predicted completion time are listed and reflected in the task list. This makes it possible to analyze the predicted completion time of a task and adjust the priority based on the predicted time.

[0083] The task management unit can use the emotion estimation function to analyze the emotional state of employees and present tasks in an order that will cause less stress. For example, the task management unit uses a generation AI to analyze the emotional state of employees and present tasks in an order that will cause less stress. For example, it prioritizes listing tasks with low emotion scores. The task management unit also uses the emotion estimation function to build a system that analyzes the emotional state of employees and presents tasks in an order that will cause less stress. For example, it adjusts the order of tasks based on the emotion score. The task management unit also analyzes the emotional state of employees and presents tasks in an order that will cause less stress. For example, it sends reminders for tasks with low emotion scores. This makes it possible to use the emotion estimation function to analyze the emotional state of employees and present tasks in an order that will cause less stress.

[0084] The task management unit can analyze the importance of tasks and set priorities based on the importance. For example, the generation AI in the task management unit analyzes the importance of tasks and automatically sets priorities based on the importance. For example, it lists tasks with high importance first. The task management unit also builds a system that analyzes the importance of tasks and sets priorities. For example, it postpones tasks with low importance. The task management unit also builds a system that analyzes the importance of tasks and automatically generates priorities based on the importance. For example, it lists tasks with high importance and reflects this in the task list. This makes it possible to analyze the importance of tasks and set priorities based on the importance.

[0085] The task management unit can analyze the urgency of tasks and adjust priorities based on the urgency. For example, the generation AI in the task management unit analyzes the urgency of tasks and automatically adjusts priorities based on the urgency. For example, it lists tasks with high urgency first. The task management unit also builds a system that analyzes the urgency of tasks and adjusts priorities. For example, it postpones tasks with low urgency. The task management unit also builds a system that analyzes the urgency of tasks and automatically generates priorities based on the urgency. For example, it lists tasks with high urgency and reflects this in the task list. This makes it possible to analyze the urgency of tasks and adjust priorities based on the urgency.

[0086] The task management unit can use the emotion estimation function to identify the time periods when employees can concentrate best and assign important tasks to those time periods. For example, the task management unit uses a generation AI to analyze the emotional state of employees and identify the time periods when they can concentrate best. For example, important tasks are assigned to time periods with high emotion scores. The task management unit also uses the emotion estimation function to build a system that identifies the time periods when employees can concentrate best and assigns important tasks to those time periods. For example, it adjusts task assignment based on the emotion scores. The task management unit also analyzes the emotional state of employees and assigns important tasks to time periods when they can concentrate best. For example, it sends reminders during time periods with high emotion scores. This allows the emotion estimation function to identify the time periods when employees can concentrate best and assign important tasks to those time periods.

[0087] The task management unit can analyze employees' location information and notify them of tasks at the appropriate time. For example, the task management unit uses a generation AI to analyze employees' location information and notify them of tasks at the appropriate time. For example, it can notify an employee of a task when they arrive at the office. The task management unit can also build a system that notifies employees of tasks when they are in a specific location based on location information. For example, it can notify them of meeting-related tasks when they are in a conference room. The task management unit can also analyze employees' location information and notify them of tasks at the appropriate time. For example, it can notify an employee of remote work-related tasks when they are at home. This makes it possible to analyze employees' location information and notify them of tasks at the appropriate time.

[0088] The task management unit can analyze employees' device usage and send notifications on the optimal device. For example, the task management unit uses a generation AI to analyze employees' device usage and send task notifications on the optimal device. For example, when an employee is using a PC, notifications are sent on the PC. The task management unit also builds a system that sends task notifications on the optimal device based on device usage. For example, when an employee is using a smartphone, notifications are sent on the smartphone. The task management unit also analyzes employees' device usage and sends task notifications on the optimal device. For example, when an employee is using a tablet, notifications are sent on the tablet. This makes it possible to analyze employees' device usage and send notifications on the optimal device.

[0089] The task management unit can use the emotion estimation function to select a notification method according to the emotional state of an employee. For example, the task management unit uses a generation AI to analyze the emotional state of an employee and select a notification method according to that emotional state. For example, if the emotion score is low, the notification is sent in a gentle tone. The task management unit also uses the emotion estimation function to build a system that selects a notification method according to the emotional state of an employee. For example, if the emotion score is high, the notification is sent with an encouraging message. The task management unit also analyzes the emotional state of an employee and selects a notification method according to that emotional state. For example, if the emotion score is low, the reminder is sent more gently. In this way, the emotion estimation function can be used to select a notification method according to the emotional state of an employee.

[0090] The task management unit can analyze employees' calendars and set reminders based on their schedules. For example, the task management unit uses a generation AI to analyze employees' calendars and automatically set reminders based on their schedules. For example, sending a reminder before a meeting. The task management unit also builds a system that sets reminders based on the schedules, based on the contents of the calendar. For example, sending a reminder before a project deadline. The task management unit also analyzes employees' calendars and automatically generates reminders based on their schedules. For example, sending a reminder before an important event. This makes it possible to analyze employees' calendars and set reminders based on their schedules.

[0091] The task management unit analyzes the reply status of employees' emails and chats and can send reminders at the appropriate time. For example, the task management unit uses a generation AI to analyze the reply status of employees' emails and chats and send reminders at the appropriate time. For example, it sends a reminder if a reply is delayed. The task management unit also builds a system that sends reminders at the appropriate time based on the reply status of emails and chats. For example, it sends reminders for tasks that require a reply. The task management unit also analyzes the reply status of employees' emails and chats and can send reminders at the appropriate time. For example, it sends a reminder for tasks that are delayed in replying. This makes it possible to analyze the reply status of employees' emails and chats and send reminders at the appropriate time.

[0092] The task management unit can use the emotion estimation function to send reminders during times when employees are most relaxed. For example, the task management unit uses a generation AI to analyze the emotional state of employees and send reminders during times when they are most relaxed. For example, reminders are sent during times when the emotion score is high. The task management unit also uses the emotion estimation function to build a system that sends reminders during times when employees are most relaxed. For example, the timing of reminders is adjusted based on the emotion score. The task management unit also analyzes the emotional state of employees and sends reminders during times when they are most relaxed. For example, reminders are sent during times when the emotion score is high. In this way, the emotion estimation function can be used to send reminders during times when employees are most relaxed.

[0093] The task management unit can predict task completion and provide feedback based on the prediction. In the task management unit, for example, a generation AI predicts task completion and provides feedback based on the prediction. For example, early feedback is provided for tasks with a long predicted completion time. The task management unit also builds a system that predicts task completion and provides feedback based on the prediction. For example, feedback is provided for tasks with a short predicted completion time. In the task management unit, a generation AI predicts task completion and provides feedback based on the prediction. For example, early feedback is provided for tasks with a long predicted completion time. This makes it possible to predict task completion and provide feedback based on the prediction.

[0094] The task management unit can use the emotion estimation function to analyze the emotional state of employees and provide feedback that increases motivation. For example, the task management unit uses a generation AI to analyze the emotional state of employees and provide feedback that increases motivation. For example, if the emotion score is low, an encouraging message is sent. The task management unit also uses the emotion estimation function to build a system that analyzes the emotional state of employees and provides feedback that increases motivation. For example, the content of the feedback is adjusted based on the emotion score. The task management unit also analyzes the emotional state of employees and provides feedback that increases motivation. For example, if the emotion score is low, positive feedback is sent. In this way, the emotion estimation function can be used to analyze the emotional state of employees and provide feedback that increases motivation.

[0095] The task management unit can visualize the progress of tasks and display it in graphs and charts. For example, the generation AI in the task management unit visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a bar graph or pie chart. The task management unit also builds a system that visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a line graph. The task management unit also visualizes the progress of tasks and displays it in graphs and charts. For example, the task progress is displayed in a heat map. This makes it possible to visualize the progress of tasks and display it in graphs and charts.

[0096] The task management unit can integrate the progress of tasks with other project management tools to manage them in an integrated manner. For example, the generation AI in the task management unit integrates the progress of tasks with other project management tools to manage them in an integrated manner. For example, the task progress is managed in collaboration with Trello or Asana. The task management unit also integrates the progress of tasks with other project management tools to build a system for integrated management. For example, the task progress is managed in collaboration with JIRA or Basecamp. The task management unit also integrates the progress of tasks with other project management tools to manage them in an integrated manner. For example, the task progress is managed in collaboration with Microsoft Project or Monday.com. This allows the progress of tasks to be integrated with other project management tools to manage them in an integrated manner.

[0097] The task management unit can use the emotion estimation function to provide feedback at the timing when employees are most likely to accept it. For example, the task management unit uses a generation AI to analyze the emotional state of employees and provide feedback at the timing when employees are most likely to accept it. For example, feedback is sent during a time period when the emotion score is high. The task management unit also uses the emotion estimation function to build a system that provides feedback at the timing when employees are most likely to accept it. For example, the timing of feedback is adjusted based on the emotion score. The task management unit also analyzes the emotional state of employees and provides feedback at the timing when employees are most likely to accept it. For example, positive feedback is sent during a time period when the emotion score is high. In this way, the emotion estimation function can be used to provide feedback at the timing when employees are most likely to accept it.

[0098] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0099] The business task management system may further include a dependency display unit that visually displays task dependencies. For example, the dependency display unit may visually display the dependencies between tasks using graphs or charts, making it easier for employees to grasp the overall picture of the tasks. The dependency display unit may also dynamically update the dependencies according to the progress of the tasks, supporting real-time task management. Furthermore, the dependency display unit may automatically adjust priorities based on task dependencies, enabling efficient task management.

[0100] The business task management system may further include a progress prediction unit that predicts the progress of a task. For example, the progress prediction unit may analyze past task data and predict the current progress of the task. The progress prediction unit may also adjust the priority of the task based on the predicted progress, supporting efficient task management. Furthermore, the progress prediction unit may provide feedback according to the task progress to support employees in completing the task.

[0101] The business task management system may further include an importance evaluation unit that evaluates the importance of a task. For example, the importance evaluation unit may analyze the content of the task and the importance of the related project to evaluate the importance of the task. The importance evaluation unit may also automatically set the priority of the task based on the evaluated importance, thereby realizing efficient task management. Furthermore, the importance evaluation unit may send reminders according to the importance of the task to prevent employees from missing important tasks.

[0102] The business task management system may further include an urgency assessment unit that assesses the urgency of a task. For example, the urgency assessment unit may analyze the deadline for the task and the progress of related projects to assess the urgency of the task. The urgency assessment unit may also automatically set the priority of the task based on the assessed urgency, thereby achieving efficient task management. Furthermore, the urgency assessment unit may send reminders according to the urgency of the task to prevent employees from overlooking urgent tasks.

[0103] The business task management system may further include a completion prediction unit that evaluates the estimated completion time of a task. For example, the completion prediction unit may analyze past task data and evaluate the estimated completion time of a current task. The completion prediction unit may also automatically set task priorities based on the estimated estimated completion times, thereby achieving efficient task management. Furthermore, the completion prediction unit may send reminders according to the estimated completion times of the tasks to support employees in completing tasks efficiently.

[0104] The work task management system can further use an emotion estimation function to analyze the emotional state of employees and provide feedback that enhances motivation. For example, the emotion estimation function can analyze the content of employees' emails and chats to evaluate their emotional state. The emotion estimation function can also send positive feedback or encouraging messages based on the evaluated emotional state to enhance employee motivation. Furthermore, the emotion estimation function can adjust task priorities according to the employee's emotional state and present tasks in an order that minimizes stress.

[0105] The work task management system can further use the emotion estimation function to identify the time periods when employees can concentrate best and assign important tasks to those time periods. For example, the emotion estimation function analyzes an employee's emotional state and evaluates the time periods when they can concentrate best. The emotion estimation function can also improve employee productivity by assigning important tasks based on the evaluated time periods. Furthermore, the emotion estimation function can send reminders based on the employee's emotional state and notify them of tasks at the optimal time.

[0106] The work task management system can further use an emotion estimation function to select a notification method according to the employee's emotional state. For example, the emotion estimation function analyzes the employee's emotional state and evaluates the notification method according to the emotional state. The emotion estimation function can also reduce the employee's stress by notifying them with a gentle tone or an encouraging message based on the evaluated emotional state. Furthermore, the emotion estimation function can adjust the timing of reminders according to the employee's emotional state and notify them at the optimal time.

[0107] The work task management system can further use the emotion estimation function to send reminders at times when employees are most relaxed. For example, the emotion estimation function analyzes the emotional state of employees and evaluates the time of day when they are most relaxed. The emotion estimation function can also send reminders based on the evaluated time of day, thereby reducing employee stress and supporting efficient task management. Furthermore, the emotion estimation function can adjust the content of reminders according to the employee's emotional state and send notifications at the optimal time.

[0108] The work task management system can further use an emotion estimation function to provide feedback at the timing when employees are most receptive to feedback. For example, the emotion estimation function analyzes an employee's emotional state and evaluates the timing when they are most receptive to feedback. Based on the evaluated timing, the emotion estimation function can also increase employee motivation by sending positive feedback or encouraging messages. Furthermore, the emotion estimation function can adjust the content of feedback according to the employee's emotional state and provide feedback at the optimal timing.

[0109] The processing flow of the second embodiment will be briefly explained below.

[0110] Step 1: The work task collection unit collects employees' work tasks. For example, the work task collection unit automatically collects work tasks from tools and systems that employees use on a daily basis (e.g., email, chat, project management tools, etc.). The work task collection unit can also analyze employees' voice conversations in real time and extract tasks from the conversation content. Furthermore, the work task collection unit can analyze employees' digital calendars and automatically generate tasks from schedules. Step 2: The work task analysis unit analyzes the collected work tasks. For example, the work task analysis unit analyzes task patterns using data mining technology. The work task analysis unit can also analyze task content using text analysis technology. Furthermore, the work task analysis unit can also analyze task importance using pattern recognition technology. Step 3: The stakeholder task learning unit learns the work tasks of stakeholders. For example, the stakeholder task learning unit learns the tasks and progress status of other members in the project team. The stakeholder task learning unit can also analyze the minutes of project meetings and automatically extract the tasks of stakeholders. Furthermore, the stakeholder task learning unit can analyze the task progress reports of stakeholders and learn the task dependencies. Step 4: The task management unit manages work tasks. For example, the task management unit manages tasks based on the collected and analyzed work tasks and the learned relevance of the work tasks of the relevant parties. The task management unit can also manage the progress of tasks and provide feedback to employees if progress is delayed. Furthermore, the task management unit can analyze task dependencies and set priorities based on the dependencies. Step 5: The priority setting unit sets the priority of the tasks. For example, the priority setting unit automatically sets the priority of the tasks according to the importance or urgency. The priority setting unit can also use an emotion estimation function to analyze the emotional state of employees and present tasks in an order that will cause the least stress. Furthermore, the priority setting unit can analyze the estimated completion time of the tasks and adjust the priority based on the estimated time.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0113] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0116] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0117] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0118] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0119] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0120] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0121] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0125] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0126] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0127] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0128] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0145] 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.

[0146] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0147] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0148] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0150] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0151] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0152] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0153] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0154] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0155] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0156] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0157] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0158] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0159] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0160] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0161] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0162] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0163] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0164] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0165] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0166] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0167] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0168] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0169] 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.

[0170] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0171] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0172] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0173] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0174] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0175] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0176] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0177] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a task collection unit that collects employee task information; a task analysis unit that analyzes the task collected by the task collection unit; a stakeholder task learning unit that learns the business tasks of stakeholders; a task management unit that manages business tasks based on information obtained by the business task analysis unit and the participant task learning unit; a priority setting unit that sets the priority of the business tasks managed by the task management unit. A system characterized by:

2. The business task collection unit Automatically collect work tasks from the tools and systems employees use every day 2. The system of claim 1.

3. The participant task learning unit: Learn about the tasks and progress of other members of the project team and adjust the priority of each employee's tasks accordingly 2. The system of claim 1.

4. The priority setting unit Automatically prioritize tasks based on importance and urgency 2. The system of claim 1.

5. The task management unit Send notifications to employees about upcoming deadlines and high priority tasks 2. The system of claim 1.

6. The task management unit Track task progress and provide feedback to employees if progress is slow 2. The system of claim 1.

7. The business task collection unit Analyze the employee's voice conversation in real time and extract tasks from the conversation content.

2. The system of claim 1.

8. The business task collection unit Analyze the employee's digital calendar and automatically generate tasks from the schedule.

2. The system of claim 1.

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