system

Abstract

The system according to this embodiment aims to appropriately determine task priorities and support efficient task management and effective use of time. [Solution] The system according to the embodiment comprises a reception unit, a synchronization unit, an analysis unit, a decision unit, and a provision unit. The reception unit inputs the details of a task. The synchronization unit synchronizes the task details entered by the reception unit with a calendar or project management tool. The analysis unit analyzes the deadline, importance, and dependencies of the tasks synchronized by the synchronization unit. The decision unit determines the priority of the tasks based on the information analyzed by the analysis unit. The provision unit provides reminders and priority advice based on the priority determined by the decision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for company employees with multiple tasks to appropriately allocate the priority and importance of tasks, and there is a risk of a decrease in work efficiency.

[0005] The system according to the embodiment aims to appropriately determine the priority of tasks and support efficient task management and effective utilization of time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a synchronization unit, an analysis unit, a decision unit, and a delivery unit. The reception unit inputs task details. The synchronization unit synchronizes the task details entered by the reception unit with a calendar or project management tool. The analysis unit analyzes the deadline, importance, and dependencies of the tasks synchronized by the synchronization unit. The decision unit determines the priority of the tasks based on the information analyzed by the analysis unit. The delivery unit provides reminders and priority advice based on the priority determined by the decision unit. [Effects of the Invention]

[0007] The system according to this embodiment can appropriately determine task priorities and support efficient task management and effective use of time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management agent system according to an embodiment of the present invention is a system that uses a generating AI to support the task management of company employees. In this task management agent system, employees input task details (deadline, importance, etc.), and the generating AI synchronizes with calendars and project management tools to analyze task deadlines, importance, and dependencies, thereby determining task priorities and providing reminders and priority advice. This allows employees to manage tasks efficiently and make effective use of their time. For example, an employee inputs task details (deadline, importance, etc.). At this time, information such as the task deadline, importance, and dependencies is entered. For example, for a task called "Create a report for Project A," the deadline is entered as "December 1, 2023," the importance as "High," and the dependency as "After completion of data collection for Project B." Next, the generating AI synchronizes with calendars and project management tools. For example, the generating AI collaborates with the calendar app or project management tool used by the employee to obtain task information. This allows the generating AI to understand the employee's schedule and project progress. The generating AI analyzes the task deadline, importance, and dependencies. For example, tasks with approaching deadlines or those with a significant impact on the project are prioritized. Tasks are also efficiently allocated, considering their work time and schedule consistency. The AI ​​determines task priorities and provides reminders and priority advice. For instance, it might determine that the task "Create a report for Project A" has a high priority due to its approaching deadline and send a reminder. It also visualizes task progress, allowing employees to track their completion rate. This clarifies task priorities for employees, improving productivity. Furthermore, it prevents missed deadlines and reduces the risk of project delays. In addition, the automation of planning improves time efficiency. Thus, the task management agent system can streamline employee task management and support effective time management.

[0029] The task management agent system according to this embodiment comprises a reception unit, a synchronization unit, an analysis unit, a decision unit, and a delivery unit. The reception unit receives task details from employees. When employees enter task details, they input information such as the task deadline, importance, and dependencies. For example, an employee might enter "Create report for Project A" as the deadline, "December 1, 2023", the importance as "High", and the dependencies as "After completion of data collection for Project B". The synchronization unit synchronizes the task details entered by the reception unit with a calendar or project management tool. For example, the generation AI works in conjunction with the calendar app or project management tool used by the employee to obtain task information. This allows the synchronization unit to understand the employee's schedule and project progress. The analysis unit analyzes the deadlines, importance, and dependencies of the tasks synchronized by the synchronization unit. For example, it prioritizes tasks with approaching deadlines or tasks that have a significant impact on the project. The analysis unit also considers the consistency of task work time and schedules to efficiently allocate tasks. The decision unit determines task priorities based on the information analyzed by the analysis unit. For example, it prioritizes tasks with approaching deadlines or those of high importance. The provision unit provides reminders and priority advice based on the priorities determined by the decision unit. For example, it determines that the task "Create a report for Project A" has a high priority because its deadline is approaching, and sends a reminder. The provision unit also visualizes the progress of tasks, allowing employees to understand their task completion rate. As a result, the task management agent system according to this embodiment can streamline employee task management and support the effective use of time.

[0030] The reception desk receives task details from employees. When employees enter task details, they input information such as the task deadline, importance level, and dependencies. Specifically, for a task like "Create a report for Project A," an employee might enter "December 1, 2023" as the deadline, "High" as the importance level, and "After completion of data collection for Project B" as the dependency level. The reception desk accurately receives this information and stores it within the system. Furthermore, the reception desk stores task details in a standardized format, making it easily accessible to other departments. For example, task details are stored in a database, and each task is assigned a unique identifier. This makes task tracking and management easier. The reception desk also performs error checking during task entry and provides feedback to prevent input errors and incomplete information. For example, if the deadline is a past date or the importance level is not entered, an error message is displayed, prompting the employee to correct it. This allows the reception desk to collect accurate and complete task information, improving the overall reliability of the system. Additionally, the reception desk provides support functions to reduce the burden on employees when entering tasks. For example, the system can automatically suggest similar tasks that have been entered in the past, making it easy for employees to select them. It can also utilize voice input and natural language processing technologies to allow employees to verbally input task details. This allows the reception department to streamline employee task entry and improve system utilization.

[0031] The synchronization unit synchronizes task details entered by the reception unit with calendars and project management tools. For example, the AI ​​generates tasks in conjunction with the calendar and project management tools used by employees to retrieve task information. Specifically, the synchronization unit accesses calendar and project management tools via APIs and automatically synchronizes task details. This allows for monitoring of employee schedules and project progress. Furthermore, the synchronization unit synchronizes in real time when tasks are changed or updated, reflecting the latest information. For example, if a task deadline is changed or a new dependency is added, it is immediately reflected in the calendar and project management tools. This allows employees to always see the latest task information, making it easier to adjust schedules and prioritize tasks. The synchronization unit can also integrate and centrally manage multiple calendars and project management tools. For example, even if an employee is involved in multiple projects, all task information can be viewed in a single interface. This prevents duplication and inconsistencies in information across different tools, allowing employees to manage tasks efficiently. In addition, the synchronization unit monitors task progress in real time and sends alerts and notifications as needed. For example, if a task deadline is approaching or if dependent tasks are not completed, the system sends notifications to employees to prompt appropriate action. This allows the synchronous team to support employees' task management and prevent project delays and errors.

[0032] The analytics department analyzes the deadlines, importance, and dependencies of tasks synchronized by the synchronization department. Specifically, it prioritizes tasks with approaching deadlines and those with a significant impact on the project. The analytics department also considers the consistency of task work time and schedules to efficiently allocate tasks. For example, it uses AI to evaluate task importance and dependencies and determine the optimal task order. Based on historical data and statistics, the AI ​​predicts the time and resources required to complete tasks and adjusts the schedule accordingly. This allows the analytics department to evenly distribute the workload among employees and efficiently advance tasks. Furthermore, the analytics department monitors task progress in real time and reallocates resources and adjusts schedules as needed. For example, if a particular task is behind schedule, it changes the priority of other tasks and concentrates resources. The analytics department also considers task dependencies to optimize the overall project progress. For example, if a dependent task is not completed, it adjusts the start of the next task. This allows the analytics department to minimize project delays and risks and support smooth progress. Furthermore, the analysis department simulates the impact of changes in task priorities and schedules, and proposes optimal countermeasures. For example, it evaluates the impact of changing the priority of a specific task and proposes an optimal schedule. In this way, the analysis department can support employees' task management and contribute to the success of projects.

[0033] The decision-making unit determines task priorities based on information analyzed by the analysis unit. Specifically, it prioritizes tasks with approaching deadlines and those of high importance. The decision-making unit uses AI to automatically determine task priorities and notifies employees. For example, the AI ​​calculates the optimal task order based on information such as task deadlines, importance, dependencies, and work time. This allows employees to work efficiently and prevent project delays and errors. Furthermore, the decision-making unit dynamically adjusts task priorities and responds to real-time changes. For example, if a new task is added or information on an existing task changes, the priority is immediately recalculated to reflect the latest information. This ensures that employees always work based on the most up-to-date priorities, enabling efficient work. The decision-making unit also collects feedback on task priorities and continuously improves its algorithms. For example, it reviews the priority determination criteria based on employee feedback to achieve more accurate prioritization. This allows the decision-making unit to provide flexible task management tailored to employee needs, improving system reliability and satisfaction. Furthermore, the decision-making unit optimizes resource allocation based on task priorities, improving overall project efficiency. For example, by concentrating resources on high-priority tasks and completing them quickly, it ensures smooth project progress. In this way, the decision-making unit can support employees' task management and contribute to project success.

[0034] The service provider provides reminders and priority advice based on the priorities determined by the decision-making department. Specifically, if the task "Create a report for Project A" is nearing its deadline, the service provider determines it has a high priority and sends a reminder. The service provider sends reminders according to the employee's schedule and visualizes the progress of tasks. For example, as the task deadline approaches, it sends a reminder notification to the employee's smartphone or computer to draw their attention. The service provider also displays the task progress in graphs and charts, allowing employees to see the completion rate of tasks at a glance. This allows employees to always be aware of the progress of their tasks and work efficiently. Furthermore, the service provider provides advice based on task priorities to support employee decision-making. For example, it advises prioritizing high-priority tasks to promote efficient task management. The service provider also supports employees' work by sending reminders at appropriate times according to the progress of tasks. For example, as the task deadline approaches, it increases the frequency of reminders to draw the employee's attention. This allows the service department to streamline employee task management and prevent project delays and errors. Furthermore, the service department can collect employee feedback and continuously improve the content of reminders and advice. For example, they can provide more effective reminders based on feedback regarding the timing and content of reminders. This enables the service department to provide flexible support tailored to employee needs and improve system reliability and satisfaction.

[0035] The service provider can visualize the progress of tasks. For example, the service provider can display the progress of tasks in a graph. For example, the service provider can also display the progress of tasks using progress bars. Furthermore, the service provider can display the progress of tasks on a dashboard. By visualizing the progress of tasks, it becomes easier for employees to understand the progress of their tasks. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the progress of tasks into an AI model and have the AI ​​perform the visualization of the progress.

[0036] The service provider can track the completion rate of tasks. For example, the service provider can display the number of completed tasks and the number of tasks in progress. For example, the service provider can also display the task completion rate as a percentage. Furthermore, the service provider can display the task completion rate in a graph. This makes it easier for employees to manage the progress of tasks by understanding the task completion rate. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the task completion rate into an AI model and have the AI ​​perform the task of tracking the completion rate.

[0037] The analysis unit can consider the consistency between task work time and schedule. For example, the analysis unit can estimate task work time and compare it to the schedule. For example, the analysis unit can also evaluate the consistency between task work time and schedule. Furthermore, the analysis unit can adjust task work time and schedule. This enables efficient task management by considering the consistency between task work time and schedule. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input task work time and schedule data into an AI model and have the AI ​​perform the consistency evaluation.

[0038] The synchronization unit can integrate with the calendar apps and project management tools used by employees. For example, the synchronization unit can set the timing for syncing with calendar apps and project management tools. It can also set the types of information to sync. This allows for efficient synchronization of task information by integrating with calendar apps and project management tools. Some or all of the above processes in the synchronization unit may be performed using AI, or not. For example, the synchronization unit can input data from calendar apps and project management tools into an AI model and have the AI ​​execute the sync settings.

[0039] The reception desk can provide input assistance when users enter task details by referring to their past task history. For example, the reception desk can automatically display task details that the user has entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest task details to be used during a specific time period based on the user's past task history. This makes task input more efficient by referring to past task history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past task history data into a generating AI and have the generating AI perform input assistance.

[0040] The reception desk can customize the input form according to the task category when entering task details. For example, for project tasks, the reception desk can add input fields for the project name and dependencies. For example, for routine tasks, the reception desk can provide a simple input form. For urgent tasks, the reception desk can also provide an input form that highlights priority and deadline. By customizing the input form according to the task category, task entry is made more efficient. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input task category information into a generating AI and have the generating AI perform the customization of the input form.

[0041] The reception desk can prioritize tasks that are highly relevant to the user's geographical location when the user enters task details. For example, if the user is in the office, the reception desk will prioritize office-related tasks. If the user is out of the office, the reception desk can also prioritize tasks that can be completed while out. Furthermore, if the user is at home, the reception desk can prioritize tasks that can be completed at home. This allows the reception desk to prioritize tasks that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant tasks.

[0042] The reception desk can analyze the user's social media activity when they enter task details and suggest relevant tasks. For example, the reception desk can automatically display tasks that the user has mentioned on social media as input suggestions. For example, the reception desk can also suggest relevant events or meetings as tasks based on the user's social media activity. Furthermore, the reception desk can analyze the content of the user's social media posts and automatically generate relevant tasks. In this way, relevant tasks can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant tasks.

[0043] The synchronization unit can select the optimal synchronization method by referring to past synchronization history during synchronization. For example, the synchronization unit can select the most efficient synchronization method from past synchronization history. For example, the synchronization unit can also analyze past synchronization history and optimize the timing of synchronization. Furthermore, the synchronization unit can adjust the frequency of synchronization based on past synchronization history. This improves the efficiency of synchronization by referring to past synchronization history. Some or all of the above processes in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input past synchronization history data into a generating AI and have the generating AI select the optimal synchronization method.

[0044] The synchronization unit can apply different synchronization algorithms depending on the task category during synchronization. For example, for project tasks, the synchronization unit applies an algorithm that synchronizes with a project management tool. For example, for daily work tasks, the synchronization unit can also apply an algorithm that synchronizes with a calendar application. Furthermore, for urgent tasks, the synchronization unit can apply a real-time synchronization algorithm. This improves the efficiency of synchronization by applying different synchronization algorithms depending on the task category. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input task category information into a generating AI and have the generating AI execute the application of the synchronization algorithm.

[0045] The synchronization unit can prioritize the synchronization of highly relevant tasks by considering the user's geographical location during synchronization. For example, if the user is in the office, the synchronization unit will prioritize the synchronization of office-related tasks. For example, if the user is out of the office, the synchronization unit can also prioritize the synchronization of tasks that can be completed while out of the office. Furthermore, if the user is at home, the synchronization unit can also prioritize the synchronization of tasks that can be completed at home. In this way, highly relevant tasks can be prioritized by considering the user's geographical location. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant tasks.

[0046] The synchronization unit can analyze the user's social media activity during synchronization and synchronize related tasks. For example, the synchronization unit can automatically synchronize tasks that the user has mentioned on social media. For example, the synchronization unit can also synchronize related events and meetings as tasks based on the user's social media activity. Furthermore, the synchronization unit can analyze the content of the user's social media posts and automatically synchronize related tasks. This allows for the synchronization of related tasks by analyzing the user's social media activity. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the user's social media data into a generating AI and have the generating AI perform the synchronization of related tasks.

[0047] The analysis unit can improve the accuracy of its analysis by referring to past task history during the analysis process. For example, the analysis unit can analyze the most efficient way to proceed with tasks from past task history. For example, the analysis unit can also optimize task priorities based on past task history. Furthermore, the analysis unit can analyze task dependencies by referring to past task history. This improves the accuracy of the analysis by referring to past task history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past task history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0048] The analysis unit can apply different analytical methods to each task category during analysis. For example, in the case of project tasks, the analysis unit applies project management methods for analysis. For example, in the case of routine work tasks, the analysis unit can apply simpler analytical methods. Furthermore, in the case of urgent tasks, the analysis unit can apply rapid analytical methods. This improves the accuracy of the analysis by applying the appropriate analytical method to each task category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category information into a generating AI and have the generating AI execute the application of analytical methods.

[0049] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, if the user is in the office, the analysis unit will prioritize analyzing office-related tasks. For example, if the user is out of the office, the analysis unit can also prioritize analyzing tasks that can be completed while out of the office. Furthermore, if the user is at home, the analysis unit can also prioritize analyzing tasks that can be completed at home. This improves the accuracy of the analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0050] The analysis unit can analyze users' social media activity and perform related task analysis during the analysis process. For example, the analysis unit can automatically analyze tasks that users have mentioned on social media. For example, the analysis unit can also analyze related events and meetings as tasks from users' social media activity. Furthermore, the analysis unit can analyze the content of users' social media posts and automatically analyze related tasks. This makes it possible to analyze related tasks by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media data into a generating AI and have the generating AI perform the analysis of related tasks.

[0051] The decision unit can select the optimal decision method by referring to past task history when determining priorities. For example, the decision unit can select the most efficient priority determination method from past task history. For example, the decision unit can also analyze past task history and optimize the criteria for determining priorities. Furthermore, the decision unit can determine priorities by considering task dependencies based on past task history. This makes priority determination more efficient by referring to past task history. Some or all of the above processes in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input past task history data into a generating AI and have the generating AI select the optimal decision method.

[0052] The decision-making unit can apply different decision algorithms depending on the task category when determining priorities. For example, in the case of project tasks, the decision-making unit can apply project management methods to determine priorities. For example, in the case of routine tasks, the decision-making unit can also apply a simple priority determination method. Furthermore, in the case of urgent tasks, the decision-making unit can apply a rapid priority determination method. This makes priority determination more efficient by applying different decision algorithms depending on the task category. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input task category information into a generating AI and have the generating AI execute the application of the decision algorithm.

[0053] The decision-making unit can determine the optimal priority by considering the user's geographical location when determining priorities. For example, if the user is in the office, the decision-making unit will prioritize office-related tasks. For example, if the user is out, the decision-making unit can also prioritize tasks that can be completed while out. Furthermore, if the user is at home, the decision-making unit can also prioritize tasks that can be completed at home. In this way, the optimal priority can be determined by considering the user's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's geographical location into a generating AI and have the generating AI perform the determination of the optimal priority.

[0054] The decision-making unit can analyze the user's social media activity and determine the priority of related tasks when determining priorities. For example, the decision-making unit can automatically prioritize tasks mentioned by the user on social media. For example, the decision-making unit can also prioritize related events and meetings as tasks based on the user's social media activity. Furthermore, the decision-making unit can analyze the content of the user's social media posts and automatically prioritize related tasks. In this way, the priority of related tasks can be determined by analyzing the user's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media data into a generating AI and have the generating AI prioritize related tasks.

[0055] The service provider can select the optimal delivery method by referring to past task history when providing reminders and advice. For example, the service provider can select the most efficient reminder delivery method from past task history. For example, the service provider can also analyze past task history and optimize the timing of reminders. Furthermore, the service provider can adjust the frequency of reminders based on past task history. This makes the delivery of reminders and advice more efficient by referring to past task history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past task history data into a generating AI and have the generating AI select the optimal delivery method.

[0056] The service provider can apply different delivery methods depending on the task category when providing reminders and advice. For example, for project tasks, the service provider can apply a project management method to provide reminders. For example, for routine tasks, the service provider can apply a simple reminder delivery method. Furthermore, for urgent tasks, the service provider can apply a rapid reminder delivery method. By applying different delivery methods depending on the task category, the delivery of reminders and advice becomes more efficient. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input task category information into a generating AI and have the generating AI execute the application of the delivery method.

[0057] The service provider can select the optimal delivery method when providing reminders and advice, taking into account the user's geographical location. For example, if the user is in the office, the service provider can provide office-related reminders. For example, if the user is out, the service provider can also provide reminders for tasks that can be completed while out. Furthermore, if the user is at home, the service provider can provide reminders for tasks that can be completed at home. This allows the service provider to provide optimal reminders and advice by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0058] The service provider can analyze a user's social media activity when providing reminders and advice, and provide reminders and advice for relevant tasks. For example, the service provider can automatically provide reminders for tasks mentioned by the user on social media. For example, the service provider can also provide reminders for relevant events or meetings as tasks based on the user's social media activity. Furthermore, the service provider can analyze the content of a user's social media posts and automatically provide reminders for relevant tasks. In this way, by analyzing the user's social media activity, it is possible to provide reminders and advice for relevant tasks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute reminders and advice for relevant tasks.

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

[0060] The task management agent system can also include a feedback unit. The feedback unit collects feedback from employees upon task completion and provides it to the analysis unit. For example, after an employee completes a task, the feedback unit can request feedback on the task's difficulty, time taken, and satisfaction level. The feedback unit can also collect any problems or areas for improvement that employees encountered during the task. Furthermore, the feedback unit can analyze the collected feedback and use it to improve the entire task management agent system. This allows the task management agent system to evolve into a more efficient and user-friendly system based on employee feedback.

[0061] The task management agent system can also be equipped with a learning unit. This unit learns employee task management patterns and tendencies, and uses this information to improve future task management. For example, the learning unit can learn what tasks employees tend to perform and at what times of day. It can also learn how much time employees spend on different tasks. Furthermore, the learning unit can offer suggestions to improve the efficiency of employee task management. This allows the task management agent system to support more effective task management based on employee task management patterns and tendencies.

[0062] The task management agent system can also include a notification function. This notification function allows employees to stay informed about task progress and important events in real time. For example, it can send reminders when task deadlines are approaching. It can also notify when task dependencies are resolved. Furthermore, it can notify about project progress and the achievement of key milestones. This allows employees to stay informed about task progress and important events in real time and respond quickly.

[0063] The task management agent system can also be equipped with a forecasting unit. This unit predicts the completion date of future tasks based on the progress and schedule of employees' tasks. For example, it can predict the expected completion date of a task based on the employee's current task progress. It can also predict the priority of future tasks based on the employee's schedule. Furthermore, it can predict the expected completion date of a project based on the overall project progress. This allows employees to efficiently manage their tasks based on predictions of future task completion.

[0064] The task management agent system can also include a customization section. This customization section allows for customization of the task management agent system's functions and interface according to the individual needs and preferences of employees. For example, the customization section can set the task display format and notification method preferred by employees. Furthermore, the customization section can customize the task management agent system's functions according to the employee's job duties and position. In addition, the customization section can improve the task management agent system's functions and interface based on employee feedback. This makes the task management agent system more user-friendly, tailored to the individual needs and preferences of employees.

[0065] The task management agent system can also include a feedback unit. The feedback unit collects feedback from employees upon task completion and provides it to the analysis unit. For example, after an employee completes a task, the feedback unit can request feedback on the task's difficulty, time taken, and satisfaction level. The feedback unit can also collect any problems or areas for improvement that employees encountered during the task. Furthermore, the feedback unit can analyze the collected feedback and use it to improve the entire task management agent system. This allows the task management agent system to evolve into a more efficient and user-friendly system based on employee feedback.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the task details from the employee. When the employee enters the task details, they will enter information such as the task deadline, importance level, and dependencies. For example, for a task called "Create a report for Project A," the employee might enter "December 1, 2023" as the deadline, "High" as the importance level, and "After completion of data collection for Project B" as the dependency level. Step 2: The synchronization department synchronizes the task details entered by the reception department with calendars and project management tools. For example, the generation AI works with the calendar app and project management tool used by employees to retrieve task information. This allows the synchronization department to understand employees' schedules and project progress. Step 3: The analysis department analyzes the deadlines, importance, and dependencies of tasks synchronized by the synchronization department. For example, it prioritizes tasks with approaching deadlines or those that have a significant impact on the project. The analysis department also considers the consistency of task work time and schedules to efficiently allocate tasks. Step 4: The decision-making unit determines task priorities based on the information analyzed by the analysis unit. For example, tasks with approaching deadlines or those of high importance are prioritized. Step 5: The delivery department provides reminders and priority advice based on the priorities determined by the decision-making department. For example, if the task "Create report for Project A" is due soon, the delivery department determines it has a high priority and sends a reminder. The delivery department also visualizes the progress of tasks so that employees can understand the completion rate of tasks.

[0068] (Example of form 2) The task management agent system according to an embodiment of the present invention is a system that uses a generating AI to support the task management of company employees. In this task management agent system, employees input task details (deadline, importance, etc.), and the generating AI synchronizes with calendars and project management tools to analyze task deadlines, importance, and dependencies, thereby determining task priorities and providing reminders and priority advice. This allows employees to manage tasks efficiently and make effective use of their time. For example, an employee inputs task details (deadline, importance, etc.). At this time, information such as the task deadline, importance, and dependencies is entered. For example, for a task called "Create a report for Project A," the deadline is entered as "December 1, 2023," the importance as "High," and the dependency as "After completion of data collection for Project B." Next, the generating AI synchronizes with calendars and project management tools. For example, the generating AI collaborates with the calendar app or project management tool used by the employee to obtain task information. This allows the generating AI to understand the employee's schedule and project progress. The generating AI analyzes the task deadline, importance, and dependencies. For example, tasks with approaching deadlines or those with a significant impact on the project are prioritized. Tasks are also efficiently allocated, considering their work time and schedule consistency. The AI ​​determines task priorities and provides reminders and priority advice. For instance, it might determine that the task "Create a report for Project A" has a high priority due to its approaching deadline and send a reminder. It also visualizes task progress, allowing employees to track their completion rate. This clarifies task priorities for employees, improving productivity. Furthermore, it prevents missed deadlines and reduces the risk of project delays. In addition, the automation of planning improves time efficiency. Thus, the task management agent system can streamline employee task management and support effective time management.

[0069] The task management agent system according to this embodiment comprises a reception unit, a synchronization unit, an analysis unit, a decision unit, and a delivery unit. The reception unit receives task details from employees. When employees enter task details, they input information such as the task deadline, importance, and dependencies. For example, an employee might enter "Create report for Project A" as the deadline, "December 1, 2023", the importance as "High", and the dependencies as "After completion of data collection for Project B". The synchronization unit synchronizes the task details entered by the reception unit with a calendar or project management tool. For example, the generation AI works in conjunction with the calendar app or project management tool used by the employee to obtain task information. This allows the synchronization unit to understand the employee's schedule and project progress. The analysis unit analyzes the deadlines, importance, and dependencies of the tasks synchronized by the synchronization unit. For example, it prioritizes tasks with approaching deadlines or tasks that have a significant impact on the project. The analysis unit also considers the consistency of task work time and schedules to efficiently allocate tasks. The decision unit determines task priorities based on the information analyzed by the analysis unit. For example, it prioritizes tasks with approaching deadlines or those of high importance. The provision unit provides reminders and priority advice based on the priorities determined by the decision unit. For example, it determines that the task "Create a report for Project A" has a high priority because its deadline is approaching, and sends a reminder. The provision unit also visualizes the progress of tasks, allowing employees to understand their task completion rate. As a result, the task management agent system according to this embodiment can streamline employee task management and support the effective use of time.

[0070] The reception desk receives task details from employees. When employees enter task details, they input information such as the task deadline, importance level, and dependencies. Specifically, for a task like "Create a report for Project A," an employee might enter "December 1, 2023" as the deadline, "High" as the importance level, and "After completion of data collection for Project B" as the dependency level. The reception desk accurately receives this information and stores it within the system. Furthermore, the reception desk stores task details in a standardized format, making it easily accessible to other departments. For example, task details are stored in a database, and each task is assigned a unique identifier. This makes task tracking and management easier. The reception desk also performs error checking during task entry and provides feedback to prevent input errors and incomplete information. For example, if the deadline is a past date or the importance level is not entered, an error message is displayed, prompting the employee to correct it. This allows the reception desk to collect accurate and complete task information, improving the overall reliability of the system. Additionally, the reception desk provides support functions to reduce the burden on employees when entering tasks. For example, the system can automatically suggest similar tasks that have been entered in the past, making it easy for employees to select them. It can also utilize voice input and natural language processing technologies to allow employees to verbally input task details. This allows the reception department to streamline employee task entry and improve system utilization.

[0071] The synchronization unit synchronizes task details entered by the reception unit with calendars and project management tools. For example, the AI ​​generates tasks in conjunction with the calendar and project management tools used by employees to retrieve task information. Specifically, the synchronization unit accesses calendar and project management tools via APIs and automatically synchronizes task details. This allows for monitoring of employee schedules and project progress. Furthermore, the synchronization unit synchronizes in real time when tasks are changed or updated, reflecting the latest information. For example, if a task deadline is changed or a new dependency is added, it is immediately reflected in the calendar and project management tools. This allows employees to always see the latest task information, making it easier to adjust schedules and prioritize tasks. The synchronization unit can also integrate and centrally manage multiple calendars and project management tools. For example, even if an employee is involved in multiple projects, all task information can be viewed in a single interface. This prevents duplication and inconsistencies in information across different tools, allowing employees to manage tasks efficiently. In addition, the synchronization unit monitors task progress in real time and sends alerts and notifications as needed. For example, if a task deadline is approaching or if dependent tasks are not completed, the system sends notifications to employees to prompt appropriate action. This allows the synchronous team to support employees' task management and prevent project delays and errors.

[0072] The analytics department analyzes the deadlines, importance, and dependencies of tasks synchronized by the synchronization department. Specifically, it prioritizes tasks with approaching deadlines and those with a significant impact on the project. The analytics department also considers the consistency of task work time and schedules to efficiently allocate tasks. For example, it uses AI to evaluate task importance and dependencies and determine the optimal task order. Based on historical data and statistics, the AI ​​predicts the time and resources required to complete tasks and adjusts the schedule accordingly. This allows the analytics department to evenly distribute the workload among employees and efficiently advance tasks. Furthermore, the analytics department monitors task progress in real time and reallocates resources and adjusts schedules as needed. For example, if a particular task is behind schedule, it changes the priority of other tasks and concentrates resources. The analytics department also considers task dependencies to optimize the overall project progress. For example, if a dependent task is not completed, it adjusts the start of the next task. This allows the analytics department to minimize project delays and risks and support smooth progress. Furthermore, the analysis department simulates the impact of changes in task priorities and schedules, and proposes optimal countermeasures. For example, it evaluates the impact of changing the priority of a specific task and proposes an optimal schedule. In this way, the analysis department can support employees' task management and contribute to the success of projects.

[0073] The decision-making unit determines task priorities based on information analyzed by the analysis unit. Specifically, it prioritizes tasks with approaching deadlines and those of high importance. The decision-making unit uses AI to automatically determine task priorities and notifies employees. For example, the AI ​​calculates the optimal task order based on information such as task deadlines, importance, dependencies, and work time. This allows employees to work efficiently and prevent project delays and errors. Furthermore, the decision-making unit dynamically adjusts task priorities and responds to real-time changes. For example, if a new task is added or information on an existing task changes, the priority is immediately recalculated to reflect the latest information. This ensures that employees always work based on the most up-to-date priorities, enabling efficient work. The decision-making unit also collects feedback on task priorities and continuously improves its algorithms. For example, it reviews the priority determination criteria based on employee feedback to achieve more accurate prioritization. This allows the decision-making unit to provide flexible task management tailored to employee needs, improving system reliability and satisfaction. Furthermore, the decision-making unit optimizes resource allocation based on task priorities, improving overall project efficiency. For example, by concentrating resources on high-priority tasks and completing them quickly, it ensures smooth project progress. In this way, the decision-making unit can support employees' task management and contribute to project success.

[0074] The service provider provides reminders and priority advice based on the priorities determined by the decision-making department. Specifically, if the task "Create a report for Project A" is nearing its deadline, the service provider determines it has a high priority and sends a reminder. The service provider sends reminders according to the employee's schedule and visualizes the progress of tasks. For example, as the task deadline approaches, it sends a reminder notification to the employee's smartphone or computer to draw their attention. The service provider also displays the task progress in graphs and charts, allowing employees to see the completion rate of tasks at a glance. This allows employees to always be aware of the progress of their tasks and work efficiently. Furthermore, the service provider provides advice based on task priorities to support employee decision-making. For example, it advises prioritizing high-priority tasks to promote efficient task management. The service provider also supports employees' work by sending reminders at appropriate times according to the progress of tasks. For example, as the task deadline approaches, it increases the frequency of reminders to draw the employee's attention. This allows the service department to streamline employee task management and prevent project delays and errors. Furthermore, the service department can collect employee feedback and continuously improve the content of reminders and advice. For example, they can provide more effective reminders based on feedback regarding the timing and content of reminders. This enables the service department to provide flexible support tailored to employee needs and improve system reliability and satisfaction.

[0075] The service provider can visualize the progress of tasks. For example, the service provider can display the progress of tasks in a graph. For example, the service provider can also display the progress of tasks using progress bars. Furthermore, the service provider can display the progress of tasks on a dashboard. By visualizing the progress of tasks, it becomes easier for employees to understand the progress of their tasks. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the progress of tasks into an AI model and have the AI ​​perform the visualization of the progress.

[0076] The service provider can track the completion rate of tasks. For example, the service provider can display the number of completed tasks and the number of tasks in progress. For example, the service provider can also display the task completion rate as a percentage. Furthermore, the service provider can display the task completion rate in a graph. This makes it easier for employees to manage the progress of tasks by understanding the task completion rate. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the task completion rate into an AI model and have the AI ​​perform the task of tracking the completion rate.

[0077] The analysis unit can consider the consistency between task work time and schedule. For example, the analysis unit can estimate task work time and compare it to the schedule. For example, the analysis unit can also evaluate the consistency between task work time and schedule. Furthermore, the analysis unit can adjust task work time and schedule. This enables efficient task management by considering the consistency between task work time and schedule. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input task work time and schedule data into an AI model and have the AI ​​perform the consistency evaluation.

[0078] The synchronization unit can integrate with the calendar apps and project management tools used by employees. For example, the synchronization unit can set the timing for syncing with calendar apps and project management tools. It can also set the types of information to sync. This allows for efficient synchronization of task information by integrating with calendar apps and project management tools. Some or all of the above processes in the synchronization unit may be performed using AI, or not. For example, the synchronization unit can input data from calendar apps and project management tools into an AI model and have the AI ​​execute the sync settings.

[0079] The reception desk can estimate the user's emotions and adjust the task input method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest a customizable input method. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of task details. This allows for more appropriate task input by adjusting the task input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The reception desk can provide input assistance when users enter task details by referring to their past task history. For example, the reception desk can automatically display task details that the user has entered in the past as suggestions. For example, the reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest task details to be used during a specific time period based on the user's past task history. This makes task input more efficient by referring to past task history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input past task history data into a generating AI and have the generating AI perform input assistance.

[0081] The reception desk can customize the input form according to the task category when entering task details. For example, for project tasks, the reception desk can add input fields for the project name and dependencies. For example, for routine tasks, the reception desk can provide a simple input form. For urgent tasks, the reception desk can also provide an input form that highlights priority and deadline. By customizing the input form according to the task category, task entry is made more efficient. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input task category information into a generating AI and have the generating AI perform the customization of the input form.

[0082] The reception desk can estimate the user's emotions and determine the priority of tasks to be entered based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize high-priority tasks. For example, if the user is relaxed, the reception desk may also prioritize long-term tasks. Furthermore, if the user is in a hurry, the reception desk may prioritize tasks that can be completed quickly. This allows for more appropriate task management by prioritizing tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0083] The reception desk can prioritize tasks that are highly relevant to the user's geographical location when the user enters task details. For example, if the user is in the office, the reception desk will prioritize office-related tasks. If the user is out of the office, the reception desk can also prioritize tasks that can be completed while out. Furthermore, if the user is at home, the reception desk can prioritize tasks that can be completed at home. This allows the reception desk to prioritize tasks that are highly relevant by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant tasks.

[0084] The reception desk can analyze the user's social media activity when they enter task details and suggest relevant tasks. For example, the reception desk can automatically display tasks that the user has mentioned on social media as input suggestions. For example, the reception desk can also suggest relevant events or meetings as tasks based on the user's social media activity. Furthermore, the reception desk can analyze the content of the user's social media posts and automatically generate relevant tasks. In this way, relevant tasks can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest relevant tasks.

[0085] The synchronization unit can estimate the user's emotions and adjust the synchronization timing based on the estimated emotions. For example, if the user is stressed, the synchronization unit can reduce the frequency of synchronization to alleviate the burden. For example, if the user is relaxed, the synchronization unit can increase the frequency of synchronization to provide the latest information. Also, if the user is in a hurry, the synchronization unit can synchronize in real time and quickly update information. This allows for more appropriate synchronization by adjusting the timing of synchronization according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI, or not using AI. For example, the synchronization unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0086] The synchronization unit can select the optimal synchronization method by referring to past synchronization history during synchronization. For example, the synchronization unit can select the most efficient synchronization method from past synchronization history. For example, the synchronization unit can also analyze past synchronization history and optimize the timing of synchronization. Furthermore, the synchronization unit can adjust the frequency of synchronization based on past synchronization history. This improves the efficiency of synchronization by referring to past synchronization history. Some or all of the above processes in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input past synchronization history data into a generating AI and have the generating AI select the optimal synchronization method.

[0087] The synchronization unit can apply different synchronization algorithms depending on the task category during synchronization. For example, for project tasks, the synchronization unit applies an algorithm that synchronizes with a project management tool. For example, for daily work tasks, the synchronization unit can also apply an algorithm that synchronizes with a calendar application. Furthermore, for urgent tasks, the synchronization unit can apply a real-time synchronization algorithm. This improves the efficiency of synchronization by applying different synchronization algorithms depending on the task category. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input task category information into a generating AI and have the generating AI execute the application of the synchronization algorithm.

[0088] The synchronization unit can estimate the user's emotions and determine the priority of tasks to synchronize based on the estimated emotions. For example, if the user is stressed, the synchronization unit will prioritize synchronizing high-priority tasks. For example, if the user is relaxed, the synchronization unit may also prioritize synchronizing long-term tasks. Furthermore, if the user is in a hurry, the synchronization unit may also prioritize synchronizing tasks that can be completed quickly. This allows for more appropriate task management by determining the priority of tasks to synchronize according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the synchronization unit may be performed using AI, or not using AI. For example, the synchronization unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The synchronization unit can prioritize the synchronization of highly relevant tasks by considering the user's geographical location during synchronization. For example, if the user is in the office, the synchronization unit will prioritize the synchronization of office-related tasks. For example, if the user is out of the office, the synchronization unit can also prioritize the synchronization of tasks that can be completed while out of the office. Furthermore, if the user is at home, the synchronization unit can also prioritize the synchronization of tasks that can be completed at home. In this way, highly relevant tasks can be prioritized by considering the user's geographical location. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the user's geographical location information into a generating AI and have the generating AI prioritize highly relevant tasks.

[0090] The synchronization unit can analyze the user's social media activity during synchronization and synchronize related tasks. For example, the synchronization unit can automatically synchronize tasks that the user has mentioned on social media. For example, the synchronization unit can also synchronize related events and meetings as tasks based on the user's social media activity. Furthermore, the synchronization unit can analyze the content of the user's social media posts and automatically synchronize related tasks. This allows for the synchronization of related tasks by analyzing the user's social media activity. Some or all of the above processing in the synchronization unit may be performed using AI, for example, or without AI. For example, the synchronization unit can input the user's social media data into a generating AI and have the generating AI perform the synchronization of related tasks.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit may prioritize analyzing high-priority tasks. For example, if the user is relaxed, the analysis unit may prioritize analyzing long-term tasks. Also, if the user is in a hurry, the analysis unit may prioritize analyzing tasks that can be completed quickly. By adjusting the analysis criteria according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The analysis unit can improve the accuracy of its analysis by referring to past task history during the analysis process. For example, the analysis unit can analyze the most efficient way to proceed with tasks from past task history. For example, the analysis unit can also optimize task priorities based on past task history. Furthermore, the analysis unit can analyze task dependencies by referring to past task history. This improves the accuracy of the analysis by referring to past task history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past task history data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0093] The analysis unit can apply different analytical methods to each task category during analysis. For example, in the case of project tasks, the analysis unit applies project management methods for analysis. For example, in the case of routine work tasks, the analysis unit can apply simpler analytical methods. Furthermore, in the case of urgent tasks, the analysis unit can apply rapid analytical methods. This improves the accuracy of the analysis by applying the appropriate analytical method to each task category. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input task category information into a generating AI and have the generating AI execute the application of analytical methods.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0095] The analysis unit can improve the accuracy of its analysis by considering the user's geographical location information. For example, if the user is in the office, the analysis unit will prioritize analyzing office-related tasks. For example, if the user is out of the office, the analysis unit can also prioritize analyzing tasks that can be completed while out of the office. Furthermore, if the user is at home, the analysis unit can also prioritize analyzing tasks that can be completed at home. This improves the accuracy of the analysis by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0096] The analysis unit can analyze users' social media activity and perform related task analysis during the analysis process. For example, the analysis unit can automatically analyze tasks that users have mentioned on social media. For example, the analysis unit can also analyze related events and meetings as tasks from users' social media activity. Furthermore, the analysis unit can analyze the content of users' social media posts and automatically analyze related tasks. This makes it possible to analyze related tasks by analyzing users' social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user social media data into a generating AI and have the generating AI perform the analysis of related tasks.

[0097] The decision-making unit can estimate the user's emotions and adjust the priority setting method based on the estimated emotions. For example, if the user is stressed, the decision-making unit will prioritize high-priority tasks. For example, if the user is relaxed, the decision-making unit may also prioritize long-term tasks. Furthermore, if the user is in a hurry, the decision-making unit may prioritize tasks that can be completed quickly. This allows for more appropriate task management by adjusting the priority setting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision-making unit may be performed using AI, or not using AI. For example, the decision-making unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The decision unit can select the optimal decision method by referring to past task history when determining priorities. For example, the decision unit can select the most efficient priority determination method from past task history. For example, the decision unit can also analyze past task history and optimize the criteria for determining priorities. Furthermore, the decision unit can determine priorities by considering task dependencies based on past task history. This makes priority determination more efficient by referring to past task history. Some or all of the above processes in the decision unit may be performed using AI, for example, or without AI. For example, the decision unit can input past task history data into a generating AI and have the generating AI select the optimal decision method.

[0099] The decision-making unit can apply different decision algorithms depending on the task category when determining priorities. For example, in the case of project tasks, the decision-making unit can apply project management methods to determine priorities. For example, in the case of routine tasks, the decision-making unit can also apply a simple priority determination method. Furthermore, in the case of urgent tasks, the decision-making unit can apply a rapid priority determination method. This makes priority determination more efficient by applying different decision algorithms depending on the task category. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input task category information into a generating AI and have the generating AI execute the application of the decision algorithm.

[0100] The decision unit can estimate the user's emotions and adjust the display method of priorities based on the estimated user emotions. For example, if the user is stressed, the decision unit can provide a simple and highly visible display method. For example, if the user is relaxed, the decision unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the decision unit can provide a display method that gets straight to the point. By adjusting the display method of priorities according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decision unit may be performed using AI, for example, or not using AI. For example, the decision unit can input user facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0101] The decision-making unit can determine the optimal priority by considering the user's geographical location when determining priorities. For example, if the user is in the office, the decision-making unit will prioritize office-related tasks. For example, if the user is out, the decision-making unit can also prioritize tasks that can be completed while out. Furthermore, if the user is at home, the decision-making unit can also prioritize tasks that can be completed at home. In this way, the optimal priority can be determined by considering the user's geographical location. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's geographical location into a generating AI and have the generating AI perform the determination of the optimal priority.

[0102] The decision-making unit can analyze the user's social media activity and determine the priority of related tasks when determining priorities. For example, the decision-making unit can automatically prioritize tasks mentioned by the user on social media. For example, the decision-making unit can also prioritize related events and meetings as tasks based on the user's social media activity. Furthermore, the decision-making unit can analyze the content of the user's social media posts and automatically prioritize related tasks. In this way, the priority of related tasks can be determined by analyzing the user's social media activity. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input the user's social media data into a generating AI and have the generating AI prioritize related tasks.

[0103] The service provider can estimate the user's emotions and adjust the way reminders and advice are delivered based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible reminder. For example, if the user is relaxed, the service provider can provide a reminder with more detailed information. Also, if the user is in a hurry, the service provider can provide a concise reminder. This allows for more appropriate information to be provided by adjusting the way reminders and advice are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0104] The service provider can select the optimal delivery method by referring to past task history when providing reminders and advice. For example, the service provider can select the most efficient reminder delivery method from past task history. For example, the service provider can also analyze past task history and optimize the timing of reminders. Furthermore, the service provider can adjust the frequency of reminders based on past task history. This makes the delivery of reminders and advice more efficient by referring to past task history. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input past task history data into a generating AI and have the generating AI select the optimal delivery method.

[0105] The service provider can apply different delivery methods depending on the task category when providing reminders and advice. For example, for project tasks, the service provider can apply a project management method to provide reminders. For example, for routine tasks, the service provider can apply a simple reminder delivery method. Furthermore, for urgent tasks, the service provider can apply a rapid reminder delivery method. By applying different delivery methods depending on the task category, the delivery of reminders and advice becomes more efficient. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input task category information into a generating AI and have the generating AI execute the application of the delivery method.

[0106] The service provider can estimate the user's emotions and adjust the display method of reminders and advice based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, if the user is relaxed, the service provider can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a concise display method. By adjusting the display method of reminders and advice according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0107] The service provider can select the optimal delivery method when providing reminders and advice, taking into account the user's geographical location. For example, if the user is in the office, the service provider can provide office-related reminders. For example, if the user is out, the service provider can also provide reminders for tasks that can be completed while out. Furthermore, if the user is at home, the service provider can provide reminders for tasks that can be completed at home. This allows the service provider to provide optimal reminders and advice by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's geographical location information into a generating AI and have the generating AI select the optimal delivery method.

[0108] The service provider can analyze a user's social media activity when providing reminders and advice, and provide reminders and advice for relevant tasks. For example, the service provider can automatically provide reminders for tasks mentioned by the user on social media. For example, the service provider can also provide reminders for relevant events or meetings as tasks based on the user's social media activity. Furthermore, the service provider can analyze the content of a user's social media posts and automatically provide reminders for relevant tasks. In this way, by analyzing the user's social media activity, it is possible to provide reminders and advice for relevant tasks. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media data into a generating AI and have the generating AI execute reminders and advice for relevant tasks.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The task management agent system can also include a feedback unit. The feedback unit collects feedback from employees upon task completion and provides it to the analysis unit. For example, after an employee completes a task, the feedback unit can request feedback on the task's difficulty, time taken, and satisfaction level. The feedback unit can also collect any problems or areas for improvement that employees encountered during the task. Furthermore, the feedback unit can analyze the collected feedback and use it to improve the entire task management agent system. This allows the task management agent system to evolve into a more efficient and user-friendly system based on employee feedback.

[0111] The task management agent system can also be equipped with a learning unit. This unit learns employee task management patterns and tendencies, and uses this information to improve future task management. For example, the learning unit can learn what tasks employees tend to perform and at what times of day. It can also learn how much time employees spend on different tasks. Furthermore, the learning unit can offer suggestions to improve the efficiency of employee task management. This allows the task management agent system to support more effective task management based on employee task management patterns and tendencies.

[0112] The task management agent system can also include a notification function. This notification function allows employees to stay informed about task progress and important events in real time. For example, it can send reminders when task deadlines are approaching. It can also notify when task dependencies are resolved. Furthermore, it can notify about project progress and the achievement of key milestones. This allows employees to stay informed about task progress and important events in real time and respond quickly.

[0113] The task management agent system can also be equipped with a forecasting unit. This unit predicts the completion date of future tasks based on the progress and schedule of employees' tasks. For example, it can predict the expected completion date of a task based on the employee's current task progress. It can also predict the priority of future tasks based on the employee's schedule. Furthermore, it can predict the expected completion date of a project based on the overall project progress. This allows employees to efficiently manage their tasks based on predictions of future task completion.

[0114] The task management agent system can also include a customization section. This customization section allows for customization of the task management agent system's functions and interface according to the individual needs and preferences of employees. For example, the customization section can set the task display format and notification method preferred by employees. Furthermore, the customization section can customize the task management agent system's functions according to the employee's job duties and position. In addition, the customization section can improve the task management agent system's functions and interface based on employee feedback. This makes the task management agent system more user-friendly, tailored to the individual needs and preferences of employees.

[0115] The task management agent system can also be equipped with an emotion estimation unit. This unit estimates an employee's emotions and adjusts the task management method based on the estimated emotions. For example, if an employee is feeling stressed, the emotion estimation unit can adjust task priorities to reduce their workload. It can also assign more tasks if an employee is relaxed. Furthermore, if an employee is in a hurry, the emotion estimation unit can prioritize assigning tasks that can be completed quickly. This allows the task management agent system to support more appropriate task management based on the employee's emotions.

[0116] The task management agent system can also include a motivation function. This function estimates the employee's emotions and suggests actions to improve motivation based on those estimates. For example, if an employee is feeling stressed, the motivation function can suggest a break to help them relax. If the employee is relaxed, it can suggest a challenging task. Furthermore, if the employee is in a hurry, the motivation function can provide advice on how to complete tasks efficiently. This allows the task management agent system to support employees in improving their motivation based on their emotions.

[0117] The task management agent system can also include a stress management unit. This unit estimates the employee's emotions and proposes actions to reduce stress based on those estimates. For example, if an employee is feeling stressed, the stress management unit can suggest relaxation exercises or meditation. If the employee is relaxed, the stress management unit can also provide advice to prevent stress. Furthermore, if an employee is in a hurry, the stress management unit can provide time management advice to efficiently complete tasks. In this way, the task management agent system can provide support to reduce stress according to the employee's emotions.

[0118] The task management agent system can also include a communication unit. This unit estimates the employee's emotions and adjusts the communication method based on those estimates. For example, if an employee is stressed, the communication unit can communicate simply and clearly. Conversely, if an employee is relaxed, it can communicate with more detailed information. Furthermore, if an employee is in a hurry, the communication unit can communicate quickly to convey information rapidly. This allows the task management agent system to support more appropriate communication based on the employee's emotions.

[0119] The task management agent system can also include a feedback unit. The feedback unit collects feedback from employees upon task completion and provides it to the analysis unit. For example, after an employee completes a task, the feedback unit can request feedback on the task's difficulty, time taken, and satisfaction level. The feedback unit can also collect any problems or areas for improvement that employees encountered during the task. Furthermore, the feedback unit can analyze the collected feedback and use it to improve the entire task management agent system. This allows the task management agent system to evolve into a more efficient and user-friendly system based on employee feedback.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk receives the task details from the employee. When the employee enters the task details, they will enter information such as the task deadline, importance level, and dependencies. For example, for a task called "Create a report for Project A," the employee might enter "December 1, 2023" as the deadline, "High" as the importance level, and "After completion of data collection for Project B" as the dependency level. Step 2: The synchronization department synchronizes the task details entered by the reception department with calendars and project management tools. For example, the generation AI works with the calendar app and project management tool used by employees to retrieve task information. This allows the synchronization department to understand employees' schedules and project progress. Step 3: The analysis department analyzes the deadlines, importance, and dependencies of tasks synchronized by the synchronization department. For example, it prioritizes tasks with approaching deadlines or those that have a significant impact on the project. The analysis department also considers the consistency of task work time and schedules to efficiently allocate tasks. Step 4: The decision-making unit determines task priorities based on the information analyzed by the analysis unit. For example, tasks with approaching deadlines or those of high importance are prioritized. Step 5: The delivery department provides reminders and priority advice based on the priorities determined by the decision-making department. For example, if the task "Create report for Project A" is due soon, the delivery department determines it has a high priority and sends a reminder. The delivery department also visualizes the progress of tasks so that employees can understand the completion rate of tasks.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, synchronization unit, analysis unit, decision unit, and delivery unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where employees input task details. The synchronization unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and synchronizes the task details with a calendar or project management tool. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, and analyzes the task deadline, importance, and dependencies. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, and determines the task priority. The delivery unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides reminders and priority advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, synchronization unit, analysis unit, decision unit, and delivery unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where employees input task details. The synchronization unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, which synchronizes the task details with a calendar or project management tool. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes task deadlines, importance, and dependencies. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which determines the task priority. The delivery unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, which provides reminders and priority advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] Each of the multiple elements described above, including the reception unit, synchronization unit, analysis unit, decision unit, and delivery unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where employees input task details. The synchronization unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, which synchronizes task details with a calendar or project management tool. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes task deadlines, importance, and dependencies. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which determines the priority of tasks. The delivery unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, which provides reminders and priority advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] Each of the multiple elements described above, including the reception unit, synchronization unit, analysis unit, decision unit, and delivery unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where employees input task details. The synchronization unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, which synchronizes task details with a calendar or project management tool. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes task deadlines, importance, and dependencies. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12, which determines task priorities. The delivery unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, which provides reminders and priority advice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) The reception desk where you enter the task details, A synchronization unit synchronizes the details of tasks entered by the reception unit with a calendar or project management tool. An analysis unit analyzes the deadline, importance, and dependencies of tasks synchronized by the aforementioned synchronization unit. A decision unit that determines the priority of tasks based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides reminders and priority advice based on the priority determined by the aforementioned determination unit. A system characterized by the following features. (Note 2) The aforementioned supply unit is, Visualize the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Track the completion rate of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is Consider the consistency between task duration and schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned synchronization unit, It integrates with the calendar apps and project management tools that employees use. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering task details, the system provides input assistance by referring to past task history. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering task details, customize the input form according to the task category. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of input tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering task details, the system prioritizes tasks that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter task details, the system analyzes their social media activity and suggests related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned synchronization unit, It estimates the user's emotions and adjusts the synchronization timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned synchronization unit, During synchronization, the system selects the optimal synchronization method by referring to past synchronization history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned synchronization unit, During synchronization, different synchronization algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned synchronization unit, It estimates the user's emotions and determines the priority of synchronization tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned synchronization unit, During synchronization, the system prioritizes syncing highly relevant tasks by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned synchronization unit, During synchronization, the system analyzes users' social media activity and synchronizes related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During analysis, refer to past task history to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During the analysis, different analytical methods are applied to each task category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, the accuracy of the analysis is improved by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During the analysis, we will analyze users' social media activity and related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned determination unit, It estimates the user's emotions and adjusts the prioritization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned determination unit, When determining priorities, refer to past task history to select the optimal decision-making method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned determination unit, When determining priorities, different decision algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned determination unit, It estimates the user's emotions and adjusts the display priority based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned determination unit, When determining priorities, the user's geographical location information is taken into consideration to determine the optimal priority. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned determination unit, When prioritizing tasks, we analyze users' social media activity and determine the priority of related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts how reminders and advice are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing reminders or advice, refer to past task history to select the most appropriate delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing reminders or advice, apply different delivery methods depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, It estimates the user's emotions and adjusts how reminders and advice are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing reminders or advice, the system selects the optimal delivery method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing reminders and advice, the system analyzes the user's social media activity and provides reminders and advice on relevant tasks. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk where you enter the task details, A synchronization unit synchronizes the details of tasks entered by the reception unit with a calendar or project management tool. An analysis unit analyzes the deadline, importance, and dependencies of tasks synchronized by the aforementioned synchronization unit. A decision unit that determines the priority of tasks based on the information analyzed by the aforementioned analysis unit, The system includes a provisioning unit that provides reminders and priority advice based on the priority determined by the aforementioned determination unit. A system characterized by the following features.

2. The aforementioned supply unit is, Visualize the progress of tasks. The system according to feature 1.

3. The aforementioned supply unit is, Track the completion rate of tasks. The system according to feature 1.

4. The aforementioned analysis unit is Consider the consistency between task duration and schedule. The system according to feature 1.

5. The aforementioned synchronization unit, It integrates with the calendar apps and project management tools that employees use. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned reception unit is When entering task details, the system provides input assistance by referring to past task history. The system according to feature 1.

8. The aforementioned reception unit is When entering task details, customize the input form according to the task category. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of input tasks based on the estimated user emotions. The system according to feature 1.

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