system

Abstract

The system according to this embodiment aims to automatically prioritize multiple business tasks and perform efficient schedule management. [Solution] The system according to the embodiment comprises an analysis unit, a decision unit, a scheduling unit, a rebalancing unit, a sharing unit, and an adjustment unit. The analysis unit analyzes the progress and urgency of tasks. The decision unit determines the priority of tasks based on the results analyzed by the analysis unit. The scheduling unit creates a daily schedule based on the priorities determined by the decision unit. The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency. The sharing unit shares the task progress of colleagues and other team members in real time. The adjustment unit adjusts the schedule based on the task progress shared by the sharing 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, since the prioritization and schedule adjustment of multiple business tasks are performed manually, there is a problem that efficient task management is difficult.

[0005] The system according to the embodiment aims to automatically prioritize multiple business tasks and perform efficient schedule management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a decision unit, a scheduling unit, a rebalancing unit, a sharing unit, and an adjustment unit. The analysis unit analyzes the progress and urgency of tasks. The decision unit determines the priority of tasks based on the results analyzed by the analysis unit. The scheduling unit creates a daily schedule based on the priorities determined by the decision unit. The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency. The sharing unit shares the task progress of colleagues and other team members in real time. The adjustment unit adjusts the schedule based on the task progress shared by the sharing unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically prioritize multiple business tasks and perform efficient schedule management. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F manages communications 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) An agent system according to an embodiment of the present invention is a system that uses generative AI to automatically prioritize multiple work tasks and create a daily schedule. This agent system analyzes the progress and urgency of tasks in real time and flexibly rebalances the schedule. It also has a cross-team collaboration function that allows for the real-time sharing of task progress of colleagues and other team members, enabling efficient task synchronization. This allows for the adjustment of each member's schedule and reduces wasted time. For example, the agent system uses generative AI to analyze the progress and urgency of each task in real time. At this time, the generative AI determines the priority of tasks based on information such as the importance and deadline of the task. For example, it can be set to prioritize tasks with approaching deadlines or high importance. Next, the generative AI automatically creates a daily schedule based on the priority determined by the generative AI. The generative AI considers the time required for each task and dependencies to create an optimal schedule. For example, it can create an efficient schedule by placing tasks that require concentration in the morning and lighter tasks in the afternoon. Furthermore, the generative AI flexibly rebalances the schedule in response to changes in the progress and urgency of tasks. For example, if an urgent task arises or if existing tasks are behind schedule, the generating AI can automatically adjust the schedule and reorder tasks to optimize their functionality. It also features cross-team collaboration capabilities, allowing for real-time sharing of task progress among colleagues and other team members, ensuring efficient task synchronization. The generating AI analyzes each member's task progress and adjusts schedules to reduce wasted time and enable efficient collaboration. For instance, it can adjust schedules to avoid team members working on the same task simultaneously. In this way, the generating AI can provide an agent that automatically prioritizes multiple work tasks and creates a daily schedule. This allows business professionals and project managers to work more efficiently and reduce wasted time.This allows the agent system to automatically prioritize multiple work tasks and create a daily schedule.

[0029] The agent system according to this embodiment comprises an analysis unit, a decision unit, a scheduling unit, a rebalancing unit, a sharing unit, and an adjustment unit. The analysis unit analyzes the progress and urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. The decision unit determines the priority of tasks based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or high importance. The decision unit can also determine priority by considering the dependencies between tasks. For example, the decision-making unit prioritizes tasks with approaching deadlines or high importance. The decision-making unit can also determine priorities by considering task dependencies. The scheduling unit creates a daily schedule based on the priorities determined by the decision-making unit. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency.The rebalancing department adjusts schedules, for example, when urgent tasks arise or when existing tasks are behind schedule. The rebalancing department can also reset task priorities. The sharing department shares the task progress of colleagues and other team members in real time. For example, the sharing department shares task progress in real time and adjusts schedules to avoid team members working on the same task simultaneously. The sharing department can also share task progress in real time and enable efficient collaboration. For example, the sharing department shares task progress in real time and adjusts schedules to avoid team members working on the same task simultaneously. The sharing department can also share task progress in real time and enable efficient collaboration. For example, the shared department can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. The shared department can also share task progress in real time and enable efficient collaboration. The coordination department adjusts schedules based on the task progress shared by the shared department. The coordination department can, for example, adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities. For example, the coordination department can adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities. For example, the coordination department can adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities.As a result, the agent system according to the embodiment can efficiently manage tasks by analyzing the progress and urgency of tasks, determining priorities, creating schedules, rebalancing, sharing, and coordinating them.

[0030] The analysis unit analyzes the progress and urgency of tasks. For example, the analysis unit monitors task progress in real time and evaluates the degree of progress. Specifically, the analysis unit tracks the progress of each task in detail and quantitatively measures task progress to evaluate the degree of task completion and the amount of work remaining. This includes collecting and analyzing data such as the elapsed time since the start of the task, the number of completed subtasks, and the number of incomplete subtasks. The analysis unit can also consider the task deadline and importance to evaluate the urgency of the task. For example, if the deadline for a task is approaching or if the task plays a crucial role in the overall progress of the project, the task is judged to be highly urgent. The analysis unit comprehensively evaluates these factors and provides basic data for determining task priorities. Furthermore, the analysis unit can use AI to automatically analyze task progress and urgency. The AI ​​learns from past data and patterns and builds models to predict task progress and urgency. This allows the analysis unit to more accurately assess the progress and urgency of tasks, supporting efficient task management.

[0031] The decision unit determines task priorities based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or those of high importance. Specifically, the decision unit dynamically adjusts the priority of each task based on the progress and urgency data provided by the analysis unit. It also considers task dependencies, setting priorities based on dependencies if a particular task depends on the completion of other tasks. For example, if a task is a prerequisite for another task, prioritizing that task ensures smooth overall project progress. Furthermore, to evaluate the importance of tasks, the decision unit considers the impact a task has on the overall project and how much its completion contributes to the progress of other tasks and the project. This allows the decision unit to optimize task priorities and achieve efficient task management. In addition, the decision unit can automatically determine task priorities using AI. The AI ​​learns from past data and patterns to build models for predicting task priorities. This allows the decision unit to set task priorities more accurately and support efficient task management.

[0032] The scheduling unit creates a daily schedule based on the priorities determined by the decision-making unit. For example, the scheduling unit creates the schedule considering the duration and dependencies of each task. Specifically, it accurately estimates the duration of each task and optimizes the schedule considering task dependencies. For instance, if one task must wait for the completion of another, it adjusts the schedule to account for those dependencies. The scheduling unit can also adjust the schedule in response to changes in task progress and urgency. For example, if a task is behind schedule or a new urgent task arises, it readjusts the schedule and resets priorities. This enables the scheduling unit to achieve flexible and efficient schedule management. Furthermore, the scheduling unit can automatically optimize the schedule using AI. The AI ​​learns from past data and patterns to build models for predicting task duration and dependencies. This allows the scheduling unit to create more accurate schedules and support efficient task management.

[0033] The Rebalancing Unit rebalances the schedule created by the Scheduling Unit in accordance with changes in task progress and urgency. For example, the Rebalancing Unit adjusts the schedule when an urgent task arises or when existing tasks are behind schedule. Specifically, the Rebalancing Unit monitors changes in task progress and urgency in real time and dynamically adjusts the schedule. For example, when an urgent task arises, it evaluates the urgency and importance of that task and resets the priority of existing tasks. Also, if existing tasks are behind schedule, it analyzes the cause and reallocates resources as needed to minimize schedule delays. In this way, the Rebalancing Unit achieves flexible and efficient schedule management. Furthermore, the Rebalancing Unit can automate schedule rebalancing using AI. The AI ​​learns from past data and patterns and builds models to predict changes in task progress and urgency. This allows the Rebalancing Unit to adjust the schedule more accurately and support efficient task management.

[0034] The shared workspace allows for real-time sharing of task progress among colleagues and other team members. For example, it can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. Specifically, the shared workspace provides a platform for tracking the progress of each task in detail and sharing it with team members in real time. This allows team members to understand the progress of others and avoid duplicate work. Furthermore, the shared workspace can facilitate efficient collaboration by sharing task progress in real time. For example, it can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. It can also facilitate efficient collaboration by sharing task progress in real time. In addition, the shared workspace can leverage AI to automatically analyze task progress and provide team members with the most relevant information. The AI ​​learns from past data and patterns to build models for predicting task progress. This allows the shared workspace to have a more accurate understanding of task progress and support more efficient collaboration.

[0035] The coordination unit adjusts the schedule based on the task progress shared by the sharing unit. For example, the coordination unit adjusts the schedule to avoid having team members work on the same task simultaneously. Specifically, the coordination unit dynamically adjusts the priority and schedule of each task based on the progress of tasks provided by the sharing unit. For example, if one task must wait for the completion of another task, the coordination unit adjusts the schedule considering that dependency. The coordination unit can also reset the priority of tasks. For example, if an urgent task arises or the progress of an existing task is behind schedule, it readjusts the schedule and resets the priority. This enables the coordination unit to achieve flexible and efficient schedule management. Furthermore, the coordination unit can use AI to automate schedule adjustments. The AI ​​learns from past data and patterns to build models to predict changes in task progress and urgency. This allows the coordination unit to adjust the schedule more accurately and support efficient task management.

[0036] The analysis unit can analyze the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. This makes it possible to analyze the progress and urgency of tasks more accurately by analyzing based on information such as the importance and deadline of the tasks. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input information such as the importance and deadline of tasks into a generation AI and have the generation AI perform the analysis of the progress and urgency of tasks.

[0037] The decision unit can prioritize tasks that are due soon or are of high importance based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks that are due soon or are of high importance. For example, the decision unit prioritizes tasks that are due soon or are of high importance. For example, the decision unit prioritizes tasks that are due soon or are of high importance. This allows for efficient management of important tasks by prioritizing tasks that are due soon or are of high importance. Some or all of the above-described processing in the decision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision unit can input the results analyzed by the analysis unit into a generative AI and have the generative AI determine the task priorities.

[0038] The scheduling unit can create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. This enables efficient schedule management by creating a schedule that considers the time required for each task and its dependencies. Some or all of the above processing in the scheduling unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the scheduling unit can input information on the time required for each task and its dependencies into a generation AI and have the generation AI create the schedule.

[0039] The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. This allows for flexible schedule management by adjusting the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. Some or all of the above processing in the rebalancing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the rebalancing unit can input information about the emergence of sudden tasks or delays in the progress of existing tasks into the generation AI and have the generation AI perform the schedule adjustments.

[0040] The sharing section can share the task progress of colleagues and other team members in real time. For example, the sharing section can share the task progress of colleagues and other team members in real time. This enables efficient collaboration by sharing the task progress of colleagues and other team members in real time. Some or all of the above processing in the sharing section may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing section can input information on the task progress of colleagues and other team members into a generative AI and have the generative AI perform the real-time sharing.

[0041] The adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously, based on the task progress shared by the sharing unit. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. By adjusting the schedule to avoid team members working on the same task simultaneously, wasted time can be reduced and efficient task management can be achieved. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the adjustment unit can input task progress information shared by the sharing unit into a generative AI and have the generative AI perform the schedule adjustment.

[0042] The analysis unit can improve the accuracy of its analysis by referring to past task history when analyzing the progress of a task. For example, the analysis unit uses a generation AI to analyze past task history and predict the current task progress based on the progress patterns of similar tasks. For example, the analysis unit uses a generation AI to detect a tendency for a particular task to be slow from past task history and reflects this in the current task progress. For example, the analysis unit uses a generation AI to refer to past task history, and if a particular task is progressing quickly, it applies that pattern to the current task progress. By referring to past task history, the analysis accuracy is improved, enabling a more accurate analysis of the task progress. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input information from past task history into a generation AI and have the generation AI perform the analysis of the task progress.

[0043] The analysis unit can perform the analysis of task urgency while considering task dependencies. For example, the analysis unit may use a generating AI to analyze task dependencies and set the urgency of dependent tasks to a high level. For example, the analysis unit may use a generating AI to consider task dependencies and set the urgency of undependent tasks to a low level. For example, the analysis unit may use a generating AI to analyze task dependencies and adjust the urgency based on the progress of dependent tasks. This allows for a more accurate analysis of task urgency by considering task dependencies. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input task dependency information into a generating AI and have the generating AI perform the task urgency analysis.

[0044] The analysis unit can perform analysis of task progress while taking into account the user's geographical location information. For example, the analysis unit may use a generating AI to consider the user's current location and prioritize analyzing nearby tasks. For example, the analysis unit may use a generating AI to analyze the user's movement patterns and prioritize analyzing tasks that can be completed while moving. For example, the analysis unit may use a generating AI to prioritize analyzing tasks that need to be completed at a specific location based on the user's geographical location information. By performing analysis while taking the user's geographical location information into account, a more accurate analysis of task progress becomes possible. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating 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 progress analysis.

[0045] The analysis unit can analyze users' social media activity to obtain relevant information when analyzing the urgency of tasks. For example, the analysis unit uses a generative AI to analyze users' social media activity and obtain information related to high-urgency tasks. For example, the analysis unit uses a generative AI to adjust the urgency of specific tasks based on users' social media activity. For example, the analysis unit uses a generative AI to analyze users' social media activity and identify factors that affect the urgency of tasks. This allows for a more accurate analysis of task urgency by analyzing users' social media activity and obtaining relevant information. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information on users' social media activity into a generative AI and have the generative AI perform the task urgency analysis.

[0046] The decision unit can adjust task priorities based on their importance when determining task priorities. For example, the decision unit may use a generative AI to analyze task importance and prioritize tasks with higher importance. The decision unit may use a generative AI to dynamically adjust priorities based on task importance. The decision unit may use a generative AI to consider task importance and postpone tasks with lower importance. By adjusting priorities based on task importance, it becomes possible to determine more appropriate task priorities. Some or all of the above processes in the decision unit may be performed using a generative AI, or not. For example, the decision unit may input task importance information into a generative AI and have the generative AI perform the task priority adjustment.

[0047] The decision-making unit can set task priorities by considering task dependencies when determining task priorities. For example, the decision-making unit may have a generative AI analyze task dependencies and prioritize tasks with dependencies. For example, the decision-making unit may have a generative AI consider task dependencies and postpone tasks without dependencies. For example, the decision-making unit may have a generative AI analyze task dependencies and set a higher priority for tasks with dependencies. This makes it possible to determine task priorities more appropriately by considering task dependencies. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit may input task dependency information into a generative AI and have the generative AI perform task priority setting.

[0048] The decision-making unit can set task priorities while considering the user's geographical location. For example, the decision-making unit may use a generative AI to consider the user's current location and prioritize nearby tasks. For example, the decision-making unit may use a generative AI to analyze the user's movement patterns and prioritize tasks that can be completed while moving. For example, the decision-making unit may use a generative AI to prioritize tasks that need to be completed at a specific location based on the user's geographical location. This allows for more appropriate task prioritization by considering the user's geographical location. Some or all of the above-described processes in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit can input the user's geographical location information into a generative AI and have the generative AI perform task prioritization.

[0049] The decision-making unit can analyze the user's social media activity and obtain relevant information when determining task priorities. For example, the decision-making unit may use a generative AI to analyze the user's social media activity and obtain information related to high-priority tasks. For example, the decision-making unit may use a generative AI to adjust the priority of specific tasks based on the user's social media activity. For example, the decision-making unit may use a generative AI to analyze the user's social media activity and identify factors that influence task priorities. This makes it possible to determine more appropriate task priorities by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit may input information about the user's social media activity into a generative AI and have the generative AI perform task priority determination.

[0050] The scheduling unit can create an optimal schedule by considering the time required for each task. For example, the scheduling unit can use a generation AI to analyze the time required for each task and create an optimal schedule. For example, the scheduling unit can use a generation AI to create an efficient schedule based on the time required for each task. For example, the scheduling unit can use a generation AI to create a streamlined schedule by considering the time required for each task. This enables efficient schedule management by creating a schedule that considers the time required for each task. Some or all of the above-described processes in the scheduling unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the scheduling unit can input information on the time required for each task into a generation AI and have the generation AI create the schedule.

[0051] The scheduling unit can set a schedule while considering task dependencies when creating it. For example, the scheduling unit may use a generation AI to analyze task dependencies and prioritize scheduling tasks with dependencies. For example, the scheduling unit may use a generation AI to consider task dependencies and postpone tasks without dependencies. For example, the scheduling unit may use a generation AI to analyze task dependencies and prioritize scheduling tasks with dependencies. This makes it possible to create a more appropriate schedule by setting a schedule while considering task dependencies. Some or all of the above processing in the scheduling unit may be performed using a generation AI, or not. For example, the scheduling unit may input task dependency information into a generation AI and have the generation AI perform the schedule setting.

[0052] The scheduling unit can create an optimal schedule by considering the user's geographical location information. For example, the scheduling unit's generating AI considers the user's current location and prioritizes scheduling nearby tasks. For example, the scheduling unit's generating AI analyzes the user's travel patterns and prioritizes scheduling tasks that can be completed while traveling. For example, the scheduling unit's generating AI uses the user's geographical location information to prioritize scheduling tasks that need to be completed at a specific location. This makes it possible to create a more appropriate schedule by considering the user's geographical location information. Some or all of the above processing in the scheduling unit may be performed using, for example, the generating AI, or without the generating AI. For example, the scheduling unit can input the user's geographical location information into the generating AI and have the generating AI create the schedule.

[0053] The scheduling unit can analyze the user's social media activity and obtain relevant information when creating a schedule. For example, the scheduling unit can use a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the schedule. For example, the scheduling unit can use a generative AI to adjust the schedule of a specific task based on the user's social media activity. For example, the scheduling unit can use a generative AI to analyze the user's social media activity and identify factors that affect the task schedule. This makes it possible to create a more appropriate schedule by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the scheduling unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the scheduling unit can input information about the user's social media activity into a generative AI and have the generative AI create the schedule.

[0054] The rebalancing unit can adjust the schedule in response to the emergence of urgent tasks when performing rebalancing. For example, the rebalancing unit's generation AI detects the emergence of urgent tasks and adjusts the existing schedule. For example, the rebalancing unit's generation AI resets priorities in response to the emergence of urgent tasks. For example, the rebalancing unit's generation AI considers the emergence of urgent tasks and reconstructs the optimal schedule. This allows for flexible schedule management by adjusting the schedule in response to the emergence of urgent tasks. Some or all of the above processes in the rebalancing unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the rebalancing unit can input information about the emergence of urgent tasks into the generation AI and have the generation AI perform the schedule adjustment.

[0055] The rebalancing unit can readjust the schedule in response to delays in the progress of existing tasks when performing rebalancing. For example, the rebalancing unit's generation AI detects delays in the progress of existing tasks and readjusts the schedule. For example, the rebalancing unit's generation AI readjusts the priority in response to delays in the progress of existing tasks. For example, the rebalancing unit's generation AI considers delays in the progress of existing tasks and reconstructs the optimal schedule. This allows for flexible schedule management by readjusting the schedule in response to delays in the progress of existing tasks. Some or all of the above processes in the rebalancing unit may be performed using, for example, the generation AI, or without the generation AI. For example, the rebalancing unit can input information about delays in the progress of existing tasks into the generation AI and have the generation AI readjust the schedule.

[0056] The rebalancing unit can select the optimal rebalancing method by considering the user's geographical location information when performing rebalancing. For example, the rebalancing unit may use a generation AI to consider the user's current location and prioritize rebalancing nearby tasks. For example, the rebalancing unit may use a generation AI to analyze the user's movement patterns and prioritize rebalancing tasks that can be completed while moving. For example, the rebalancing unit may use a generation AI to prioritize rebalancing tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a rebalancing method that considers the user's geographical location information, a more appropriate rebalancing becomes possible. Some or all of the above processes in the rebalancing unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the rebalancing unit can input the user's geographical location information into a generation AI and have the generation AI select a rebalancing method.

[0057] The rebalancing unit can analyze the user's social media activity and obtain relevant information when performing rebalancing. For example, the rebalancing unit uses a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the rebalancing. For example, the rebalancing unit uses a generative AI to adjust the rebalancing of specific tasks based on the user's social media activity. For example, the rebalancing unit uses a generative AI to analyze the user's social media activity and identify factors that affect task rebalancing. This allows for more appropriate rebalancing by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the rebalancing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the rebalancing unit can input information about the user's social media activity into a generative AI and have the generative AI perform the rebalancing.

[0058] The sharing function can improve the accuracy of task sharing by referring to the past task history of colleagues and other team members when sharing task progress. For example, the sharing function uses a generative AI to analyze the past task history of colleagues and other team members to improve sharing accuracy. For example, the sharing function uses a generative AI to share relevant task progress based on past task history. For example, the sharing function uses a generative AI to refer to the past task history of colleagues and other team members and select the optimal sharing method. This improves sharing accuracy and enables more appropriate sharing of task progress by referring to the past task history of colleagues and other team members. Some or all of the above processing in the sharing function may be performed using a generative AI, or not. For example, the sharing function can input information on the past task history of colleagues and other team members into a generative AI and have the generative AI perform task progress sharing.

[0059] The sharing function can prioritize sharing highly relevant information by considering the user's geographical location when sharing task progress. For example, the sharing function's generating AI can consider the user's current location and prioritize sharing nearby task progress. For example, the sharing function's generating AI can analyze the user's travel patterns and prioritize sharing task progress relevant to the user's travel. For example, the sharing function's generating AI can use the user's geographical location to prioritize sharing task progress that needs to be completed at a specific location. This allows for more appropriate sharing of task progress by prioritizing the sharing of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, the generating AI, or without the generating AI. For example, the sharing function can input the user's geographical location information into the generating AI and have the generating AI perform task progress sharing.

[0060] The sharing function can prioritize sharing highly relevant information by considering the user's geographical location when sharing task progress. For example, the sharing function's generating AI can consider the user's current location and prioritize sharing nearby task progress. For example, the sharing function's generating AI can analyze the user's travel patterns and prioritize sharing task progress relevant to the user's travel. For example, the sharing function's generating AI can use the user's geographical location to prioritize sharing task progress that needs to be completed at a specific location. This allows for more appropriate sharing of task progress by prioritizing the sharing of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, the generating AI, or without the generating AI. For example, the sharing function can input the user's geographical location information into the generating AI and have the generating AI perform task progress sharing.

[0061] The sharing unit can analyze the user's social media activity and obtain relevant information when sharing task progress. For example, the sharing unit uses a generative AI to analyze the user's social media activity, obtain event information related to the task, and reflect it in the sharing. For example, the sharing unit uses a generative AI to share specific task progress based on the user's social media activity. For example, the sharing unit uses a generative AI to analyze the user's social media activity and identify factors that influence the sharing of task progress. This makes it possible to share task progress more appropriately by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input information about the user's social media activity into a generative AI and have the generative AI perform the task progress sharing.

[0062] The scheduling unit can make optimal adjustments when adjusting schedules, taking into account the task progress of colleagues and other team members. For example, the scheduling unit uses a generating AI to analyze the task progress of colleagues and other team members and adjust schedules. For example, the scheduling unit uses a generating AI to make efficient schedule adjustments based on task progress. For example, the scheduling unit uses a generating AI to make efficient schedule adjustments, taking into account the task progress of colleagues and other team members. This makes it possible to make more appropriate schedule adjustments by taking into account the task progress of colleagues and other team members. Some or all of the above processes in the scheduling unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the scheduling unit can input information on the task progress of colleagues and other team members into a generating AI and have the generating AI perform schedule adjustments.

[0063] The scheduling unit can select the optimal scheduling method by considering the user's geographical location information when adjusting the schedule. For example, the scheduling unit may use a generating AI to consider the user's current location and prioritize scheduling nearby tasks. For example, the scheduling unit may use a generating AI to analyze the user's travel patterns and prioritize scheduling tasks that can be completed while traveling. For example, the scheduling unit may use a generating AI to prioritize scheduling tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a scheduling method that considers the user's geographical location information, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using a generating AI, or without using a generating AI. For example, the scheduling unit may input the user's geographical location information into a generating AI and have the generating AI perform the scheduling adjustments.

[0064] The scheduling unit can select the optimal scheduling method by considering the user's geographical location information when adjusting the schedule. For example, the scheduling unit may use a generating AI to consider the user's current location and prioritize scheduling nearby tasks. For example, the scheduling unit may use a generating AI to analyze the user's travel patterns and prioritize scheduling tasks that can be completed while traveling. For example, the scheduling unit may use a generating AI to prioritize scheduling tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a scheduling method that considers the user's geographical location information, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using a generating AI, or without using a generating AI. For example, the scheduling unit may input the user's geographical location information into a generating AI and have the generating AI perform the scheduling adjustments.

[0065] The scheduling unit can analyze the user's social media activity and obtain relevant information when adjusting the schedule. For example, the scheduling unit may use a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the schedule adjustment. For example, the scheduling unit may use a generative AI to adjust the schedule of a specific task based on the user's social media activity. For example, the scheduling unit may use a generative AI to analyze the user's social media activity and identify factors that affect the scheduling of tasks. By analyzing the user's social media activity and obtaining relevant information, it becomes possible to adjust the schedule more appropriately. Some or all of the above processing in the scheduling unit may be performed using a generative AI, or not. For example, the scheduling unit may input information about the user's social media activity into a generative AI and have the generative AI perform the schedule adjustment.

[0066] The adjustment unit can select the optimal adjustment method when adjusting the schedule, taking into account the user's health condition. For example, the adjustment unit's generating AI considers the user's health condition and adjusts the schedule to reduce the workload if the user is tired. For example, the adjustment unit's generating AI suggests a slightly longer route to encourage healthy exercise based on the user's health condition. For example, the adjustment unit's generating AI analyzes the user's health condition and suggests a schedule that includes rest points if the user is feeling unwell. By selecting an adjustment method that takes the user's health condition into account, it becomes possible to adjust the schedule more appropriately. Some or all of the above processes in the adjustment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the adjustment unit can input information about the user's health condition into the generating AI and have the generating AI perform the schedule adjustment.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The agent system can prioritize tasks by considering the user's geographical location. For example, if a user is in a specific location, it can be set to prioritize tasks that need to be completed at that location. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized. Furthermore, it can prioritize nearby tasks by considering the user's current location. This allows for flexible adjustment of task priorities based on the user's geographical location, enabling more efficient task management.

[0069] The agent system can analyze a user's social media activity to determine task priorities. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be prioritized. Furthermore, the system can adjust the priority of specific tasks based on the user's social media activity. It can also analyze the user's social media activity to identify factors influencing task priorities. This allows for flexible task prioritization considering the user's social media activity, enabling more effective task management.

[0070] The agent system can prioritize tasks while considering the user's health condition. For example, if the user is tired, the system can be set to prioritize lighter tasks. It can also suggest a slightly longer route to encourage healthy exercise based on the user's health status. Furthermore, if the user is unwell, the system can suggest a schedule that includes rest points. This allows for flexible adjustment of task priorities based on the user's health condition, enabling more appropriate task management.

[0071] The agent system can create schedules that take the user's geographical location into consideration. For example, if a user is in a specific location, tasks that need to be completed at that location can be prioritized. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized. Furthermore, tasks near the user's current location can be prioritized. This allows for flexible scheduling adjustments based on the user's geographical location, enabling more efficient schedule management.

[0072] The agent system can analyze a user's social media activity and create a schedule. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be reflected in the schedule. Furthermore, the system can adjust the schedule of specific tasks based on the user's social media activity. It can also analyze the user's social media activity and identify factors that influence task scheduling. This allows for flexible schedule adjustments that take the user's social media activity into consideration, enabling more effective schedule management.

[0073] The agent system can rebalance tasks while considering the user's geographical location. For example, if a user is in a specific location, tasks that need to be completed at that location can be prioritized in the rebalancing process. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized in the rebalancing process. Furthermore, tasks near the user's current location can be prioritized in the rebalancing process. This allows for flexible adjustment of rebalancing based on the user's geographical location, enabling more efficient task management.

[0074] The agent system can analyze and rebalance users' social media activity. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be reflected in the rebalancing. Furthermore, the rebalancing of specific tasks can be adjusted based on the user's social media activity. In addition, the system can analyze the user's social media activity and identify factors that influence task rebalancing. This allows for flexible adjustment of rebalancing to take the user's social media activity into account, enabling more appropriate task management.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The analysis unit analyzes the progress and urgency of the task. For example, the analysis unit monitors the task's progress in real time and evaluates the degree of progress. It can also consider the task's deadline and importance in order to assess the urgency of the task. Step 2: The decision unit determines task priorities based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or those of high importance. It can also determine priorities by considering task dependencies. Step 3: The scheduling department creates a daily schedule based on the priorities determined by the decision-making department. The scheduling department creates the schedule by considering, for example, the time required for each task and its dependencies. It can also adjust the schedule according to changes in the progress and urgency of tasks. Step 4: The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency. For example, the rebalancing unit adjusts the schedule when an urgent task arises or when the progress of an existing task is behind schedule. It can also reset the priority of tasks. Step 5: The shared department shares the task progress of colleagues and other team members in real time. For example, the shared department can share task progress in real time and adjust schedules to avoid team members working on the same task at the same time. This can also enable more efficient collaboration. Step 6: The Coordination Team adjusts the schedule based on the task progress shared by the Sharing Team. For example, the Coordination Team adjusts the schedule to avoid team members working on the same task at the same time. They may also reprioritize tasks.

[0077] (Example of form 2) An agent system according to an embodiment of the present invention is a system that uses generative AI to automatically prioritize multiple work tasks and create a daily schedule. This agent system analyzes the progress and urgency of tasks in real time and flexibly rebalances the schedule. It also has a cross-team collaboration function that allows for the real-time sharing of task progress of colleagues and other team members, enabling efficient task synchronization. This allows for the adjustment of each member's schedule and reduces wasted time. For example, the agent system uses generative AI to analyze the progress and urgency of each task in real time. At this time, the generative AI determines the priority of tasks based on information such as the importance and deadline of the task. For example, it can be set to prioritize tasks with approaching deadlines or high importance. Next, the generative AI automatically creates a daily schedule based on the priority determined by the generative AI. The generative AI considers the time required for each task and dependencies to create an optimal schedule. For example, it can create an efficient schedule by placing tasks that require concentration in the morning and lighter tasks in the afternoon. Furthermore, the generative AI flexibly rebalances the schedule in response to changes in the progress and urgency of tasks. For example, if an urgent task arises or if existing tasks are behind schedule, the generating AI can automatically adjust the schedule and reorder tasks to optimize their functionality. It also features cross-team collaboration capabilities, allowing for real-time sharing of task progress among colleagues and other team members, ensuring efficient task synchronization. The generating AI analyzes each member's task progress and adjusts schedules to reduce wasted time and enable efficient collaboration. For instance, it can adjust schedules to avoid team members working on the same task simultaneously. In this way, the generating AI can provide an agent that automatically prioritizes multiple work tasks and creates a daily schedule. This allows business professionals and project managers to work more efficiently and reduce wasted time.This allows the agent system to automatically prioritize multiple work tasks and create a daily schedule.

[0078] The agent system according to this embodiment comprises an analysis unit, a decision unit, a scheduling unit, a rebalancing unit, a sharing unit, and an adjustment unit. The analysis unit analyzes the progress and urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. For example, the analysis unit monitors the progress of tasks in real time and evaluates the degree of progress. The analysis unit can also consider the deadline and importance of tasks in order to evaluate the urgency of tasks. The decision unit determines the priority of tasks based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or high importance. The decision unit can also determine priority by considering the dependencies between tasks. For example, the decision-making unit prioritizes tasks with approaching deadlines or high importance. The decision-making unit can also determine priorities by considering task dependencies. The scheduling unit creates a daily schedule based on the priorities determined by the decision-making unit. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. For example, the scheduling unit creates a schedule considering the time required for each task and its dependencies. The scheduling unit can also adjust the schedule according to changes in task progress and urgency. The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency.The rebalancing department adjusts schedules, for example, when urgent tasks arise or when existing tasks are behind schedule. The rebalancing department can also reset task priorities. The sharing department shares the task progress of colleagues and other team members in real time. For example, the sharing department shares task progress in real time and adjusts schedules to avoid team members working on the same task simultaneously. The sharing department can also share task progress in real time and enable efficient collaboration. For example, the sharing department shares task progress in real time and adjusts schedules to avoid team members working on the same task simultaneously. The sharing department can also share task progress in real time and enable efficient collaboration. For example, the shared department can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. The shared department can also share task progress in real time and enable efficient collaboration. The coordination department adjusts schedules based on the task progress shared by the shared department. The coordination department can, for example, adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities. For example, the coordination department can adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities. For example, the coordination department can adjust schedules to avoid team members working on the same task simultaneously. The coordination department can also reset task priorities.As a result, the agent system according to the embodiment can efficiently manage tasks by analyzing the progress and urgency of tasks, determining priorities, creating schedules, rebalancing, sharing, and coordinating them.

[0079] The analysis unit analyzes the progress and urgency of tasks. For example, the analysis unit monitors task progress in real time and evaluates the degree of progress. Specifically, the analysis unit tracks the progress of each task in detail and quantitatively measures task progress to evaluate the degree of task completion and the amount of work remaining. This includes collecting and analyzing data such as the elapsed time since the start of the task, the number of completed subtasks, and the number of incomplete subtasks. The analysis unit can also consider the task deadline and importance to evaluate the urgency of the task. For example, if the deadline for a task is approaching or if the task plays a crucial role in the overall progress of the project, the task is judged to be highly urgent. The analysis unit comprehensively evaluates these factors and provides basic data for determining task priorities. Furthermore, the analysis unit can use AI to automatically analyze task progress and urgency. The AI ​​learns from past data and patterns and builds models to predict task progress and urgency. This allows the analysis unit to more accurately assess the progress and urgency of tasks, supporting efficient task management.

[0080] The decision unit determines task priorities based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or those of high importance. Specifically, the decision unit dynamically adjusts the priority of each task based on the progress and urgency data provided by the analysis unit. It also considers task dependencies, setting priorities based on dependencies if a particular task depends on the completion of other tasks. For example, if a task is a prerequisite for another task, prioritizing that task ensures smooth overall project progress. Furthermore, to evaluate the importance of tasks, the decision unit considers the impact a task has on the overall project and how much its completion contributes to the progress of other tasks and the project. This allows the decision unit to optimize task priorities and achieve efficient task management. In addition, the decision unit can automatically determine task priorities using AI. The AI ​​learns from past data and patterns to build models for predicting task priorities. This allows the decision unit to set task priorities more accurately and support efficient task management.

[0081] The scheduling unit creates a daily schedule based on the priorities determined by the decision-making unit. For example, the scheduling unit creates the schedule considering the duration and dependencies of each task. Specifically, it accurately estimates the duration of each task and optimizes the schedule considering task dependencies. For instance, if one task must wait for the completion of another, it adjusts the schedule to account for those dependencies. The scheduling unit can also adjust the schedule in response to changes in task progress and urgency. For example, if a task is behind schedule or a new urgent task arises, it readjusts the schedule and resets priorities. This enables the scheduling unit to achieve flexible and efficient schedule management. Furthermore, the scheduling unit can automatically optimize the schedule using AI. The AI ​​learns from past data and patterns to build models for predicting task duration and dependencies. This allows the scheduling unit to create more accurate schedules and support efficient task management.

[0082] The Rebalancing Unit rebalances the schedule created by the Scheduling Unit in accordance with changes in task progress and urgency. For example, the Rebalancing Unit adjusts the schedule when an urgent task arises or when existing tasks are behind schedule. Specifically, the Rebalancing Unit monitors changes in task progress and urgency in real time and dynamically adjusts the schedule. For example, when an urgent task arises, it evaluates the urgency and importance of that task and resets the priority of existing tasks. Also, if existing tasks are behind schedule, it analyzes the cause and reallocates resources as needed to minimize schedule delays. In this way, the Rebalancing Unit achieves flexible and efficient schedule management. Furthermore, the Rebalancing Unit can automate schedule rebalancing using AI. The AI ​​learns from past data and patterns and builds models to predict changes in task progress and urgency. This allows the Rebalancing Unit to adjust the schedule more accurately and support efficient task management.

[0083] The shared workspace allows for real-time sharing of task progress among colleagues and other team members. For example, it can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. Specifically, the shared workspace provides a platform for tracking the progress of each task in detail and sharing it with team members in real time. This allows team members to understand the progress of others and avoid duplicate work. Furthermore, the shared workspace can facilitate efficient collaboration by sharing task progress in real time. For example, it can share task progress in real time and adjust schedules to avoid team members working on the same task simultaneously. It can also facilitate efficient collaboration by sharing task progress in real time. In addition, the shared workspace can leverage AI to automatically analyze task progress and provide team members with the most relevant information. The AI ​​learns from past data and patterns to build models for predicting task progress. This allows the shared workspace to have a more accurate understanding of task progress and support more efficient collaboration.

[0084] The coordination unit adjusts the schedule based on the task progress shared by the sharing unit. For example, the coordination unit adjusts the schedule to avoid having team members work on the same task simultaneously. Specifically, the coordination unit dynamically adjusts the priority and schedule of each task based on the progress of tasks provided by the sharing unit. For example, if one task must wait for the completion of another task, the coordination unit adjusts the schedule considering that dependency. The coordination unit can also reset the priority of tasks. For example, if an urgent task arises or the progress of an existing task is behind schedule, it readjusts the schedule and resets the priority. This enables the coordination unit to achieve flexible and efficient schedule management. Furthermore, the coordination unit can use AI to automate schedule adjustments. The AI ​​learns from past data and patterns to build models to predict changes in task progress and urgency. This allows the coordination unit to adjust the schedule more accurately and support efficient task management.

[0085] The analysis unit can analyze the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. For example, the analysis unit analyzes the progress and urgency of tasks based on information such as the importance and deadline of the tasks. This makes it possible to analyze the progress and urgency of tasks more accurately by analyzing based on information such as the importance and deadline of the tasks. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input information such as the importance and deadline of tasks into a generation AI and have the generation AI perform the analysis of the progress and urgency of tasks.

[0086] The decision unit can prioritize tasks that are due soon or are of high importance based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks that are due soon or are of high importance. For example, the decision unit prioritizes tasks that are due soon or are of high importance. For example, the decision unit prioritizes tasks that are due soon or are of high importance. This allows for efficient management of important tasks by prioritizing tasks that are due soon or are of high importance. Some or all of the above-described processing in the decision unit may be performed using, for example, a generative AI, or without a generative AI. For example, the decision unit can input the results analyzed by the analysis unit into a generative AI and have the generative AI determine the task priorities.

[0087] The scheduling unit can create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. The scheduling unit can, for example, create a schedule considering the time required for each task and its dependencies. This enables efficient schedule management by creating a schedule that considers the time required for each task and its dependencies. Some or all of the above processing in the scheduling unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the scheduling unit can input information on the time required for each task and its dependencies into a generation AI and have the generation AI create the schedule.

[0088] The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The rebalancing unit can adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. This allows for flexible schedule management by adjusting the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. Some or all of the above processing in the rebalancing unit may be performed using, for example, a generation AI, or without a generation AI. For example, the rebalancing unit can input information about the emergence of sudden tasks or delays in the progress of existing tasks into the generation AI and have the generation AI perform the schedule adjustments.

[0089] The sharing section can share the task progress of colleagues and other team members in real time. For example, the sharing section can share the task progress of colleagues and other team members in real time. This enables efficient collaboration by sharing the task progress of colleagues and other team members in real time. Some or all of the above processing in the sharing section may be performed using, for example, a generative AI, or without a generative AI. For example, the sharing section can input information on the task progress of colleagues and other team members into a generative AI and have the generative AI perform the real-time sharing.

[0090] The adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously, based on the task progress shared by the sharing unit. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. For example, the adjustment unit can adjust the schedule to avoid team members working on the same task simultaneously. By adjusting the schedule to avoid team members working on the same task simultaneously, wasted time can be reduced and efficient task management can be achieved. Some or all of the above processing in the adjustment unit may be performed using, for example, a generative AI, or without a generative AI. For example, the adjustment unit can input task progress information shared by the sharing unit into a generative AI and have the generative AI perform the schedule adjustment.

[0091] The analysis unit can estimate the user's emotions and adjust the analysis method for task progress and urgency based on the estimated user emotions. For example, if the user is stressed, the analysis unit's generating AI will display a simplified version of the task progress, highlighting only the most urgent tasks. If the user is relaxed, the analysis unit's generating AI will display a detailed version of the task progress, including less urgent tasks. If the user is in a hurry, the analysis unit's generating AI will quickly analyze the task progress and prioritize displaying the most urgent tasks. By adjusting the analysis method based on the user's emotions, a more appropriate analysis of task progress and urgency becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generating AI. The generating AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generating AI, for example, or without a generating AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the analysis method for task progress and urgency.

[0092] The analysis unit can improve the accuracy of its analysis by referring to past task history when analyzing the progress of a task. For example, the analysis unit uses a generation AI to analyze past task history and predict the current task progress based on the progress patterns of similar tasks. For example, the analysis unit uses a generation AI to detect a tendency for a particular task to be slow from past task history and reflects this in the current task progress. For example, the analysis unit uses a generation AI to refer to past task history, and if a particular task is progressing quickly, it applies that pattern to the current task progress. By referring to past task history, the analysis accuracy is improved, enabling a more accurate analysis of the task progress. Some or all of the above processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input information from past task history into a generation AI and have the generation AI perform the analysis of the task progress.

[0093] The analysis unit can perform the analysis of task urgency while considering task dependencies. For example, the analysis unit may use a generating AI to analyze task dependencies and set the urgency of dependent tasks to a high level. For example, the analysis unit may use a generating AI to consider task dependencies and set the urgency of undependent tasks to a low level. For example, the analysis unit may use a generating AI to analyze task dependencies and adjust the urgency based on the progress of dependent tasks. This allows for a more accurate analysis of task urgency by considering task dependencies. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or not. For example, the analysis unit can input task dependency information into a generating AI and have the generating AI perform the task urgency analysis.

[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 tense, the generation AI provides a simple and highly visible display method. If the user is relaxed, the generation AI provides a display method that includes detailed information. If the user is in a hurry, the generation AI provides a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generation AI, for example, or without a generation AI. For example, the analysis unit can input user emotion data into a generation AI and have the generation AI adjust the display method of the analysis results.

[0095] The analysis unit can perform analysis of task progress while taking into account the user's geographical location information. For example, the analysis unit may use a generating AI to consider the user's current location and prioritize analyzing nearby tasks. For example, the analysis unit may use a generating AI to analyze the user's movement patterns and prioritize analyzing tasks that can be completed while moving. For example, the analysis unit may use a generating AI to prioritize analyzing tasks that need to be completed at a specific location based on the user's geographical location information. By performing analysis while taking the user's geographical location information into account, a more accurate analysis of task progress becomes possible. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, or they may be performed without a generating 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 progress analysis.

[0096] The analysis unit can analyze users' social media activity to obtain relevant information when analyzing the urgency of tasks. For example, the analysis unit uses a generative AI to analyze users' social media activity and obtain information related to high-urgency tasks. For example, the analysis unit uses a generative AI to adjust the urgency of specific tasks based on users' social media activity. For example, the analysis unit uses a generative AI to analyze users' social media activity and identify factors that affect the urgency of tasks. This allows for a more accurate analysis of task urgency by analyzing users' social media activity and obtaining relevant information. Some or all of the above processes in the analysis unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the analysis unit can input information on users' social media activity into a generative AI and have the generative AI perform the task urgency analysis.

[0097] The decision unit can estimate the user's emotions and adjust the method of prioritizing tasks based on the estimated user emotions. For example, if the user is stressed, the decision unit may have the generative AI prioritize only high-urgency tasks. If the user is relaxed, the decision unit may have the generative AI set detailed task priorities. If the user is in a hurry, the decision unit may have the generative AI quickly determine task priorities. By adjusting the method of prioritizing tasks based on the user's emotions, more appropriate task prioritization becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the decision unit may be performed using a generative AI, or not. For example, the decision unit can input user emotion data into a generative AI and have the generative AI adjust the method of determining task priorities.

[0098] The decision unit can adjust task priorities based on their importance when determining task priorities. For example, the decision unit may use a generative AI to analyze task importance and prioritize tasks with higher importance. The decision unit may use a generative AI to dynamically adjust priorities based on task importance. The decision unit may use a generative AI to consider task importance and postpone tasks with lower importance. By adjusting priorities based on task importance, it becomes possible to determine more appropriate task priorities. Some or all of the above processes in the decision unit may be performed using a generative AI, or not. For example, the decision unit may input task importance information into a generative AI and have the generative AI perform the task priority adjustment.

[0099] The decision-making unit can set task priorities by considering task dependencies when determining task priorities. For example, the decision-making unit may have a generative AI analyze task dependencies and prioritize tasks with dependencies. For example, the decision-making unit may have a generative AI consider task dependencies and postpone tasks without dependencies. For example, the decision-making unit may have a generative AI analyze task dependencies and set a higher priority for tasks with dependencies. This makes it possible to determine task priorities more appropriately by considering task dependencies. Some or all of the above processing in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit may input task dependency information into a generative AI and have the generative AI perform task priority setting.

[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 tense, the decision unit's generating AI provides a simple and highly visible display method. If the user is relaxed, the decision unit's generating AI provides a display method that includes detailed information. If the user is in a hurry, the decision unit's generating AI provides a display method that gets straight to the point. This allows for a more appropriate display by adjusting the display method of priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the decision unit may be performed using a generating AI, or not using a generating AI. For example, the decision unit can input user emotion data into a generating AI and have the generating AI adjust the display method of priorities.

[0101] The decision-making unit can set task priorities while considering the user's geographical location. For example, the decision-making unit may use a generative AI to consider the user's current location and prioritize nearby tasks. For example, the decision-making unit may use a generative AI to analyze the user's movement patterns and prioritize tasks that can be completed while moving. For example, the decision-making unit may use a generative AI to prioritize tasks that need to be completed at a specific location based on the user's geographical location. This allows for more appropriate task prioritization by considering the user's geographical location. Some or all of the above-described processes in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit can input the user's geographical location information into a generative AI and have the generative AI perform task prioritization.

[0102] The decision-making unit can analyze the user's social media activity and obtain relevant information when determining task priorities. For example, the decision-making unit may use a generative AI to analyze the user's social media activity and obtain information related to high-priority tasks. For example, the decision-making unit may use a generative AI to adjust the priority of specific tasks based on the user's social media activity. For example, the decision-making unit may use a generative AI to analyze the user's social media activity and identify factors that influence task priorities. This makes it possible to determine more appropriate task priorities by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the decision-making unit may be performed using a generative AI, or not. For example, the decision-making unit may input information about the user's social media activity into a generative AI and have the generative AI perform task priority determination.

[0103] The scheduling unit can estimate the user's emotions and adjust how the schedule is created based on the estimated emotions. For example, if the user is stressed, the scheduling unit's generative AI will create a schedule that reduces the workload. If the user is relaxed, the scheduling unit's generative AI will create a detailed schedule. If the user is in a hurry, the scheduling unit's generative AI will create a quick schedule. This allows for the creation of more appropriate schedules by adjusting how the schedule is created based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the scheduling unit may be performed using or without a generative AI. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI adjust how the schedule is created.

[0104] The scheduling unit can create an optimal schedule by considering the time required for each task. For example, the scheduling unit can use a generation AI to analyze the time required for each task and create an optimal schedule. For example, the scheduling unit can use a generation AI to create an efficient schedule based on the time required for each task. For example, the scheduling unit can use a generation AI to create a streamlined schedule by considering the time required for each task. This enables efficient schedule management by creating a schedule that considers the time required for each task. Some or all of the above-described processes in the scheduling unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the scheduling unit can input information on the time required for each task into a generation AI and have the generation AI create the schedule.

[0105] The scheduling unit can set a schedule while considering task dependencies when creating it. For example, the scheduling unit may use a generation AI to analyze task dependencies and prioritize scheduling tasks with dependencies. For example, the scheduling unit may use a generation AI to consider task dependencies and postpone tasks without dependencies. For example, the scheduling unit may use a generation AI to analyze task dependencies and prioritize scheduling tasks with dependencies. This makes it possible to create a more appropriate schedule by setting a schedule while considering task dependencies. Some or all of the above processing in the scheduling unit may be performed using a generation AI, or not. For example, the scheduling unit may input task dependency information into a generation AI and have the generation AI perform the schedule setting.

[0106] The scheduling unit can estimate the user's emotions and adjust the schedule display method based on the estimated emotions. For example, if the user is stressed, the generating AI provides a simple and highly visible display method. If the user is relaxed, the generating AI provides a display method that includes detailed information. If the user is in a hurry, the generating AI provides a display method that gets straight to the point. By adjusting the schedule display method based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the scheduling unit may be performed using a generating AI, or not. For example, the scheduling unit can input user emotion data into a generating AI and have the generating AI adjust the schedule display method.

[0107] The scheduling unit can create an optimal schedule by considering the user's geographical location information. For example, the scheduling unit's generating AI considers the user's current location and prioritizes scheduling nearby tasks. For example, the scheduling unit's generating AI analyzes the user's travel patterns and prioritizes scheduling tasks that can be completed while traveling. For example, the scheduling unit's generating AI uses the user's geographical location information to prioritize scheduling tasks that need to be completed at a specific location. This makes it possible to create a more appropriate schedule by considering the user's geographical location information. Some or all of the above processing in the scheduling unit may be performed using, for example, the generating AI, or without the generating AI. For example, the scheduling unit can input the user's geographical location information into the generating AI and have the generating AI create the schedule.

[0108] The scheduling unit can analyze the user's social media activity and obtain relevant information when creating a schedule. For example, the scheduling unit can use a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the schedule. For example, the scheduling unit can use a generative AI to adjust the schedule of a specific task based on the user's social media activity. For example, the scheduling unit can use a generative AI to analyze the user's social media activity and identify factors that affect the task schedule. This makes it possible to create a more appropriate schedule by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the scheduling unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the scheduling unit can input information about the user's social media activity into a generative AI and have the generative AI create the schedule.

[0109] The rebalancing unit can estimate the user's emotions and adjust the rebalancing method based on the estimated emotions. For example, if the user is stressed, the rebalancing unit uses a generative AI to rebalance the task load to reduce it. For example, if the user is relaxed, the rebalancing unit uses a generative AI to perform a detailed rebalancing. For example, if the user is in a hurry, the rebalancing unit uses a generative AI to perform a rapid rebalancing. This allows for more appropriate rebalancing by adjusting the rebalancing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the rebalancing unit may be performed using a generative AI, or not. For example, the rebalancing unit can input user emotion data into a generative AI and have the generative AI adjust the rebalancing method.

[0110] The rebalancing unit can adjust the schedule in response to the emergence of urgent tasks when performing rebalancing. For example, the rebalancing unit's generation AI detects the emergence of urgent tasks and adjusts the existing schedule. For example, the rebalancing unit's generation AI resets priorities in response to the emergence of urgent tasks. For example, the rebalancing unit's generation AI considers the emergence of urgent tasks and reconstructs the optimal schedule. This allows for flexible schedule management by adjusting the schedule in response to the emergence of urgent tasks. Some or all of the above processes in the rebalancing unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the rebalancing unit can input information about the emergence of urgent tasks into the generation AI and have the generation AI perform the schedule adjustment.

[0111] The rebalancing unit can readjust the schedule in response to delays in the progress of existing tasks when performing rebalancing. For example, the rebalancing unit's generation AI detects delays in the progress of existing tasks and readjusts the schedule. For example, the rebalancing unit's generation AI readjusts the priority in response to delays in the progress of existing tasks. For example, the rebalancing unit's generation AI considers delays in the progress of existing tasks and reconstructs the optimal schedule. This allows for flexible schedule management by readjusting the schedule in response to delays in the progress of existing tasks. Some or all of the above processes in the rebalancing unit may be performed using, for example, the generation AI, or without the generation AI. For example, the rebalancing unit can input information about delays in the progress of existing tasks into the generation AI and have the generation AI readjust the schedule.

[0112] The rebalancing unit can estimate the user's emotions and determine rebalancing priorities based on the estimated emotions. For example, if the user is stressed, the rebalancing unit's generative AI will prioritize rebalancing high-urgency tasks. If the user is relaxed, the rebalancing unit's generative AI will perform a detailed rebalancing. If the user is in a hurry, the rebalancing unit's generative AI will perform a rapid rebalancing. This allows for more appropriate rebalancing by determining rebalancing priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the rebalancing unit may be performed using a generative AI, or not. For example, the rebalancing unit can input user emotion data into a generative AI and have the generative AI determine the rebalancing priorities.

[0113] The rebalancing unit can select the optimal rebalancing method by considering the user's geographical location information when performing rebalancing. For example, the rebalancing unit may use a generation AI to consider the user's current location and prioritize rebalancing nearby tasks. For example, the rebalancing unit may use a generation AI to analyze the user's movement patterns and prioritize rebalancing tasks that can be completed while moving. For example, the rebalancing unit may use a generation AI to prioritize rebalancing tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a rebalancing method that considers the user's geographical location information, a more appropriate rebalancing becomes possible. Some or all of the above processes in the rebalancing unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the rebalancing unit can input the user's geographical location information into a generation AI and have the generation AI select a rebalancing method.

[0114] The rebalancing unit can analyze the user's social media activity and obtain relevant information when performing rebalancing. For example, the rebalancing unit uses a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the rebalancing. For example, the rebalancing unit uses a generative AI to adjust the rebalancing of specific tasks based on the user's social media activity. For example, the rebalancing unit uses a generative AI to analyze the user's social media activity and identify factors that affect task rebalancing. This allows for more appropriate rebalancing by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processes in the rebalancing unit may be performed using a generative AI, or they may be performed without a generative AI. For example, the rebalancing unit can input information about the user's social media activity into a generative AI and have the generative AI perform the rebalancing.

[0115] The sharing unit can estimate the user's emotions and adjust the way task progress is shared based on the estimated emotions. For example, if the user is nervous, the generative AI provides a simple and visually clear sharing method. If the user is relaxed, the generative AI provides a sharing method that includes detailed information. If the user is in a hurry, the generative AI provides a sharing method that gets straight to the point. This allows for more appropriate sharing by adjusting the task progress sharing method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI adjust the way task progress is shared.

[0116] The sharing function can improve the accuracy of task sharing by referring to the past task history of colleagues and other team members when sharing task progress. For example, the sharing function uses a generative AI to analyze the past task history of colleagues and other team members to improve sharing accuracy. For example, the sharing function uses a generative AI to share relevant task progress based on past task history. For example, the sharing function uses a generative AI to refer to the past task history of colleagues and other team members and select the optimal sharing method. This improves sharing accuracy and enables more appropriate sharing of task progress by referring to the past task history of colleagues and other team members. Some or all of the above processing in the sharing function may be performed using a generative AI, or not. For example, the sharing function can input information on the past task history of colleagues and other team members into a generative AI and have the generative AI perform task progress sharing.

[0117] The sharing function can prioritize sharing highly relevant information by considering the user's geographical location when sharing task progress. For example, the sharing function's generating AI can consider the user's current location and prioritize sharing nearby task progress. For example, the sharing function's generating AI can analyze the user's travel patterns and prioritize sharing task progress relevant to the user's travel. For example, the sharing function's generating AI can use the user's geographical location to prioritize sharing task progress that needs to be completed at a specific location. This allows for more appropriate sharing of task progress by prioritizing the sharing of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, the generating AI, or without the generating AI. For example, the sharing function can input the user's geographical location information into the generating AI and have the generating AI perform task progress sharing.

[0118] The sharing unit can estimate the user's emotions and determine the priority of task progress to share based on the estimated emotions. For example, if the user is stressed, the generative AI will prioritize sharing high-urgency task progress. If the user is relaxed, the generative AI will share detailed task progress. If the user is in a hurry, the generative AI will quickly share task progress. This allows for more appropriate sharing of task progress by determining the priority of task progress to share based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input user emotion data into a generative AI and have the generative AI determine the priority of task progress.

[0119] The sharing function can prioritize sharing highly relevant information by considering the user's geographical location when sharing task progress. For example, the sharing function's generating AI can consider the user's current location and prioritize sharing nearby task progress. For example, the sharing function's generating AI can analyze the user's travel patterns and prioritize sharing task progress relevant to the user's travel. For example, the sharing function's generating AI can use the user's geographical location to prioritize sharing task progress that needs to be completed at a specific location. This allows for more appropriate sharing of task progress by prioritizing the sharing of highly relevant information by considering the user's geographical location. Some or all of the above processing in the sharing function may be performed using, for example, the generating AI, or without the generating AI. For example, the sharing function can input the user's geographical location information into the generating AI and have the generating AI perform task progress sharing.

[0120] The sharing unit can analyze the user's social media activity and obtain relevant information when sharing task progress. For example, the sharing unit uses a generative AI to analyze the user's social media activity, obtain event information related to the task, and reflect it in the sharing. For example, the sharing unit uses a generative AI to share specific task progress based on the user's social media activity. For example, the sharing unit uses a generative AI to analyze the user's social media activity and identify factors that influence the sharing of task progress. This makes it possible to share task progress more appropriately by analyzing the user's social media activity and obtaining relevant information. Some or all of the above processing in the sharing unit may be performed using a generative AI, or not. For example, the sharing unit can input information about the user's social media activity into a generative AI and have the generative AI perform the task progress sharing.

[0121] The adjustment unit can estimate the user's emotions and adjust the schedule adjustment method based on the estimated user emotions. For example, if the user is stressed, the adjustment unit will have the generative AI adjust the schedule to reduce the workload. For example, if the user is relaxed, the adjustment unit will have the generative AI make detailed schedule adjustments. For example, if the user is in a hurry, the adjustment unit will have the generative AI quickly adjust the schedule. This allows for more appropriate schedule adjustments by adjusting the schedule adjustment method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using the generative AI, or not using the generative AI. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the schedule adjustment method.

[0122] The scheduling unit can make optimal adjustments when adjusting schedules, taking into account the task progress of colleagues and other team members. For example, the scheduling unit uses a generating AI to analyze the task progress of colleagues and other team members and adjust schedules. For example, the scheduling unit uses a generating AI to make efficient schedule adjustments based on task progress. For example, the scheduling unit uses a generating AI to make efficient schedule adjustments, taking into account the task progress of colleagues and other team members. This makes it possible to make more appropriate schedule adjustments by taking into account the task progress of colleagues and other team members. Some or all of the above processes in the scheduling unit may be performed using a generating AI, or they may be performed without a generating AI. For example, the scheduling unit can input information on the task progress of colleagues and other team members into a generating AI and have the generating AI perform schedule adjustments.

[0123] The scheduling unit can select the optimal scheduling method by considering the user's geographical location information when adjusting the schedule. For example, the scheduling unit may use a generating AI to consider the user's current location and prioritize scheduling nearby tasks. For example, the scheduling unit may use a generating AI to analyze the user's travel patterns and prioritize scheduling tasks that can be completed while traveling. For example, the scheduling unit may use a generating AI to prioritize scheduling tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a scheduling method that considers the user's geographical location information, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using a generating AI, or without using a generating AI. For example, the scheduling unit may input the user's geographical location information into a generating AI and have the generating AI perform the scheduling adjustments.

[0124] The adjustment unit can estimate the user's emotions and determine the priority of tasks to be adjusted based on the estimated user emotions. For example, if the user is stressed, the adjustment unit's generative AI will prioritize tasks of high urgency. If the user is relaxed, the adjustment unit's generative AI will perform detailed task adjustments. If the user is in a hurry, the adjustment unit's generative AI will perform rapid task adjustments. This allows for more appropriate task adjustments by determining the priority of tasks to be adjusted based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using a generative AI, or not using a generative AI. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI determine task priorities.

[0125] The scheduling unit can select the optimal scheduling method by considering the user's geographical location information when adjusting the schedule. For example, the scheduling unit may use a generating AI to consider the user's current location and prioritize scheduling nearby tasks. For example, the scheduling unit may use a generating AI to analyze the user's travel patterns and prioritize scheduling tasks that can be completed while traveling. For example, the scheduling unit may use a generating AI to prioritize scheduling tasks that need to be completed at a specific location based on the user's geographical location information. By selecting a scheduling method that considers the user's geographical location information, more appropriate scheduling becomes possible. Some or all of the above processing in the scheduling unit may be performed using a generating AI, or without using a generating AI. For example, the scheduling unit may input the user's geographical location information into a generating AI and have the generating AI perform the scheduling adjustments.

[0126] The scheduling unit can analyze the user's social media activity and obtain relevant information when adjusting the schedule. For example, the scheduling unit may use a generative AI to analyze the user's social media activity, obtain event information related to tasks, and reflect it in the schedule adjustment. For example, the scheduling unit may use a generative AI to adjust the schedule of a specific task based on the user's social media activity. For example, the scheduling unit may use a generative AI to analyze the user's social media activity and identify factors that affect the scheduling of tasks. By analyzing the user's social media activity and obtaining relevant information, it becomes possible to adjust the schedule more appropriately. Some or all of the above processing in the scheduling unit may be performed using a generative AI, or not. For example, the scheduling unit may input information about the user's social media activity into a generative AI and have the generative AI perform the schedule adjustment.

[0127] The adjustment unit can select the optimal adjustment method when adjusting the schedule, taking into account the user's health condition. For example, the adjustment unit's generating AI considers the user's health condition and adjusts the schedule to reduce the workload if the user is tired. For example, the adjustment unit's generating AI suggests a slightly longer route to encourage healthy exercise based on the user's health condition. For example, the adjustment unit's generating AI analyzes the user's health condition and suggests a schedule that includes rest points if the user is feeling unwell. By selecting an adjustment method that takes the user's health condition into account, it becomes possible to adjust the schedule more appropriately. Some or all of the above processes in the adjustment unit may be performed using, for example, the generating AI, or without using the generating AI. For example, the adjustment unit can input information about the user's health condition into the generating AI and have the generating AI perform the schedule adjustment.

[0128] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0129] The agent system can estimate the user's emotions and prioritize tasks based on those emotions. For example, if the user is stressed, the system can be set to prioritize only high-priority tasks. If the user is relaxed, the system can set detailed task priorities to ensure efficient progress. Furthermore, if the user is in a hurry, the system can quickly prioritize tasks and provide an optimal schedule. This allows for flexible adjustment of task priorities based on the user's emotions, enabling more effective task management.

[0130] The agent system can prioritize tasks by considering the user's geographical location. For example, if a user is in a specific location, it can be set to prioritize tasks that need to be completed at that location. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized. Furthermore, it can prioritize nearby tasks by considering the user's current location. This allows for flexible adjustment of task priorities based on the user's geographical location, enabling more efficient task management.

[0131] The agent system can analyze a user's social media activity to determine task priorities. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be prioritized. Furthermore, the system can adjust the priority of specific tasks based on the user's social media activity. It can also analyze the user's social media activity to identify factors influencing task priorities. This allows for flexible task prioritization considering the user's social media activity, enabling more effective task management.

[0132] The agent system can prioritize tasks while considering the user's health condition. For example, if the user is tired, the system can be set to prioritize lighter tasks. It can also suggest a slightly longer route to encourage healthy exercise based on the user's health status. Furthermore, if the user is unwell, the system can suggest a schedule that includes rest points. This allows for flexible adjustment of task priorities based on the user's health condition, enabling more appropriate task management.

[0133] The agent system can estimate the user's emotions and adjust how it creates schedules based on those estimates. For example, if the user is stressed, the system can create a schedule that reduces the workload. If the user is relaxed, the system can create a detailed schedule that allows tasks to progress efficiently. Furthermore, if the user is in a hurry, the system can quickly create a schedule and provide the optimal order of tasks. This allows for flexible adjustment of how schedules are created based on the user's emotions, enabling more effective schedule management.

[0134] The agent system can create schedules that take the user's geographical location into consideration. For example, if a user is in a specific location, tasks that need to be completed at that location can be prioritized. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized. Furthermore, tasks near the user's current location can be prioritized. This allows for flexible scheduling adjustments based on the user's geographical location, enabling more efficient schedule management.

[0135] The agent system can analyze a user's social media activity and create a schedule. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be reflected in the schedule. Furthermore, the system can adjust the schedule of specific tasks based on the user's social media activity. It can also analyze the user's social media activity and identify factors that influence task scheduling. This allows for flexible schedule adjustments that take the user's social media activity into consideration, enabling more effective schedule management.

[0136] The agent system can estimate the user's emotions and adjust the rebalancing method based on those estimates. For example, if the user is stressed, the system can rebalance to reduce the task load. If the user is relaxed, the system can perform a more detailed rebalancing to allow for more efficient task progress. Furthermore, if the user is in a hurry, the system can quickly rebalance to provide the optimal task order. This allows for flexible adjustment of the rebalancing method based on the user's emotions, enabling more effective task management.

[0137] The agent system can rebalance tasks while considering the user's geographical location. For example, if a user is in a specific location, tasks that need to be completed at that location can be prioritized in the rebalancing process. Similarly, if a user is on the move, tasks that can be completed while traveling can be prioritized in the rebalancing process. Furthermore, tasks near the user's current location can be prioritized in the rebalancing process. This allows for flexible adjustment of rebalancing based on the user's geographical location, enabling more efficient task management.

[0138] The agent system can analyze and rebalance users' social media activity. For example, if a user plans to participate in a specific event on social media, tasks related to that event can be reflected in the rebalancing. Furthermore, the rebalancing of specific tasks can be adjusted based on the user's social media activity. In addition, the system can analyze the user's social media activity and identify factors that influence task rebalancing. This allows for flexible adjustment of rebalancing to take the user's social media activity into account, enabling more appropriate task management.

[0139] The following briefly describes the processing flow for example form 2.

[0140] Step 1: The analysis unit analyzes the progress and urgency of the task. For example, the analysis unit monitors the task's progress in real time and evaluates the degree of progress. It can also consider the task's deadline and importance in order to assess the urgency of the task. Step 2: The decision unit determines task priorities based on the results analyzed by the analysis unit. For example, the decision unit prioritizes tasks with approaching deadlines or those of high importance. It can also determine priorities by considering task dependencies. Step 3: The scheduling department creates a daily schedule based on the priorities determined by the decision-making department. The scheduling department creates the schedule by considering, for example, the time required for each task and its dependencies. It can also adjust the schedule according to changes in the progress and urgency of tasks. Step 4: The rebalancing unit rebalances the schedule created by the scheduling unit according to changes in task progress and urgency. For example, the rebalancing unit adjusts the schedule when an urgent task arises or when the progress of an existing task is behind schedule. It can also reset the priority of tasks. Step 5: The shared department shares the task progress of colleagues and other team members in real time. For example, the shared department can share task progress in real time and adjust schedules to avoid team members working on the same task at the same time. This can also enable more efficient collaboration. Step 6: The Coordination Team adjusts the schedule based on the task progress shared by the Sharing Team. For example, the Coordination Team adjusts the schedule to avoid team members working on the same task at the same time. They may also reprioritize tasks.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, decision unit, scheduling unit, rebalancing unit, sharing unit, and adjustment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the progress and urgency of tasks in real time. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the priority of tasks based on the analysis results. The scheduling unit is implemented by the control unit 46A of the smart device 14 and creates a daily schedule based on the determined priorities. The rebalancing unit is implemented by the specific processing unit 290 of the data processing unit 12 and rebalances the schedule in accordance with changes in the progress and urgency of tasks. The sharing unit shares task progress in real time via the communication I / F 44 of the smart device 14. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the shared task progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, decision unit, scheduling unit, rebalancing unit, sharing unit, and adjustment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the progress and urgency of tasks in real time. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the priority of tasks based on the analysis results. The scheduling unit is implemented by the control unit 46A of the smart glasses 214 and creates a daily schedule based on the determined priorities. The rebalancing unit is implemented by the specific processing unit 290 of the data processing unit 12 and rebalances the schedule in accordance with changes in the progress and urgency of tasks. The sharing unit shares task progress in real time via the communication I / F 44 of the smart glasses 214. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the shared task progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] Each of the multiple elements described above, including the analysis unit, decision unit, scheduling unit, rebalancing unit, sharing unit, and adjustment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the progress and urgency of tasks in real time. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the priority of tasks based on the analysis results. The scheduling unit is implemented by the control unit 46A of the headset terminal 314 and creates a daily schedule based on the determined priorities. The rebalancing unit is implemented by the specific processing unit 290 of the data processing unit 12 and rebalances the schedule in accordance with changes in the progress and urgency of tasks. The sharing unit shares task progress in real time via the communication I / F 44 of the headset terminal 314. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the shared task progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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).

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.).

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Each of the multiple elements described above, including the analysis unit, decision unit, scheduling unit, rebalancing unit, sharing unit, and adjustment unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the progress and urgency of tasks in real time. The decision unit is implemented by the specific processing unit 290 of the data processing unit 12 and determines the priority of tasks based on the analysis results. The scheduling unit is implemented by the control unit 46A of the robot 414 and creates a daily schedule based on the determined priorities. The rebalancing unit is implemented by the specific processing unit 290 of the data processing unit 12 and rebalances the schedule in accordance with changes in the progress and urgency of tasks. The sharing unit shares task progress in real time via the communication I / F 44 of the robot 414. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the schedule based on the shared task progress. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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."

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] (Note 1) The analysis unit analyzes the progress and urgency of tasks, A decision unit that determines the priority of tasks based on the results analyzed by the aforementioned analysis unit, A scheduling unit creates a daily schedule based on the priority determined by the aforementioned decision unit, A rebalancing unit rebalances the schedule created by the aforementioned scheduling unit according to changes in the progress and urgency of tasks. A sharing section for sharing the task progress of colleagues and other team members in real time, The system includes an adjustment unit that adjusts the schedule based on the task progress shared by the shared unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The progress and urgency of tasks are analyzed based on information such as the importance and deadline of each task. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned determination unit, Based on the results analyzed by the aforementioned analysis unit, tasks with approaching deadlines or high importance are prioritized for processing. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned scheduling unit is Create a schedule that takes into account the time required for each task and its dependencies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned rebalancing unit is Adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned shared portion is, Share the task progress of colleagues and other team members in real time. The system described in Appendix 1, characterized by the features described herein. (Note 7) The adjustment unit is, Based on the task progress shared by the aforementioned shared unit, the schedule is adjusted to avoid team members working on the same task simultaneously. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis of task progress and urgency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing task progress, referencing past task history improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, When analyzing the urgency of a task, the task dependencies should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, When analyzing task progress, the analysis takes into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing the urgency of a task, we analyze users' social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned determination unit, Adjust the method for estimating user emotions and determining task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned determination unit, When determining task priorities, adjust priorities based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned determination unit, When determining task priorities, consider the dependencies between tasks when setting priorities. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned determination unit, It estimates the user's emotions and adjusts the display priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned determination unit, When determining task priorities, consider the user's geographical location when setting priorities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned determination unit, When determining task priorities, we analyze users' social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit is It estimates the user's emotions and adjusts how the schedule is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit is When creating a schedule, consider the time required for each task to create the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit is When creating a schedule, consider the dependencies between tasks when setting the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned scheduling unit is It estimates the user's emotions and adjusts how the schedule is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned scheduling unit is When creating a schedule, we take the user's geographical location into consideration to create the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned scheduling unit is When creating a schedule, we analyze the user's social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned rebalancing unit is It estimates the user's emotions and adjusts the rebalancing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned rebalancing unit is When rebalancing, adjust the schedule in response to sudden tasks. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned rebalancing unit is When rebalancing, the schedule is readjusted according to any delays in the progress of existing tasks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned rebalancing unit is The system estimates user sentiment and determines rebalancing priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned rebalancing unit is When performing a rebalancing, the optimal rebalancing method is selected by considering the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned rebalancing unit is When performing rebalancing, we analyze users' social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned shared portion is, It estimates the user's emotions and adjusts how task progress is shared based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned shared portion is, When sharing task progress, referencing the past task history of colleagues and other team members improves the accuracy of the sharing process. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned shared portion is, When sharing task progress, prioritize sharing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned shared portion is, It estimates the user's emotions and determines the priority of shared task progress based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned shared portion is, When sharing task progress, prioritize sharing highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned shared portion is, When sharing task progress, we analyze users' social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The adjustment unit is, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The adjustment unit is, When adjusting schedules, make the best adjustments by considering the task progress of colleagues and other team members. The system described in Appendix 1, characterized by the features described herein. (Note 40) The adjustment unit is, When adjusting schedules, the system selects the optimal adjustment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The adjustment unit is, Prioritize tasks that estimate user emotions and adjust based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The adjustment unit is, When adjusting schedules, the system selects the optimal adjustment method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The adjustment unit is, When scheduling, we analyze users' social media activity to obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 44) The adjustment unit is, When adjusting schedules, we select the most appropriate adjustment method while taking the user's health condition into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0213] 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 analysis unit analyzes the progress and urgency of tasks, A decision unit that determines the priority of tasks based on the results analyzed by the aforementioned analysis unit, A scheduling unit creates a daily schedule based on the priority determined by the aforementioned decision unit, A rebalancing unit rebalances the schedule created by the aforementioned scheduling unit according to changes in the progress and urgency of tasks. A sharing section for sharing the task progress of colleagues and other team members in real time, The system includes an adjustment unit that adjusts the schedule based on the task progress shared by the shared unit. A system characterized by the following features.

2. The aforementioned analysis unit, The progress and urgency of tasks are analyzed based on information such as the importance and deadline of each task. The system according to feature 1.

3. The aforementioned determination unit, Based on the results analyzed by the aforementioned analysis unit, tasks with approaching deadlines or high importance are prioritized for processing. The system according to feature 1.

4. The aforementioned scheduling unit is Create a schedule that takes into account the time required for each task and its dependencies. The system according to feature 1.

5. The aforementioned rebalancing unit is Adjust the schedule in response to the emergence of sudden tasks or delays in the progress of existing tasks. The system according to feature 1.

6. The aforementioned shared portion is, Share the task progress of colleagues and other team members in real time. The system according to feature 1.

7. The adjustment unit is, Based on the task progress shared by the aforementioned shared unit, the schedule is adjusted to avoid team members working on the same task simultaneously. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis of task progress and urgency based on the estimated user emotions. The system according to feature 1.

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