System

Abstract

An object of a system according to an embodiment is to efficiently manage and appropriately allocate an enormous number of tasks.SOLUTION: A system includes an analysis unit, a priority determination unit, an allocation unit, and a reminder unit. The analysis unit analyzes the content of the task and calculates the required time. The priority determination unit determines the priority of the task based on the information analyzed by the analysis unit. The allocation unit allocates the task to an available time on the calendar based on the priority determined by the priority determination unit and the required time. The reminding unit monitors a progress status of the task allocated by the allocating unit and notifies the user of the progress status.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have faced the challenge of making it difficult to efficiently manage and appropriately allocate a huge number of tasks.

[0005] The system according to the embodiment aims to efficiently manage and appropriately allocate a huge number of tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a priority determination unit, an allocation unit, and a reminder unit. The analysis unit analyzes the content of a task and calculates the required time. The priority determination unit determines the priority of the task based on the information analyzed by the analysis unit. The allocation unit allocates the task to an available time slot on a calendar based on the priority and required time determined by the priority determination unit. The reminder unit monitors the progress of the task allocated by the allocation unit and notifies the user. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage and appropriately allocate a huge number of tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A task management system according to an embodiment of the present invention uses a generation AI to link a calendar and a task management system to support efficient task processing. In the task management system, a user enters the details of tasks in the calendar and task management system, and the generation AI calculates the entered task details and approximate required times. The generation AI allocates tasks to available time slots on the calendar based on the task's priority and required time. Furthermore, the generation AI notifies the user of the task's progress using a reminder function. For example, in a task management system, a user inputs tasks such as "preparing for a meeting" or "writing a report" into the calendar and task management system. This information is input into the generation AI. The generation AI then analyzes the entered task details and calculates approximate required times. The generation AI estimates the required times based on past data and the task details. For example, it calculates the required time as two hours for "preparing for a meeting" and three hours for "writing a report." The generation AI allocates tasks to available time slots on the calendar based on the task's priority and required time. For example, tasks are efficiently allocated to time slots free of scheduled meetings or appointments. This allows users to efficiently process tasks. Furthermore, a reminder function is used to notify the user of the progress of a task. For example, a reminder is sent when the start or end time of a task is approaching. This allows the user to grasp the progress of the task and proceed with the task efficiently. This enables the task management system to efficiently process tasks even when a huge number of tasks are submitted all at once. This allows the task management system to efficiently process tasks based on the priority and required time of the task by linking the user's calendar with the task management system. Furthermore, the reminder function makes it easier to grasp the progress of a task, realizing efficient task processing.

[0029] A task management system according to an embodiment includes an analysis unit, a priority determination unit, an allocation unit, and a reminder unit. The analysis unit analyzes the content of a task and calculates the required time. For example, the analysis unit analyzes the content of a task and calculates the required time. The analysis unit can also use a generation AI to analyze the content of a task and calculate the required time. For example, the analysis unit estimates the required time based on past data or the content of the task. The priority determination unit determines the priority of the task based on the information analyzed by the analysis unit. The priority determination unit determines the priority based on, for example, the urgency or importance of the task. The priority determination unit can also use the generation AI to determine the priority of the task. The allocation unit allocates tasks to available time slots on a calendar based on the priorities and required time slots determined by the priority determination unit. For example, the allocation unit efficiently allocates tasks to available time slots on the calendar. The allocation unit can also allocate tasks using the generation AI. The reminder unit monitors the progress of the tasks allocated by the allocation unit and notifies the user. The reminder unit sends a reminder when, for example, the start or end time of a task is approaching. The reminder unit can also use a generation AI to monitor the progress of a task and notify the user. As a result, the task management system according to the embodiment can support efficient task processing by analyzing the content of tasks, efficiently allocating tasks based on priority and required time, and notifying the user of the progress using the reminder function.

[0030] The task management system includes an adjustment unit that allows a user to manually adjust tasks. The adjustment unit allows a user to manually adjust tasks. The adjustment unit adjusts tasks using, for example, drag and drop. The adjustment unit can also adjust tasks using an input form. The adjustment unit can also allow a user to manually adjust tasks using a generation AI. This allows a user to manually adjust tasks, enabling flexible task management.

[0031] The task management system includes a reallocation unit that monitors the progress of tasks in real time and reallocates tasks as needed. The reallocation unit monitors the progress of tasks in real time and reallocates tasks as needed. The reallocation unit performs updates, for example, on a second-by-second basis. The reallocation unit can also perform updates on a minute-by-minute basis. The reallocation unit can also use a generation AI to monitor the progress of tasks in real time and reallocate tasks as needed. This enables efficient task management by reallocating tasks in real time according to the progress of tasks.

[0032] The analysis unit can estimate the required time based on historical data and task content. The analysis unit estimates the required time based on historical data and task content. The analysis unit estimates the required time based on, for example, past task completion times. The analysis unit can also estimate the required time based on past task content. The analysis unit can also estimate the required time based on historical data and task content using generation AI. This makes it possible to calculate the required time more accurately by estimating the required time based on past data and task content.

[0033] The reminder unit can issue a reminder when the start time or end time of a task is approaching. The reminder unit issues a reminder when the start time or end time of a task is approaching. The reminder unit, for example, issues an email notification. The reminder unit can also issue a push notification. The reminder unit can also display an alert. The reminder unit can also use a generation AI to issue a reminder when the start time or end time of a task is approaching. This makes it easier for the user to grasp the progress of the task by issuing a reminder when the start time or end time of a task is approaching.

[0034] When analyzing the content of a task, the analysis unit can estimate the required time by referring to the user's past task completion times. When analyzing the content of a task, the analysis unit estimates the required time by referring to the user's past task completion times. In the analysis unit, for example, the generation AI estimates the required time based on the time it took the user to complete a similar task in the past. The analysis unit can also have the generation AI estimate the required time based on the average of the user's past task completion times. The analysis unit can also analyze the distribution of the user's past task completion times, and have the generation AI estimate the optimal required time. In this way, by referring to the user's past task completion times, the accuracy of the required time estimation is improved.

[0035] When analyzing the content of a task, the analysis unit can dynamically adjust the required time according to the complexity of the task. When analyzing the content of a task, the analysis unit dynamically adjusts the required time according to the complexity of the task. For example, if the content of the task is complex, the analysis unit causes the generation AI to set a longer required time. If the content of the task is simple, the analysis unit can also cause the generation AI to set a shorter required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the content of the task and set an optimal time. In this way, by dynamically adjusting the required time according to the complexity of the task, a more appropriate required time can be set.

[0036] When analyzing the content of a task, the analysis unit can estimate the required time by taking into account task dependencies. When analyzing the content of a task, the analysis unit estimates the required time by taking into account task dependencies. For example, if a task depends on other tasks, the analysis unit allows the generation AI to estimate the required time by taking into account the dependencies. If the task dependencies are complex, the analysis unit can also allow the generation AI to set the required time longer. If the task dependencies are simple, the analysis unit can also allow the generation AI to set the required time shorter. In this way, by taking task dependencies into account, more accurate estimation of the required time becomes possible.

[0037] When analyzing the content of a task, the analysis unit can estimate the required time by taking into account the user's current project status. When analyzing the content of a task, the analysis unit estimates the required time by taking into account the user's current project status. For example, if the user has multiple projects, the analysis unit allows the generation AI to estimate the required time by taking into account the progress of each project. The analysis unit can also allow the generation AI to adjust the required time based on the priority of the user's current project. The analysis unit can also allow the generation AI to dynamically adjust the required time according to the progress of the user's project. This improves the accuracy of the required time estimation by taking into account the user's current project status.

[0038] When analyzing the content of a task, the analysis unit can adjust the required time according to the user's skill level. When analyzing the content of a task, the analysis unit adjusts the required time according to the user's skill level. For example, when the user's skill level is high, the analysis unit causes the generation AI to set a shorter required time. When the user's skill level is low, the analysis unit can also cause the generation AI to set a longer required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the user's skill level. This makes it possible to set a more appropriate required time by adjusting the required time according to the user's skill level.

[0039] When analyzing the content of a task, the analysis unit can dynamically adjust the required time based on the priority of the task. When analyzing the content of a task, the analysis unit dynamically adjusts the required time based on the priority of the task. For example, when the priority of a task is high, the analysis unit causes the generation AI to set a shorter required time. When the priority of a task is low, the analysis unit can also cause the generation AI to set a longer required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the priority of the task. This makes it possible to set a more appropriate required time by dynamically adjusting the required time based on the priority of the task.

[0040] The priority determination unit can refer to the user's past task completion history when determining the priority of a task. The priority determination unit refers to the user's past task completion history when determining the priority of a task. In the priority determination unit, for example, the generation AI determines the priority of a new task based on the priorities of tasks previously completed by the user. The priority determination unit can also analyze the user's past task completion history, and the generation AI can determine the optimal priority. The priority determination unit can also cause the generation AI to dynamically adjust the priority of a task based on the user's past task completion history. In this way, by referring to the user's past task completion history, the accuracy of task priority determination is improved.

[0041] When determining the priority of a task, the priority determination unit can dynamically adjust the priority based on the urgency of the task. When determining the priority of a task, the priority determination unit dynamically adjusts the priority based on the urgency of the task. In the priority determination unit, for example, if the urgency of the task is high, the generation AI sets the priority to the highest priority. In the priority determination unit, if the urgency of the task is low, the generation AI can also set the priority to a lower level. In the priority determination unit, the generation AI can also dynamically adjust the priority according to the urgency of the task. In this way, by dynamically adjusting the priority based on the urgency of the task, more appropriate priority setting becomes possible.

[0042] When determining the priority of a task, the priority determination unit can determine the priority by taking into account the dependency relationships between tasks. When determining the priority of a task, the priority determination unit determines the priority by taking into account the dependency relationships between tasks. For example, when a task depends on another task, the priority determination unit determines the priority by having the generation AI take into account the dependency relationships. When the dependency relationships between tasks are complex, the priority determination unit can also have the generation AI set the priority higher. When the dependency relationships between tasks are simple, the priority determination unit can also have the generation AI set the priority lower. This makes it possible to determine priorities more accurately by taking into account the dependency relationships between tasks.

[0043] The priority determination unit can take into account the user's current project status when determining the priority of a task. The priority determination unit takes into account the user's current project status when determining the priority of a task. For example, if the user has multiple projects, the priority determination unit determines the priority by having the generation AI take into account the progress status of each project. The priority determination unit can also have the generation AI adjust the priority of a task based on the priority of the user's current project. The priority determination unit can also have the generation AI dynamically adjust the priority of a task according to the progress status of the user's project. This improves the accuracy of task priority determination by taking into account the user's current project status.

[0044] When determining the priority of a task, the priority determination unit can adjust the priority according to the skill level of the user. When determining the priority of a task, the priority determination unit adjusts the priority according to the skill level of the user. For example, when the user's skill level is high, the priority determination unit causes the generation AI to set a high priority for the task. When the user's skill level is low, the priority determination unit can also cause the generation AI to set a low priority for the task. The priority determination unit can also cause the generation AI to dynamically adjust the priority of the task according to the skill level of the user. This makes it possible to set more appropriate priorities by adjusting the priority according to the user's skill level.

[0045] When determining the priority of a task, the priority determination unit can dynamically adjust the priority based on the time required for the task. When determining the priority of a task, the priority determination unit dynamically adjusts the priority based on the time required for the task. In the priority determination unit, for example, if the time required for the task is short, the generation AI can set the priority higher. In the priority determination unit, if the time required for the task is long, the generation AI can also set the priority lower. In the priority determination unit, the generation AI can also dynamically adjust the priority according to the time required for the task. In this way, by dynamically adjusting the priority based on the time required for the task, more appropriate priority can be set.

[0046] The allocation unit can refer to the user's past task completion history when allocating tasks. The allocation unit refers to the user's past task completion history when allocating tasks. In the allocation unit, for example, the generation AI allocates new tasks based on the allocation method of tasks previously completed by the user. The allocation unit can also analyze the user's past task completion history, and the generation AI can determine the optimal allocation method. The allocation unit can also dynamically adjust task allocation based on the user's past task completion history. In this way, the accuracy of task allocation is improved by referring to the user's past task completion history.

[0047] The allocation unit can take task dependency relationships into consideration when allocating tasks. The allocation unit takes task dependency relationships into consideration when allocating tasks. For example, if a task depends on another task, the allocation unit has the generation AI take the dependency relationships into consideration when allocating tasks. If task dependency relationships are complex, the allocation unit can also have the generation AI prioritize allocation. If task dependency relationships are simple, the allocation unit can also have the generation AI postpone allocation. In this way, by taking task dependency relationships into consideration, more appropriate task allocation is possible.

[0048] When allocating tasks, the allocation unit can dynamically adjust the allocation based on the urgency of the tasks. When allocating tasks, the allocation unit dynamically adjusts the allocation based on the urgency of the tasks. For example, if the urgency of a task is high, the allocation unit assigns the task to the generating AI with the highest priority. If the urgency of a task is low, the allocation unit can also assign the task to the generating AI later. The allocation unit can also have the generating AI dynamically adjust the allocation based on the urgency of the task. This makes it possible to allocate tasks more appropriately by dynamically adjusting the allocation based on the urgency of the task.

[0049] The allocation unit can take into account the user's current project status when allocating tasks. The allocation unit takes into account the user's current project status when allocating tasks. For example, if the user has multiple projects, the allocation unit allocates tasks by having the generation AI take into account the progress status of each project. The allocation unit can also have the generation AI adjust task allocation based on the priority of the user's current project. The allocation unit can also have the generation AI dynamically adjust task allocation according to the progress status of the user's project. This improves the accuracy of task allocation by taking into account the user's current project status.

[0050] When allocating tasks, the allocation unit can adjust the allocation according to the user's skill level. When allocating tasks, the allocation unit adjusts the allocation according to the user's skill level. For example, if the user's skill level is high, the allocation unit causes the generation AI to allocate more tasks. If the user's skill level is low, the allocation unit can also cause the generation AI to allocate fewer tasks. The allocation unit can also cause the generation AI to dynamically adjust the task allocation according to the user's skill level. This makes it possible to allocate tasks more appropriately by adjusting the allocation according to the user's skill level.

[0051] When allocating tasks, the allocation unit can dynamically adjust the allocation based on the time required for the task. When allocating tasks, the allocation unit dynamically adjusts the allocation based on the time required for the task. For example, if the time required for the task is short, the allocation unit allows the generation AI to prioritize allocation. If the time required for the task is long, the allocation unit can also allow the generation AI to postpone allocation. The allocation unit also allows the generation AI to dynamically adjust the allocation according to the time required for the task. This makes it possible to allocate tasks more appropriately by dynamically adjusting the allocation based on the time required for the task.

[0052] The reminding unit can refer to the user's past reminder history when reminding. The reminding unit refers to the user's past reminder history when reminding. In the reminding unit, for example, the generation AI sets the optimal reminder timing based on the timing when the user received a reminder in the past. The reminding unit can also analyze the user's past reminder history and the generation AI can adjust the frequency of reminders. The reminding unit can also dynamically adjust the timing of reminders based on the user's past reminder history. In this way, the accuracy of reminders is improved by referring to the user's past reminder history.

[0053] When reminding, the reminding unit can dynamically adjust the frequency of reminders based on the urgency of the task. When reminding, the reminding unit dynamically adjusts the frequency of reminders based on the urgency of the task. For example, when the urgency of the task is high, the generation AI of the reminding unit can set the frequency of reminders higher. When the urgency of the task is low, the generation AI of the reminding unit can also set the frequency of reminders lower. The reminding unit can also dynamically adjust the frequency of reminders based on the urgency of the task. This makes it possible to provide more appropriate reminders by dynamically adjusting the frequency of reminders based on the urgency of the task.

[0054] The reminding unit can take task dependency relationships into consideration when reminding. The reminding unit takes task dependency relationships into consideration when reminding. For example, if a task is dependent on another task, the reminding unit allows the generation AI to take the dependency relationships into consideration when reminding. If the task dependency relationships are complex, the reminding unit can also allow the generation AI to set the frequency of reminders more frequently. If the task dependency relationships are simple, the reminding unit can also allow the generation AI to set the frequency of reminders less frequently. This makes it possible to give more appropriate reminders by taking task dependency relationships into consideration.

[0055] The reminding unit can take into account the user's current project status when reminding. The reminding unit takes into account the user's current project status when reminding. For example, if the user has multiple projects, the generation AI will take into account the progress status of each project when reminding. The reminding unit can also have the generation AI adjust the timing of reminders based on the priority of the user's current projects. The reminding unit can also have the generation AI dynamically adjust the timing of reminders according to the progress status of the user's projects. This improves the accuracy of reminders by taking into account the user's current project status.

[0056] When giving a reminder, the reminder unit can adjust the content of the reminder according to the user's skill level. When giving a reminder, the reminder unit adjusts the content of the reminder according to the user's skill level. For example, when the user's skill level is high, the generation AI of the reminder unit provides detailed reminder content. When the user's skill level is low, the generation AI of the reminder unit can also provide simple reminder content. The generation AI of the reminder unit can also dynamically adjust the content of the reminder according to the user's skill level. This allows for more appropriate reminders by adjusting the content of the reminder according to the user's skill level.

[0057] When reminding, the reminding unit can dynamically adjust the timing of the reminder based on the time required for the task. When reminding, the reminding unit dynamically adjusts the timing of the reminder based on the time required for the task. For example, if the time required for the task is short, the generation AI of the reminding unit sets the timing of the reminder earlier. If the time required for the task is long, the generation AI of the reminding unit can also set the timing of the reminder later. The reminding unit can also dynamically adjust the timing of the reminder based on the time required for the task. This makes it possible to provide more appropriate reminders by dynamically adjusting the timing of the reminder based on the time required for the task.

[0058] The adjustment unit can refer to the user's past task completion history when adjusting a task. The adjustment unit refers to the user's past task completion history when adjusting a task. In the adjustment unit, for example, the generation AI adjusts a new task based on the adjustment method of a task that the user previously completed. The adjustment unit can also analyze the user's past task completion history, and the generation AI can determine the optimal adjustment method. The adjustment unit can also cause the generation AI to dynamically adjust the task based on the user's past task completion history. In this way, the accuracy of task adjustment is improved by referring to the user's past task completion history.

[0059] When adjusting tasks, the adjustment unit can take task dependency relationships into consideration. When adjusting tasks, the adjustment unit takes task dependency relationships into consideration. For example, when a task depends on another task, the adjustment unit allows the generation AI to make adjustments by taking the dependency relationships into consideration. When task dependency relationships are complex, the adjustment unit can also allow the generation AI to make adjustments on a priority basis. When task dependency relationships are simple, the adjustment unit can also allow the generation AI to make adjustments on a later basis. In this way, by taking task dependency relationships into consideration, more appropriate task adjustment is possible.

[0060] The adjustment unit can take into account the user's current project status when adjusting tasks. The adjustment unit takes into account the user's current project status when adjusting tasks. For example, if the user has multiple projects, the adjustment unit allows the generation AI to make adjustments by taking into account the progress status of each project. The adjustment unit can also allow the generation AI to adjust tasks based on the priority of the user's current projects. The adjustment unit can also allow the generation AI to dynamically adjust tasks according to the progress status of the user's projects. In this way, the accuracy of task adjustment is improved by taking into account the user's current project status.

[0061] When adjusting a task, the adjustment unit can make adjustments according to the skill level of the user. When adjusting a task, the adjustment unit makes adjustments according to the skill level of the user. For example, when the user's skill level is high, the adjustment unit causes the generation AI to adjust the task less. When the user's skill level is low, the adjustment unit can also cause the generation AI to adjust the task more. The adjustment unit can also cause the generation AI to dynamically adjust the task according to the user's skill level. This makes it possible to adjust the task more appropriately by making adjustments according to the user's skill level.

[0062] The reallocation unit can refer to the user's past task completion history when reallocating tasks. The reallocation unit refers to the user's past task completion history when reallocating tasks. In the reallocation unit, for example, the generation AI reallocates a new task based on the reallocation method used by the user to reallocate tasks completed in the past. The reallocation unit can also analyze the user's past task completion history, and the generation AI can determine the optimal reallocation method. The reallocation unit can also dynamically reallocate tasks based on the user's past task completion history. In this way, the accuracy of task reallocation is improved by referring to the user's past task completion history.

[0063] When reallocating tasks, the reallocation unit can take task dependency relationships into consideration. When reallocating tasks, the reallocation unit takes task dependency relationships into consideration. For example, if a task depends on another task, the reallocation unit allows the generation AI to reallocate the task while taking the dependency relationships into consideration. When task dependency relationships are complex, the reallocation unit can also allow the generation AI to prioritize reallocation. When task dependency relationships are simple, the reallocation unit can also allow the generation AI to postpone reallocation. In this way, by taking task dependency relationships into consideration, more appropriate task reallocation is possible.

[0064] The reallocation unit can take into account the user's current project status when reallocating tasks. The reallocation unit takes into account the user's current project status when reallocating tasks. For example, if the user has multiple projects, the reallocation unit reallocates tasks by having the generation AI take into account the progress status of each project. The reallocation unit can also have the generation AI reallocate tasks based on the priority of the user's current projects. The reallocation unit can also have the generation AI dynamically reallocate tasks according to the progress status of the user's projects. In this way, by taking into account the user's current project status, the accuracy of task reallocation is improved.

[0065] When reallocating tasks, the reallocation unit can adjust the reallocation according to the skill level of the user. When reallocating tasks, the reallocation unit adjusts the reallocation according to the skill level of the user. For example, when the user's skill level is high, the reallocation unit causes the generation AI to reallocate tasks less. When the user's skill level is low, the reallocation unit can also cause the generation AI to reallocate tasks more. The reallocation unit can also cause the generation AI to dynamically reallocate tasks according to the user's skill level. This makes it possible to adjust the reallocation according to the user's skill level, thereby reallocating tasks more appropriately.

[0066] When reallocating a task, the reallocation unit can dynamically adjust the reallocation based on the required time for the task. When reallocating a task, the reallocation unit dynamically adjusts the reallocation based on the required time for the task. For example, when the required time for the task is short, the reallocation unit allows the generation AI to prioritize reallocation. When the required time for the task is long, the reallocation unit can also allow the generation AI to postpone reallocation. The reallocation unit can also allow the generation AI to dynamically adjust the reallocation according to the required time for the task. This makes it possible to dynamically adjust the reallocation based on the required time for the task, thereby enabling more appropriate task reallocation.

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

[0068] The task management system can further include a location information management unit that takes into account the user's geographic location information. The location information management unit collects information about the user's current location and movement history and provides it to the analysis unit. Based on this information, the analysis unit can propose a task schedule that takes into account the user's travel time. For example, if the user is commuting, the generation AI can allocate tasks taking into account the commuting time. Also, if the user is on a business trip, the generation AI can propose tasks for the business trip destination. This enables flexible task management based on the user's geographic location information.

[0069] The task management system can further include a device management unit that takes into account the user's device usage. The device management unit collects information about the device used by the user and provides it to the analysis unit. The analysis unit can then use this information to suggest tasks that are appropriate for the user's device usage. For example, if the user frequently uses a smartphone, the generation AI can suggest tasks that can be completed on the smartphone. Also, if the user uses a computer, the generation AI can suggest tasks that prioritize computer tasks. This enables flexible task management based on the user's device usage.

[0070] The task management system can further include a network management unit that takes into account the user's network connection status. The network management unit collects information about the user's Internet connection status and provides it to the analysis unit. The analysis unit can then use this information to suggest tasks that are appropriate for the user's network connection status. For example, if the user's Internet connection is unstable, the generation AI can suggest tasks that can be completed offline. Alternatively, if the user has a high-speed Internet connection, the generation AI can suggest tasks that prioritize online work. This enables flexible task management based on the user's network connection status.

[0071] The task management system can further include an energy management unit that takes into account the user's energy consumption. The energy management unit collects information about the remaining battery level and energy consumption of the user's device and provides it to the analysis unit. The analysis unit can suggest tasks to optimize the user's energy consumption based on this information. For example, if the user's device has low battery, the generation AI can suggest tasks that consume less battery. Also, if the user is charging, the generation AI can suggest tasks that consume more energy. This enables flexible task management based on the user's energy consumption.

[0072] The task management system can further include an environmental sound management unit that takes into account the user's environmental sounds. The environmental sound management unit collects information about the sound environment around the user and provides it to the analysis unit. The analysis unit can suggest tasks that correspond to the user's sound environment based on this information. For example, if the user is in a quiet environment, the generation AI can suggest tasks that require concentration. Also, if the user is in a noisy environment, the generation AI can suggest easy tasks. This enables flexible task management based on the user's sound environment.

[0073] The processing flow of the first embodiment will be briefly explained below.

[0074] Step 1: The analysis unit analyzes the content of the task and calculates the required time. The analysis unit can also use generative AI to estimate the required time based on past data and the content of the task. Step 2: The priority determination unit determines the priority of the tasks based on the information analyzed by the analysis unit. The priority determination unit determines the priority based on the urgency and importance of the tasks, and can also determine the priority using a generation AI. Step 3: The allocation unit allocates tasks to available time slots on the calendar based on the priorities and required times determined by the priority determination unit. The allocation unit can also use generation AI to efficiently allocate tasks to available time slots on the calendar. Step 4: The reminder unit monitors the progress of the tasks assigned by the allocation unit and notifies the user. The reminder unit notifies the user of reminders when the start or end time of a task is approaching, and can also monitor the progress of a task using the generation AI and notify the user.

[0075] (Example 2) A task management system according to an embodiment of the present invention uses a generation AI to link a calendar and a task management system to support efficient task processing. In the task management system, a user enters the details of tasks in the calendar and task management system, and the generation AI calculates the entered task details and approximate required times. The generation AI allocates tasks to available time slots on the calendar based on the task's priority and required time. Furthermore, the generation AI notifies the user of the task's progress using a reminder function. For example, in a task management system, a user inputs tasks such as "preparing for a meeting" or "writing a report" into the calendar and task management system. This information is input into the generation AI. The generation AI then analyzes the entered task details and calculates approximate required times. The generation AI estimates the required times based on past data and the task details. For example, it calculates the required time as two hours for "preparing for a meeting" and three hours for "writing a report." The generation AI allocates tasks to available time slots on the calendar based on the task's priority and required time. For example, tasks are efficiently allocated to time slots free of scheduled meetings or appointments. This allows users to efficiently process tasks. Furthermore, a reminder function is used to notify the user of the progress of a task. For example, a reminder is sent when the start or end time of a task is approaching. This allows the user to grasp the progress of the task and proceed with the task efficiently. This enables the task management system to efficiently process tasks even when a huge number of tasks are submitted all at once. This allows the task management system to efficiently process tasks based on the priority and required time of the task by linking the user's calendar with the task management system. Furthermore, the reminder function makes it easier to grasp the progress of a task, realizing efficient task processing.

[0076] A task management system according to an embodiment includes an analysis unit, a priority determination unit, an allocation unit, and a reminder unit. The analysis unit analyzes the content of a task and calculates the required time. For example, the analysis unit analyzes the content of a task and calculates the required time. The analysis unit can also use a generation AI to analyze the content of a task and calculate the required time. For example, the analysis unit estimates the required time based on past data or the content of the task. The priority determination unit determines the priority of the task based on the information analyzed by the analysis unit. The priority determination unit determines the priority based on, for example, the urgency or importance of the task. The priority determination unit can also use the generation AI to determine the priority of the task. The allocation unit allocates tasks to available time slots on a calendar based on the priorities and required time slots determined by the priority determination unit. For example, the allocation unit efficiently allocates tasks to available time slots on the calendar. The allocation unit can also allocate tasks using the generation AI. The reminder unit monitors the progress of the tasks allocated by the allocation unit and notifies the user. The reminder unit sends a reminder when, for example, the start or end time of a task is approaching. The reminder unit can also use a generation AI to monitor the progress of a task and notify the user. As a result, the task management system according to the embodiment can support efficient task processing by analyzing the content of tasks, efficiently allocating tasks based on priority and required time, and notifying the user of the progress using the reminder function.

[0077] The task management system includes an adjustment unit that allows a user to manually adjust tasks. The adjustment unit allows a user to manually adjust tasks. The adjustment unit adjusts tasks using, for example, drag and drop. The adjustment unit can also adjust tasks using an input form. The adjustment unit can also allow a user to manually adjust tasks using a generation AI. This allows a user to manually adjust tasks, enabling flexible task management.

[0078] The task management system includes a reallocation unit that monitors the progress of tasks in real time and reallocates tasks as needed. The reallocation unit monitors the progress of tasks in real time and reallocates tasks as needed. The reallocation unit performs updates, for example, on a second-by-second basis. The reallocation unit can also perform updates on a minute-by-minute basis. The reallocation unit can also use a generation AI to monitor the progress of tasks in real time and reallocate tasks as needed. This enables efficient task management by reallocating tasks in real time according to the progress of tasks.

[0079] The analysis unit can estimate the required time based on historical data and task content. The analysis unit estimates the required time based on historical data and task content. The analysis unit estimates the required time based on, for example, past task completion times. The analysis unit can also estimate the required time based on past task content. The analysis unit can also estimate the required time based on historical data and task content using generation AI. This makes it possible to calculate the required time more accurately by estimating the required time based on past data and task content.

[0080] The reminder unit can issue a reminder when the start time or end time of a task is approaching. The reminder unit issues a reminder when the start time or end time of a task is approaching. The reminder unit, for example, issues an email notification. The reminder unit can also issue a push notification. The reminder unit can also display an alert. The reminder unit can also use a generation AI to issue a reminder when the start time or end time of a task is approaching. This makes it easier for the user to grasp the progress of the task by issuing a reminder when the start time or end time of a task is approaching.

[0081] The analysis unit can estimate the user's emotional state and adjust the required time for the task based on the estimated emotional state. The analysis unit can estimate the user's emotional state and adjust the required time for the task based on the estimated emotional state. For example, if the user is feeling stressed, the analysis unit can cause the generation AI to set the required time for the task longer to allow for ample time. If the user is relaxed, the analysis unit can also cause the generation AI to set the required time for the task shorter to allow for efficient progress. If the user is in a hurry, the analysis unit can also cause the generation AI to set the required time for the task as short as possible to complete it quickly. This enables flexible task management according to the user's state by adjusting the required time for the task based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0082] When analyzing the content of a task, the analysis unit can estimate the required time by referring to the user's past task completion times. When analyzing the content of a task, the analysis unit estimates the required time by referring to the user's past task completion times. In the analysis unit, for example, the generation AI estimates the required time based on the time it took the user to complete a similar task in the past. The analysis unit can also have the generation AI estimate the required time based on the average of the user's past task completion times. The analysis unit can also analyze the distribution of the user's past task completion times, and have the generation AI estimate the optimal required time. In this way, by referring to the user's past task completion times, the accuracy of the required time estimation is improved.

[0083] When analyzing the content of a task, the analysis unit can dynamically adjust the required time according to the complexity of the task. When analyzing the content of a task, the analysis unit dynamically adjusts the required time according to the complexity of the task. For example, if the content of the task is complex, the analysis unit causes the generation AI to set a longer required time. If the content of the task is simple, the analysis unit can also cause the generation AI to set a shorter required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the content of the task and set an optimal time. In this way, by dynamically adjusting the required time according to the complexity of the task, a more appropriate required time can be set.

[0084] When analyzing the content of a task, the analysis unit can estimate the required time by taking into account task dependencies. When analyzing the content of a task, the analysis unit estimates the required time by taking into account task dependencies. For example, if a task depends on other tasks, the analysis unit allows the generation AI to estimate the required time by taking into account the dependencies. If the task dependencies are complex, the analysis unit can also allow the generation AI to set the required time longer. If the task dependencies are simple, the analysis unit can also allow the generation AI to set the required time shorter. In this way, by taking task dependencies into account, more accurate estimation of the required time becomes possible.

[0085] The analysis unit can estimate the user's emotional state and adjust the display method of the task analysis results based on the estimated emotional state of the user. The analysis unit can estimate the user's emotional state and adjust the display method of the task analysis results based on the estimated emotional state of the user. For example, if the user is feeling stressed, the generation AI can provide a simple display method. If the user is relaxed, the analysis unit can also display detailed analysis results. If the user is in a hurry, the analysis unit can also display analysis results that focus on the main points. This allows the display method of the analysis results to be adjusted based on the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] When analyzing the content of a task, the analysis unit can estimate the required time by taking into account the user's current project status. When analyzing the content of a task, the analysis unit estimates the required time by taking into account the user's current project status. For example, if the user has multiple projects, the analysis unit allows the generation AI to estimate the required time by taking into account the progress of each project. The analysis unit can also allow the generation AI to adjust the required time based on the priority of the user's current project. The analysis unit can also allow the generation AI to dynamically adjust the required time according to the progress of the user's project. This improves the accuracy of the required time estimation by taking into account the user's current project status.

[0087] When analyzing the content of a task, the analysis unit can adjust the required time according to the user's skill level. When analyzing the content of a task, the analysis unit adjusts the required time according to the user's skill level. For example, when the user's skill level is high, the analysis unit causes the generation AI to set a shorter required time. When the user's skill level is low, the analysis unit can also cause the generation AI to set a longer required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the user's skill level. This makes it possible to set a more appropriate required time by adjusting the required time according to the user's skill level.

[0088] When analyzing the content of a task, the analysis unit can dynamically adjust the required time based on the priority of the task. When analyzing the content of a task, the analysis unit dynamically adjusts the required time based on the priority of the task. For example, when the priority of a task is high, the analysis unit causes the generation AI to set a shorter required time. When the priority of a task is low, the analysis unit can also cause the generation AI to set a longer required time. The analysis unit can also cause the generation AI to dynamically adjust the required time according to the priority of the task. This makes it possible to set a more appropriate required time by dynamically adjusting the required time based on the priority of the task.

[0089] The priority determination unit can estimate the user's emotional state and adjust the priority of tasks based on the estimated emotional state of the user. The priority determination unit can estimate the user's emotional state and adjust the priority of tasks based on the estimated emotional state of the user. For example, if the user is feeling stressed, the priority determination unit can cause the generation AI to set a lower priority for the task. If the user is relaxed, the priority determination unit can also cause the generation AI to set a higher priority for the task. If the user is in a hurry, the priority determination unit can also cause the generation AI to set the priority of the task to the highest priority. This enables flexible task management according to the user's state by adjusting the priority of the task based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0090] The priority determination unit can refer to the user's past task completion history when determining the priority of a task. The priority determination unit refers to the user's past task completion history when determining the priority of a task. In the priority determination unit, for example, the generation AI determines the priority of a new task based on the priorities of tasks previously completed by the user. The priority determination unit can also analyze the user's past task completion history, and the generation AI can determine the optimal priority. The priority determination unit can also cause the generation AI to dynamically adjust the priority of a task based on the user's past task completion history. In this way, by referring to the user's past task completion history, the accuracy of task priority determination is improved.

[0091] When determining the priority of a task, the priority determination unit can dynamically adjust the priority based on the urgency of the task. When determining the priority of a task, the priority determination unit dynamically adjusts the priority based on the urgency of the task. In the priority determination unit, for example, if the urgency of the task is high, the generation AI sets the priority to the highest priority. In the priority determination unit, if the urgency of the task is low, the generation AI can also set the priority to a lower level. In the priority determination unit, the generation AI can also dynamically adjust the priority according to the urgency of the task. In this way, by dynamically adjusting the priority based on the urgency of the task, more appropriate priority setting becomes possible.

[0092] When determining the priority of a task, the priority determination unit can determine the priority by taking into account the dependency relationships between tasks. When determining the priority of a task, the priority determination unit determines the priority by taking into account the dependency relationships between tasks. For example, when a task depends on another task, the priority determination unit determines the priority by having the generation AI take into account the dependency relationships. When the dependency relationships between tasks are complex, the priority determination unit can also have the generation AI set the priority higher. When the dependency relationships between tasks are simple, the priority determination unit can also have the generation AI set the priority lower. This makes it possible to determine priorities more accurately by taking into account the dependency relationships between tasks.

[0093] The priority determination unit can estimate the user's emotions and adjust the method for displaying task priorities based on the estimated user emotions. The priority determination unit can estimate the user's emotions and adjust the method for displaying task priorities based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI can provide a simple display method. If the user is relaxed, the priority determination unit can also provide a detailed priority display. If the user is in a hurry, the priority determination unit can also provide a priority display that focuses on the main points. This allows the priority display method to be adjusted based on the user's emotions, resulting in a display that is easy for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The priority determination unit can take into account the user's current project status when determining the priority of a task. The priority determination unit takes into account the user's current project status when determining the priority of a task. For example, if the user has multiple projects, the priority determination unit determines the priority by having the generation AI take into account the progress status of each project. The priority determination unit can also have the generation AI adjust the priority of a task based on the priority of the user's current project. The priority determination unit can also have the generation AI dynamically adjust the priority of a task according to the progress status of the user's project. This improves the accuracy of task priority determination by taking into account the user's current project status.

[0095] When determining the priority of a task, the priority determination unit can adjust the priority according to the skill level of the user. When determining the priority of a task, the priority determination unit adjusts the priority according to the skill level of the user. For example, when the user's skill level is high, the priority determination unit causes the generation AI to set a high priority for the task. When the user's skill level is low, the priority determination unit can also cause the generation AI to set a low priority for the task. The priority determination unit can also cause the generation AI to dynamically adjust the priority of the task according to the skill level of the user. This makes it possible to set more appropriate priorities by adjusting the priority according to the user's skill level.

[0096] When determining the priority of a task, the priority determination unit can dynamically adjust the priority based on the time required for the task. When determining the priority of a task, the priority determination unit dynamically adjusts the priority based on the time required for the task. In the priority determination unit, for example, if the time required for the task is short, the generation AI can set the priority higher. In the priority determination unit, if the time required for the task is long, the generation AI can also set the priority lower. In the priority determination unit, the generation AI can also dynamically adjust the priority according to the time required for the task. In this way, by dynamically adjusting the priority based on the time required for the task, more appropriate priority can be set.

[0097] The allocation unit can estimate the user's emotional state and adjust the task allocation method based on the estimated user's emotional state. The allocation unit can estimate the user's emotional state and adjust the task allocation method based on the estimated user's emotional state. For example, if the user is feeling stressed, the allocation unit causes the generation AI to allocate fewer tasks. If the user is relaxed, the allocation unit can also cause the generation AI to allocate more tasks. If the user is in a hurry, the allocation unit can also cause the generation AI to allocate tasks with the highest priority. This enables flexible task management according to the user's state by adjusting the task allocation method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0098] The allocation unit can refer to the user's past task completion history when allocating tasks. The allocation unit refers to the user's past task completion history when allocating tasks. In the allocation unit, for example, the generation AI allocates new tasks based on the allocation method of tasks previously completed by the user. The allocation unit can also analyze the user's past task completion history, and the generation AI can determine the optimal allocation method. The allocation unit can also dynamically adjust task allocation based on the user's past task completion history. In this way, the accuracy of task allocation is improved by referring to the user's past task completion history.

[0099] The allocation unit can take task dependency relationships into consideration when allocating tasks. The allocation unit takes task dependency relationships into consideration when allocating tasks. For example, if a task depends on another task, the allocation unit has the generation AI take the dependency relationships into consideration when allocating tasks. If task dependency relationships are complex, the allocation unit can also have the generation AI prioritize allocation. If task dependency relationships are simple, the allocation unit can also have the generation AI postpone allocation. In this way, by taking task dependency relationships into consideration, more appropriate task allocation is possible.

[0100] When allocating tasks, the allocation unit can dynamically adjust the allocation based on the urgency of the tasks. When allocating tasks, the allocation unit dynamically adjusts the allocation based on the urgency of the tasks. For example, if the urgency of a task is high, the allocation unit assigns the task to the generating AI with the highest priority. If the urgency of a task is low, the allocation unit can also assign the task to the generating AI later. The allocation unit can also have the generating AI dynamically adjust the allocation based on the urgency of the task. This makes it possible to allocate tasks more appropriately by dynamically adjusting the allocation based on the urgency of the task.

[0101] The allocation unit can estimate the user's emotional state and adjust the display method of the task allocation results based on the estimated emotional state of the user. The allocation unit can estimate the user's emotional state and adjust the display method of the task allocation results based on the estimated emotional state of the user. For example, if the user is stressed, the allocation unit can have the generation AI provide a simple display method. If the user is relaxed, the allocation unit can also have the generation AI display a detailed allocation result. If the user is in a hurry, the allocation unit can also display a summary allocation result. This allows the display method of the allocation results to be adjusted based on the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0102] The allocation unit can take into account the user's current project status when allocating tasks. The allocation unit takes into account the user's current project status when allocating tasks. For example, if the user has multiple projects, the allocation unit allocates tasks by having the generation AI take into account the progress status of each project. The allocation unit can also have the generation AI adjust task allocation based on the priority of the user's current project. The allocation unit can also have the generation AI dynamically adjust task allocation according to the progress status of the user's project. This improves the accuracy of task allocation by taking into account the user's current project status.

[0103] When allocating tasks, the allocation unit can adjust the allocation according to the user's skill level. When allocating tasks, the allocation unit adjusts the allocation according to the user's skill level. For example, if the user's skill level is high, the allocation unit causes the generation AI to allocate more tasks. If the user's skill level is low, the allocation unit can also cause the generation AI to allocate fewer tasks. The allocation unit can also cause the generation AI to dynamically adjust the task allocation according to the user's skill level. This makes it possible to allocate tasks more appropriately by adjusting the allocation according to the user's skill level.

[0104] When allocating tasks, the allocation unit can dynamically adjust the allocation based on the time required for the task. When allocating tasks, the allocation unit dynamically adjusts the allocation based on the time required for the task. For example, if the time required for the task is short, the allocation unit allows the generation AI to prioritize allocation. If the time required for the task is long, the allocation unit can also allow the generation AI to postpone allocation. The allocation unit also allows the generation AI to dynamically adjust the allocation according to the time required for the task. This makes it possible to allocate tasks more appropriately by dynamically adjusting the allocation based on the time required for the task.

[0105] The reminder unit can estimate the user's emotional state and adjust the timing of reminders based on the estimated emotional state of the user. The reminder unit can estimate the user's emotional state and adjust the timing of reminders based on the estimated emotional state of the user. For example, if the user is feeling stressed, the generation AI of the reminder unit can set the frequency of reminders to be low. If the user is relaxed, the generation AI of the reminder unit can also set the frequency of reminders to be high. If the user is in a hurry, the generation AI of the reminder unit can also optimize the timing of reminders. This allows for flexible reminders according to the user's state by adjusting the timing of reminders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0106] The reminding unit can refer to the user's past reminder history when reminding. The reminding unit refers to the user's past reminder history when reminding. In the reminding unit, for example, the generation AI sets the optimal reminder timing based on the timing when the user received a reminder in the past. The reminding unit can also analyze the user's past reminder history and the generation AI can adjust the frequency of reminders. The reminding unit can also dynamically adjust the timing of reminders based on the user's past reminder history. In this way, the accuracy of reminders is improved by referring to the user's past reminder history.

[0107] When reminding, the reminding unit can dynamically adjust the frequency of reminders based on the urgency of the task. When reminding, the reminding unit dynamically adjusts the frequency of reminders based on the urgency of the task. For example, when the urgency of the task is high, the generation AI of the reminding unit can set the frequency of reminders higher. When the urgency of the task is low, the generation AI of the reminding unit can also set the frequency of reminders lower. The reminding unit can also dynamically adjust the frequency of reminders based on the urgency of the task. This makes it possible to provide more appropriate reminders by dynamically adjusting the frequency of reminders based on the urgency of the task.

[0108] The reminding unit can take task dependency relationships into consideration when reminding. The reminding unit takes task dependency relationships into consideration when reminding. For example, if a task is dependent on another task, the reminding unit allows the generation AI to take the dependency relationships into consideration when reminding. If the task dependency relationships are complex, the reminding unit can also allow the generation AI to set the frequency of reminders more frequently. If the task dependency relationships are simple, the reminding unit can also allow the generation AI to set the frequency of reminders less frequently. This makes it possible to give more appropriate reminders by taking task dependency relationships into consideration.

[0109] The reminder unit can estimate the user's emotions and adjust the reminder display method based on the estimated user emotions. The reminder unit can estimate the user's emotions and adjust the reminder display method based on the estimated user emotions. For example, if the user is feeling stressed, the generation AI of the reminder unit provides a simple display method. If the user is relaxed, the generation AI of the reminder unit can also provide a detailed reminder display. If the user is in a hurry, the generation AI can also provide a reminder display that focuses on the main points. This allows the reminder display method to be adjusted based on the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0110] The reminding unit can take into account the user's current project status when reminding. The reminding unit takes into account the user's current project status when reminding. For example, if the user has multiple projects, the generation AI will take into account the progress status of each project when reminding. The reminding unit can also have the generation AI adjust the timing of reminders based on the priority of the user's current projects. The reminding unit can also have the generation AI dynamically adjust the timing of reminders according to the progress status of the user's projects. This improves the accuracy of reminders by taking into account the user's current project status.

[0111] When giving a reminder, the reminder unit can adjust the content of the reminder according to the user's skill level. When giving a reminder, the reminder unit adjusts the content of the reminder according to the user's skill level. For example, when the user's skill level is high, the generation AI of the reminder unit provides detailed reminder content. When the user's skill level is low, the generation AI of the reminder unit can also provide simple reminder content. The generation AI of the reminder unit can also dynamically adjust the content of the reminder according to the user's skill level. This allows for more appropriate reminders by adjusting the content of the reminder according to the user's skill level.

[0112] When reminding, the reminding unit can dynamically adjust the timing of the reminder based on the time required for the task. When reminding, the reminding unit dynamically adjusts the timing of the reminder based on the time required for the task. For example, if the time required for the task is short, the generation AI of the reminding unit sets the timing of the reminder earlier. If the time required for the task is long, the generation AI of the reminding unit can also set the timing of the reminder later. The reminding unit can also dynamically adjust the timing of the reminder based on the time required for the task. This makes it possible to provide more appropriate reminders by dynamically adjusting the timing of the reminder based on the time required for the task.

[0113] The adjustment unit can estimate the user's emotional state and adjust the task adjustment method based on the estimated user's emotional state. The adjustment unit estimates the user's emotional state and adjusts the task adjustment method based on the estimated user's emotional state. For example, if the user is feeling stressed, the adjustment unit causes the generation AI to adjust the task less. If the user is relaxed, the adjustment unit can also cause the generation AI to adjust the task more. If the user is in a hurry, the adjustment unit can also cause the generation AI to adjust the task more quickly. This enables flexible task management according to the user's state by adjusting the task adjustment method based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0114] The adjustment unit can refer to the user's past task completion history when adjusting a task. The adjustment unit refers to the user's past task completion history when adjusting a task. In the adjustment unit, for example, the generation AI adjusts a new task based on the adjustment method of a task that the user previously completed. The adjustment unit can also analyze the user's past task completion history, and the generation AI can determine the optimal adjustment method. The adjustment unit can also cause the generation AI to dynamically adjust the task based on the user's past task completion history. In this way, the accuracy of task adjustment is improved by referring to the user's past task completion history.

[0115] When adjusting tasks, the adjustment unit can take task dependency relationships into consideration. When adjusting tasks, the adjustment unit takes task dependency relationships into consideration. For example, when a task depends on another task, the adjustment unit allows the generation AI to make adjustments by taking the dependency relationships into consideration. When task dependency relationships are complex, the adjustment unit can also allow the generation AI to make adjustments on a priority basis. When task dependency relationships are simple, the adjustment unit can also allow the generation AI to make adjustments on a later basis. In this way, by taking task dependency relationships into consideration, more appropriate task adjustment is possible.

[0116] The adjustment unit can estimate the user's emotions and adjust the method for displaying the task adjustment results based on the estimated user emotions. The adjustment unit estimates the user's emotions and adjusts the method for displaying the task adjustment results based on the estimated user emotions. For example, when the user is feeling stressed, the adjustment unit causes the generation AI to provide a simple display method. When the user is relaxed, the adjustment unit can also cause the generation AI to display detailed adjustment results. When the user is in a hurry, the adjustment unit can also cause the generation AI to display adjustment results that focus on the main points. This allows the display of the adjustment results to be easily viewed by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0117] The adjustment unit can take into account the user's current project status when adjusting tasks. The adjustment unit takes into account the user's current project status when adjusting tasks. For example, if the user has multiple projects, the adjustment unit allows the generation AI to make adjustments by taking into account the progress status of each project. The adjustment unit can also allow the generation AI to adjust tasks based on the priority of the user's current projects. The adjustment unit can also allow the generation AI to dynamically adjust tasks according to the progress status of the user's projects. In this way, the accuracy of task adjustment is improved by taking into account the user's current project status.

[0118] When adjusting a task, the adjustment unit can make adjustments according to the skill level of the user. When adjusting a task, the adjustment unit makes adjustments according to the skill level of the user. For example, when the user's skill level is high, the adjustment unit causes the generation AI to adjust the task less. When the user's skill level is low, the adjustment unit can also cause the generation AI to adjust the task more. The adjustment unit can also cause the generation AI to dynamically adjust the task according to the user's skill level. This makes it possible to adjust the task more appropriately by making adjustments according to the user's skill level.

[0119] The reallocation unit can estimate the user's emotional state and adjust the task reallocation method based on the estimated user's emotional state. The reallocation unit can estimate the user's emotional state and adjust the task reallocation method based on the estimated user's emotional state. For example, if the user is feeling stressed, the reallocation unit causes the generation AI to reallocate tasks less frequently. If the user is relaxed, the reallocation unit can also cause the generation AI to reallocate tasks more frequently. If the user is in a hurry, the reallocation unit can also quickly reallocate tasks. This enables flexible task management according to the user's state by adjusting the task reallocation method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0120] The reallocation unit can refer to the user's past task completion history when reallocating tasks. The reallocation unit refers to the user's past task completion history when reallocating tasks. In the reallocation unit, for example, the generation AI reallocates a new task based on the reallocation method used by the user to reallocate tasks completed in the past. The reallocation unit can also analyze the user's past task completion history, and the generation AI can determine the optimal reallocation method. The reallocation unit can also dynamically reallocate tasks based on the user's past task completion history. In this way, the accuracy of task reallocation is improved by referring to the user's past task completion history.

[0121] When reallocating tasks, the reallocation unit can take task dependency relationships into consideration. When reallocating tasks, the reallocation unit takes task dependency relationships into consideration. For example, if a task depends on another task, the reallocation unit allows the generation AI to reallocate the task while taking the dependency relationships into consideration. When task dependency relationships are complex, the reallocation unit can also allow the generation AI to prioritize reallocation. When task dependency relationships are simple, the reallocation unit can also allow the generation AI to postpone reallocation. In this way, by taking task dependency relationships into consideration, more appropriate task reallocation is possible.

[0122] The reallocation unit can estimate the user's emotions and adjust the display method of the task reallocation results based on the estimated user emotions. The reallocation unit can estimate the user's emotions and adjust the display method of the task reallocation results based on the estimated user emotions. For example, if the user is stressed, the reallocation unit generates a simple display method using the generation AI. If the user is relaxed, the reallocation unit can also display detailed reallocation results. If the user is in a hurry, the reallocation unit can also display reallocation results that focus on the main points. This allows the display method of the reallocation results to be adjusted based on the user's emotions, making it easier for the user to view. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] The reallocation unit can take into account the user's current project status when reallocating tasks. The reallocation unit takes into account the user's current project status when reallocating tasks. For example, if the user has multiple projects, the reallocation unit reallocates tasks by having the generation AI take into account the progress status of each project. The reallocation unit can also have the generation AI reallocate tasks based on the priority of the user's current projects. The reallocation unit can also have the generation AI dynamically reallocate tasks according to the progress status of the user's projects. In this way, by taking into account the user's current project status, the accuracy of task reallocation is improved.

[0124] When reallocating tasks, the reallocation unit can adjust the reallocation according to the skill level of the user. When reallocating tasks, the reallocation unit adjusts the reallocation according to the skill level of the user. For example, when the user's skill level is high, the reallocation unit causes the generation AI to reallocate tasks less. When the user's skill level is low, the reallocation unit can also cause the generation AI to reallocate tasks more. The reallocation unit can also cause the generation AI to dynamically reallocate tasks according to the user's skill level. This makes it possible to adjust the reallocation according to the user's skill level, thereby reallocating tasks more appropriately.

[0125] When reallocating a task, the reallocation unit can dynamically adjust the reallocation based on the required time for the task. When reallocating a task, the reallocation unit dynamically adjusts the reallocation based on the required time for the task. For example, when the required time for the task is short, the reallocation unit allows the generation AI to prioritize reallocation. When the required time for the task is long, the reallocation unit can also allow the generation AI to postpone reallocation. The reallocation unit can also allow the generation AI to dynamically adjust the reallocation according to the required time for the task. This makes it possible to dynamically adjust the reallocation based on the required time for the task, thereby enabling more appropriate task reallocation. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, priority determination unit, allocation unit, remind unit, adjustment unit, and reallocation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the priority determination unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the allocation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the remind unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the reallocation unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the plurality of elements including the above-described analysis unit, priority determination unit, allocation unit, remind unit, adjustment unit, and reallocation unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the priority determination unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the allocation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the remind unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the reallocation unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, priority determination unit, allocation unit, reminding unit, adjustment unit, and reallocation unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the priority determination unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the allocation unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the reminding unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the reallocation unit is realized by the control unit 46 A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12 . === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, priority determination unit, allocation unit, reminding unit, adjustment unit, and reallocation unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the priority determination unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the allocation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the reminding unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the adjustment unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the reallocation unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0126] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0127] The task management system can further include a health management unit that monitors the user's health condition. The health management unit collects health data such as the user's heart rate and sleep data and provides it to the analysis unit. The analysis unit estimates the user's physical condition based on this health data, and can set the task duration longer if the user is not feeling well. For example, if the user is not getting enough sleep, the generation AI can set the task duration longer to create a more reasonable schedule. Also, if the heart rate is high, it can add a break for relaxation. This enables flexible task management according to the user's health condition.

[0128] The task management system can further include a hobby management unit that takes into account the user's hobbies and interests. The hobby management unit collects information about hobbies and interests entered by the user and provides it to the analysis unit. The analysis unit can suggest tasks to increase the user's motivation based on this information. For example, if the user likes listening to music, the generation AI can suggest time to listen to music between tasks. Also, if the user likes reading, the generation AI can incorporate reading time into the schedule. This makes it possible to manage tasks based on the user's hobbies and interests.

[0129] The task management system may further include a social management unit that takes into account the user's sociability. The social management unit collects information about the user's social activities and relationships and provides it to the analysis unit. The analysis unit can suggest tasks to satisfy the user's social needs based on this information. For example, if the user places importance on interacting with friends, the generation AI can include time for conversations and meetings with friends in the schedule. Also, if the user likes to participate in networking events, the generation AI can suggest such events. This enables task management based on the user's social needs.

[0130] The task management system can further include a learning management unit that manages the user's learning progress. The learning management unit collects information about the content and progress of the user's learning and provides it to the analysis unit. Based on this information, the analysis unit can suggest tasks to improve the user's learning efficiency. For example, if the user is learning a new skill, the generation AI can incorporate tasks related to that skill into the schedule. Also, if the user is studying for an exam, the generation AI can suggest time to review. This makes it possible to manage tasks based on the user's learning progress.

[0131] The task management system may further include a feedback unit that collects user feedback. The feedback unit collects feedback provided by the user to the task management system and provides it to the analysis unit. The analysis unit can improve the functionality of the task management system based on this feedback. For example, if the user feels that a particular task takes too long, the generation AI can adjust the next task duration taking that feedback into account. Also, if the user feels that reminders are being sent too frequently, the generation AI can reduce the frequency of reminders. This enables flexible task management based on user feedback.

[0132] The task management system can further include a location information management unit that takes into account the user's geographic location information. The location information management unit collects information about the user's current location and movement history and provides it to the analysis unit. Based on this information, the analysis unit can propose a task schedule that takes into account the user's travel time. For example, if the user is commuting, the generation AI can allocate tasks taking into account the commuting time. Also, if the user is on a business trip, the generation AI can propose tasks for the business trip destination. This enables flexible task management based on the user's geographic location information.

[0133] The task management system can further include a device management unit that takes into account the user's device usage. The device management unit collects information about the device used by the user and provides it to the analysis unit. The analysis unit can then use this information to suggest tasks that are appropriate for the user's device usage. For example, if the user frequently uses a smartphone, the generation AI can suggest tasks that can be completed on the smartphone. Also, if the user uses a computer, the generation AI can suggest tasks that prioritize computer tasks. This enables flexible task management based on the user's device usage.

[0134] The task management system can further include a network management unit that takes into account the user's network connection status. The network management unit collects information about the user's Internet connection status and provides it to the analysis unit. The analysis unit can then use this information to suggest tasks that are appropriate for the user's network connection status. For example, if the user's Internet connection is unstable, the generation AI can suggest tasks that can be completed offline. Alternatively, if the user has a high-speed Internet connection, the generation AI can suggest tasks that prioritize online work. This enables flexible task management based on the user's network connection status.

[0135] The task management system can further include an energy management unit that takes into account the user's energy consumption. The energy management unit collects information about the remaining battery level and energy consumption of the user's device and provides it to the analysis unit. The analysis unit can suggest tasks to optimize the user's energy consumption based on this information. For example, if the user's device has low battery, the generation AI can suggest tasks that consume less battery. Also, if the user is charging, the generation AI can suggest tasks that consume more energy. This enables flexible task management based on the user's energy consumption.

[0136] The task management system can further include an environmental sound management unit that takes into account the user's environmental sounds. The environmental sound management unit collects information about the sound environment around the user and provides it to the analysis unit. The analysis unit can suggest tasks that correspond to the user's sound environment based on this information. For example, if the user is in a quiet environment, the generation AI can suggest tasks that require concentration. Also, if the user is in a noisy environment, the generation AI can suggest easy tasks. This enables flexible task management based on the user's sound environment.

[0137] The processing flow of the second embodiment will be briefly explained below.

[0138] Step 1: The analysis unit analyzes the content of the task and calculates the required time. The analysis unit can also use generative AI to estimate the required time based on past data and the content of the task. Step 2: The priority determination unit determines the priority of the tasks based on the information analyzed by the analysis unit. The priority determination unit determines the priority based on the urgency and importance of the tasks, and can also determine the priority using a generation AI. Step 3: The allocation unit allocates tasks to available time slots on the calendar based on the priorities and required times determined by the priority determination unit. The allocation unit can also use generation AI to efficiently allocate tasks to available time slots on the calendar. Step 4: The reminder unit monitors the progress of the tasks assigned by the allocation unit and notifies the user. The reminder unit notifies the user of reminders when the start or end time of a task is approaching, and can also monitor the progress of a task using the generation AI and notify the user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0144] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0145] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0146] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0147] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0149] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0150] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0151] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0160] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0169] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0176] 7, a 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.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0183] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0184] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0185] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0186] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0187] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0188] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0189] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0190] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0191] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0192] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0193] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0194] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0195] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0196] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0197] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0198] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0199] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0200] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0201] 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.

[0202] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0203] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0204] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0205] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0206] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0207] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0208] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0209] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0210] [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of a task and calculates the required time; a priority determination unit that determines the priority of a task based on the information analyzed by the analysis unit; an allocation unit that allocates tasks to available time slots on a calendar based on the priorities and required times determined by the priority determination unit; a reminder unit that monitors the progress of the tasks assigned by the assignment unit and notifies the user; Equipped with A system characterized by:

2. Equipped with an adjustment section that allows users to manually adjust tasks 2. The system of claim 1.

3. It has a reallocation unit that monitors the progress of tasks in real time and reallocates them as needed.

2. The system of claim 1.

4. The analysis unit Estimate duration based on historical data and task details 2. The system of claim 1.

5. The reminding unit Reminders when a task is about to start or finish 2. The system of claim 1.

6. The analysis unit Estimate the user's emotional state and adjust the task duration based on the estimated emotional state.

2. The system of claim 1.

7. The analysis unit When analyzing the content of a task, the required time is estimated based on the user's past task completion times.

2. The system of claim 1.

8. The analysis unit When analyzing the content of a task, the time required is dynamically adjusted depending on the complexity of the task.

2. The system of claim 1.

9. The analysis unit When analyzing task content, estimate the required time by taking into account task dependencies.

2. The system of claim 1.

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