system

Abstract

The system according to this embodiment aims to flexibly respond to changes in the user's schedule and shifts in task priority, thereby achieving efficient task management. [Solution] The system according to the embodiment comprises an analysis unit, a proposal unit, a reconstruction unit, a presentation unit, and an analysis unit. The analysis unit analyzes the user's schedule and task priorities. The proposal unit proposes an optimal task progress plan based on the data analyzed by the analysis unit. The reconstruction unit reconstructs the optimal task progress plan based on the task progress plan proposed by the proposal unit in the event of an unexpected schedule change. The presentation unit presents the new schedule reconstructed by the reconstruction unit to the user. The analysis unit analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to flexibly respond to changes in the user's schedule and fluctuations in task priorities, and there is a problem that efficient task management cannot be achieved.

[0005] The system according to the embodiment aims to flexibly respond to changes in the user's schedule and fluctuations in task priorities and realize efficient task management.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a reconstruction unit, a presentation unit, and an analysis unit. The analysis unit analyzes the user's schedule and task priorities. The proposal unit proposes an optimal task progress plan based on the data analyzed by the analysis unit. The reconstruction unit reconstructs the optimal task progress plan based on the task progress plan proposed by the proposal unit in the event of an unexpected schedule change. The presentation unit presents the new schedule reconstructed by the reconstruction unit to the user. The analysis unit analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized. [Effects of the Invention]

[0007] The system according to this embodiment can flexibly respond to changes in the user's schedule and shifts in task priority, enabling efficient task management. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The TaskMaster AI Agent System according to an embodiment of the present invention is a personal assistant system that analyzes the user's schedule and task priorities, and proposes the optimal task progress plan for the day, taking into account interactions and requests made through communication tools. This TaskMaster AI Agent System autonomously re-evaluates task priorities, and in the event of a sudden schedule change, immediately reconstructs the optimal task progress plan and presents the new schedule to the user. It also analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized and adjusts task scheduling to match that timing. For example, the TaskMaster AI Agent System collects data from the user's calendar and task management tools to analyze the user's schedule and task priorities. Next, based on the collected data, the TaskMaster AI Agent System evaluates task priorities and proposes the optimal task progress plan. For example, it prioritizes scheduling important meetings and tasks with approaching deadlines. Furthermore, the TaskMaster AI Agent System takes into account interactions and requests made through communication tools. For example, it analyzes task requests made through chat apps and emails and re-evaluates task priorities. This allows tasks that the user might easily overlook to be appropriately incorporated into the schedule. In the event of unexpected schedule changes, the TaskMaster AI agent system immediately reconstructs the optimal task progress plan. For example, if a meeting is added unexpectedly or a task is changed, the TaskMaster AI agent system presents the user with a new schedule, allowing tasks to proceed efficiently. It also analyzes past task completion times and performance data to identify the times when the user's concentration is maximized. For example, if the user is most focused in the morning, important tasks are scheduled for that time. This maximizes the user's productivity. In this way, the TaskMaster AI agent system improves user productivity by analyzing the user's schedule and task priorities and proposing the optimal task progress plan.Furthermore, it responds immediately to sudden schedule changes, enabling efficient task management. This allows the TaskMaster AI agent system to efficiently analyze, suggest, restructure, present, and analyze the user's schedule and task priorities.

[0029] The TaskMaster AI agent system according to this embodiment comprises an analysis unit, a proposal unit, a reconstruction unit, a presentation unit, and an analysis unit. The analysis unit analyzes the user's schedule and task priorities. The analysis unit collects data from, for example, the user's calendar or task management tools and evaluates task priorities. The proposal unit proposes an optimal task progress plan based on the data analyzed by the analysis unit. The proposal unit, for example, prioritizes scheduling important meetings and tasks with approaching deadlines. The reconstruction unit reconstructs the optimal task progress plan based on the task progress plan proposed by the proposal unit in the event of unexpected schedule changes. The reconstruction unit, for example, reconstructs a new schedule when there are sudden additions of meetings or changes to tasks. The presentation unit presents the new schedule reconstructed by the reconstruction unit to the user. The presentation unit, for example, notifies the user of the new schedule and enables them to efficiently proceed with tasks. The analysis unit analyzes past task completion times and performance data to identify the time periods when the user's concentration is maximized. The analysis department, for example, will schedule important tasks for the morning if the user has high concentration levels during that time. This allows the TaskMaster AI agent system to efficiently analyze, suggest, restructure, present, and analyze the user's schedule and task priorities.

[0030] The analysis unit analyzes the user's schedule and task priorities. For example, the analysis unit collects data from the user's calendar and task management tools and evaluates task priorities. Specifically, the analysis unit obtains data from the user's calendar application and task management tools via APIs and collects information such as the due date, importance, and related resources for each task. Furthermore, the analysis unit uses natural language processing technology to analyze the content of tasks and automatically evaluate their urgency and importance. For example, it analyzes the task description and related notes, extracts specific keywords and phrases, and determines the task priority. In addition, the analysis unit learns the user's task processing patterns based on the user's past task completion history and performance data, enabling more accurate priority evaluation. This allows the analysis unit to accurately understand the user's schedule and task priorities and support efficient task management. Furthermore, the analysis unit can detect free time and overlapping tasks in the user's schedule and optimize the schedule. For example, if multiple tasks overlap at the same time, the analysis unit compares the priorities of those tasks and adjusts the schedule to prioritize the most important task. This allows users to complete tasks efficiently.

[0031] The proposal department proposes an optimal task progress plan based on data analyzed by the analysis department. For example, the proposal department prioritizes scheduling important meetings and tasks with approaching deadlines. Specifically, the proposal department runs an algorithm to optimize the user's schedule based on task priority information provided by the analysis department. This algorithm considers the importance, urgency, duration, and dependencies of the user's tasks to generate an optimal task progress plan. For example, the proposal department prioritizes scheduling important meetings and tasks with approaching project deadlines, and appropriately places other tasks. The proposal department also identifies the time of day when the user is most efficient, based on the user's past performance data, and places important tasks during that time. Furthermore, the proposal department can propose a flexible task progress plan that takes into account the user's individual work style and preferences. For example, if the user prefers to work intensely for short periods, the proposal department prioritizes tasks that can be completed quickly, and places tasks requiring longer work periods with appropriate breaks in between. This allows the proposal department to maximize the user's work efficiency and ensure smooth task progress.

[0032] The Reconstruction Unit reconstructs the optimal task progress plan based on the task progress plan proposed by the Proposal Unit in the event of unexpected schedule changes. For example, the Reconstruction Unit reconstructs a new schedule when there are sudden additions to meetings or changes to tasks. Specifically, when a change occurs in the user's schedule, the Reconstruction Unit executes an algorithm to generate a new schedule in real time. This algorithm considers the user's current schedule, task priorities, required time, dependencies, etc., to reconstruct the optimal task progress plan. For example, if a sudden meeting is added, the Reconstruction Unit incorporates that meeting into the schedule and adjusts the placement of other tasks. Also, if there are changes to tasks, the Reconstruction Unit generates a new schedule that reflects those changes. Furthermore, the Reconstruction Unit can reconstruct the schedule based on user feedback. For example, if a user wants to change the priority of a particular task, the Reconstruction Unit generates a new schedule that reflects that change. In this way, the Reconstruction Unit can flexibly respond to changes in the user's schedule and provide the optimal task progress plan.

[0033] The presentation unit presents the user with the new schedule reconstructed by the reconstruction unit. For example, the presentation unit notifies the user of the new schedule, enabling efficient task progress. Specifically, the presentation unit provides an interface for displaying the new schedule on the user's device. This interface is designed for intuitive user interaction, making it easy to review and modify the new schedule. For example, the presentation unit can send push notifications to the user's smartphone or tablet, allowing for immediate confirmation of the new schedule. The presentation unit can also place a widget on the user's device to constantly display a schedule overview. Furthermore, the presentation unit can integrate with voice assistants to provide voice notifications of the new schedule. This allows users to receive not only visual confirmation but also voice notifications. The presentation unit can collect user feedback and continuously improve how the schedule is presented. For example, if a user prefers a specific notification method, it can be set to prioritize that method. This allows the presentation unit to efficiently and effectively present the new schedule to the user and support task progress.

[0034] The analytics department analyzes past task completion times and performance data to identify the times when users' concentration levels are maximized. For example, if a user's concentration is high in the morning, the analytics department will schedule important tasks during that time. Specifically, the analytics department collects users' past task completion times and performance data and analyzes their work patterns using statistical methods. For example, if a user completes tasks quickly during a particular time period, it will be determined that this is the time when the user's concentration is highest. The analytics department can also consider users' physiological and environmental data to perform more accurate analyses. For example, it can evaluate the user's physical condition and fatigue level based on their heart rate and sleep data to identify the optimal work time. Furthermore, the analytics department can continuously improve accuracy by refining the analysis results based on user feedback. As a result, the analytics department can identify the times when users' concentration levels are maximized and schedule important tasks during those times, thereby improving users' work efficiency.

[0035] The Communication Analysis Department analyzes interactions and requests made through communication tools. For example, the Communication Analysis Department analyzes task requests made via chat applications or email and re-evaluates the priority of those tasks. This allows for an appropriate evaluation of task priorities by analyzing interactions and requests made through communication tools. Some or all of the above-described processes in the Communication Analysis Department may be performed using AI, or not. For example, the Communication Analysis Department can input task requests from chat applications into an AI and have the AI ​​perform the re-evaluation of task priorities.

[0036] The learning unit learns the user's characteristics and provides a customized schedule based on them. For example, the learning unit learns the user's work style, preferences, and past behavioral patterns, and customizes the schedule based on that. This improves the user's productivity by providing a customized schedule based on the user's characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's past behavioral patterns into the AI ​​and have the AI ​​perform the schedule customization.

[0037] The analysis unit can collect data from the user's calendar and task management tools. For example, the analysis unit can retrieve appointments from the user's calendar and collect task information from the task management tool. By collecting data from the user's calendar and task management tools, it can accurately analyze the priority of schedules and tasks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input calendar data into AI and have the AI ​​perform schedule analysis.

[0038] The proposal department can evaluate task priorities based on collected data and propose an optimal task progress plan. For example, the proposal department can prioritize scheduling important meetings and tasks with approaching deadlines. This allows it to propose an optimal task progress plan by evaluating task priorities based on collected data. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input collected data into an AI and have the AI ​​perform the task priority evaluation.

[0039] The reconstruction unit can reconstruct an optimal task progress plan in the event of unexpected schedule changes. For example, the reconstruction unit can reconstruct a new schedule if there are sudden additions to meetings or changes to tasks. This enables efficient task management by reconstructing an optimal task progress plan in the event of unexpected schedule changes. Some or all of the above-described processes in the reconstruction unit may be performed using AI, or not. For example, the reconstruction unit can input schedule change data into the AI ​​and have the AI ​​perform the reconstruction of the task progress plan.

[0040] The presentation unit can present the reconstructed new schedule to the user. For example, the presentation unit can notify the user of the new schedule and enable them to efficiently proceed with their tasks. By presenting the reconstructed new schedule to the user, the user can efficiently proceed with their tasks. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the data for the new schedule into the AI ​​and have the AI ​​perform the task of presenting the schedule.

[0041] The analytics unit can analyze past task completion times and performance data to identify the times when a user's concentration is maximized. For example, if a user's concentration is high in the morning, the analytics unit can schedule important tasks for that time. This improves user productivity by identifying the times when a user's concentration is maximized. Some or all of the above processing in the analytics unit may be performed using AI, or not. For example, the analytics unit can input past performance data into an AI and have the AI ​​identify the times when concentration is maximized.

[0042] The analysis unit can analyze the user's past schedule history and select the optimal analysis method. For example, the analysis unit prioritizes analyzing tasks that the user has frequently performed in the past. The analysis unit analyzes tasks that are concentrated in specific time periods from the user's past schedule history. The analysis unit re-evaluates task priorities based on the user's past schedule history. In this way, the optimal analysis method can be selected by analyzing the user's past schedule history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past schedule history into AI and have the AI ​​select the optimal analysis method.

[0043] The analysis unit can filter schedules and tasks based on the user's current projects and areas of interest. For example, the analysis unit prioritizes analyzing tasks related to projects the user is currently working on. The analysis unit prioritizes analyzing tasks related to the user's areas of interest. The analysis unit re-evaluates task priorities based on the progress of the user's current projects. This allows for the analysis of more relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0044] The analysis unit can prioritize the analysis of highly relevant tasks by considering the user's geographical location when analyzing schedules and tasks. For example, the analysis unit prioritizes tasks that the user can perform near their current location. Based on the user's geographical location, the analysis unit re-evaluates task priorities considering travel time. Based on the user's geographical location, the analysis unit prioritizes the analysis of tasks that can be performed in a specific region. This enables efficient task management by prioritizing the analysis of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into AI and have the AI ​​perform the analysis of highly relevant tasks.

[0045] The analysis unit can analyze users' social media activity and identify related tasks when analyzing schedules and tasks. For example, the analysis unit can prioritize the analysis of related tasks based on users' social media activity. The analysis unit can re-evaluate task priorities based on users' social media activity. The analysis unit can analyze users' social media activity and prioritize the analysis of tasks related to specific events. This enables more appropriate task management by analyzing users' social media activity and identifying related tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity data into AI and have the AI ​​perform the analysis of related tasks.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal unit will provide a detailed proposal for high-importance tasks and a concise proposal for low-importance tasks. The proposal unit will also adjust the priority of proposals according to the importance of the task. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input task importance data into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, for project management tasks, the proposal unit applies a proposal algorithm specialized for project management. For daily work tasks, the proposal unit applies a proposal algorithm specialized for daily work. For urgent tasks, the proposal unit applies a proposal algorithm that emphasizes rapid response. By applying different proposal algorithms depending on the task category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task category data into AI and have the AI ​​execute the application of the proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the task submission deadlines. For example, the proposal department will prioritize tasks with approaching deadlines. It will propose to postpone tasks with later submission deadlines. The proposal department adjusts the priority of proposals according to the submission deadlines. This allows for more appropriate proposals by determining the priority of proposals based on task submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input task submission deadline data into an AI and have the AI ​​determine the priority of proposals.

[0049] The proposal unit can adjust the order of proposals based on the relevance of the tasks. For example, the proposal unit will prioritize proposing tasks that are highly relevant. The proposal unit will propose to postpone tasks that are less relevant. The proposal unit adjusts the order of proposals according to the relevance of the tasks. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task relevance data into AI and have the AI ​​perform the adjustment of the order of proposals.

[0050] The reconstruction unit can improve the accuracy of reconstruction by considering the interrelationships of tasks during the reconstruction process. For example, the reconstruction unit can improve the accuracy of reconstruction by considering the dependencies between tasks. The reconstruction unit can improve the accuracy of reconstruction based on the priority of tasks. The reconstruction unit analyzes the interrelationships of tasks and selects the optimal reconstruction method. This allows for more appropriate task management by improving the accuracy of reconstruction by considering the interrelationships of tasks. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input data on the interrelationships of tasks into the AI ​​and have the AI ​​perform the task of improving the accuracy of reconstruction.

[0051] The reconstruction unit can perform reconstruction while considering the attribute information of the task submitter. For example, the reconstruction unit can determine the reconstruction priority based on the job title of the task submitter. The reconstruction unit can improve the accuracy of reconstruction by considering the past performance data of the task submitter. The reconstruction unit can select the optimal reconstruction method based on the attribute information of the task submitter. This makes it possible to perform more appropriate task management by performing reconstruction while considering the attribute information of the task submitter. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or not using AI. For example, the reconstruction unit can input the submitter's attribute information into AI and have the AI ​​perform the reconstruction.

[0052] The reconstruction unit can perform reconstruction while considering the geographical distribution of tasks. For example, the reconstruction unit can prioritize reconstructing tasks that the user will perform in locations close to their current location. The reconstruction unit can reconstruct tasks while considering travel time based on the user's geographical location information. The reconstruction unit can prioritize reconstructing tasks that will be performed in specific regions based on the user's geographical location information. By performing reconstruction while considering the geographical distribution of tasks, more appropriate task management becomes possible. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input geographical distribution data into AI and have the AI ​​perform the reconstruction.

[0053] The reconstruction unit can improve the accuracy of the reconstruction by referring to relevant literature for the task during the reconstruction process. For example, the reconstruction unit improves the accuracy of the reconstruction based on relevant literature for the task. The reconstruction unit selects the optimal reconstruction method by referring to relevant literature for the task. The reconstruction unit analyzes relevant literature for the task and improves the accuracy of the reconstruction. This makes it possible to manage tasks more appropriately by improving the accuracy of the reconstruction by referring to relevant literature for the task. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input data from relevant literature into AI and have the AI ​​perform the reconstruction.

[0054] The display unit can select the optimal display method by referring to the user's past operation history when presenting information. For example, the display unit may prioritize providing display methods that the user has previously preferred. The display unit may suggest the optimal display method based on the user's past operation history. The display unit may customize the display method based on the user's past operation history. This allows for more appropriate schedule management by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input data from past operation history into AI and have the AI ​​select the optimal display method.

[0055] The display unit can customize its display content based on the user's current situation at the time of presentation. For example, if the user is on the move, the display unit provides concise content. If the user is working at a desk, the display unit provides detailed content. The display unit customizes its display content according to the user's current situation. This allows for more appropriate schedule management by customizing the display content based on the user's current situation. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the display content.

[0056] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. If the user is using a tablet, the display unit provides a display method optimized for a larger screen. If the user is using a smartwatch, the display unit provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, more appropriate schedule management becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device information data into AI and have the AI ​​select the optimal display method.

[0057] The display unit can analyze the user's social media activity and adjust the displayed content at the time of presentation. For example, the display unit prioritizes displaying tasks relevant to the user's social media activity. The display unit customizes the displayed content based on the user's social media activity. The display unit analyzes the user's social media activity and proposes the optimal displayed content. This enables more appropriate schedule management by analyzing the user's social media activity and adjusting the displayed content. Some or all of the above processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input social media activity data into AI and have the AI ​​perform the adjustment of the displayed content.

[0058] The analysis unit can optimize its analysis algorithm by referring to past task completion times and performance data during analysis. For example, the analysis unit selects the optimal analysis algorithm based on past task completion times. The analysis unit optimizes the analysis algorithm by referring to past performance data. The analysis unit improves the accuracy of the analysis algorithm based on past data. This makes it possible to perform more appropriate data analysis by optimizing the analysis algorithm by referring to past task completion times and performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past performance data into AI and have AI perform the optimization of the analysis algorithm.

[0059] The analysis unit can customize the analysis content based on the user's current situation during the analysis. For example, if the user is on the move, the analysis unit will provide a concise analysis. If the user is working at a desk, the analysis unit will provide a detailed analysis. The analysis unit customizes the analysis content according to the user's current situation. This allows for more appropriate data analysis by customizing the analysis content based on the user's current situation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the analysis content.

[0060] The analysis unit can weight the analysis data based on task completion time and performance data submission timing during analysis. For example, the analysis unit can prioritize weighting data with recent submission dates, and reduce the weighting of data with later submission dates. The analysis unit adjusts the weighting of the analysis data according to the submission timing. This allows for more appropriate data analysis by weighting the analysis data based on task completion time and performance data submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input submission timing data into AI and have the AI ​​perform the weighting of the analysis data.

[0061] The analysis unit can analyze users' social media activity and adjust the analysis content during the analysis process. For example, the analysis unit prioritizes analyzing relevant data from users' social media activity. The analysis unit customizes the analysis content based on users' social media activity. The analysis unit analyzes users' social media activity and proposes the optimal analysis content. This allows for more appropriate data analysis by analyzing users' social media activity and adjusting the analysis content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media activity data into AI and have the AI ​​perform the adjustment of the analysis content.

[0062] The communication analysis unit can select the optimal analysis method by referring to the user's past communication history when analyzing interactions and requests made through communication tools. For example, the communication analysis unit selects the optimal analysis method based on the user's past communication history. The communication analysis unit prioritizes the analysis of high-priority interactions from the user's past communication history. The communication analysis unit analyzes the user's past communication history and proposes the optimal analysis method. This enables more appropriate task management by selecting the optimal analysis method by referring to the user's past communication history. Some or all of the above processes in the communication analysis unit may be performed using AI, for example, or without AI. For example, the communication analysis unit can input data from past communication history into AI and have the AI ​​select the optimal analysis method.

[0063] The communication analysis unit can customize the analysis content based on the user's current situation when analyzing interactions and requests in communication tools. For example, if the user is on the move, the communication analysis unit provides a concise analysis. If the user is working at a desk, the communication analysis unit provides a detailed analysis. The communication analysis unit customizes the analysis content according to the user's current situation. This allows for more appropriate task management by customizing the analysis content based on the user's current situation. Some or all of the above processing in the communication analysis unit may be performed using AI, for example, or without AI. For example, the communication analysis unit can input data on the current situation into AI and have AI perform the customization of the analysis content.

[0064] The communication analysis unit can prioritize the analysis of highly relevant interactions and requests when analyzing interactions and requests made through communication tools, taking into account the user's geographical location. For example, the communication analysis unit prioritizes the analysis of interactions that take place near the user's current location. Based on the user's geographical location, the communication analysis unit determines the priority of interactions, taking travel time into account. Based on the user's geographical location, the communication analysis unit prioritizes the analysis of interactions that take place in a specific region. This enables more appropriate task management by prioritizing the analysis of highly relevant interactions and requests, taking into account the user's geographical location. Some or all of the above processing in the communication analysis unit may be performed using AI, for example, or not. For example, the communication analysis unit can input geographical location data into AI and have the AI ​​perform the analysis of highly relevant interactions and requests.

[0065] The Communication Analysis Unit can analyze users' social media activity to identify relevant interactions and requests when analyzing interactions and requests on communication tools. For example, the Communication Analysis Unit prioritizes the analysis of relevant interactions from the user's social media activity. The Communication Analysis Unit determines the priority of interactions based on the user's social media activity. The Communication Analysis Unit analyzes the user's social media activity and proposes the optimal analysis method. This enables more appropriate task management by analyzing users' social media activity to identify relevant interactions and requests. Some or all of the above processes in the Communication Analysis Unit may be performed using AI, for example, or not. For example, the Communication Analysis Unit can input social media activity data into AI and have the AI ​​perform the analysis of relevant interactions and requests.

[0066] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit optimizes the learning algorithm by referring to past learning data. The learning unit improves the accuracy of the learning algorithm based on past data. This allows for more appropriate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0067] The learning unit can customize the learning content based on the user's current situation during learning. For example, if the user is on the go, the learning unit provides concise learning content. If the user is working at a desk, the learning unit provides detailed learning content. The learning unit customizes the learning content according to the user's current situation. This allows for more appropriate learning by customizing the learning content based on the user's current situation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the learning content.

[0068] The learning unit can weight the training data based on user characteristics during training. For example, the learning unit can prioritize weighting important training data based on user characteristics. The learning unit adjusts the weighting of the training data according to user characteristics. The learning unit analyzes user characteristics and performs optimal weighting of the training data. This enables more appropriate training by weighting the training data based on user characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user characteristic data into AI and have the AI ​​perform the weighting of the training data.

[0069] The learning unit can analyze the user's social media activity during learning and adjust the learning content accordingly. For example, the learning unit prioritizes providing relevant learning content based on the user's social media activity. The learning unit customizes the learning content based on the user's social media activity. The learning unit analyzes the user's social media activity and proposes the most suitable learning content. This allows for more appropriate learning by analyzing the user's social media activity and adjusting the learning content. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input social media activity data into AI and have the AI ​​perform the adjustment of the learning content.

[0070] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0071] The analysis unit can consider the user's health data when analyzing the user's schedule and task priorities. For example, it can collect the user's sleep data and heart rate data and adjust task priorities according to the user's physical condition. If the user is tired, it will prioritize analyzing low-priority tasks, and if the user is energetic, it will prioritize analyzing high-priority tasks. This allows for more appropriate task management by analyzing task priorities based on the user's health status.

[0072] The reconstruction unit can improve the accuracy of task reconstruction by considering the user's past task completion times. For example, it can prioritize reconstructing tasks that the user completed quickly in the past, while delaying tasks that took the user a long time in the past. By improving the accuracy of reconstruction by considering the user's past task completion times, more appropriate task management becomes possible.

[0073] The analysis unit can take into account the user's hobbies and interests when analyzing the user's schedule and task priorities. For example, it can prioritize tasks related to the user's hobbies. By prioritizing tasks based on the user's interests, it enables more appropriate task management by considering the user's hobbies and interests when analyzing task priorities.

[0074] The reconstruction unit can improve the accuracy of task reconstruction by considering the user's current project progress. For example, it can prioritize the reconstruction of tasks related to the user's current project. By prioritizing the reconstruction of relevant tasks based on the user's project progress, it improves the accuracy of reconstruction by considering the user's current project progress, enabling more appropriate task management.

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

[0076] Step 1: The analysis unit analyzes the user's schedule and task priorities. For example, the analysis unit collects data from the user's calendar and task management tools and evaluates task priorities. Step 2: The proposal team proposes the optimal task progress plan based on the data analyzed by the analysis team. For example, the proposal team prioritizes scheduling important meetings and tasks with approaching deadlines. Step 3: The Restructuring Department reconstructs the task progress plan based on the proposed task progress plan, in case of unexpected schedule changes. For example, the Restructuring Department will reconstruct the schedule if there are sudden additions to meetings or changes to tasks. Step 4: The presentation unit presents the user with the new schedule reconstructed by the reconstruction unit. The presentation unit, for example, notifies the user of the new schedule and enables them to efficiently proceed with their tasks. Step 5: The analytics team analyzes past task completion times and performance data to identify the times when users are most focused. For example, if a user is most focused in the morning, the analytics team will schedule important tasks for that time slot.

[0077] (Example of form 2) The TaskMaster AI Agent System according to an embodiment of the present invention is a personal assistant system that analyzes the user's schedule and task priorities, and proposes the optimal task progress plan for the day, taking into account interactions and requests made through communication tools. This TaskMaster AI Agent System autonomously re-evaluates task priorities, and in the event of a sudden schedule change, immediately reconstructs the optimal task progress plan and presents the new schedule to the user. It also analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized and adjusts task scheduling to match that timing. For example, the TaskMaster AI Agent System collects data from the user's calendar and task management tools to analyze the user's schedule and task priorities. Next, based on the collected data, the TaskMaster AI Agent System evaluates task priorities and proposes the optimal task progress plan. For example, it prioritizes scheduling important meetings and tasks with approaching deadlines. Furthermore, the TaskMaster AI Agent System takes into account interactions and requests made through communication tools. For example, it analyzes task requests made through chat apps and emails and re-evaluates task priorities. This allows tasks that the user might easily overlook to be appropriately incorporated into the schedule. In the event of unexpected schedule changes, the TaskMaster AI agent system immediately reconstructs the optimal task progress plan. For example, if a meeting is added unexpectedly or a task is changed, the TaskMaster AI agent system presents the user with a new schedule, allowing tasks to proceed efficiently. It also analyzes past task completion times and performance data to identify the times when the user's concentration is maximized. For example, if the user is most focused in the morning, important tasks are scheduled for that time. This maximizes the user's productivity. In this way, the TaskMaster AI agent system improves user productivity by analyzing the user's schedule and task priorities and proposing the optimal task progress plan.Furthermore, it responds immediately to sudden schedule changes, enabling efficient task management. This allows the TaskMaster AI agent system to efficiently analyze, suggest, restructure, present, and analyze the user's schedule and task priorities.

[0078] The TaskMaster AI agent system according to this embodiment comprises an analysis unit, a proposal unit, a reconstruction unit, a presentation unit, and an analysis unit. The analysis unit analyzes the user's schedule and task priorities. The analysis unit collects data from, for example, the user's calendar or task management tools and evaluates task priorities. The proposal unit proposes an optimal task progress plan based on the data analyzed by the analysis unit. The proposal unit, for example, prioritizes scheduling important meetings and tasks with approaching deadlines. The reconstruction unit reconstructs the optimal task progress plan based on the task progress plan proposed by the proposal unit in the event of unexpected schedule changes. The reconstruction unit, for example, reconstructs a new schedule when there are sudden additions of meetings or changes to tasks. The presentation unit presents the new schedule reconstructed by the reconstruction unit to the user. The presentation unit, for example, notifies the user of the new schedule and enables them to efficiently proceed with tasks. The analysis unit analyzes past task completion times and performance data to identify the time periods when the user's concentration is maximized. The analysis department, for example, will schedule important tasks for the morning if the user has high concentration levels during that time. This allows the TaskMaster AI agent system to efficiently analyze, suggest, restructure, present, and analyze the user's schedule and task priorities.

[0079] The analysis unit analyzes the user's schedule and task priorities. For example, the analysis unit collects data from the user's calendar and task management tools and evaluates task priorities. Specifically, the analysis unit obtains data from the user's calendar application and task management tools via APIs and collects information such as the due date, importance, and related resources for each task. Furthermore, the analysis unit uses natural language processing technology to analyze the content of tasks and automatically evaluate their urgency and importance. For example, it analyzes the task description and related notes, extracts specific keywords and phrases, and determines the task priority. In addition, the analysis unit learns the user's task processing patterns based on the user's past task completion history and performance data, enabling more accurate priority evaluation. This allows the analysis unit to accurately understand the user's schedule and task priorities and support efficient task management. Furthermore, the analysis unit can detect free time and overlapping tasks in the user's schedule and optimize the schedule. For example, if multiple tasks overlap at the same time, the analysis unit compares the priorities of those tasks and adjusts the schedule to prioritize the most important task. This allows users to complete tasks efficiently.

[0080] The proposal department proposes an optimal task progress plan based on data analyzed by the analysis department. For example, the proposal department prioritizes scheduling important meetings and tasks with approaching deadlines. Specifically, the proposal department runs an algorithm to optimize the user's schedule based on task priority information provided by the analysis department. This algorithm considers the importance, urgency, duration, and dependencies of the user's tasks to generate an optimal task progress plan. For example, the proposal department prioritizes scheduling important meetings and tasks with approaching project deadlines, and appropriately places other tasks. The proposal department also identifies the time of day when the user is most efficient, based on the user's past performance data, and places important tasks during that time. Furthermore, the proposal department can propose a flexible task progress plan that takes into account the user's individual work style and preferences. For example, if the user prefers to work intensely for short periods, the proposal department prioritizes tasks that can be completed quickly, and places tasks requiring longer work periods with appropriate breaks in between. This allows the proposal department to maximize the user's work efficiency and ensure smooth task progress.

[0081] The Reconstruction Unit reconstructs the optimal task progress plan based on the task progress plan proposed by the Proposal Unit in the event of unexpected schedule changes. For example, the Reconstruction Unit reconstructs a new schedule when there are sudden additions to meetings or changes to tasks. Specifically, when a change occurs in the user's schedule, the Reconstruction Unit executes an algorithm to generate a new schedule in real time. This algorithm considers the user's current schedule, task priorities, required time, dependencies, etc., to reconstruct the optimal task progress plan. For example, if a sudden meeting is added, the Reconstruction Unit incorporates that meeting into the schedule and adjusts the placement of other tasks. Also, if there are changes to tasks, the Reconstruction Unit generates a new schedule that reflects those changes. Furthermore, the Reconstruction Unit can reconstruct the schedule based on user feedback. For example, if a user wants to change the priority of a particular task, the Reconstruction Unit generates a new schedule that reflects that change. In this way, the Reconstruction Unit can flexibly respond to changes in the user's schedule and provide the optimal task progress plan.

[0082] The presentation unit presents the user with the new schedule reconstructed by the reconstruction unit. For example, the presentation unit notifies the user of the new schedule, enabling efficient task progress. Specifically, the presentation unit provides an interface for displaying the new schedule on the user's device. This interface is designed for intuitive user interaction, making it easy to review and modify the new schedule. For example, the presentation unit can send push notifications to the user's smartphone or tablet, allowing for immediate confirmation of the new schedule. The presentation unit can also place a widget on the user's device to constantly display a schedule overview. Furthermore, the presentation unit can integrate with voice assistants to provide voice notifications of the new schedule. This allows users to receive not only visual confirmation but also voice notifications. The presentation unit can collect user feedback and continuously improve how the schedule is presented. For example, if a user prefers a specific notification method, it can be set to prioritize that method. This allows the presentation unit to efficiently and effectively present the new schedule to the user and support task progress.

[0083] The analytics department analyzes past task completion times and performance data to identify the times when users' concentration levels are maximized. For example, if a user's concentration is high in the morning, the analytics department will schedule important tasks during that time. Specifically, the analytics department collects users' past task completion times and performance data and analyzes their work patterns using statistical methods. For example, if a user completes tasks quickly during a particular time period, it will be determined that this is the time when the user's concentration is highest. The analytics department can also consider users' physiological and environmental data to perform more accurate analyses. For example, it can evaluate the user's physical condition and fatigue level based on their heart rate and sleep data to identify the optimal work time. Furthermore, the analytics department can continuously improve accuracy by refining the analysis results based on user feedback. As a result, the analytics department can identify the times when users' concentration levels are maximized and schedule important tasks during those times, thereby improving users' work efficiency.

[0084] The Communication Analysis Department analyzes interactions and requests made through communication tools. For example, the Communication Analysis Department analyzes task requests made via chat applications or email and re-evaluates the priority of those tasks. This allows for an appropriate evaluation of task priorities by analyzing interactions and requests made through communication tools. Some or all of the above-described processes in the Communication Analysis Department may be performed using AI, or not. For example, the Communication Analysis Department can input task requests from chat applications into an AI and have the AI ​​perform the re-evaluation of task priorities.

[0085] The learning unit learns the user's characteristics and provides a customized schedule based on them. For example, the learning unit learns the user's work style, preferences, and past behavioral patterns, and customizes the schedule based on that. This improves the user's productivity by providing a customized schedule based on the user's characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's past behavioral patterns into the AI ​​and have the AI ​​perform the schedule customization.

[0086] The analysis unit can collect data from the user's calendar and task management tools. For example, the analysis unit can retrieve appointments from the user's calendar and collect task information from the task management tool. By collecting data from the user's calendar and task management tools, it can accurately analyze the priority of schedules and tasks. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input calendar data into AI and have the AI ​​perform schedule analysis.

[0087] The proposal department can evaluate task priorities based on collected data and propose an optimal task progress plan. For example, the proposal department can prioritize scheduling important meetings and tasks with approaching deadlines. This allows it to propose an optimal task progress plan by evaluating task priorities based on collected data. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input collected data into an AI and have the AI ​​perform the task priority evaluation.

[0088] The reconstruction unit can reconstruct an optimal task progress plan in the event of unexpected schedule changes. For example, the reconstruction unit can reconstruct a new schedule if there are sudden additions to meetings or changes to tasks. This enables efficient task management by reconstructing an optimal task progress plan in the event of unexpected schedule changes. Some or all of the above-described processes in the reconstruction unit may be performed using AI, or not. For example, the reconstruction unit can input schedule change data into the AI ​​and have the AI ​​perform the reconstruction of the task progress plan.

[0089] The presentation unit can present the reconstructed new schedule to the user. For example, the presentation unit can notify the user of the new schedule and enable them to efficiently proceed with their tasks. By presenting the reconstructed new schedule to the user, the user can efficiently proceed with their tasks. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the data for the new schedule into the AI ​​and have the AI ​​perform the task of presenting the schedule.

[0090] The analytics unit can analyze past task completion times and performance data to identify the times when a user's concentration is maximized. For example, if a user's concentration is high in the morning, the analytics unit can schedule important tasks for that time. This improves user productivity by identifying the times when a user's concentration is maximized. Some or all of the above processing in the analytics unit may be performed using AI, or not. For example, the analytics unit can input past performance data into an AI and have the AI ​​identify the times when concentration is maximized.

[0091] The analysis unit can estimate the user's emotions and analyze schedules and task priorities based on the estimated emotions. For example, if the user is stressed, the analysis unit will postpone less important tasks and prioritize tasks that help the user relax. If the user is relaxed, the analysis unit will prioritize scheduling important tasks that require concentration. If the user is in a hurry, the analysis unit will analyze and prioritize tasks that can be completed quickly. This allows for more appropriate task management by analyzing schedules and task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform an analysis of task priorities based on emotions.

[0092] The analysis unit can analyze the user's past schedule history and select the optimal analysis method. For example, the analysis unit prioritizes analyzing tasks that the user has frequently performed in the past. The analysis unit analyzes tasks that are concentrated in specific time periods from the user's past schedule history. The analysis unit re-evaluates task priorities based on the user's past schedule history. In this way, the optimal analysis method can be selected by analyzing the user's past schedule history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past schedule history into AI and have the AI ​​select the optimal analysis method.

[0093] The analysis unit can filter schedules and tasks based on the user's current projects and areas of interest. For example, the analysis unit prioritizes analyzing tasks related to projects the user is currently working on. The analysis unit prioritizes analyzing tasks related to the user's areas of interest. The analysis unit re-evaluates task priorities based on the progress of the user's current projects. This allows for the analysis of more relevant tasks by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current projects and areas of interest into an AI and have the AI ​​perform the filtering.

[0094] The analysis unit can estimate the user's emotions and determine the priority of tasks to analyze based on the estimated emotions. For example, if the user is tired, the analysis unit will prioritize analyzing low-priority tasks. If the user is focused, the analysis unit will prioritize analyzing high-priority tasks. If the user is stressed, the analysis unit will prioritize analyzing tasks that help them relax. This allows for more appropriate task management by determining the priority of tasks to analyze based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the task prioritization based on emotions.

[0095] The analysis unit can prioritize the analysis of highly relevant tasks by considering the user's geographical location when analyzing schedules and tasks. For example, the analysis unit prioritizes tasks that the user can perform near their current location. Based on the user's geographical location, the analysis unit re-evaluates task priorities considering travel time. Based on the user's geographical location, the analysis unit prioritizes the analysis of tasks that can be performed in a specific region. This enables efficient task management by prioritizing the analysis of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location information into AI and have the AI ​​perform the analysis of highly relevant tasks.

[0096] The analysis unit can analyze users' social media activity and identify related tasks when analyzing schedules and tasks. For example, the analysis unit can prioritize the analysis of related tasks based on users' social media activity. The analysis unit can re-evaluate task priorities based on users' social media activity. The analysis unit can analyze users' social media activity and prioritize the analysis of tasks related to specific events. This enables more appropriate task management by analyzing users' social media activity and identifying related tasks. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input social media activity data into AI and have the AI ​​perform the analysis of related tasks.

[0097] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is stressed, the suggestion unit will suggest postponing lower-priority tasks. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the way suggestions are presented based on those emotions.

[0098] The proposal unit can adjust the level of detail of a proposal based on the importance of the task. For example, the proposal unit will provide a detailed proposal for high-importance tasks and a concise proposal for low-importance tasks. The proposal unit will also adjust the priority of proposals according to the importance of the task. This allows for more appropriate proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input task importance data into the AI ​​and have the AI ​​adjust the level of detail of the proposals.

[0099] The proposal unit can apply different proposal algorithms depending on the task category when making a proposal. For example, for project management tasks, the proposal unit applies a proposal algorithm specialized for project management. For daily work tasks, the proposal unit applies a proposal algorithm specialized for daily work. For urgent tasks, the proposal unit applies a proposal algorithm that emphasizes rapid response. By applying different proposal algorithms depending on the task category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input task category data into AI and have the AI ​​execute the application of the proposal algorithm.

[0100] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is stressed, the suggestion unit will suggest postponing lower-priority tasks. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the length of suggestions based on the emotion.

[0101] The proposal department can determine the priority of proposals based on the task submission deadlines. For example, the proposal department will prioritize tasks with approaching deadlines. It will propose to postpone tasks with later submission deadlines. The proposal department adjusts the priority of proposals according to the submission deadlines. This allows for more appropriate proposals by determining the priority of proposals based on task submission deadlines. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input task submission deadline data into an AI and have the AI ​​determine the priority of proposals.

[0102] The proposal unit can adjust the order of proposals based on the relevance of the tasks. For example, the proposal unit will prioritize proposing tasks that are highly relevant. The proposal unit will propose to postpone tasks that are less relevant. The proposal unit adjusts the order of proposals according to the relevance of the tasks. This allows for more appropriate proposals by adjusting the order of proposals based on the relevance of the tasks. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input task relevance data into AI and have the AI ​​perform the adjustment of the order of proposals.

[0103] The restructuring unit can estimate the user's emotions and adjust the restructured task schedule based on those emotions. For example, if the user is stressed, the restructuring unit will postpone less important tasks and prioritize tasks that help the user relax. If the user is relaxed, the restructuring unit will prioritize scheduling important tasks that require concentration. If the user is in a hurry, the restructuring unit will prioritize tasks that can be completed quickly. This allows for more appropriate task management by adjusting the restructured task schedule based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the restructuring unit may be performed using AI or not. For example, the restructuring unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the task schedule based on the emotions.

[0104] The reconstruction unit can improve the accuracy of reconstruction by considering the interrelationships of tasks during the reconstruction process. For example, the reconstruction unit can improve the accuracy of reconstruction by considering the dependencies between tasks. The reconstruction unit can improve the accuracy of reconstruction based on the priority of tasks. The reconstruction unit analyzes the interrelationships of tasks and selects the optimal reconstruction method. This allows for more appropriate task management by improving the accuracy of reconstruction by considering the interrelationships of tasks. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input data on the interrelationships of tasks into the AI ​​and have the AI ​​perform the task of improving the accuracy of reconstruction.

[0105] The reconstruction unit can perform reconstruction while considering the attribute information of the task submitter. For example, the reconstruction unit can determine the reconstruction priority based on the job title of the task submitter. The reconstruction unit can improve the accuracy of reconstruction by considering the past performance data of the task submitter. The reconstruction unit can select the optimal reconstruction method based on the attribute information of the task submitter. This makes it possible to perform more appropriate task management by performing reconstruction while considering the attribute information of the task submitter. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or not using AI. For example, the reconstruction unit can input the submitter's attribute information into AI and have the AI ​​perform the reconstruction.

[0106] The restructuring unit can estimate the user's emotions and determine the priorities of the task schedule to be restructured based on the estimated user emotions. For example, if the user is stressed, the restructuring unit will postpone less important tasks and prioritize tasks that help the user relax. If the user is relaxed, the restructuring unit will prioritize scheduling important tasks that require concentration. If the user is in a hurry, the restructuring unit will prioritize tasks that can be completed quickly. This allows for more appropriate task management by determining the priorities of the task schedule to be restructured based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the restructuring unit may be performed using AI or not. For example, the restructuring unit can input user emotion data into a generative AI and have the generative AI perform the determination of priority for the task schedule based on emotions.

[0107] The reconstruction unit can perform reconstruction while considering the geographical distribution of tasks. For example, the reconstruction unit can prioritize reconstructing tasks that the user will perform in locations close to their current location. The reconstruction unit can reconstruct tasks while considering travel time based on the user's geographical location information. The reconstruction unit can prioritize reconstructing tasks that will be performed in specific regions based on the user's geographical location information. By performing reconstruction while considering the geographical distribution of tasks, more appropriate task management becomes possible. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input geographical distribution data into AI and have the AI ​​perform the reconstruction.

[0108] The reconstruction unit can improve the accuracy of the reconstruction by referring to relevant literature for the task during the reconstruction process. For example, the reconstruction unit improves the accuracy of the reconstruction based on relevant literature for the task. The reconstruction unit selects the optimal reconstruction method by referring to relevant literature for the task. The reconstruction unit analyzes relevant literature for the task and improves the accuracy of the reconstruction. This makes it possible to manage tasks more appropriately by improving the accuracy of the reconstruction by referring to relevant literature for the task. Some or all of the above processes in the reconstruction unit may be performed using AI, for example, or without AI. For example, the reconstruction unit can input data from relevant literature into AI and have the AI ​​perform the reconstruction.

[0109] The presentation unit can estimate the user's emotions and adjust the way the schedule is displayed based on the estimated emotions. For example, if the user is stressed, the presentation unit provides a simple and highly visible display. If the user is relaxed, the presentation unit provides a display that includes detailed information. If the user is in a hurry, the presentation unit provides a concise display. This allows for more appropriate schedule management by adjusting the way the schedule is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input user emotion data into the generative AI and have the generative AI adjust the way the schedule is displayed based on the emotions.

[0110] The display unit can select the optimal display method by referring to the user's past operation history when presenting information. For example, the display unit may prioritize providing display methods that the user has previously preferred. The display unit may suggest the optimal display method based on the user's past operation history. The display unit may customize the display method based on the user's past operation history. This allows for more appropriate schedule management by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit may input data from past operation history into AI and have the AI ​​select the optimal display method.

[0111] The display unit can customize its display content based on the user's current situation at the time of presentation. For example, if the user is on the move, the display unit provides concise content. If the user is working at a desk, the display unit provides detailed content. The display unit customizes its display content according to the user's current situation. This allows for more appropriate schedule management by customizing the display content based on the user's current situation. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the display content.

[0112] The presentation unit can estimate the user's emotions and determine the priority of the schedule to present based on the estimated emotions. For example, if the user is stressed, the presentation unit will postpone less important tasks and prioritize tasks that help the user relax. If the user is relaxed, the presentation unit will prioritize important tasks that require concentration in the schedule. If the user is in a hurry, the presentation unit will prioritize presenting tasks that can be completed quickly. This allows for more appropriate schedule management by determining the priority of the schedule based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI or not using AI. For example, the presentation unit can input user emotion data into a generative AI and have the generative AI perform the determination of schedule priorities based on emotions.

[0113] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. If the user is using a tablet, the display unit provides a display method optimized for a larger screen. If the user is using a smartwatch, the display unit provides a concise and highly visible display method. By selecting the optimal display method considering the user's device information, more appropriate schedule management becomes possible. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input device information data into AI and have the AI ​​select the optimal display method.

[0114] The display unit can analyze the user's social media activity and adjust the displayed content at the time of presentation. For example, the display unit prioritizes displaying tasks relevant to the user's social media activity. The display unit customizes the displayed content based on the user's social media activity. The display unit analyzes the user's social media activity and proposes the optimal displayed content. This enables more appropriate schedule management by analyzing the user's social media activity and adjusting the displayed content. Some or all of the above processes in the display unit may be performed using AI, for example, or without AI. For example, the display unit can input social media activity data into AI and have the AI ​​perform the adjustment of the displayed content.

[0115] The analysis unit can estimate the user's emotions and select data to analyze based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize analyzing data that promotes relaxation. If the user is relaxed, the analysis unit will prioritize analyzing data that requires concentration. If the user is in a hurry, the analysis unit will prioritize analyzing data that can be completed quickly. This allows for more appropriate data analysis by selecting data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform the selection of emotion-based data.

[0116] The analysis unit can optimize its analysis algorithm by referring to past task completion times and performance data during analysis. For example, the analysis unit selects the optimal analysis algorithm based on past task completion times. The analysis unit optimizes the analysis algorithm by referring to past performance data. The analysis unit improves the accuracy of the analysis algorithm based on past data. This makes it possible to perform more appropriate data analysis by optimizing the analysis algorithm by referring to past task completion times and performance data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past performance data into AI and have AI perform the optimization of the analysis algorithm.

[0117] The analysis unit can customize the analysis content based on the user's current situation during the analysis. For example, if the user is on the move, the analysis unit will provide a concise analysis. If the user is working at a desk, the analysis unit will provide a detailed analysis. The analysis unit customizes the analysis content according to the user's current situation. This allows for more appropriate data analysis by customizing the analysis content based on the user's current situation. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the analysis content.

[0118] The analysis unit can estimate the user's emotions and adjust the frequency of analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit reduces the frequency of analysis. If the user is relaxed, the analysis unit increases the frequency of analysis. If the user is in a hurry, the analysis unit performs analysis quickly. By adjusting the frequency of analysis based on the user's emotions, more appropriate data analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the frequency of analysis based on emotions.

[0119] The analysis unit can weight the analysis data based on task completion time and performance data submission timing during analysis. For example, the analysis unit can prioritize weighting data with recent submission dates, and reduce the weighting of data with later submission dates. The analysis unit adjusts the weighting of the analysis data according to the submission timing. This allows for more appropriate data analysis by weighting the analysis data based on task completion time and performance data submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input submission timing data into AI and have the AI ​​perform the weighting of the analysis data.

[0120] The analysis unit can analyze users' social media activity and adjust the analysis content during the analysis process. For example, the analysis unit prioritizes analyzing relevant data from users' social media activity. The analysis unit customizes the analysis content based on users' social media activity. The analysis unit analyzes users' social media activity and proposes the optimal analysis content. This allows for more appropriate data analysis by analyzing users' social media activity and adjusting the analysis content. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input social media activity data into AI and have the AI ​​perform the adjustment of the analysis content.

[0121] The communication analysis unit can estimate the user's emotions and analyze interactions and requests in communication tools based on the estimated user emotions. For example, if the user is stressed, the communication analysis unit will postpone less important interactions. If the user is relaxed, the communication analysis unit will prioritize analyzing high-importance interactions. If the user is in a hurry, the communication analysis unit will prioritize analyzing interactions that can be completed quickly. This enables more appropriate task management by analyzing interactions and requests in communication tools based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication analysis unit may be performed using AI, or not using AI. For example, the communication analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion-based analysis of interactions and requests.

[0122] The communication analysis unit can select the optimal analysis method by referring to the user's past communication history when analyzing interactions and requests made through communication tools. For example, the communication analysis unit selects the optimal analysis method based on the user's past communication history. The communication analysis unit prioritizes the analysis of high-priority interactions from the user's past communication history. The communication analysis unit analyzes the user's past communication history and proposes the optimal analysis method. This enables more appropriate task management by selecting the optimal analysis method by referring to the user's past communication history. Some or all of the above processes in the communication analysis unit may be performed using AI, for example, or without AI. For example, the communication analysis unit can input data from past communication history into AI and have the AI ​​select the optimal analysis method.

[0123] The communication analysis unit can customize the analysis content based on the user's current situation when analyzing interactions and requests in communication tools. For example, if the user is on the move, the communication analysis unit provides a concise analysis. If the user is working at a desk, the communication analysis unit provides a detailed analysis. The communication analysis unit customizes the analysis content according to the user's current situation. This allows for more appropriate task management by customizing the analysis content based on the user's current situation. Some or all of the above processing in the communication analysis unit may be performed using AI, for example, or without AI. For example, the communication analysis unit can input data on the current situation into AI and have AI perform the customization of the analysis content.

[0124] The communication analysis unit can estimate the user's emotions and, based on the estimated emotions, determine the priority of interactions and requests in the communication tool. For example, if the user is stressed, the communication analysis unit will postpone less important interactions. If the user is relaxed, the communication analysis unit will prioritize analyzing high-importance interactions. If the user is in a hurry, the communication analysis unit will prioritize analyzing interactions that can be completed quickly. This enables more appropriate task management by determining the priority of interactions and requests in the communication tool based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the communication analysis unit may be performed using AI, for example, or not using AI. For example, the communication analysis unit can input user emotion data into a generative AI and have the generative AI perform the determination of priority of interactions and requests based on emotions.

[0125] The communication analysis unit can prioritize the analysis of highly relevant interactions and requests when analyzing interactions and requests made through communication tools, taking into account the user's geographical location. For example, the communication analysis unit prioritizes the analysis of interactions that take place near the user's current location. Based on the user's geographical location, the communication analysis unit determines the priority of interactions, taking travel time into account. Based on the user's geographical location, the communication analysis unit prioritizes the analysis of interactions that take place in a specific region. This enables more appropriate task management by prioritizing the analysis of highly relevant interactions and requests, taking into account the user's geographical location. Some or all of the above processing in the communication analysis unit may be performed using AI, for example, or not. For example, the communication analysis unit can input geographical location data into AI and have the AI ​​perform the analysis of highly relevant interactions and requests.

[0126] The Communication Analysis Unit can analyze users' social media activity to identify relevant interactions and requests when analyzing interactions and requests on communication tools. For example, the Communication Analysis Unit prioritizes the analysis of relevant interactions from the user's social media activity. The Communication Analysis Unit determines the priority of interactions based on the user's social media activity. The Communication Analysis Unit analyzes the user's social media activity and proposes the optimal analysis method. This enables more appropriate task management by analyzing users' social media activity to identify relevant interactions and requests. Some or all of the above processes in the Communication Analysis Unit may be performed using AI, for example, or not. For example, the Communication Analysis Unit can input social media activity data into AI and have the AI ​​perform the analysis of relevant interactions and requests.

[0127] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will prioritize selecting training data that promotes relaxation. If the user is relaxed, the learning unit will prioritize selecting training data that requires concentration. If the user is in a hurry, the learning unit will prioritize selecting training data that can be completed quickly. This allows for more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform the selection of training data based on emotions.

[0128] The learning unit can optimize the learning algorithm by referring to past learning data during the learning process. For example, the learning unit selects the optimal learning algorithm based on past learning data. The learning unit optimizes the learning algorithm by referring to past learning data. The learning unit improves the accuracy of the learning algorithm based on past data. This allows for more appropriate learning by optimizing the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0129] The learning unit can customize the learning content based on the user's current situation during learning. For example, if the user is on the go, the learning unit provides concise learning content. If the user is working at a desk, the learning unit provides detailed learning content. The learning unit customizes the learning content according to the user's current situation. This allows for more appropriate learning by customizing the learning content based on the user's current situation. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input data on the current situation into the AI ​​and have the AI ​​perform the customization of the learning content.

[0130] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit reduces the learning frequency. If the user is relaxed, the learning unit increases the learning frequency. If the user is in a hurry, the learning unit performs learning quickly. This allows for more appropriate learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into the generative AI and have the generative AI adjust the learning frequency based on emotions.

[0131] The learning unit can weight the training data based on user characteristics during training. For example, the learning unit can prioritize weighting important training data based on user characteristics. The learning unit adjusts the weighting of the training data according to user characteristics. The learning unit analyzes user characteristics and performs optimal weighting of the training data. This enables more appropriate training by weighting the training data based on user characteristics. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user characteristic data into AI and have the AI ​​perform the weighting of the training data.

[0132] The learning unit can analyze the user's social media activity during learning and adjust the learning content accordingly. For example, the learning unit prioritizes providing relevant learning content based on the user's social media activity. The learning unit customizes the learning content based on the user's social media activity. The learning unit analyzes the user's social media activity and proposes the most suitable learning content. This allows for more appropriate learning by analyzing the user's social media activity and adjusting the learning content. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input social media activity data into AI and have the AI ​​perform the adjustment of the learning content.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The analysis unit can consider the user's health data when analyzing the user's schedule and task priorities. For example, it can collect the user's sleep data and heart rate data and adjust task priorities according to the user's physical condition. If the user is tired, it will prioritize analyzing low-priority tasks, and if the user is energetic, it will prioritize analyzing high-priority tasks. This allows for more appropriate task management by analyzing task priorities based on the user's health status.

[0135] The suggestion function can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is relaxed, the suggestion will be made earlier. If the user is stressed, the suggestion will be made later. If the user is in a hurry, the suggestion will be made quickly. By adjusting the timing of suggestions based on the user's emotions, more appropriate suggestions can be made.

[0136] The reconstruction unit can improve the accuracy of task reconstruction by considering the user's past task completion times. For example, it can prioritize reconstructing tasks that the user completed quickly in the past, while delaying tasks that took the user a long time in the past. By improving the accuracy of reconstruction by considering the user's past task completion times, more appropriate task management becomes possible.

[0137] The display unit can estimate the user's emotions and adjust the color and design of the schedule displayed based on those emotions. For example, if the user is relaxed, calm colors and designs are used. If the user is stressed, bright colors and simple designs are used. If the user is in a hurry, a highly visible design is used. By adjusting the color and design of the schedule displayed based on the user's emotions, more appropriate schedule management becomes possible.

[0138] The analytics department can estimate the user's emotions and prioritize the data to be analyzed based on those emotions. For example, if the user is stressed, it will prioritize analyzing data that promotes relaxation. If the user is relaxed, it will prioritize analyzing data that requires concentration. If the user is in a hurry, it will prioritize analyzing data that can be completed quickly. By prioritizing the data to be analyzed based on the user's emotions, more appropriate data analysis becomes possible.

[0139] The analysis unit can take into account the user's hobbies and interests when analyzing the user's schedule and task priorities. For example, it can prioritize tasks related to the user's hobbies. By prioritizing tasks based on the user's interests, it enables more appropriate task management by considering the user's hobbies and interests when analyzing task priorities.

[0140] The suggestion function can estimate the user's emotions and adjust the content of the suggestions based on those emotions. For example, if the user is relaxed, it will provide detailed suggestions. If the user is stressed, it will provide concise suggestions. If the user is in a hurry, it will provide suggestions that can be completed quickly. By adjusting the content of suggestions based on the user's emotions, it becomes possible to provide more appropriate suggestions.

[0141] The reconstruction unit can improve the accuracy of task reconstruction by considering the user's current project progress. For example, it can prioritize the reconstruction of tasks related to the user's current project. By prioritizing the reconstruction of relevant tasks based on the user's project progress, it improves the accuracy of reconstruction by considering the user's current project progress, enabling more appropriate task management.

[0142] The notification unit can estimate the user's emotions and adjust the way it presents schedule notifications based on those emotions. For example, if the user is relaxed, a gentle notification sound is used. If the user is stressed, a simple notification sound is used. If the user is in a hurry, a notification sound that can be quickly noticed is used. This allows for more appropriate schedule management by adjusting the way schedule notifications are presented based on the user's emotions.

[0143] The analytics department can estimate the user's emotions and prioritize the data to be analyzed based on those emotions. For example, if the user is stressed, it will prioritize analyzing data that promotes relaxation. If the user is relaxed, it will prioritize analyzing data that requires concentration. If the user is in a hurry, it will prioritize analyzing data that can be completed quickly. By prioritizing the data to be analyzed based on the user's emotions, more appropriate data analysis becomes possible.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The analysis unit analyzes the user's schedule and task priorities. For example, the analysis unit collects data from the user's calendar and task management tools and evaluates task priorities. Step 2: The proposal team proposes the optimal task progress plan based on the data analyzed by the analysis team. For example, the proposal team prioritizes scheduling important meetings and tasks with approaching deadlines. Step 3: The Restructuring Department reconstructs the task progress plan based on the proposed task progress plan, in case of unexpected schedule changes. For example, the Restructuring Department will reconstruct the schedule if there are sudden additions to meetings or changes to tasks. Step 4: The presentation unit presents the user with the new schedule reconstructed by the reconstruction unit. The presentation unit, for example, notifies the user of the new schedule and enables them to efficiently proceed with their tasks. Step 5: The analytics team analyzes past task completion times and performance data to identify the times when users are most focused. For example, if a user is most focused in the morning, the analytics team will schedule important tasks for that time slot.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0148] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the analysis unit, proposal unit, reconstruction unit, presentation unit, analysis unit, communication analysis unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14, which collects data from the user's calendar and task management tools and evaluates task priorities. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes an optimal task progress plan based on the analyzed data. The reconstruction unit is implemented by the control unit 46A of the smart device 14, which reconstructs the optimal task progress plan in the event of a sudden schedule change. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12, which presents the new schedule to the user. The analysis unit is implemented by the control unit 46A of the smart device 14, which analyzes past task completion times and performance data to identify the time periods when the user's concentration is maximized. The communication analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes task requests in chat applications and emails and re-evaluates task priorities. The learning unit is implemented, for example, by the control unit 46A of the smart device 14, which learns the user's characteristics and provides a customized schedule based on them. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the analysis unit, proposal unit, reconstruction unit, presentation unit, analysis unit, communication analysis unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214, which collects data from the user's calendar and task management tools and evaluates task priorities. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes an optimal task progress plan based on the analyzed data. The reconstruction unit is implemented by the control unit 46A of the smart glasses 214, which reconstructs the optimal task progress plan in the event of a sudden schedule change. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12, which presents the new schedule to the user. The analysis unit is implemented by the control unit 46A of the smart glasses 214, which analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized. The communication analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes task requests in chat applications and emails and re-evaluates task priorities. The learning unit is implemented, for example, by the control unit 46A of the smart glasses 214, which learns the user's characteristics and provides a customized schedule based on them. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the analysis unit, proposal unit, reconstruction unit, presentation unit, analysis unit, communication analysis unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314, which collects data from the user's calendar and task management tools and evaluates task priorities. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes an optimal task progress plan based on the analyzed data. The reconstruction unit is implemented by the control unit 46A of the headset terminal 314, which reconstructs the optimal task progress plan in the event of a sudden schedule change. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12, which presents the new schedule to the user. The analysis unit is implemented by the control unit 46A of the headset terminal 314, which analyzes past task completion times and performance data to identify the time period when the user's concentration is maximized. The communication analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes task requests in chat applications and emails and re-evaluates task priorities. The learning unit is implemented, for example, by the control unit 46A of the headset terminal 314, which learns the user's characteristics and provides a customized schedule based on them. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0188] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0189] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0190] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0197] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements described above, including the analysis unit, proposal unit, reconstruction unit, presentation unit, analysis unit, communication analysis unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414, which collects data from the user's calendar and task management tools and evaluates task priorities. The proposal unit is implemented by the identification processing unit 290 of the data processing unit 12, which proposes an optimal task progress plan based on the analyzed data. The reconstruction unit is implemented by the control unit 46A of the robot 414, which reconstructs an optimal task progress plan in the event of a sudden schedule change. The presentation unit is implemented by the identification processing unit 290 of the data processing unit 12, which presents a new schedule to the user. The analysis unit is implemented by the control unit 46A of the robot 414, which analyzes past task completion times and performance data to identify the time periods when the user's concentration is maximized. The communication analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes task requests via chat applications and emails and re-evaluates task priorities. The learning unit is implemented, for example, by the control unit 46A of the robot 414, which learns the user's characteristics and provides a customized schedule based on them. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0199] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0202] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0203] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0205] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0208] 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.

[0209] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0214] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0215] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0216] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0217] (Note 1) An analysis unit that analyzes the user's schedule and task priorities, A proposal unit proposes an optimal task progress plan based on the data analyzed by the aforementioned analysis unit, A reconstruction unit reconstructs the optimal task progress plan when an unexpected schedule change occurs based on the task progress plan proposed by the aforementioned proposal unit, A presentation unit presents the user with the new schedule reconstructed by the reconstruction unit, It includes an analysis unit that analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized. A system characterized by the following features. (Note 2) It includes a communication analysis unit that analyzes interactions and requests made through communication tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) It features a learning unit that learns the user's characteristics and provides a customized schedule based on those characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Collect data from users' calendars and task management tools. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the collected data, we evaluate task priorities and propose the optimal task progress plan. The system described in Appendix 1, characterized by the features described herein. (Note 6) The reconstruction unit, Reconstruct the optimal task progress plan in case of unexpected schedule changes. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned display unit is, Present the user with a newly restructured schedule. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is By analyzing past task completion times and performance data, we identify the time periods when users are most focused. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and analyzes the priority of schedules and tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Analyze the user's past schedule history and select the optimal analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing schedules and tasks, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, Estimate user emotions and prioritize tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, When analyzing schedules and tasks, the system prioritizes analyzing highly relevant tasks by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing schedules and tasks, the system analyzes users' social media activity and identifies related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When making a proposal, apply a different proposal algorithm depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on the task submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making proposals, adjust the order of the proposals based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The reconstruction unit, Estimate user emotions and adjust the task progress plan to reconstruct it based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The reconstruction unit, During reconstruction, consider the interrelationships between tasks to improve the accuracy of the reconstruction. The system described in Appendix 1, characterized by the features described herein. (Note 23) The reconstruction unit, During reconstruction, the attribute information of the task submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The reconstruction unit, Estimate user emotions and prioritize task progress plans based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The reconstruction unit, During reconstruction, the geographical distribution of tasks should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The reconstruction unit, During reconstruction, we improve the accuracy of the reconstruction by referring to relevant literature for the task. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, It estimates the user's emotions and adjusts how the schedule is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting information, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting, the displayed content is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, It estimates the user's emotions and determines the priority of the schedule to present based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is, When presenting the information, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is, When presenting content, the system analyzes the user's social media activity and adjusts the displayed content accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is We estimate the user's emotions and select data for analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit is During analysis, the analysis algorithm is optimized by referring to past task completion times and performance data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit is During analysis, the analysis content is customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit is It estimates the user's sentiment and adjusts the frequency of analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned analysis unit is During analysis, the analysis data is weighted based on task completion time and the timing of performance data submission. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and adjust the analysis accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned communication analysis unit, It estimates the user's emotions and analyzes interactions and requests in communication tools based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned communication analysis unit, When analyzing interactions and requests made through communication tools, the system selects the optimal analysis method by referring to the user's past communication history. The system described in Appendix 2, characterized by the features described herein. (Note 41) The aforementioned communication analysis unit, When analyzing interactions and requests in communication tools, the analysis content is customized based on the user's current situation. The system described in Appendix 2, characterized by the features described herein. (Note 42) The aforementioned communication analysis unit, It estimates the user's emotions and, based on those estimated emotions, determines the priority of interactions and requests within communication tools. The system described in Appendix 2, characterized by the features described herein. (Note 43) The aforementioned communication analysis unit, When analyzing interactions and requests from communication tools, the system prioritizes analyzing highly relevant interactions and requests by considering the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 44) The aforementioned communication analysis unit, When analyzing interactions and requests on communication tools, the system analyzes users' social media activity to identify related interactions and requests. The system described in Appendix 2, characterized by the features described herein. (Note 45) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 46) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 3, characterized by the features described herein. (Note 47) The learning unit customizes learning content based on the user's current situation during learning The system according to Supplementary Note 3, characterized in that. (Supplementary Note 48) The learning unit estimates the user's emotion and adjusts the learning frequency based on the estimated user emotion The system according to Supplementary Note 3, characterized in that. (Supplementary Note 49) The learning unit performs weighting of learning data based on the user's characteristics during learning The system according to Supplementary Note 3, characterized in that. (Supplementary Note 50) The learning unit analyzes the user's social media activities and adjusts the learning content during learning The system according to Supplementary Note 3, characterized in that.

Explanation of Signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes the user's schedule and task priorities, A proposal unit proposes an optimal task progress plan based on the data analyzed by the aforementioned analysis unit, A reconstruction unit reconstructs the optimal task progress plan when an unexpected schedule change occurs based on the task progress plan proposed by the aforementioned proposal unit, A presentation unit presents the user with the new schedule reconstructed by the reconstruction unit, It includes an analysis unit that analyzes past task completion times and performance data to identify the time of day when the user's concentration is maximized. A system characterized by the following features.

2. It includes a communication analysis unit that analyzes interactions and requests made through communication tools. The system according to feature 1.

3. It features a learning unit that learns the user's characteristics and provides a customized schedule based on those characteristics. The system according to feature 1.

4. The aforementioned analysis unit, Collect data from users' calendars and task management tools. The system according to feature 1.

5. The aforementioned proposal section is, Based on the collected data, we evaluate task priorities and propose the optimal task progress plan. The system according to feature 1.

6. The reconstruction unit, Reconstruct the optimal task progress plan in case of unexpected schedule changes. The system according to feature 1.

7. The aforementioned display unit is, Present the user with a newly restructured schedule. The system according to feature 1.

8. The aforementioned analysis unit is By analyzing past task completion times and performance data, we identify the time periods when users are most focused. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and analyzes the priority of schedules and tasks based on those estimated emotions. The system according to feature 1.

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