system

Abstract

The system according to this embodiment aims to automatically schedule tasks that a user has to do and prevent procrastination. [Solution] The system according to the embodiment comprises a reception unit, a division unit, a scheduling unit, and a presentation unit. The reception unit receives input from the user regarding the content of the task, the required time, and the deadline. The division unit divides the task into manageable granularity based on the information received by the reception unit. The scheduling unit automatically schedules the tasks divided by the division unit. The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and presents to the user what they should do now.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 prior art, it is troublesome for a user with many tasks to identify and schedule tasks, and there is a risk of procrastination.

[0005] The system according to the embodiment aims to automatically schedule the tasks of the user and prevent procrastination.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a division unit, a scheduling unit, and a presentation unit. The reception unit receives input from the user regarding the content of a task, the required time, and the deadline. The division unit divides the task into manageable granularities based on the information received by the reception unit. The scheduling unit automatically schedules the tasks divided by the division unit. The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and presents to the user what they should do now. [Effects of the Invention]

[0007] The system according to this embodiment can automatically schedule tasks that a user has to do and prevent procrastination. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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 task scheduling system according to an embodiment of the present invention is a system that automates task scheduling using generative AI. This task scheduling system is particularly effective for people who tend to procrastinate or who have characteristics of ADHD. The task scheduling system accepts input from the user via voice or text, including the task content, required time, and deadline. The task scheduling system analyzes this information, divides the task into manageable granularities, and automatically schedules it. A timer activates based on the schedule, indicating to the user what needs to be done now. Once a task is completed, the task scheduling system reschedules and adjusts the schedule. For example, the task scheduling system accepts input from the user via voice or text, including the task content, required time, and deadline. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting," the task scheduling system analyzes this information and divides the task into manageable granularities. Next, the task scheduling system automatically schedules the task using generative AI. For example, the task scheduling system sets a schedule to prepare for the meeting in the morning. Furthermore, the task scheduling system activates a timer based on the schedule, indicating to the user what needs to be done now. For example, the task scheduling system might notify the user, "Please begin preparing for the meeting at 9:00 AM." Once the task is completed, the task scheduling system reschedules and adjusts the schedule. For instance, if the meeting preparation is completed earlier than planned, the task scheduling system adjusts the schedule to allow the next task to start earlier. This allows the task scheduling system to prevent users from procrastinating and to complete tasks efficiently. In this way, the task scheduling system can efficiently divide, schedule, and present the user's tasks.

[0029] The task scheduling system according to this embodiment comprises a reception unit, a division unit, a scheduling unit, and a presentation unit. The reception unit receives input from the user regarding the content of a task, the required time, and the deadline. The reception unit accepts, for example, the user to input the content of a task by voice. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting" by voice, the reception unit accepts this information. The reception unit also accepts the user to input the content of a task by text. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting" by text, the reception unit accepts this information. Furthermore, the reception unit accepts the user to input the required time and the deadline for the task. For example, if the user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception unit accepts this information. The division unit divides the task into manageable granularity based on the information received by the reception unit. The division unit divides the task using, for example, a generation AI. For example, the division unit divides meeting preparation into smaller tasks such as "creating materials," "practicing the presentation," and "preparing the meeting room." Furthermore, the division unit can adjust the granularity of the division based on the content and duration of the tasks. For example, the division unit can divide long tasks into smaller parts. The scheduling unit automatically schedules the tasks divided by the division unit. The scheduling unit can schedule tasks using, for example, generative AI. For example, the scheduling unit can set a schedule to complete meeting preparations between 9:00 AM and 11:00 AM. The scheduling unit can also adjust the schedule based on task priority and deadlines. For example, the scheduling unit can prioritize scheduling tasks with approaching deadlines. The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and shows the user what they should do now. The presentation unit can, for example, activate a timer and notify the user. For example, the presentation unit might notify the user, "Please start preparing for the meeting at 9:00 AM." The presentation unit can also adjust the timer according to the progress of the tasks. For example, if a task is completed earlier than planned, the presentation unit might notify the user to start the next task earlier.As a result, the task scheduling system according to the embodiment can efficiently divide, schedule, and present tasks to the user.

[0030] The reception desk accepts input from users regarding the task content, required time, and deadline. For example, the reception desk accepts task content input by voice. Specifically, it uses speech recognition technology to convert the user's voice input into text data, accurately understanding the task content. For example, if a user voice-inputs "It will take 2 hours to prepare for tomorrow's meeting," the speech recognition technology analyzes this voice and converts it into text data: "It will take 2 hours to prepare for tomorrow's meeting." The reception desk also accepts task content input by text. For example, if a user texts "It will take 2 hours to prepare for tomorrow's meeting," the reception desk accepts this information. Furthermore, the reception desk accepts input from users regarding the required time and deadline. For example, if a user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception desk accepts this information. The reception desk centrally manages this input information and stores it as data necessary for subsequent processing. This allows the reception desk to accommodate diverse user input methods and collect task information accurately and efficiently. Furthermore, the reception unit also has a function to analyze user input and automatically evaluate the importance and urgency of tasks. For example, it can automatically set task priorities based on the task content and deadline, and reflect this in subsequent processing. In this way, the reception unit streamlines user task management and enables smooth task scheduling.

[0031] The task division unit divides tasks into manageable granularity based on information received by the reception unit. For example, the division unit uses generative AI to divide tasks. Specifically, it analyzes the task content using natural language processing technology and divides it into appropriate subtasks. For instance, the division unit divides meeting preparation into smaller tasks such as "document creation," "presentation practice," and "meeting room preparation." The division unit can also adjust the granularity of the division based on the task content and required time. For example, it can break down long tasks into smaller, more manageable subtasks to make them easier for users to complete. Furthermore, the division unit considers task dependencies. For instance, if presentation practice cannot begin until document creation is complete, it will divide the task so that document creation is done first. This allows the division unit to support users in efficiently completing tasks. The division unit also has a function to learn the optimal division method by referencing the user's past task history and performance data. This enables the division unit to achieve flexible task division tailored to the user's work style and habits, improving the efficiency of task management.

[0032] The scheduling unit automatically schedules tasks divided by the division unit. The scheduling unit uses, for example, generation AI to schedule tasks. Specifically, it generates an optimal schedule considering the user's schedule, task priorities, and deadlines. For example, the scheduling unit might schedule meeting preparations to be completed between 9:00 AM and 11:00 AM. The scheduling unit can also adjust the schedule based on task priorities and deadlines. For example, it might prioritize tasks with approaching deadlines and set high-priority tasks to be completed first. Furthermore, the scheduling unit has the ability to learn the optimal scheduling method by referencing the user's past scheduling history and performance data. This allows the scheduling unit to achieve flexible scheduling tailored to the user's work style and habits, improving the efficiency of task management. The scheduling unit can also update the schedule in real time and respond flexibly to the user's situation. For example, if the user makes a sudden change of plans or adds a new task, the scheduling unit immediately readjusts the schedule and presents the optimal schedule. This allows the scheduling unit to streamline the user's task management and support smooth task execution.

[0033] The notification unit activates a timer based on tasks scheduled by the scheduling unit, informing the user of what they should do now. For example, the notification unit activates the timer and notifies the user. Specifically, it sends a notification to the user's device informing them of the task's start and end times. For example, the notification unit might notify the user, "Please start preparing for the meeting at 9:00 AM." The notification unit can also adjust the timer according to the progress of the task. For example, if a task is completed earlier than planned, it will notify the user to start the next task earlier. In this way, the notification unit supports the user in efficiently completing tasks. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user behavior data after receiving a notification and optimize the timing and content of notifications. The notification unit can also reliably transmit information using multiple notification methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with quick and reliable instructions, improving the efficiency of task management.

[0034] The scheduling unit can reschedule tasks once they are completed. For example, when the scheduling unit detects that a task has been completed, it schedules the next task. For example, if the scheduling unit completes preparations for a meeting earlier than planned, it adjusts the schedule to start the next task earlier. The scheduling unit can also adjust the schedule according to the progress of tasks. For example, if a task is behind schedule, the scheduling unit adjusts the schedules of other tasks to make up for the delay. This allows for schedule adjustments by rescheduling after a task is completed. Some or all of the above processes in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input the progress of tasks into a generative AI, which can then readjust the schedule.

[0035] The task division unit can be used by a generating AI to counsel the user and divide the task into manageable granularities. For example, the task division unit can interact with the user using a generating AI and divide the task. For instance, if the user inputs "Preparing for the meeting is difficult," the generating AI will ask, "Specifically, which part is difficult?" and divide the task based on the user's answer. The task division unit can also divide the task considering the user's situation and emotions. For example, if the user is feeling stressed, the task division unit will break it down into smaller, manageable parts. This allows the generating AI to counsel the user and divide the task into manageable granularities. Some or all of the above-described processes in the task division unit may be performed using a generating AI or not. For example, the task division unit can pass user input to a generating AI, which can then perform the task division.

[0036] The presentation unit can activate a timer based on a schedule. For example, the presentation unit can set a timer based on a schedule and notify the user. For example, the presentation unit can notify the user, "Please start preparing for the meeting at 9:00 AM." The presentation unit can also adjust the timer according to the progress of tasks. For example, if a task is completed earlier than scheduled, the presentation unit can notify the user to start the next task earlier. In this way, by activating a timer based on a schedule, the presentation unit can show the user what they should be doing now. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input schedule information into a generative AI, and the generative AI can set a timer.

[0037] The reception desk can analyze the user's past task input history and suggest the optimal input method. For example, the reception desk can automatically display the content of tasks that the user has frequently entered in the past as suggestions. For example, if the reception desk has frequently entered "Prepare for a meeting" in the past, it will display "Prepare for a meeting" as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest the content of tasks to be entered at a specific time of day based on the user's past input history. For example, if the reception desk has entered "Prepare for a meeting" in the morning in the past, it will suggest "Prepare for a meeting" in the morning. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI, and the generative AI can suggest the optimal input method.

[0038] The input system can filter input content based on the user's current situation and areas of interest when a task is entered. For example, the input system prioritizes tasks related to the project the user is currently working on. For instance, if the user enters "Create documents for Project A," the input system will prioritize the related task "Prepare for a meeting for Project A." The input system can also automatically suggest relevant tasks based on the user's areas of interest. For example, if the user is interested in "Marketing," the input system will suggest the related task "Marketing Research." Furthermore, the input system can also input appropriate tasks depending on the user's current situation (e.g., at work, on vacation). For example, if the user is at work, the input system will prioritize tasks related to work. This allows for the input of appropriate tasks by filtering input content based on the user's current situation and areas of interest. Some or all of the above processing in the input system may be performed using, for example, a generative AI, or not. For example, the input system can input data on the user's current situation and areas of interest into a generative AI, which can then filter the input content.

[0039] The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, the reception desk can prioritize tasks related to the user's current location. For instance, if the user is in the office, the reception desk will prioritize tasks to be performed in the office. The reception desk can also suggest relevant tasks based on the user's travel plans. For example, if the user has a business trip planned, the reception desk will suggest tasks related to the business trip. Furthermore, the reception desk can input the most suitable tasks based on the user's geographical location. For example, if the user is at home, the reception desk will prioritize tasks to be performed at home. This allows for the priority input of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then suggest highly relevant tasks.

[0040] The reception desk can analyze a user's social media activity when a task is entered and input relevant tasks. For example, the reception desk can automatically input tasks that the user has mentioned on social media. For instance, if the reception desk mentions "meeting preparation" on social media, it will automatically input "meeting preparation." The reception desk can also suggest relevant tasks based on the user's social media activity. For example, if the reception desk frequently posts about "marketing," it will suggest "marketing research." Furthermore, the reception desk can analyze the user's social media activity and input the most appropriate task. For example, if the reception desk frequently posts about "Project A," it will suggest "creating materials for Project A." In this way, relevant tasks can be entered by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input the user's social media data into a generative AI, which can then suggest relevant tasks.

[0041] The task splitting function can adjust the level of detail in task splitting based on the importance of the task. For example, it can split high-importance tasks into detailed parts and low-importance tasks into broader parts. For instance, if preparing for a meeting is a high-importance task, the splitting function will split it into detailed steps such as "creating materials," "practicing the presentation," and "preparing the meeting room." The splitting function can also split tasks with approaching deadlines into detailed parts and tasks with distant deadlines into broader parts. For example, it can split tasks with a deadline the next day into detailed parts and tasks with a deadline one week away into broad parts. Furthermore, the splitting function can adjust how tasks are split based on the user's priorities. For example, if the user wants to prioritize "preparing for the meeting," the splitting function will split it into detailed parts. This allows important tasks to be split into detailed parts by adjusting the level of detail in the splitting function based on the importance of the task. Some or all of the above processing in the splitting function may be performed using, for example, generative AI, or not. For example, the division unit can input task importance data into a generating AI, which can then adjust the level of detail in the division.

[0042] The task division unit can apply different division algorithms depending on the task category when dividing tasks. For example, the division unit can apply a detailed division algorithm to project management tasks. For instance, it can divide project management tasks into detailed steps such as "planning," "progress management," and "deliverable verification." The division unit can also apply a simplified division algorithm to daily work tasks. For example, it can divide daily work tasks into broad steps such as "checking emails" and "attending meetings." Furthermore, the division unit can apply a flexible division algorithm to creative tasks. For example, it can divide creative tasks into flexible steps such as "idea generation" and "design creation." This allows for the selection of an appropriate division method by applying different division algorithms depending on the task category. Some or all of the above processing in the division unit may be performed using, for example, a generative AI, or without a generative AI. For example, the division unit can input task category data into a generative AI, which can then apply an appropriate division algorithm.

[0043] The task splitting unit can determine the priority of task splitting based on the task submission deadline. For example, the splitting unit can prioritize splitting tasks with approaching deadlines. For example, it can prioritize splitting tasks with a deadline of the next day. The splitting unit can also postpone tasks with distant submission deadlines. For example, it can postpone tasks with a submission deadline of one week away. Furthermore, the splitting unit can adjust the order of task splitting based on the submission deadlines. For example, it can prioritize splitting tasks with approaching submission deadlines and postpone tasks with distant submission deadlines. In this way, by determining the priority of splitting based on the task submission deadlines, splitting can be performed according to the submission deadlines. Some or all of the above processing in the splitting unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the splitting unit can input task submission deadline data into a generating AI, and the generating AI can determine the priority of splitting.

[0044] The splitting unit can adjust the order of splitting tasks based on their relevance. For example, the splitting unit may prioritize splitting highly relevant tasks. For example, the splitting unit may split highly relevant tasks consecutively. The splitting unit can also postpone less relevant tasks. For example, the splitting unit may postpone less relevant tasks. Furthermore, the splitting unit can adjust the order of splitting based on the relevance of the tasks. For example, the splitting unit may prioritize splitting highly relevant tasks and postpone less relevant tasks. In this way, by adjusting the order of splitting based on the relevance of the tasks, highly relevant tasks can be prioritized for splitting. Some or all of the above processing in the splitting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the splitting unit can input task relevance data into a generative AI, and the generative AI can adjust the order of splitting.

[0045] The scheduling unit can improve scheduling accuracy by considering the interrelationships between tasks during scheduling. For example, the scheduling unit can schedule dependent tasks consecutively. For example, the scheduling unit can schedule "document creation" and "presentation practice" consecutively. The scheduling unit can also schedule related tasks at the same time. For example, the scheduling unit can schedule "meeting preparation" and "meeting room preparation" at the same time. Furthermore, the scheduling unit can set an optimal schedule by considering the interrelationships between tasks. For example, the scheduling unit can set an efficient schedule by considering the interrelationships between tasks. This improves scheduling accuracy by considering the interrelationships between tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input task interrelationship data into a generative AI, and the generative AI can adjust the schedule.

[0046] The scheduling unit can schedule tasks while considering the attribute information of the task submitter. For example, the scheduling unit can set an optimal schedule based on the submitter's position and job responsibilities. For example, if the submitter is a manager, the scheduling unit will set a schedule suitable for managerial duties. The scheduling unit can also set a schedule considering the submitter's working hours and vacation plans. For example, if the submitter has a vacation planned for the afternoon, the scheduling unit will schedule important tasks for the morning. Furthermore, the scheduling unit can set an appropriate schedule according to the submitter's skill level. For example, if the submitter is a beginner, the scheduling unit will set a schedule with ample leeway. In this way, an appropriate schedule can be set by considering the attribute information of the task submitter. Some or all of the above processing in the scheduling unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scheduling unit can input the submitter's attribute information data into the generating AI, and the generating AI can adjust the schedule.

[0047] The scheduling unit can schedule tasks while considering their geographical distribution. For example, the scheduling unit can schedule tasks that require the user to travel consecutively. For example, the scheduling unit can schedule "meeting preparation" and "meeting room preparation" consecutively. The scheduling unit can also schedule geographically close tasks at the same time. For example, the scheduling unit can schedule "document creation" and "presentation practice" at the same time. Furthermore, the scheduling unit can set an optimal schedule by considering the geographical distribution of tasks. For example, the scheduling unit can set an efficient schedule by considering the geographical distribution of tasks. This allows for the setting of an efficient schedule by considering the geographical distribution of tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input geographical distribution data of tasks into a generative AI, and the generative AI can adjust the schedule.

[0048] The scheduling unit can improve the accuracy of scheduling by referring to relevant literature for tasks during scheduling. For example, the scheduling unit can refer to relevant literature for tasks and set an optimal schedule. For example, the scheduling unit can refer to literature related to "meeting preparation" and set an efficient schedule. The scheduling unit can also adjust the schedule based on background information for tasks. For example, the scheduling unit can adjust the schedule based on background information related to "presentation practice". Furthermore, the scheduling unit can analyze relevant literature for tasks and set an efficient schedule. For example, the scheduling unit can analyze literature related to "document creation" and set an efficient schedule. In this way, the accuracy of scheduling can be improved by referring to relevant literature for tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input relevant literature data for tasks into a generative AI, and the generative AI can adjust the schedule.

[0049] The presentation unit can select the optimal presentation method when presenting tasks by referring to the user's past operation history. For example, the presentation unit may prioritize providing presentation methods that the user has preferred to use in the past. For example, if the presentation unit has preferred to use the "list format" in the past, it will prioritize providing the "list format". The presentation unit can also select the most efficient presentation method from the user's past operation history. For example, if the presentation unit has efficiently used the "calendar format" in the past, it will select the "calendar format". Furthermore, the presentation unit can analyze the user's operation history and suggest the optimal presentation method. For example, if the presentation unit has frequently used the "timeline format" in the past, it will suggest the "timeline format". In this way, the optimal presentation method can be selected by referring to the user's past operation history. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the presentation unit can input the user's operation history data into a generative AI, and the generative AI can suggest the optimal presentation method.

[0050] The presentation unit can filter the presented tasks based on the user's current situation and areas of interest. For example, the presentation unit can prioritize tasks related to the project the user is currently working on. For instance, if the user is "creating documents for Project A," the presentation unit will prioritize "preparing for a meeting for Project A." The presentation unit can also automatically present relevant tasks based on the user's areas of interest. For example, if the user is interested in "marketing," the presentation unit will present relevant "marketing research." Furthermore, the presentation unit can present appropriate tasks according to the user's current situation (e.g., at work, on vacation). For example, if the user is at work, the presentation unit will prioritize tasks related to work. This allows the presentation unit to present appropriate tasks by filtering the presented content based on the user's current situation and areas of interest. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input data on the user's current situation and areas of interest into a generative AI, which can then filter the presented content.

[0051] The presentation unit can select the optimal presentation method when presenting tasks, taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a presentation method adapted to the screen size. For example, the presentation unit can provide a "list format" optimized for the small screen of a smartphone. The presentation unit can also provide a presentation method optimized for the larger screen if the user is using a tablet. For example, the presentation unit can provide a "calendar format" optimized for the large screen of a tablet. Furthermore, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible presentation method. For example, the presentation unit can provide a "notification format" optimized for the small screen of a smartwatch. This allows the presentation unit to select the optimal presentation method by taking into account the user's device information. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input user device information data into a generative AI, which can then propose the optimal presentation method.

[0052] The presentation unit can provide multilingual presentations when presenting tasks, depending on the user's language settings. For example, the presentation unit can automatically set the task presentation language based on the user's device language settings. For example, if the user's device is set to English, the presentation unit will present the task in English. The presentation unit can also provide a language switching function if the user uses multiple languages. For example, if the user uses English and Japanese, the presentation unit will provide a language switching function. Furthermore, if the user selects a specific language, the presentation unit can present the task in that language. For example, if the user selects Japanese, the presentation unit will present the task in Japanese. By providing multilingual presentations according to the user's language settings, presentations can be made easier for the user to understand. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the user's language setting data into a generative AI, which can then provide multilingual presentations.

[0053] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0054] The reception desk can analyze a user's past task input history and suggest the optimal input method. For example, it can automatically display the content of tasks that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest the content of tasks that the user will enter at a specific time based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0055] The task splitting function can adjust the level of detail based on the importance of each task. For example, high-priority tasks can be split into more detail, while low-priority tasks can be split into broader parts. Furthermore, tasks with approaching deadlines can be split into more detail, while tasks with distant deadlines can be split into broader parts. This allows for the detailed splitting of important tasks by adjusting the level of detail based on their importance.

[0056] The scheduling unit can improve scheduling accuracy by considering the interrelationships between tasks during scheduling. For example, it can schedule dependent tasks consecutively. It can also schedule related tasks within the same time frame. In this way, the accuracy of scheduling can be improved by considering the interrelationships between tasks.

[0057] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Similarly, if the user is using a tablet, it can provide a display method optimized for the larger screen. This allows the system to select the most suitable display method by considering the user's device information.

[0058] The reception system can filter input content based on the user's current situation and areas of interest when they enter a task. For example, it can prioritize tasks related to the project the user is currently working on. It can also automatically suggest relevant tasks based on the user's areas of interest. This allows users to enter appropriate tasks by filtering input content based on their current situation and areas of interest.

[0059] The following briefly describes the processing flow for example form 1.

[0060] Step 1: The reception desk receives input from users regarding the task content, required time, and deadline. For example, if a user inputs "It will take 2 hours to prepare for tomorrow's meeting" via voice or text, the reception desk will receive this information. Similarly, if the user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception desk will also receive this information. Step 2: The division unit divides the tasks into manageable granularities based on the information received by the reception unit. For example, using a generation AI, meeting preparations can be divided into smaller tasks such as "creating materials," "practicing the presentation," and "preparing the meeting room." The granularity of the division can also be adjusted based on the content and time required for each task. Step 3: The scheduling unit automatically schedules the tasks divided by the division unit. For example, it can use a generation AI to schedule meeting preparations to take place between 9:00 AM and 11:00 AM. It can also adjust the schedule based on task priority and deadlines. Step 4: The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and presents the user with what they should do now. For example, it activates the timer and notifies the user, "Please start preparing for the meeting at 9:00 AM." It also adjusts the timer according to the progress of the tasks and notifies the user to start the next task earlier if the task is completed ahead of schedule.

[0061] (Example of form 2) The task scheduling system according to an embodiment of the present invention is a system that automates task scheduling using generative AI. This task scheduling system is particularly effective for people who tend to procrastinate or who have characteristics of ADHD. The task scheduling system accepts input from the user via voice or text, including the task content, required time, and deadline. The task scheduling system analyzes this information, divides the task into manageable granularities, and automatically schedules it. A timer activates based on the schedule, indicating to the user what needs to be done now. Once a task is completed, the task scheduling system reschedules and adjusts the schedule. For example, the task scheduling system accepts input from the user via voice or text, including the task content, required time, and deadline. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting," the task scheduling system analyzes this information and divides the task into manageable granularities. Next, the task scheduling system automatically schedules the task using generative AI. For example, the task scheduling system sets a schedule to prepare for the meeting in the morning. Furthermore, the task scheduling system activates a timer based on the schedule, indicating to the user what needs to be done now. For example, the task scheduling system might notify the user, "Please begin preparing for the meeting at 9:00 AM." Once the task is completed, the task scheduling system reschedules and adjusts the schedule. For instance, if the meeting preparation is completed earlier than planned, the task scheduling system adjusts the schedule to allow the next task to start earlier. This allows the task scheduling system to prevent users from procrastinating and to complete tasks efficiently. In this way, the task scheduling system can efficiently divide, schedule, and present the user's tasks.

[0062] The task scheduling system according to this embodiment comprises a reception unit, a division unit, a scheduling unit, and a presentation unit. The reception unit receives input from the user regarding the content of a task, the required time, and the deadline. The reception unit accepts, for example, the user to input the content of a task by voice. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting" by voice, the reception unit accepts this information. The reception unit also accepts the user to input the content of a task by text. For example, if the user inputs "It will take 2 hours to prepare for tomorrow's meeting" by text, the reception unit accepts this information. Furthermore, the reception unit accepts the user to input the required time and the deadline for the task. For example, if the user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception unit accepts this information. The division unit divides the task into manageable granularity based on the information received by the reception unit. The division unit divides the task using, for example, a generation AI. For example, the division unit divides meeting preparation into smaller tasks such as "creating materials," "practicing the presentation," and "preparing the meeting room." Furthermore, the division unit can adjust the granularity of the division based on the content and duration of the tasks. For example, the division unit can divide long tasks into smaller parts. The scheduling unit automatically schedules the tasks divided by the division unit. The scheduling unit can schedule tasks using, for example, generative AI. For example, the scheduling unit can set a schedule to complete meeting preparations between 9:00 AM and 11:00 AM. The scheduling unit can also adjust the schedule based on task priority and deadlines. For example, the scheduling unit can prioritize scheduling tasks with approaching deadlines. The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and shows the user what they should do now. The presentation unit can, for example, activate a timer and notify the user. For example, the presentation unit might notify the user, "Please start preparing for the meeting at 9:00 AM." The presentation unit can also adjust the timer according to the progress of the tasks. For example, if a task is completed earlier than planned, the presentation unit might notify the user to start the next task earlier.As a result, the task scheduling system according to the embodiment can efficiently divide, schedule, and present tasks to the user.

[0063] The reception desk accepts input from users regarding the task content, required time, and deadline. For example, the reception desk accepts task content input by voice. Specifically, it uses speech recognition technology to convert the user's voice input into text data, accurately understanding the task content. For example, if a user voice-inputs "It will take 2 hours to prepare for tomorrow's meeting," the speech recognition technology analyzes this voice and converts it into text data: "It will take 2 hours to prepare for tomorrow's meeting." The reception desk also accepts task content input by text. For example, if a user texts "It will take 2 hours to prepare for tomorrow's meeting," the reception desk accepts this information. Furthermore, the reception desk accepts input from users regarding the required time and deadline. For example, if a user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception desk accepts this information. The reception desk centrally manages this input information and stores it as data necessary for subsequent processing. This allows the reception desk to accommodate diverse user input methods and collect task information accurately and efficiently. Furthermore, the reception unit also has a function to analyze user input and automatically evaluate the importance and urgency of tasks. For example, it can automatically set task priorities based on the task content and deadline, and reflect this in subsequent processing. In this way, the reception unit streamlines user task management and enables smooth task scheduling.

[0064] The task division unit divides tasks into manageable granularity based on information received by the reception unit. For example, the division unit uses generative AI to divide tasks. Specifically, it analyzes the task content using natural language processing technology and divides it into appropriate subtasks. For instance, the division unit divides meeting preparation into smaller tasks such as "document creation," "presentation practice," and "meeting room preparation." The division unit can also adjust the granularity of the division based on the task content and required time. For example, it can break down long tasks into smaller, more manageable subtasks to make them easier for users to complete. Furthermore, the division unit considers task dependencies. For instance, if presentation practice cannot begin until document creation is complete, it will divide the task so that document creation is done first. This allows the division unit to support users in efficiently completing tasks. The division unit also has a function to learn the optimal division method by referencing the user's past task history and performance data. This enables the division unit to achieve flexible task division tailored to the user's work style and habits, improving the efficiency of task management.

[0065] The scheduling unit automatically schedules tasks divided by the division unit. The scheduling unit uses, for example, generation AI to schedule tasks. Specifically, it generates an optimal schedule considering the user's schedule, task priorities, and deadlines. For example, the scheduling unit might schedule meeting preparations to be completed between 9:00 AM and 11:00 AM. The scheduling unit can also adjust the schedule based on task priorities and deadlines. For example, it might prioritize tasks with approaching deadlines and set high-priority tasks to be completed first. Furthermore, the scheduling unit has the ability to learn the optimal scheduling method by referencing the user's past scheduling history and performance data. This allows the scheduling unit to achieve flexible scheduling tailored to the user's work style and habits, improving the efficiency of task management. The scheduling unit can also update the schedule in real time and respond flexibly to the user's situation. For example, if the user makes a sudden change of plans or adds a new task, the scheduling unit immediately readjusts the schedule and presents the optimal schedule. This allows the scheduling unit to streamline the user's task management and support smooth task execution.

[0066] The notification unit activates a timer based on tasks scheduled by the scheduling unit, informing the user of what they should do now. For example, the notification unit activates the timer and notifies the user. Specifically, it sends a notification to the user's device informing them of the task's start and end times. For example, the notification unit might notify the user, "Please start preparing for the meeting at 9:00 AM." The notification unit can also adjust the timer according to the progress of the task. For example, if a task is completed earlier than planned, it will notify the user to start the next task earlier. In this way, the notification unit supports the user in efficiently completing tasks. Furthermore, the notification unit can collect user feedback and continuously improve the accuracy and effectiveness of its notifications. For example, it can analyze user behavior data after receiving a notification and optimize the timing and content of notifications. The notification unit can also reliably transmit information using multiple notification methods. For example, it can reliably deliver important information by using not only smartphone notifications but also voice calls, SMS, and email. In this way, the notification unit can provide users with quick and reliable instructions, improving the efficiency of task management.

[0067] The scheduling unit can reschedule tasks once they are completed. For example, when the scheduling unit detects that a task has been completed, it schedules the next task. For example, if the scheduling unit completes preparations for a meeting earlier than planned, it adjusts the schedule to start the next task earlier. The scheduling unit can also adjust the schedule according to the progress of tasks. For example, if a task is behind schedule, the scheduling unit adjusts the schedules of other tasks to make up for the delay. This allows for schedule adjustments by rescheduling after a task is completed. Some or all of the above processes in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input the progress of tasks into a generative AI, which can then readjust the schedule.

[0068] The task division unit can be used by a generating AI to counsel the user and divide the task into manageable granularities. For example, the task division unit can interact with the user using a generating AI and divide the task. For instance, if the user inputs "Preparing for the meeting is difficult," the generating AI will ask, "Specifically, which part is difficult?" and divide the task based on the user's answer. The task division unit can also divide the task considering the user's situation and emotions. For example, if the user is feeling stressed, the task division unit will break it down into smaller, manageable parts. This allows the generating AI to counsel the user and divide the task into manageable granularities. Some or all of the above-described processes in the task division unit may be performed using a generating AI or not. For example, the task division unit can pass user input to a generating AI, which can then perform the task division.

[0069] The presentation unit can activate a timer based on a schedule. For example, the presentation unit can set a timer based on a schedule and notify the user. For example, the presentation unit can notify the user, "Please start preparing for the meeting at 9:00 AM." The presentation unit can also adjust the timer according to the progress of tasks. For example, if a task is completed earlier than scheduled, the presentation unit can notify the user to start the next task earlier. In this way, by activating a timer based on a schedule, the presentation unit can show the user what they should be doing now. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input schedule information into a generative AI, and the generative AI can set a timer.

[0070] The reception desk can estimate the user's emotions and adjust the task input method based on the estimated emotions. For example, the reception desk can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input, allowing for quick input of task details, duration, and deadline. This reduces the user's burden by adjusting the task input method according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using or without a generative AI. For example, the reception desk can input user facial data into a generative AI, which can then estimate emotions.

[0071] The reception desk can analyze the user's past task input history and suggest the optimal input method. For example, the reception desk can automatically display the content of tasks that the user has frequently entered in the past as suggestions. For example, if the reception desk has frequently entered "Prepare for a meeting" in the past, it will display "Prepare for a meeting" as a suggestion. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. Furthermore, the reception desk can predict and suggest the content of tasks to be entered at a specific time of day based on the user's past input history. For example, if the reception desk has entered "Prepare for a meeting" in the morning in the past, it will suggest "Prepare for a meeting" in the morning. In this way, by analyzing the user's past input history, the reception desk can suggest the optimal input method. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or not using a generative AI. For example, the reception desk can input the user's past input history data into a generative AI, and the generative AI can suggest the optimal input method.

[0072] The input system can filter input content based on the user's current situation and areas of interest when a task is entered. For example, the input system prioritizes tasks related to the project the user is currently working on. For instance, if the user enters "Create documents for Project A," the input system will prioritize the related task "Prepare for a meeting for Project A." The input system can also automatically suggest relevant tasks based on the user's areas of interest. For example, if the user is interested in "Marketing," the input system will suggest the related task "Marketing Research." Furthermore, the input system can also input appropriate tasks depending on the user's current situation (e.g., at work, on vacation). For example, if the user is at work, the input system will prioritize tasks related to work. This allows for the input of appropriate tasks by filtering input content based on the user's current situation and areas of interest. Some or all of the above processing in the input system may be performed using, for example, a generative AI, or not. For example, the input system can input data on the user's current situation and areas of interest into a generative AI, which can then filter the input content.

[0073] The reception desk can estimate the user's emotions and determine the priority of input tasks based on the estimated emotions. For example, the reception desk can estimate emotions by analyzing the user's facial expressions and voice. For example, if the reception desk is stressed, it may postpone less important tasks. Conversely, if the user is relaxed, it may prioritize high-importance tasks. Furthermore, if the user is in a hurry, it may prioritize tasks with approaching deadlines. In this way, by determining task priorities according to the user's emotions, important tasks can be processed preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using a generative AI, or not using a generative AI. For example, the reception desk can input user facial expression data into a generative AI, which can estimate emotions.

[0074] The reception desk can prioritize the input of highly relevant tasks by considering the user's geographical location when tasks are entered. For example, the reception desk can prioritize tasks related to the user's current location. For instance, if the user is in the office, the reception desk will prioritize tasks to be performed in the office. The reception desk can also suggest relevant tasks based on the user's travel plans. For example, if the user has a business trip planned, the reception desk will suggest tasks related to the business trip. Furthermore, the reception desk can input the most suitable tasks based on the user's geographical location. For example, if the user is at home, the reception desk will prioritize tasks to be performed at home. This allows for the priority input of highly relevant tasks by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using, for example, a generative AI, or without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then suggest highly relevant tasks.

[0075] The reception desk can analyze a user's social media activity when a task is entered and input relevant tasks. For example, the reception desk can automatically input tasks that the user has mentioned on social media. For instance, if the reception desk mentions "meeting preparation" on social media, it will automatically input "meeting preparation." The reception desk can also suggest relevant tasks based on the user's social media activity. For example, if the reception desk frequently posts about "marketing," it will suggest "marketing research." Furthermore, the reception desk can analyze the user's social media activity and input the most appropriate task. For example, if the reception desk frequently posts about "Project A," it will suggest "creating materials for Project A." In this way, relevant tasks can be entered by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk can input the user's social media data into a generative AI, which can then suggest relevant tasks.

[0076] The task division unit can estimate the user's emotions and adjust the task division method based on the estimated user emotions. For example, the task division unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the task division unit can divide the task into smaller steps. Conversely, if the user is relaxed, the task division unit can also divide the task into larger steps. Furthermore, if the user is in a hurry, the task division unit can divide the task to allow for quick completion. This allows for efficient task division by adjusting the task division method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 task division unit may be performed using a generative AI, or not. For example, the task division unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0077] The task splitting function can adjust the level of detail in task splitting based on the importance of the task. For example, it can split high-importance tasks into detailed parts and low-importance tasks into broader parts. For instance, if preparing for a meeting is a high-importance task, the splitting function will split it into detailed steps such as "creating materials," "practicing the presentation," and "preparing the meeting room." The splitting function can also split tasks with approaching deadlines into detailed parts and tasks with distant deadlines into broader parts. For example, it can split tasks with a deadline the next day into detailed parts and tasks with a deadline one week away into broad parts. Furthermore, the splitting function can adjust how tasks are split based on the user's priorities. For example, if the user wants to prioritize "preparing for the meeting," the splitting function will split it into detailed parts. This allows important tasks to be split into detailed parts by adjusting the level of detail in the splitting function based on the importance of the task. Some or all of the above processing in the splitting function may be performed using, for example, generative AI, or not. For example, the division unit can input task importance data into a generating AI, which can then adjust the level of detail in the division.

[0078] The task division unit can apply different division algorithms depending on the task category when dividing tasks. For example, the division unit can apply a detailed division algorithm to project management tasks. For instance, it can divide project management tasks into detailed steps such as "planning," "progress management," and "deliverable verification." The division unit can also apply a simplified division algorithm to daily work tasks. For example, it can divide daily work tasks into broad steps such as "checking emails" and "attending meetings." Furthermore, the division unit can apply a flexible division algorithm to creative tasks. For example, it can divide creative tasks into flexible steps such as "idea generation" and "design creation." This allows for the selection of an appropriate division method by applying different division algorithms depending on the task category. Some or all of the above processing in the division unit may be performed using, for example, a generative AI, or without a generative AI. For example, the division unit can input task category data into a generative AI, which can then apply an appropriate division algorithm.

[0079] The task division unit can estimate the user's emotions and adjust the length of task divisions based on the estimated emotions. For example, the task division unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the task division unit can divide the task into shorter tasks. If the user is relaxed, the task division unit can also divide the task into longer tasks. Furthermore, if the user is in a hurry, the task division unit can divide the task into shorter tasks that can be completed quickly. This allows for efficient task division by adjusting the length of task divisions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task division unit may be performed using a generative AI, or not. For example, the task division unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0080] The task splitting unit can determine the priority of task splitting based on the task submission deadline. For example, the splitting unit can prioritize splitting tasks with approaching deadlines. For example, it can prioritize splitting tasks with a deadline of the next day. The splitting unit can also postpone tasks with distant submission deadlines. For example, it can postpone tasks with a submission deadline of one week away. Furthermore, the splitting unit can adjust the order of task splitting based on the submission deadlines. For example, it can prioritize splitting tasks with approaching submission deadlines and postpone tasks with distant submission deadlines. In this way, by determining the priority of splitting based on the task submission deadlines, splitting can be performed according to the submission deadlines. Some or all of the above processing in the splitting unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the splitting unit can input task submission deadline data into a generating AI, and the generating AI can determine the priority of splitting.

[0081] The splitting unit can adjust the order of splitting tasks based on their relevance. For example, the splitting unit may prioritize splitting highly relevant tasks. For example, the splitting unit may split highly relevant tasks consecutively. The splitting unit can also postpone less relevant tasks. For example, the splitting unit may postpone less relevant tasks. Furthermore, the splitting unit can adjust the order of splitting based on the relevance of the tasks. For example, the splitting unit may prioritize splitting highly relevant tasks and postpone less relevant tasks. In this way, by adjusting the order of splitting based on the relevance of the tasks, highly relevant tasks can be prioritized for splitting. Some or all of the above processing in the splitting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the splitting unit can input task relevance data into a generative AI, and the generative AI can adjust the order of splitting.

[0082] The scheduling unit can estimate the user's emotions and adjust scheduling criteria based on the estimated emotions. For example, the scheduling unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the scheduling unit can set a schedule with ample leeway. The scheduling unit can also set an efficient schedule if the user is relaxed. Furthermore, if the user is in a hurry, the scheduling unit can set a schedule that can be completed quickly. In this way, an appropriate schedule can be set by adjusting the scheduling criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using a generative AI, or not using a generative AI. For example, the scheduling unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0083] The scheduling unit can improve scheduling accuracy by considering the interrelationships between tasks during scheduling. For example, the scheduling unit can schedule dependent tasks consecutively. For example, the scheduling unit can schedule "document creation" and "presentation practice" consecutively. The scheduling unit can also schedule related tasks at the same time. For example, the scheduling unit can schedule "meeting preparation" and "meeting room preparation" at the same time. Furthermore, the scheduling unit can set an optimal schedule by considering the interrelationships between tasks. For example, the scheduling unit can set an efficient schedule by considering the interrelationships between tasks. This improves scheduling accuracy by considering the interrelationships between tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input task interrelationship data into a generative AI, and the generative AI can adjust the schedule.

[0084] The scheduling unit can schedule tasks while considering the attribute information of the task submitter. For example, the scheduling unit can set an optimal schedule based on the submitter's position and job responsibilities. For example, if the submitter is a manager, the scheduling unit will set a schedule suitable for managerial duties. The scheduling unit can also set a schedule considering the submitter's working hours and vacation plans. For example, if the submitter has a vacation planned for the afternoon, the scheduling unit will schedule important tasks for the morning. Furthermore, the scheduling unit can set an appropriate schedule according to the submitter's skill level. For example, if the submitter is a beginner, the scheduling unit will set a schedule with ample leeway. In this way, an appropriate schedule can be set by considering the attribute information of the task submitter. Some or all of the above processing in the scheduling unit may be performed using, for example, a generating AI, or not using a generating AI. For example, the scheduling unit can input the submitter's attribute information data into the generating AI, and the generating AI can adjust the schedule.

[0085] The scheduling unit can estimate the user's emotions and adjust the order in which scheduling results are displayed based on the estimated emotions. For example, the scheduling unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the scheduling unit may postpone less important tasks. Also, if the user is relaxed, the scheduling unit may prioritize displaying more important tasks. Furthermore, if the user is in a hurry, the scheduling unit may prioritize displaying tasks with approaching deadlines. In this way, important tasks can be prioritized by adjusting the order in which scheduling results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using a generative AI, or not. For example, the scheduling unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0086] The scheduling unit can schedule tasks while considering their geographical distribution. For example, the scheduling unit can schedule tasks that require the user to travel consecutively. For example, the scheduling unit can schedule "meeting preparation" and "meeting room preparation" consecutively. The scheduling unit can also schedule geographically close tasks at the same time. For example, the scheduling unit can schedule "document creation" and "presentation practice" at the same time. Furthermore, the scheduling unit can set an optimal schedule by considering the geographical distribution of tasks. For example, the scheduling unit can set an efficient schedule by considering the geographical distribution of tasks. This allows for the setting of an efficient schedule by considering the geographical distribution of tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input geographical distribution data of tasks into a generative AI, and the generative AI can adjust the schedule.

[0087] The scheduling unit can improve the accuracy of scheduling by referring to relevant literature for tasks during scheduling. For example, the scheduling unit can refer to relevant literature for tasks and set an optimal schedule. For example, the scheduling unit can refer to literature related to "meeting preparation" and set an efficient schedule. The scheduling unit can also adjust the schedule based on background information for tasks. For example, the scheduling unit can adjust the schedule based on background information related to "presentation practice". Furthermore, the scheduling unit can analyze relevant literature for tasks and set an efficient schedule. For example, the scheduling unit can analyze literature related to "document creation" and set an efficient schedule. In this way, the accuracy of scheduling can be improved by referring to relevant literature for tasks. Some or all of the above processing in the scheduling unit may be performed using, for example, a generative AI, or without a generative AI. For example, the scheduling unit can input relevant literature data for tasks into a generative AI, and the generative AI can adjust the schedule.

[0088] The presentation unit can estimate the user's emotions and adjust the task presentation method based on the estimated user emotions. For example, the presentation unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the presentation unit can provide a simple and highly visible presentation method. If the user is relaxed, the presentation unit can also provide a presentation method that includes detailed information. Furthermore, if the user is in a hurry, the presentation unit can provide a concise presentation method. In this way, by adjusting the task presentation method according to the user's emotions, the optimal presentation method can be provided to the user. 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 a generative AI, or not using a generative AI. For example, the presentation unit can input user facial expression data into a generative AI, and the generative AI can estimate emotions.

[0089] The presentation unit can select the optimal presentation method when presenting tasks by referring to the user's past operation history. For example, the presentation unit may prioritize providing presentation methods that the user has preferred to use in the past. For example, if the presentation unit has preferred to use the "list format" in the past, it will prioritize providing the "list format". The presentation unit can also select the most efficient presentation method from the user's past operation history. For example, if the presentation unit has efficiently used the "calendar format" in the past, it will select the "calendar format". Furthermore, the presentation unit can analyze the user's operation history and suggest the optimal presentation method. For example, if the presentation unit has frequently used the "timeline format" in the past, it will suggest the "timeline format". In this way, the optimal presentation method can be selected by referring to the user's past operation history. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the presentation unit can input the user's operation history data into a generative AI, and the generative AI can suggest the optimal presentation method.

[0090] The presentation unit can filter the presented tasks based on the user's current situation and areas of interest. For example, the presentation unit can prioritize tasks related to the project the user is currently working on. For instance, if the user is "creating documents for Project A," the presentation unit will prioritize "preparing for a meeting for Project A." The presentation unit can also automatically present relevant tasks based on the user's areas of interest. For example, if the user is interested in "marketing," the presentation unit will present relevant "marketing research." Furthermore, the presentation unit can present appropriate tasks according to the user's current situation (e.g., at work, on vacation). For example, if the user is at work, the presentation unit will prioritize tasks related to work. This allows the presentation unit to present appropriate tasks by filtering the presented content based on the user's current situation and areas of interest. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input data on the user's current situation and areas of interest into a generative AI, which can then filter the presented content.

[0091] The presentation unit can estimate the user's emotions and determine the priority of task presentations based on the estimated emotions. For example, the presentation unit can estimate emotions by analyzing the user's facial expressions and voice. For example, if the user is stressed, the presentation unit will postpone less important tasks. Conversely, if the user is relaxed, the presentation unit can prioritize presenting more important tasks. Furthermore, if the user is in a hurry, the presentation unit can prioritize presenting tasks with approaching deadlines. In this way, by determining the priority of task presentations according to the user's emotions, important tasks can be presented preferentially. 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 a generative AI, or not using a generative AI. For example, the presentation unit can input user facial expression data into a generative AI, which can then estimate emotions.

[0092] The presentation unit can select the optimal presentation method when presenting tasks, taking into account the user's device information. For example, if the user is using a smartphone, the presentation unit can provide a presentation method adapted to the screen size. For example, the presentation unit can provide a "list format" optimized for the small screen of a smartphone. The presentation unit can also provide a presentation method optimized for the larger screen if the user is using a tablet. For example, the presentation unit can provide a "calendar format" optimized for the large screen of a tablet. Furthermore, if the user is using a smartwatch, the presentation unit can provide a concise and highly visible presentation method. For example, the presentation unit can provide a "notification format" optimized for the small screen of a smartwatch. This allows the presentation unit to select the optimal presentation method by taking into account the user's device information. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input user device information data into a generative AI, which can then propose the optimal presentation method.

[0093] The presentation unit can provide multilingual presentations when presenting tasks, depending on the user's language settings. For example, the presentation unit can automatically set the task presentation language based on the user's device language settings. For example, if the user's device is set to English, the presentation unit will present the task in English. The presentation unit can also provide a language switching function if the user uses multiple languages. For example, if the user uses English and Japanese, the presentation unit will provide a language switching function. Furthermore, if the user selects a specific language, the presentation unit can present the task in that language. For example, if the user selects Japanese, the presentation unit will present the task in Japanese. By providing multilingual presentations according to the user's language settings, presentations can be made easier for the user to understand. Some or all of the above processing in the presentation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the presentation unit can input the user's language setting data into a generative AI, which can then provide multilingual presentations.

[0094] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0095] The reception desk can analyze a user's past task input history and suggest the optimal input method. For example, it can automatically display the content of tasks that the user has frequently entered in the past as suggestions. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest the content of tasks that the user will enter at a specific time based on their past input history. In this way, by analyzing the user's past input history, the system can suggest the optimal input method.

[0096] The task splitting function can adjust the level of detail based on the importance of each task. For example, high-priority tasks can be split into more detail, while low-priority tasks can be split into broader parts. Furthermore, tasks with approaching deadlines can be split into more detail, while tasks with distant deadlines can be split into broader parts. This allows for the detailed splitting of important tasks by adjusting the level of detail based on their importance.

[0097] The scheduling unit can improve scheduling accuracy by considering the interrelationships between tasks during scheduling. For example, it can schedule dependent tasks consecutively. It can also schedule related tasks within the same time frame. In this way, the accuracy of scheduling can be improved by considering the interrelationships between tasks.

[0098] The display unit can select the optimal display method by considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. Similarly, if the user is using a tablet, it can provide a display method optimized for the larger screen. This allows the system to select the most suitable display method by considering the user's device information.

[0099] The reception desk can estimate the user's emotions and adjust the task input method based on that estimation. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. Conversely, if the user is relaxed, it can provide more detailed input options. This reduces the user's burden by adjusting the task input method according to their emotions.

[0100] The task division function can estimate the user's emotions and adjust the task division method based on those emotions. For example, if the user is stressed, the task can be divided into smaller steps. Conversely, if the user is relaxed, the task can be divided into larger steps. This allows for efficient task division by adjusting the task division method according to the user's emotions.

[0101] The scheduling unit can estimate the user's emotions and adjust the scheduling criteria based on those emotions. For example, if the user is feeling stressed, it can set a schedule with more leeway. Conversely, if the user is relaxed, it can set a more efficient schedule. In this way, by adjusting the scheduling criteria according to the user's emotions, an appropriate schedule can be set.

[0102] The presentation unit can estimate the user's emotions and adjust the task presentation method based on the estimated emotions. For example, if the user is stressed, it can provide a simple and highly visible presentation method. Conversely, if the user is relaxed, it can provide a presentation method that includes detailed information. By adjusting the task presentation method according to the user's emotions, the system can provide the optimal presentation method for the user.

[0103] The presentation unit can estimate the user's emotions and determine the priority of task presentations based on those emotions. For example, if the user is stressed, it can postpone less important tasks. Conversely, if the user is relaxed, it can prioritize presenting more important tasks. In this way, by prioritizing task presentations according to the user's emotions, important tasks can be presented preferentially.

[0104] The reception system can filter input content based on the user's current situation and areas of interest when they enter a task. For example, it can prioritize tasks related to the project the user is currently working on. It can also automatically suggest relevant tasks based on the user's areas of interest. This allows users to enter appropriate tasks by filtering input content based on their current situation and areas of interest.

[0105] The following briefly describes the processing flow for example form 2.

[0106] Step 1: The reception desk receives input from users regarding the task content, required time, and deadline. For example, if a user inputs "It will take 2 hours to prepare for tomorrow's meeting" via voice or text, the reception desk will receive this information. Similarly, if the user inputs "I want to finish preparing for the meeting by tomorrow morning," the reception desk will also receive this information. Step 2: The division unit divides the tasks into manageable granularities based on the information received by the reception unit. For example, using a generation AI, meeting preparations can be divided into smaller tasks such as "creating materials," "practicing the presentation," and "preparing the meeting room." The granularity of the division can also be adjusted based on the content and time required for each task. Step 3: The scheduling unit automatically schedules the tasks divided by the division unit. For example, it can use a generation AI to schedule meeting preparations to take place between 9:00 AM and 11:00 AM. It can also adjust the schedule based on task priority and deadlines. Step 4: The presentation unit activates a timer based on the tasks scheduled by the scheduling unit and presents the user with what they should do now. For example, it activates the timer and notifies the user, "Please start preparing for the meeting at 9:00 AM." It also adjusts the timer according to the progress of the tasks and notifies the user to start the next task earlier if the task is completed ahead of schedule.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, division unit, scheduling unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives voice and text input from the user. The division unit is implemented by the specific processing unit 290 of the data processing unit 12 and divides tasks using a generation AI. The scheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically schedules the divided tasks. The presentation unit is implemented by the control unit 46A of the smart device 14 and notifies the user by activating a timer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, division unit, scheduling unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives voice and text input from the user. The division unit is implemented by the specific processing unit 290 of the data processing unit 12 and divides tasks using generating AI. The scheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically schedules the divided tasks. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and notifies the user by activating a timer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0127] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, division unit, scheduling unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives voice and text input from the user. The division unit is implemented by the specific processing unit 290 of the data processing unit 12 and divides tasks using a generation AI. The scheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically schedules the divided tasks. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and notifies the user by activating a timer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] Each of the multiple elements described above, including the reception unit, division unit, scheduling unit, and presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives voice and text input from the user. The division unit is implemented by the specific processing unit 290 of the data processing unit 12 and divides tasks using a generation AI. The scheduling unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically schedules the divided tasks. The presentation unit is implemented by the control unit 46A of the robot 414 and notifies the user by activating a timer. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] (Note 1) A reception desk that accepts input from users regarding the task details, required time, and deadline, A division unit divides the task into manageable granularities based on the information received by the reception unit, A scheduling unit that automatically schedules the tasks divided by the division unit, The system includes a presentation unit that activates a timer based on the tasks scheduled by the scheduling unit and presents the user with what they should do now. A system characterized by the following features. (Note 2) The aforementioned scheduling unit, Once the task is complete, reschedule it. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned divided portion is The generating AI counsels and breaks down tasks into manageable granularities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is, Activate the timer based on the schedule. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the task input method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It analyzes the user's past task input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering a task, the input content is filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the input tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned divided portion is It estimates the user's emotions and adjusts the task division method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned divided portion is When splitting tasks, adjust the level of detail in the splits based on the importance of each task. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned divided portion is When splitting tasks, apply different splitting algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned divided portion is It estimates the user's emotions and adjusts the length of task divisions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned divided portion is When splitting tasks, prioritize the splits based on the task submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned divided portion is When splitting tasks, adjust the order of splitting based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned scheduling unit, It estimates the user's emotions and adjusts scheduling criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned scheduling unit, When scheduling, consider the interrelationships between tasks to improve scheduling accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned scheduling unit, When scheduling, the attribute information of the task submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned scheduling unit, It estimates the user's emotions and adjusts the order in which scheduling results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned scheduling unit, When scheduling, consider the geographical distribution of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned scheduling unit, When scheduling, refer to relevant literature for tasks to improve scheduling accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is, It estimates the user's emotions and adjusts how tasks are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is, When presenting tasks, the system selects the optimal presentation method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, When presenting tasks, the suggestions are filtered based on the user's current situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, The system estimates the user's emotions and prioritizes the presentation of tasks based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting tasks, the optimal presentation method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, When presenting tasks, provide multilingual support based on the user's language settings. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0179] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts input from users regarding the task details, required time, and deadline, A division unit divides the task into manageable granularities based on the information received by the reception unit, A scheduling unit that automatically schedules the tasks divided by the division unit, The system includes a presentation unit that activates a timer based on the tasks scheduled by the scheduling unit and presents the user with what they should do now. A system characterized by the following features.

2. The aforementioned scheduling unit, Once the task is complete, reschedule it. The system according to feature 1.

3. The aforementioned divided portion is The generating AI provides counseling and breaks down the task into manageable granularities. The system according to feature 1.

4. The aforementioned display unit is, Activate the timer based on the schedule. The system according to feature 1.

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

6. The aforementioned reception unit is It analyzes the user's past task input history and suggests the optimal input method. The system according to feature 1.

7. The aforementioned reception unit is When entering a task, the input content is filtered based on the user's current situation and areas of interest. The system according to feature 1.

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

9. The aforementioned reception unit is When entering tasks, the system prioritizes tasks that are highly relevant to the user, taking into account the user's geographical location. The system according to feature 1.

10. The aforementioned reception unit is When entering tasks, the system analyzes the user's social media activity and enters relevant tasks. The system according to feature 1.

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