system

Abstract

The system according to this embodiment aims to efficiently manage and schedule project tasks. [Solution] The system according to the embodiment comprises a reception unit, a sorting unit, a scheduling unit, a response unit, a checkpoint sorting unit, a correction unit, a repeating unit, and an output unit. The reception unit receives input of the project content and completion deadline. The sorting unit sorts the tasks necessary until the output based on the information received by the reception unit. The scheduling unit schedules the tasks sorted by the sorting unit. The response unit handles the tasks scheduled by the scheduling unit. The checkpoint sorting unit sorts the points that the user should check for the tasks handled by the response unit. The correction unit corrects the tasks after the user has checked them based on the points sorted by the checkpoint sorting unit. The repeating unit repeats the tasks corrected by the correction unit until the user is satisfied. The output unit finally outputs the tasks that have been repeated by the repeating unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that task management and scheduling of a project are time-consuming and difficult to proceed efficiently.

[0005] The system according to the embodiment aims to efficiently perform task management and scheduling of a project.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a sorting unit, a scheduling unit, a response unit, a checkpoint sorting unit, a correction unit, a repeating unit, and an output unit. The reception unit receives input of the project details and completion deadline. The sorting unit sorts the tasks necessary to produce the output based on the information received by the reception unit. The scheduling unit schedules the tasks sorted by the sorting unit. The response unit handles the tasks scheduled by the scheduling unit. The checkpoint sorting unit sorts the points that the user should check for the tasks handled by the response unit. The correction unit corrects the tasks after the user has checked them based on the points sorted by the checkpoint sorting unit. The repeating unit repeats the tasks corrected by the correction unit until the user is satisfied. The output unit finally outputs the tasks that have been repeated by the repeating unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently manage and schedule project tasks. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (e.g., a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management and calendar service system according to an embodiment of the present invention is a system for quickly guiding projects to output. With this system, the user simply registers the project details and completion deadline, and the generating AI handles the following seven tasks: 1. Organizing the tasks necessary to reach the output. 2. Scheduling tasks. 3. Handling various tasks. 4. Organizing the points that the user should check for each task. 5. Correcting each task after the user's check. 6. Repeating steps 4 and 5 until satisfactory. 7. Output. As a result, the generating AI takes over tasks that were traditionally mainly performed by the user, and the user takes over the parts that the generating AI previously assisted with, enabling rapid output by the user simply "checking and providing feedback." For example, the user registers the project details and completion deadline. For example, they might register something like, "Complete a marketing plan for a new product within two weeks." This information is input into the generating AI. Next, the generating AI organizes the tasks necessary to reach the output based on the project details. For example, it identifies tasks such as research, document creation, and presentation preparation necessary for creating the marketing plan. The generating AI then schedules each task. For example, it might create a schedule where research is conducted for the first three days, materials are created for the next three days, and the remaining time is used to prepare the presentation. The generating AI also handles each task. For example, it automatically collects information for research and organizes data for material creation. Furthermore, the generating AI organizes the points that the user should check for each task. For example, it clarifies the points that the user should check, such as the content of the materials or the design of the presentation slides. After the user has checked, the generating AI makes revisions to each task. For example, it might revise the content of the materials or redesign the presentation slides based on the user's feedback. This process is repeated until the user is satisfied, ultimately completing the output. For example, it will revise the materials or adjust the presentation until the user is satisfied. This system allows users to achieve rapid output simply by "checking and giving feedback."Because the generation AI handles the majority of the tasks, the user's burden is reduced, and the project progresses smoothly. This allows the task management and calendar service system to achieve rapid project output.

[0029] The task management and calendar service system according to this embodiment comprises a reception unit, an organization unit, a scheduling unit, a response unit, a checkpoint organization unit, a correction unit, a repeating unit, and an output unit. The reception unit receives input of project details and completion deadlines. The reception unit provides, for example, an interface for the user to input project details and completion deadlines. For example, the reception unit provides text boxes or drop-down menus for the user to input project details. The reception unit may also provide a calendar or date selection tool for the user to input completion deadlines. For example, when the user inputs project details, the reception unit provides fields for the user to input detailed information such as the project's purpose, scope, and requirements. Furthermore, the reception unit may also provide a calendar widget for the user to select a specific date and time when entering completion deadlines. The organization unit organizes the tasks necessary for output based on the information received by the reception unit. For example, the organization unit identifies necessary tasks based on the project details. For example, based on the project's purpose and requirements, the organization unit identifies tasks such as research, document creation, and presentation preparation. The organization unit may also evaluate the importance and urgency of tasks in order to determine task priorities. For example, the organization department prioritizes and organizes the important tasks of the project, scheduling high-priority tasks to be executed early. The scheduling department then schedules the tasks organized by the organization department. The scheduling department determines, for example, when and for how long each task will be performed. For example, the scheduling department might create a schedule where research is conducted in the first three days, materials are created in the next three days, and the remaining time is used to prepare the presentation. The scheduling department can also determine the order in which tasks are executed, taking into account their dependencies. For example, the scheduling department might schedule the creation of materials after the research is completed, and the preparation of the presentation after the materials are completed. The response department then handles the tasks scheduled by the scheduling department.The task handling unit automatically performs tasks such as gathering information for research and organizing data for document creation. For example, the task handling unit collects information from the internet, organizes the necessary data, and creates documents. The task handling unit can also monitor the progress of tasks and adjust them as needed. For example, the task handling unit monitors the progress of tasks in real time and adjusts the schedule if delays occur. The checkpoint organization unit organizes the points that users should check for tasks handled by the task handling unit. For example, the checkpoint organization unit clarifies the points that users should check, such as the content of the document or the design of the presentation slides. For example, the checkpoint organization unit organizes checkpoints such as whether the content of the document is accurate or whether the presentation slides are easy to read. The checkpoint organization unit can also provide checklists and guidelines to make it easier for users to perform checks. For example, the checkpoint organization unit lists the items that users should check and provides checkboxes. The revision unit revises tasks after the user has checked them based on the points organized by the checkpoint organization unit. For example, the revision unit revises the content of the document or redesigns the presentation slides based on user feedback. For example, the revision unit corrects errors pointed out by the user and updates the content of the document. The revision unit can also change the design of the presentation slides according to the user's requests. The iteration unit repeats the tasks corrected by the revision unit until the user is satisfied. For example, the iteration unit revises the document or adjusts the presentation until the user is satisfied. For example, the iteration unit has the user check again and repeats revisions as needed. The output unit finally outputs the tasks repeated by the iteration unit. For example, the output unit provides the user with the completed document or presentation. For example, the output unit outputs the completed document in PDF format and provides it to the user. The output unit can also provide the user with the presentation slides.As a result, the task management and calendar service system according to this embodiment can achieve rapid project output.

[0030] The reception desk accepts input of project details and completion deadlines. For example, the reception desk provides an interface for users to input project details and completion deadlines. Specifically, it provides text boxes and dropdown menus for users to enter project details. This allows users to easily enter detailed information such as the project's purpose, scope, and requirements. The reception desk can also provide a calendar or date selection tool for users to enter completion deadlines. For example, a calendar widget allows users to select a specific date and time. Furthermore, the reception desk has a function to verify input regarding project details and completion deadlines in real time, checking for input errors and omissions. For example, it verifies that the completion deadline is not a past date and that the project details are entered correctly. This ensures users can enter accurate information and improves the overall reliability of the system. The reception desk stores the information entered by users in a database, making it available for subsequent processing. This allows for centralized management of project details and completion deadlines, enabling efficient task management.

[0031] The organization department organizes the tasks necessary to produce the output based on the information received by the reception department. For example, the organization department identifies necessary tasks based on the project content. Specifically, the organization department identifies tasks such as research, document creation, and presentation preparation based on the project's objectives and requirements. This clarifies all the tasks necessary for the project's progress. The organization department can also evaluate the importance and urgency of tasks to determine their priority. For example, the organization department prioritizes important project tasks and schedules high-urgency tasks to be executed early. Furthermore, the organization department determines the execution order of tasks by considering their dependencies. This ensures that tasks proceed efficiently and prevents project delays. The organization department stores detailed task information in a database for use in subsequent processing. This allows for centralized management of information regarding task progress and priorities, enabling efficient task management. The organization department can also provide an interface that displays a list of tasks and their progress so that users can check the progress of tasks. This allows users to understand the project's progress in real time and adjust tasks as needed.

[0032] The scheduling unit schedules the tasks organized by the organization unit. For example, the scheduling unit determines the timing and duration of each task. Specifically, it might create a schedule where research is conducted for the first three days, materials are created for the next three days, and the remaining time is used for presentation preparation. The scheduling unit can also determine the execution order of tasks, taking into account their dependencies. For example, it might schedule material creation after research is complete, and presentation preparation after material creation is complete. Furthermore, the scheduling unit has the functionality to monitor task progress in real time and adjust the schedule as needed. For example, if a task is behind schedule or a new task is added, it readjusts the schedule to prevent project delays. The scheduling unit can also provide visual interfaces such as calendars and Gantt charts so that users can easily check the schedule. This allows users to grasp the timing and duration of tasks at a glance, enabling efficient task management. The scheduling unit stores schedule information in a database for use in subsequent processing. This centralizes schedule information, enabling efficient task management.

[0033] The task handling unit handles tasks scheduled by the scheduling unit. The task handling unit automatically performs tasks such as gathering information for research and organizing data for document creation. Specifically, it collects information from the internet, organizes the necessary data, and creates documents. The task handling unit can also monitor task progress and adjust tasks as needed. For example, it monitors task progress in real time and adjusts the schedule if delays occur. Furthermore, the task handling unit manages the resources necessary for task execution, supporting efficient task execution. For example, it manages information sources necessary for research and data necessary for document creation, and appropriately allocates the resources required for task execution. The task handling unit saves task progress and execution results to a database for use in subsequent processing. This allows for centralized management of information regarding task progress and execution results, enabling efficient task management. The task handling unit can also provide an interface that displays a list of tasks and their progress, allowing users to check task progress. This enables users to understand task progress in real time and adjust tasks as needed.

[0034] The checkpoint organization unit organizes the points that users should check for tasks handled by the corresponding unit. The checkpoint organization unit clarifies the points that users should verify, such as the content of documents or the design of presentation slides. Specifically, it organizes checkpoints such as whether the content of documents is accurate or whether the presentation slides are easy to read. The checkpoint organization unit can also provide checklists and guidelines to facilitate user checks. For example, it can list the items users should check and provide checkboxes. This allows users to quickly grasp the points to check and perform checks efficiently. Furthermore, the checkpoint organization unit saves the results of user checks to a database for use in subsequent processing. This centralizes checkpoint information, enabling efficient task management. Based on the user's check results, the checkpoint organization unit can also provide information to correct tasks as needed. This allows users to efficiently check tasks and quickly make necessary corrections.

[0035] The revision unit corrects tasks after they have been checked by the user, based on the points organized by the checkpoint organization unit. For example, the revision unit may revise the content of a document or redesign presentation slides based on user feedback. Specifically, the revision unit corrects errors pointed out by the user and updates the content of the document. The revision unit can also change the design of presentation slides according to the user's request. Furthermore, the revision unit saves the revisions to a database so that they can be used for subsequent processing. This allows for centralized management of information regarding revisions, enabling efficient task management. The revision unit can also provide an interface that displays a comparison of the revisions before and after, allowing the user to see the changes at a glance and make revisions efficiently. The revision unit works in conjunction with the iteration unit to perform revision work, allowing the user to repeat revisions until they are satisfied. This allows the user to revise the task until they are satisfied, improving the quality of the final output.

[0036] The iteration unit repeats the task corrected by the revision unit until the user is satisfied. For example, the iteration unit revises materials or adjusts presentations until the user is satisfied. Specifically, the iteration unit has the user check the work again and repeat the revisions as needed. This allows the user to revise the task until they are satisfied, improving the quality of the final output. The iteration unit saves the revisions and user feedback to a database for use in subsequent processing. This allows for centralized management of information regarding revisions and feedback, enabling efficient task management. The iteration unit can also provide an interface that displays a comparison of the before and after revisions so that the user can check the revisions. This allows the user to grasp the revisions at a glance and make revisions efficiently. Furthermore, the iteration unit works in conjunction with the revision unit to perform revision work so that the user can repeat the revisions until they are satisfied. This allows the user to revise the task until they are satisfied, improving the quality of the final output.

[0037] The output unit ultimately outputs the tasks repeated by the repetition unit. For example, the output unit provides the user with completed documents or presentations. Specifically, the output unit outputs completed documents in PDF format and provides them to the user. The output unit can also provide the user with presentation slides. Furthermore, the output unit saves the output content to a database for use in subsequent processing. This allows for centralized management of information regarding the output content, enabling efficient task management. The output unit can also provide an interface that displays a comparison of the output before and after, allowing the user to review the output content. This enables the user to grasp the output content at a glance and perform output efficiently. The output unit works in conjunction with the repetition unit to perform output tasks, allowing the user to repeat the output until they are satisfied. This allows the user to perform task outputs until they are satisfied, improving the quality of the final output. The output unit can also provide an interface that displays a comparison of the output before and after, allowing the user to review the output content. This enables the user to grasp the output content at a glance and perform output efficiently.

[0038] The reception desk can assist with input when users enter project details and completion deadlines by referring to their past project history. For example, the reception desk can suggest similar projects by referring to the details and completion deadlines of projects the user has completed in the past. For example, the reception desk can automatically complete the details of projects the user has entered in the past, saving input effort. The reception desk can also suggest frequently used completion deadlines from the user's past project history. This reduces input effort and enables efficient input by referring to past project history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past project history data into a generating AI and have the generating AI perform input assistance.

[0039] The reception desk can filter the input content based on the user's current work status and areas of interest when the user enters project details and completion deadlines. For example, the reception desk can prioritize displaying content related to projects the user is currently working on. For example, the reception desk can also suggest relevant project content based on the user's areas of interest. Furthermore, the reception desk can suggest an appropriate completion deadline considering the user's current work status. In this way, by filtering the input content based on the current work status and areas of interest, it suggests appropriate project content and completion deadlines. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current work status data and areas of interest data into a generating AI and have the generating AI perform the filtering of the input content.

[0040] The reception desk can prioritize the input of highly relevant projects by considering the user's geographical location when inputting project details and completion deadlines. For example, the reception desk can prioritize projects related to the user's current location. For example, the reception desk can also suggest nearby projects based on the user's geographical location. Furthermore, the reception desk can suggest projects that minimize travel time by considering the user's location. This allows for efficient project management by prioritizing the input of highly relevant projects while considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant projects.

[0041] The reception desk can analyze the user's social media activity and input related projects when the user enters project details and completion deadlines. For example, the reception desk can analyze the user's social media posts and suggest related projects. For example, the reception desk can suggest projects that the user might be interested in based on their social media activity history. The reception desk can also suggest related projects by referring to the activities of the user's social media followers and friends. This enables efficient project management by suggesting related projects through the analysis of social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest related projects.

[0042] The task organization unit can set the level of detail of tasks based on the importance of the project when organizing tasks. For example, the task organization unit will organize detailed tasks for high-importance projects. For example, the task organization unit can also organize simplified tasks for low-importance projects. Furthermore, the task organization unit can adjust the priority of tasks according to the importance of the project. This enables efficient task management by adjusting the level of detail of tasks based on the importance of the project. Some or all of the above processes in the task organization unit may be performed using AI, for example, or not using AI. For example, the task organization unit can input project importance data into a generating AI and have the generating AI set the level of detail of the tasks.

[0043] The task organization unit can apply different organization algorithms based on the project category when organizing tasks. For example, the task organization unit can apply a marketing-specific organization algorithm to a marketing project. For example, the task organization unit can also apply a development-specific organization algorithm to a development project. Furthermore, the task organization unit can apply a research-specific organization algorithm to a research project. This enables efficient task management by applying different organization algorithms according to the project category. Some or all of the above processing in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input project category data into a generating AI and have the generating AI execute the application of the organization algorithm.

[0044] The task management unit can prioritize tasks based on project submission deadlines when organizing tasks. For example, the task management unit can prioritize tasks for projects with approaching deadlines. For example, the task management unit can also postpone tasks for projects with distant deadlines. Furthermore, the task management unit can dynamically adjust task priorities according to submission deadlines. This enables efficient task management by prioritizing tasks based on project submission deadlines. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input project submission deadline data into a generating AI and have the generating AI perform the task priority determination.

[0045] The task organization unit can adjust the order of tasks based on project relevance when organizing tasks. For example, the task organization unit can group and organize highly relevant tasks. For example, the task organization unit can also organize less relevant tasks separately. Furthermore, the task organization unit can dynamically adjust the order of tasks according to project relevance. This enables efficient task management by adjusting the order of tasks based on project relevance. Some or all of the above processes in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input project relevance data into a generating AI and have the generating AI perform the task order adjustment.

[0046] The scheduling unit can adjust the level of detail in the schedule based on the importance of the tasks during scheduling. For example, the scheduling unit can create a detailed schedule for high-importance tasks. For example, the scheduling unit can also create a simplified schedule for low-importance tasks. Furthermore, the scheduling unit can adjust the priority of the schedule according to the importance of the tasks. This enables efficient scheduling by adjusting the level of detail in the schedule based on the importance of the tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0047] The scheduling unit can apply different scheduling algorithms depending on the task category during scheduling. For example, the scheduling unit can apply a scheduling algorithm specialized for marketing to marketing tasks. For example, the scheduling unit can also apply a scheduling algorithm specialized for development to development tasks. Furthermore, the scheduling unit can apply a scheduling algorithm specialized for research to research tasks. By applying different scheduling algorithms depending on the task category, efficient scheduling is achieved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task category data into a generating AI and have the generating AI execute the application of scheduling algorithms.

[0048] The scheduling unit can determine the priority of tasks based on their submission dates during scheduling. For example, the scheduling unit may prioritize tasks with approaching deadlines. For example, the scheduling unit may also postpone tasks with later deadlines. Furthermore, the scheduling unit can dynamically adjust the schedule priority according to the submission deadlines. This enables efficient scheduling by determining the schedule priority based on the task submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task submission date data into a generating AI and have the generating AI perform the task of determining the schedule priority.

[0049] The scheduling unit can adjust the order of schedules based on the relevance of tasks during scheduling. For example, the scheduling unit can group highly related tasks together and schedule them. For example, the scheduling unit can also schedule unrelated tasks separately. Furthermore, the scheduling unit can dynamically adjust the order of schedules according to the relevance of tasks. This enables efficient scheduling by adjusting the order of schedules based on the relevance of tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0050] The response unit can adjust the level of detail in its responses based on the importance of the task. For example, it can provide detailed responses to high-importance tasks, and simplified responses to low-importance tasks. It can also adjust the priority of responses according to the importance of the task. This allows for efficient task handling by adjusting the level of detail in responses based on the importance of the task. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the responses.

[0051] The response unit can apply different response algorithms depending on the task category when handling a task. For example, for a marketing task, the response unit can apply a response algorithm specialized for marketing. For example, for a development task, the response unit can apply a response algorithm specialized for development. Furthermore, for a research task, the response unit can apply a response algorithm specialized for research. This enables efficient task handling by applying different response algorithms depending on the task category. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task category data into a generating AI and have the generating AI execute the application of the response algorithm.

[0052] The task handling unit can determine the priority of tasks based on their submission dates. For example, the unit may prioritize tasks with approaching deadlines. For example, it may also postpone tasks with distant deadlines. Furthermore, the unit can dynamically adjust the priority of tasks according to their submission dates. This enables efficient task handling by determining the priority of tasks based on their submission dates. Some or all of the above processing in the task handling unit may be performed using AI, for example, or without AI. For example, the task handling unit can input task submission date data into a generating AI and have the generating AI determine the priority of tasks.

[0053] The response unit can adjust the order of tasks based on their relationships when handling tasks. For example, the response unit can group highly related tasks together and handle them. For example, the response unit can also handle unrelated tasks separately. Furthermore, the response unit can dynamically adjust the order of tasks according to their relationships. This enables efficient task handling by adjusting the order of tasks based on their relationships. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task relationship data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0054] The checkpoint organization unit can adjust the level of detail of checkpoints based on the importance of the task when organizing checkpoints. For example, the checkpoint organization unit can organize detailed checkpoints for high-importance tasks. For example, it can also organize simplified checkpoints for low-importance tasks. Furthermore, the checkpoint organization unit can adjust the priority of checkpoints according to the importance of the task. This enables efficient checkpoint organization by adjusting the level of detail of checkpoints based on the importance of the task. Some or all of the above processing in the checkpoint organization unit may be performed using AI, for example, or without AI. For example, the checkpoint organization unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the checkpoints.

[0055] The checkpoint organization unit can apply different organization algorithms depending on the task category when organizing checkpoints. For example, for marketing tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for marketing. For example, for development tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for development. Furthermore, for research tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for research. This enables efficient checkpoint organization by applying different organization algorithms depending on the task category. Some or all of the above processing in the checkpoint organization unit may be performed using AI, for example, or without AI. For example, the checkpoint organization unit can input task category data into a generating AI and have the generating AI execute the application of the organization algorithm.

[0056] The checkpoint sorting unit can determine the priority of checkpoints based on the task submission dates when sorting checkpoints. For example, the checkpoint sorting unit can prioritize sorting checkpoints for tasks with approaching deadlines. For example, the checkpoint sorting unit can also postpone sorting checkpoints for tasks with distant deadlines. Furthermore, the checkpoint sorting unit can dynamically adjust the priority of checkpoints according to the submission deadlines. This enables efficient checkpoint sorting by determining the priority of checkpoints based on the task submission dates. Some or all of the above processing in the checkpoint sorting unit may be performed using AI, for example, or without AI. For example, the checkpoint sorting unit can input task submission date data into a generating AI and have the generating AI perform the determination of checkpoint priorities.

[0057] The checkpoint sorting unit can adjust the order of checkpoints based on the relevance of tasks when sorting checkpoints. For example, the checkpoint sorting unit can group and organize highly relevant checkpoints. For example, the checkpoint sorting unit can also organize less relevant checkpoints separately. Furthermore, the checkpoint sorting unit can dynamically adjust the order according to the relevance of checkpoints. This enables efficient checkpoint sorting by adjusting the order of checkpoints based on the relevance of tasks. Some or all of the above processing in the checkpoint sorting unit may be performed using AI, for example, or without AI. For example, the checkpoint sorting unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of checkpoints.

[0058] The modification unit can adjust the level of detail of modifications based on the importance of the task when modifying it. For example, the modification unit can perform detailed modifications for high-importance tasks, and simplified modifications for low-importance tasks. The modification unit can also adjust the priority of modifications according to the importance of the task. This enables efficient task modification by adjusting the level of detail of modifications based on the importance of the task. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the modifications.

[0059] The modification unit can apply different modification algorithms depending on the task category when modifying a task. For example, for a marketing task, the modification unit can apply a modification algorithm specialized for marketing. For example, for a development task, the modification unit can apply a modification algorithm specialized for development. Furthermore, for a research task, the modification unit can apply a modification algorithm specialized for research. This enables efficient task modification by applying different modification algorithms depending on the task category. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task category data into a generating AI and have the generating AI execute the application of the modification algorithm.

[0060] The revision unit can determine the priority of revisions based on the task submission date when revising tasks. For example, the revision unit can prioritize revising tasks with approaching deadlines. For example, the revision unit can also postpone tasks with distant deadlines. Furthermore, the revision unit can dynamically adjust the revision priority according to the submission deadline. This enables efficient task revision by determining the revision priority based on the task submission date. Some or all of the above processing in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input task submission date data into a generating AI and have the generating AI perform the determination of revision priorities.

[0061] The modification unit can adjust the order of modifications based on the relevance of tasks when modifying tasks. For example, the modification unit can group highly relevant tasks together and modify them. For example, the modification unit can also modify unrelated tasks separately. Furthermore, the modification unit can dynamically adjust the order of modifications according to the relevance of tasks. This enables efficient task modification by adjusting the order of modifications based on the relevance of tasks. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the modification order.

[0062] The iteration unit can adjust the level of detail of the iteration based on the importance of the task during iteration. For example, the iteration unit can perform detailed iterations for high-importance tasks, and simplified iterations for low-importance tasks. The iteration unit can also adjust the priority of the iterations according to the importance of the tasks. This allows for efficient iteration by adjusting the level of detail of the iteration based on the importance of the tasks. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the iterations.

[0063] The iteration unit can apply different iteration algorithms depending on the task category during iteration. For example, for a marketing task, the iteration unit can apply an iteration algorithm specialized for marketing. For example, for a development task, the iteration unit can apply an iteration algorithm specialized for development. Furthermore, for a research task, the iteration unit can apply an iteration algorithm specialized for research. This enables efficient iteration by applying different iteration algorithms depending on the task category. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task category data into a generating AI and have the generating AI execute the application of the iteration algorithm.

[0064] The iteration unit can determine the priority of iterations based on the task submission timing during each iteration. For example, the iteration unit may prioritize iterating on tasks with approaching deadlines. For example, it may also postpone tasks with distant deadlines. Furthermore, the iteration unit can dynamically adjust the iteration priority according to the submission deadlines. This enables efficient iteration by determining the iteration priority based on the task submission timing. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task submission timing data into a generating AI and have the generating AI determine the iteration priority.

[0065] The iteration unit can adjust the order of repetitions based on the relevance of tasks during iteration. For example, the iteration unit can group highly relevant tasks together and repeat them. For example, the iteration unit can also repeat unrelated tasks separately. Furthermore, the iteration unit can dynamically adjust the order of repetitions according to the relevance of tasks. This enables efficient iteration by adjusting the order of repetitions based on the relevance of tasks. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the repetition order.

[0066] The output unit can adjust the level of detail of the output based on the importance of the task. For example, the output unit can provide detailed output for high-importance tasks, and simplified output for low-importance tasks. The output unit can also adjust the priority of the output according to the importance of the task. This allows for efficient output by adjusting the level of detail of the output based on the importance of the task. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task importance data into a generating AI and have the generating AI adjust the level of detail of the output.

[0067] The output unit can apply different output algorithms depending on the task category during output. For example, for a marketing task, the output unit can apply a marketing-specific output algorithm. For example, for a development task, the output unit can apply a development-specific output algorithm. Furthermore, for a research task, the output unit can apply a research-specific output algorithm. This enables efficient output by applying different output algorithms depending on the task category. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task category data into a generating AI and have the generating AI execute the application of the output algorithm.

[0068] The output unit can determine the priority of outputs based on the task submission timing at the time of output. For example, the output unit may prioritize tasks with approaching deadlines. For example, the output unit may also postpone tasks with distant deadlines. Furthermore, the output unit can dynamically adjust the output priority according to the submission deadlines. This enables efficient output by determining the output priority based on the task submission timing. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task submission timing data into a generation AI and have the generation AI determine the output priority.

[0069] The output unit can adjust the order of outputs based on the relevance of the tasks during output. For example, the output unit can group highly relevant tasks together for output. For example, the output unit can also output unrelated tasks separately. Furthermore, the output unit can dynamically adjust the order of outputs according to the relevance of the tasks. This enables efficient output by adjusting the order of outputs based on the relevance of the tasks. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the output order.

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

[0071] The reception desk can assist users with inputting project details and completion deadlines by referencing their past project history. For example, it can suggest similar projects based on the details and completion deadlines of projects the user has completed in the past. It can also automatically complete project details entered by the user in the past, saving input effort. Furthermore, it can suggest frequently used completion deadlines based on the user's past project history. In this way, referencing past project history reduces input effort and enables efficient data entry.

[0072] The task organization section allows you to set the level of detail for tasks based on the importance of the project. For example, for high-priority projects, you can organize detailed tasks. Conversely, for low-priority projects, you can organize simplified tasks. Furthermore, you can adjust the priority of tasks according to the importance of the project. This enables efficient task management by adjusting the level of detail of tasks based on the importance of the project.

[0073] The scheduling unit can determine schedule priorities based on the submission deadlines of tasks during scheduling. For example, tasks with approaching deadlines can be prioritized, while tasks with later deadlines can be postponed. Furthermore, schedule priorities can be dynamically adjusted according to the submission deadlines. This enables efficient scheduling by determining schedule priorities based on the submission deadlines.

[0074] The task handling unit can apply different handling algorithms depending on the task category when handling tasks. For example, a marketing-specific handling algorithm can be applied to marketing tasks. Similarly, a development-specific handling algorithm can be applied to development tasks. Furthermore, a research-specific handling algorithm can be applied to research tasks. By applying different handling algorithms according to the task category, efficient task handling is achieved.

[0075] The output unit can adjust the order of outputs based on the relevance of the tasks during the output process. For example, highly related tasks can be grouped together and output. Less relevant tasks can also be output separately. Furthermore, the order of outputs can be dynamically adjusted according to the relevance of the tasks. This allows for efficient output by adjusting the output order based on the relevance of the tasks.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The reception desk accepts input for project details and completion deadlines. For example, it provides text boxes or dropdown menus for users to enter project details, and a calendar or date selection tool for entering completion deadlines. Step 2: The organization department organizes the tasks necessary to produce the output based on the information received by the reception department. For example, based on the project's objectives and requirements, they identify tasks such as research, document creation, and presentation preparation, and determine the priority of these tasks. Step 3: The scheduling unit schedules the tasks that have been organized by the organization unit. For example, it determines the execution time and duration of each task and determines the execution order considering the dependencies between tasks. Step 4: The response unit handles tasks scheduled by the scheduling unit. For example, it automatically collects information for research and organizes data for document creation, monitors the progress of tasks, and adjusts tasks as needed. Step 5: The checkpoint organization team organizes the points that users should check for tasks handled by the response team. For example, they clarify the points that users should check, such as the content of the documents or the design of the presentation slides, and provide checklists and guidelines. Step 6: The revision section modifies tasks after the user has checked them, based on the points organized by the checkpoint organization section. For example, it might revise the content of a document based on user feedback or redesign presentation slides. Step 7: The repetition section repeats the task corrected by the revision section until the user is satisfied. For example, revisions to materials or adjustments to the presentation are made until the user is satisfied. Step 8: The output unit ultimately outputs the tasks that have been repeated by the loop unit. For example, it provides the user with completed documents or presentations.

[0078] (Example of form 2) The task management and calendar service system according to an embodiment of the present invention is a system for quickly guiding projects to output. With this system, the user simply registers the project details and completion deadline, and the generating AI handles the following seven tasks: 1. Organizing the tasks necessary to reach the output. 2. Scheduling tasks. 3. Handling various tasks. 4. Organizing the points that the user should check for each task. 5. Correcting each task after the user's check. 6. Repeating steps 4 and 5 until satisfactory. 7. Output. As a result, the generating AI takes over tasks that were traditionally mainly performed by the user, and the user takes over the parts that the generating AI previously assisted with, enabling rapid output by the user simply "checking and providing feedback." For example, the user registers the project details and completion deadline. For example, they might register something like, "Complete a marketing plan for a new product within two weeks." This information is input into the generating AI. Next, the generating AI organizes the tasks necessary to reach the output based on the project details. For example, it identifies tasks such as research, document creation, and presentation preparation necessary for creating the marketing plan. The generating AI then schedules each task. For example, it might create a schedule where research is conducted for the first three days, materials are created for the next three days, and the remaining time is used to prepare the presentation. The generating AI also handles each task. For example, it automatically collects information for research and organizes data for material creation. Furthermore, the generating AI organizes the points that the user should check for each task. For example, it clarifies the points that the user should check, such as the content of the materials or the design of the presentation slides. After the user has checked, the generating AI makes revisions to each task. For example, it might revise the content of the materials or redesign the presentation slides based on the user's feedback. This process is repeated until the user is satisfied, ultimately completing the output. For example, it will revise the materials or adjust the presentation until the user is satisfied. This system allows users to achieve rapid output simply by "checking and giving feedback."Because the generation AI handles the majority of the tasks, the user's burden is reduced, and the project progresses smoothly. This allows the task management and calendar service system to achieve rapid project output.

[0079] The task management and calendar service system according to this embodiment comprises a reception unit, an organization unit, a scheduling unit, a response unit, a checkpoint organization unit, a correction unit, a repeating unit, and an output unit. The reception unit receives input of project details and completion deadlines. The reception unit provides, for example, an interface for the user to input project details and completion deadlines. For example, the reception unit provides text boxes or drop-down menus for the user to input project details. The reception unit may also provide a calendar or date selection tool for the user to input completion deadlines. For example, when the user inputs project details, the reception unit provides fields for the user to input detailed information such as the project's purpose, scope, and requirements. Furthermore, the reception unit may also provide a calendar widget for the user to select a specific date and time when entering completion deadlines. The organization unit organizes the tasks necessary for output based on the information received by the reception unit. For example, the organization unit identifies necessary tasks based on the project details. For example, based on the project's purpose and requirements, the organization unit identifies tasks such as research, document creation, and presentation preparation. The organization unit may also evaluate the importance and urgency of tasks in order to determine task priorities. For example, the organization department prioritizes and organizes the important tasks of the project, scheduling high-priority tasks to be executed early. The scheduling department then schedules the tasks organized by the organization department. The scheduling department determines, for example, when and for how long each task will be performed. For example, the scheduling department might create a schedule where research is conducted in the first three days, materials are created in the next three days, and the remaining time is used to prepare the presentation. The scheduling department can also determine the order in which tasks are executed, taking into account their dependencies. For example, the scheduling department might schedule the creation of materials after the research is completed, and the preparation of the presentation after the materials are completed. The response department then handles the tasks scheduled by the scheduling department.The task handling unit automatically performs tasks such as gathering information for research and organizing data for document creation. For example, the task handling unit collects information from the internet, organizes the necessary data, and creates documents. The task handling unit can also monitor the progress of tasks and adjust them as needed. For example, the task handling unit monitors the progress of tasks in real time and adjusts the schedule if delays occur. The checkpoint organization unit organizes the points that users should check for tasks handled by the task handling unit. For example, the checkpoint organization unit clarifies the points that users should check, such as the content of the document or the design of the presentation slides. For example, the checkpoint organization unit organizes checkpoints such as whether the content of the document is accurate or whether the presentation slides are easy to read. The checkpoint organization unit can also provide checklists and guidelines to make it easier for users to perform checks. For example, the checkpoint organization unit lists the items that users should check and provides checkboxes. The revision unit revises tasks after the user has checked them based on the points organized by the checkpoint organization unit. For example, the revision unit revises the content of the document or redesigns the presentation slides based on user feedback. For example, the revision unit corrects errors pointed out by the user and updates the content of the document. The revision unit can also change the design of the presentation slides according to the user's requests. The iteration unit repeats the tasks corrected by the revision unit until the user is satisfied. For example, the iteration unit revises the document or adjusts the presentation until the user is satisfied. For example, the iteration unit has the user check again and repeats revisions as needed. The output unit finally outputs the tasks repeated by the iteration unit. For example, the output unit provides the user with the completed document or presentation. For example, the output unit outputs the completed document in PDF format and provides it to the user. The output unit can also provide the user with the presentation slides.As a result, the task management and calendar service system according to this embodiment can achieve rapid project output.

[0080] The reception desk accepts input of project details and completion deadlines. For example, the reception desk provides an interface for users to input project details and completion deadlines. Specifically, it provides text boxes and dropdown menus for users to enter project details. This allows users to easily enter detailed information such as the project's purpose, scope, and requirements. The reception desk can also provide a calendar or date selection tool for users to enter completion deadlines. For example, a calendar widget allows users to select a specific date and time. Furthermore, the reception desk has a function to verify input regarding project details and completion deadlines in real time, checking for input errors and omissions. For example, it verifies that the completion deadline is not a past date and that the project details are entered correctly. This ensures users can enter accurate information and improves the overall reliability of the system. The reception desk stores the information entered by users in a database, making it available for subsequent processing. This allows for centralized management of project details and completion deadlines, enabling efficient task management.

[0081] The organization department organizes the tasks necessary to produce the output based on the information received by the reception department. For example, the organization department identifies necessary tasks based on the project content. Specifically, the organization department identifies tasks such as research, document creation, and presentation preparation based on the project's objectives and requirements. This clarifies all the tasks necessary for the project's progress. The organization department can also evaluate the importance and urgency of tasks to determine their priority. For example, the organization department prioritizes important project tasks and schedules high-urgency tasks to be executed early. Furthermore, the organization department determines the execution order of tasks by considering their dependencies. This ensures that tasks proceed efficiently and prevents project delays. The organization department stores detailed task information in a database for use in subsequent processing. This allows for centralized management of information regarding task progress and priorities, enabling efficient task management. The organization department can also provide an interface that displays a list of tasks and their progress so that users can check the progress of tasks. This allows users to understand the project's progress in real time and adjust tasks as needed.

[0082] The scheduling unit schedules the tasks organized by the organization unit. For example, the scheduling unit determines the timing and duration of each task. Specifically, it might create a schedule where research is conducted for the first three days, materials are created for the next three days, and the remaining time is used for presentation preparation. The scheduling unit can also determine the execution order of tasks, taking into account their dependencies. For example, it might schedule material creation after research is complete, and presentation preparation after material creation is complete. Furthermore, the scheduling unit has the functionality to monitor task progress in real time and adjust the schedule as needed. For example, if a task is behind schedule or a new task is added, it readjusts the schedule to prevent project delays. The scheduling unit can also provide visual interfaces such as calendars and Gantt charts so that users can easily check the schedule. This allows users to grasp the timing and duration of tasks at a glance, enabling efficient task management. The scheduling unit stores schedule information in a database for use in subsequent processing. This centralizes schedule information, enabling efficient task management.

[0083] The task handling unit handles tasks scheduled by the scheduling unit. The task handling unit automatically performs tasks such as gathering information for research and organizing data for document creation. Specifically, it collects information from the internet, organizes the necessary data, and creates documents. The task handling unit can also monitor task progress and adjust tasks as needed. For example, it monitors task progress in real time and adjusts the schedule if delays occur. Furthermore, the task handling unit manages the resources necessary for task execution, supporting efficient task execution. For example, it manages information sources necessary for research and data necessary for document creation, and appropriately allocates the resources required for task execution. The task handling unit saves task progress and execution results to a database for use in subsequent processing. This allows for centralized management of information regarding task progress and execution results, enabling efficient task management. The task handling unit can also provide an interface that displays a list of tasks and their progress, allowing users to check task progress. This enables users to understand task progress in real time and adjust tasks as needed.

[0084] The checkpoint organization unit organizes the points that users should check for tasks handled by the corresponding unit. The checkpoint organization unit clarifies the points that users should verify, such as the content of documents or the design of presentation slides. Specifically, it organizes checkpoints such as whether the content of documents is accurate or whether the presentation slides are easy to read. The checkpoint organization unit can also provide checklists and guidelines to facilitate user checks. For example, it can list the items users should check and provide checkboxes. This allows users to quickly grasp the points to check and perform checks efficiently. Furthermore, the checkpoint organization unit saves the results of user checks to a database for use in subsequent processing. This centralizes checkpoint information, enabling efficient task management. Based on the user's check results, the checkpoint organization unit can also provide information to correct tasks as needed. This allows users to efficiently check tasks and quickly make necessary corrections.

[0085] The revision unit corrects tasks after they have been checked by the user, based on the points organized by the checkpoint organization unit. For example, the revision unit may revise the content of a document or redesign presentation slides based on user feedback. Specifically, the revision unit corrects errors pointed out by the user and updates the content of the document. The revision unit can also change the design of presentation slides according to the user's request. Furthermore, the revision unit saves the revisions to a database so that they can be used for subsequent processing. This allows for centralized management of information regarding revisions, enabling efficient task management. The revision unit can also provide an interface that displays a comparison of the revisions before and after, allowing the user to see the changes at a glance and make revisions efficiently. The revision unit works in conjunction with the iteration unit to perform revision work, allowing the user to repeat revisions until they are satisfied. This allows the user to revise the task until they are satisfied, improving the quality of the final output.

[0086] The iteration unit repeats the task corrected by the revision unit until the user is satisfied. For example, the iteration unit revises materials or adjusts presentations until the user is satisfied. Specifically, the iteration unit has the user check the work again and repeat the revisions as needed. This allows the user to revise the task until they are satisfied, improving the quality of the final output. The iteration unit saves the revisions and user feedback to a database for use in subsequent processing. This allows for centralized management of information regarding revisions and feedback, enabling efficient task management. The iteration unit can also provide an interface that displays a comparison of the before and after revisions so that the user can check the revisions. This allows the user to grasp the revisions at a glance and make revisions efficiently. Furthermore, the iteration unit works in conjunction with the revision unit to perform revision work so that the user can repeat the revisions until they are satisfied. This allows the user to revise the task until they are satisfied, improving the quality of the final output.

[0087] The output unit ultimately outputs the tasks repeated by the repetition unit. For example, the output unit provides the user with completed documents or presentations. Specifically, the output unit outputs completed documents in PDF format and provides them to the user. The output unit can also provide the user with presentation slides. Furthermore, the output unit saves the output content to a database for use in subsequent processing. This allows for centralized management of information regarding the output content, enabling efficient task management. The output unit can also provide an interface that displays a comparison of the output before and after, allowing the user to review the output content. This enables the user to grasp the output content at a glance and perform output efficiently. The output unit works in conjunction with the repetition unit to perform output tasks, allowing the user to repeat the output until they are satisfied. This allows the user to perform task outputs until they are satisfied, improving the quality of the final output. The output unit can also provide an interface that displays a comparison of the output before and after, allowing the user to review the output content. This enables the user to grasp the output content at a glance and perform output efficiently.

[0088] The reception desk can estimate the user's emotions and modify the input method for project details and deadlines based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. Also, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of project details and deadlines. This reduces the user burden and enables efficient input by adjusting the input method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The reception desk can assist with input when users enter project details and completion deadlines by referring to their past project history. For example, the reception desk can suggest similar projects by referring to the details and completion deadlines of projects the user has completed in the past. For example, the reception desk can automatically complete the details of projects the user has entered in the past, saving input effort. The reception desk can also suggest frequently used completion deadlines from the user's past project history. This reduces input effort and enables efficient input by referring to past project history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past project history data into a generating AI and have the generating AI perform input assistance.

[0090] The reception desk can filter the input content based on the user's current work status and areas of interest when the user enters project details and completion deadlines. For example, the reception desk can prioritize displaying content related to projects the user is currently working on. For example, the reception desk can also suggest relevant project content based on the user's areas of interest. Furthermore, the reception desk can suggest an appropriate completion deadline considering the user's current work status. In this way, by filtering the input content based on the current work status and areas of interest, it suggests appropriate project content and completion deadlines. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's current work status data and areas of interest data into a generating AI and have the generating AI perform the filtering of the input content.

[0091] The reception desk can estimate the user's emotions and set project priorities based on those emotions. For example, if the user is stressed, the reception desk may prioritize low-priority projects. For example, if the user is relaxed, the reception desk may prioritize high-priority projects. Also, if the user is in a hurry, the reception desk may prioritize urgent projects. This enables efficient project management by determining project priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI set project priorities.

[0092] The reception desk can prioritize the input of highly relevant projects by considering the user's geographical location when inputting project details and completion deadlines. For example, the reception desk can prioritize projects related to the user's current location. For example, the reception desk can also suggest nearby projects based on the user's geographical location. Furthermore, the reception desk can suggest projects that minimize travel time by considering the user's location. This allows for efficient project management by prioritizing the input of highly relevant projects while considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest highly relevant projects.

[0093] The reception desk can analyze the user's social media activity and input related projects when the user enters project details and completion deadlines. For example, the reception desk can analyze the user's social media posts and suggest related projects. For example, the reception desk can suggest projects that the user might be interested in based on their social media activity history. The reception desk can also suggest related projects by referring to the activities of the user's social media followers and friends. This enables efficient project management by suggesting related projects through the analysis of social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest related projects.

[0094] The task organization unit can estimate the user's emotions and modify the task organization method based on the estimated emotions. For example, if the user is stressed, the task organization unit can provide a simple task organization method. For example, if the user is relaxed, the task organization unit can provide a detailed task organization method. Furthermore, if the user is in a hurry, the task organization unit can provide a method for quickly organizing tasks. This enables efficient task management by adjusting the task organization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task organization unit may be performed using AI or not using AI. For example, the task organization unit can input user emotion data into the generative AI and have the generative AI adjust the task organization method.

[0095] The task organization unit can set the level of detail of tasks based on the importance of the project when organizing tasks. For example, the task organization unit will organize detailed tasks for high-importance projects. For example, the task organization unit can also organize simplified tasks for low-importance projects. Furthermore, the task organization unit can adjust the priority of tasks according to the importance of the project. This enables efficient task management by adjusting the level of detail of tasks based on the importance of the project. Some or all of the above processes in the task organization unit may be performed using AI, for example, or not using AI. For example, the task organization unit can input project importance data into a generating AI and have the generating AI set the level of detail of the tasks.

[0096] The task organization unit can apply different organization algorithms based on the project category when organizing tasks. For example, the task organization unit can apply a marketing-specific organization algorithm to a marketing project. For example, the task organization unit can also apply a development-specific organization algorithm to a development project. Furthermore, the task organization unit can apply a research-specific organization algorithm to a research project. This enables efficient task management by applying different organization algorithms according to the project category. Some or all of the above processing in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input project category data into a generating AI and have the generating AI execute the application of the organization algorithm.

[0097] The task management unit can estimate the user's emotions and determine task priorities based on those emotions. For example, if the user is stressed, the task management unit will prioritize tasks of lower importance. For example, if the user is relaxed, the task management unit may prioritize tasks of higher importance. Also, if the user is in a hurry, the task management unit may prioritize tasks of higher urgency. This enables efficient task management by determining task priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the task management unit may be performed using AI or not. For example, the task management unit can input user emotion data into a generative AI and have the generative AI determine task priorities.

[0098] The task management unit can prioritize tasks based on project submission deadlines when organizing tasks. For example, the task management unit can prioritize tasks for projects with approaching deadlines. For example, the task management unit can also postpone tasks for projects with distant deadlines. Furthermore, the task management unit can dynamically adjust task priorities according to submission deadlines. This enables efficient task management by prioritizing tasks based on project submission deadlines. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input project submission deadline data into a generating AI and have the generating AI perform the task priority determination.

[0099] The task organization unit can adjust the order of tasks based on project relevance when organizing tasks. For example, the task organization unit can group and organize highly relevant tasks. For example, the task organization unit can also organize less relevant tasks separately. Furthermore, the task organization unit can dynamically adjust the order of tasks according to project relevance. This enables efficient task management by adjusting the order of tasks based on project relevance. Some or all of the above processes in the task organization unit may be performed using AI, for example, or without AI. For example, the task organization unit can input project relevance data into a generating AI and have the generating AI perform the task order adjustment.

[0100] The scheduling unit can estimate the user's emotions and adjust the scheduling method based on the estimated emotions. For example, if the user is stressed, the scheduling unit can provide a simple scheduling method. For example, if the user is relaxed, the scheduling unit can also provide a detailed scheduling method. Furthermore, if the user is in a hurry, the scheduling unit can provide a method for quickly creating a schedule. This enables efficient scheduling by adjusting the scheduling method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the scheduling method.

[0101] The scheduling unit can adjust the level of detail in the schedule based on the importance of the tasks during scheduling. For example, the scheduling unit can create a detailed schedule for high-importance tasks. For example, the scheduling unit can also create a simplified schedule for low-importance tasks. Furthermore, the scheduling unit can adjust the priority of the schedule according to the importance of the tasks. This enables efficient scheduling by adjusting the level of detail in the schedule based on the importance of the tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the schedule.

[0102] The scheduling unit can apply different scheduling algorithms depending on the task category during scheduling. For example, the scheduling unit can apply a scheduling algorithm specialized for marketing to marketing tasks. For example, the scheduling unit can also apply a scheduling algorithm specialized for development to development tasks. Furthermore, the scheduling unit can apply a scheduling algorithm specialized for research to research tasks. By applying different scheduling algorithms depending on the task category, efficient scheduling is achieved. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task category data into a generating AI and have the generating AI execute the application of scheduling algorithms.

[0103] The scheduling unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is stressed, the scheduling unit will prioritize scheduling low-priority tasks. For example, if the user is relaxed, the scheduling unit can prioritize scheduling high-priority tasks. Also, if the user is in a hurry, the scheduling unit can prioritize scheduling urgent tasks. This enables efficient scheduling by determining schedule priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the scheduling unit may be performed using AI, or not using AI. For example, the scheduling unit can input user emotion data into a generative AI and have the generative AI determine schedule priorities.

[0104] The scheduling unit can determine the priority of tasks based on their submission dates during scheduling. For example, the scheduling unit may prioritize tasks with approaching deadlines. For example, the scheduling unit may also postpone tasks with later deadlines. Furthermore, the scheduling unit can dynamically adjust the schedule priority according to the submission deadlines. This enables efficient scheduling by determining the schedule priority based on the task submission dates. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task submission date data into a generating AI and have the generating AI perform the task of determining the schedule priority.

[0105] The scheduling unit can adjust the order of schedules based on the relevance of tasks during scheduling. For example, the scheduling unit can group highly related tasks together and schedule them. For example, the scheduling unit can also schedule unrelated tasks separately. Furthermore, the scheduling unit can dynamically adjust the order of schedules according to the relevance of tasks. This enables efficient scheduling by adjusting the order of schedules based on the relevance of tasks. Some or all of the above processing in the scheduling unit may be performed using AI, for example, or without AI. For example, the scheduling unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the schedule order.

[0106] The response unit can estimate the user's emotions and adjust the task response method based on the estimated user emotions. For example, if the user is stressed, the response unit can provide a simple response method. For example, if the user is relaxed, the response unit can also provide a detailed response method. Furthermore, if the user is in a hurry, the response unit can provide a quick response method. This enables efficient task response by adjusting the task response method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI, for example, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the task response method.

[0107] The response unit can adjust the level of detail in its responses based on the importance of the task. For example, it can provide detailed responses to high-importance tasks, and simplified responses to low-importance tasks. It can also adjust the priority of responses according to the importance of the task. This allows for efficient task handling by adjusting the level of detail in responses based on the importance of the task. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the responses.

[0108] The response unit can apply different response algorithms depending on the task category when handling a task. For example, for a marketing task, the response unit can apply a response algorithm specialized for marketing. For example, for a development task, the response unit can apply a response algorithm specialized for development. Furthermore, for a research task, the response unit can apply a response algorithm specialized for research. This enables efficient task handling by applying different response algorithms depending on the task category. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task category data into a generating AI and have the generating AI execute the application of the response algorithm.

[0109] The response unit can estimate the user's emotions and determine the priority of tasks to be handled based on the estimated emotions. For example, if the user is stressed, the response unit will prioritize tasks of low importance. For example, if the user is relaxed, the response unit may prioritize tasks of high importance. Also, if the user is in a hurry, the response unit may prioritize tasks of high urgency. This enables efficient task handling by determining the priority of tasks to be handled 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 response unit may be performed using AI, or not using AI. For example, the response unit can input user emotion data into a generative AI and have the generative AI determine the priority of tasks to be handled.

[0110] The task handling unit can determine the priority of tasks based on their submission dates. For example, the unit may prioritize tasks with approaching deadlines. For example, it may also postpone tasks with distant deadlines. Furthermore, the unit can dynamically adjust the priority of tasks according to their submission dates. This enables efficient task handling by determining the priority of tasks based on their submission dates. Some or all of the above processing in the task handling unit may be performed using AI, for example, or without AI. For example, the task handling unit can input task submission date data into a generating AI and have the generating AI determine the priority of tasks.

[0111] The response unit can adjust the order of tasks based on their relationships when handling tasks. For example, the response unit can group highly related tasks together and handle them. For example, the response unit can also handle unrelated tasks separately. Furthermore, the response unit can dynamically adjust the order of tasks according to their relationships. This enables efficient task handling by adjusting the order of tasks based on their relationships. Some or all of the above processing in the response unit may be performed using AI, for example, or without AI. For example, the response unit can input task relationship data into a generating AI and have the generating AI perform the adjustment of the order of tasks.

[0112] The checkpoint sorting unit can estimate the user's emotions and adjust the checkpoint sorting method based on the estimated emotions. For example, if the user is stressed, the checkpoint sorting unit can provide a simple checkpoint sorting method. For example, if the user is relaxed, the checkpoint sorting unit can also provide a detailed checkpoint sorting method. Furthermore, if the user is in a hurry, the checkpoint sorting unit can provide a method for quickly sorting checkpoints. This enables efficient checkpoint sorting by adjusting the checkpoint sorting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the checkpoint sorting unit may be performed using AI or not using AI. For example, the checkpoint sorting unit can input user emotion data into the generative AI and have the generative AI perform the adjustment of the checkpoint sorting method.

[0113] The checkpoint organization unit can adjust the level of detail of checkpoints based on the importance of the task when organizing checkpoints. For example, the checkpoint organization unit can organize detailed checkpoints for high-importance tasks. For example, it can also organize simplified checkpoints for low-importance tasks. Furthermore, the checkpoint organization unit can adjust the priority of checkpoints according to the importance of the task. This enables efficient checkpoint organization by adjusting the level of detail of checkpoints based on the importance of the task. Some or all of the above processing in the checkpoint organization unit may be performed using AI, for example, or without AI. For example, the checkpoint organization unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the checkpoints.

[0114] The checkpoint organization unit can apply different organization algorithms depending on the task category when organizing checkpoints. For example, for marketing tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for marketing. For example, for development tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for development. Furthermore, for research tasks, the checkpoint organization unit can apply a checkpoint organization algorithm specialized for research. This enables efficient checkpoint organization by applying different organization algorithms depending on the task category. Some or all of the above processing in the checkpoint organization unit may be performed using AI, for example, or without AI. For example, the checkpoint organization unit can input task category data into a generating AI and have the generating AI execute the application of the organization algorithm.

[0115] The checkpoint sorting unit can estimate the user's emotions and determine the priority of checkpoints based on the estimated emotions. For example, if the user is stressed, the checkpoint sorting unit will prioritize low-priority checkpoints. For example, if the user is relaxed, the checkpoint sorting unit may prioritize high-priority checkpoints. Also, if the user is in a hurry, the checkpoint sorting unit may prioritize high-urgency checkpoints. This enables efficient checkpoint sorting by determining the priority of checkpoints 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 checkpoint sorting unit may be performed using AI, for example, or not using AI. For example, the checkpoint sorting unit can input user emotion data into a generative AI and have the generative AI perform the determination of checkpoint priorities.

[0116] The checkpoint sorting unit can determine the priority of checkpoints based on the task submission dates when sorting checkpoints. For example, the checkpoint sorting unit can prioritize sorting checkpoints for tasks with approaching deadlines. For example, the checkpoint sorting unit can also postpone sorting checkpoints for tasks with distant deadlines. Furthermore, the checkpoint sorting unit can dynamically adjust the priority of checkpoints according to the submission deadlines. This enables efficient checkpoint sorting by determining the priority of checkpoints based on the task submission dates. Some or all of the above processing in the checkpoint sorting unit may be performed using AI, for example, or without AI. For example, the checkpoint sorting unit can input task submission date data into a generating AI and have the generating AI perform the determination of checkpoint priorities.

[0117] The checkpoint sorting unit can adjust the order of checkpoints based on the relevance of tasks when sorting checkpoints. For example, the checkpoint sorting unit can group and organize highly relevant checkpoints. For example, the checkpoint sorting unit can also organize less relevant checkpoints separately. Furthermore, the checkpoint sorting unit can dynamically adjust the order according to the relevance of checkpoints. This enables efficient checkpoint sorting by adjusting the order of checkpoints based on the relevance of tasks. Some or all of the above processing in the checkpoint sorting unit may be performed using AI, for example, or without AI. For example, the checkpoint sorting unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the order of checkpoints.

[0118] The modification unit can estimate the user's emotions and adjust the task modification method based on the estimated user emotions. For example, if the user is stressed, the modification unit can provide a simple modification method. For example, if the user is relaxed, the modification unit can also provide a more detailed modification method. Furthermore, if the user is in a hurry, the modification unit can provide a quick modification method. This enables efficient task modification by adjusting the task modification 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 modification unit may be performed using AI, or not using AI. For example, the modification unit can input user emotion data into the generative AI and have the generative AI adjust the task modification method.

[0119] The modification unit can adjust the level of detail of modifications based on the importance of the task when modifying it. For example, the modification unit can perform detailed modifications for high-importance tasks, and simplified modifications for low-importance tasks. The modification unit can also adjust the priority of modifications according to the importance of the task. This enables efficient task modification by adjusting the level of detail of modifications based on the importance of the task. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the modifications.

[0120] The modification unit can apply different modification algorithms depending on the task category when modifying a task. For example, for a marketing task, the modification unit can apply a modification algorithm specialized for marketing. For example, for a development task, the modification unit can apply a modification algorithm specialized for development. Furthermore, for a research task, the modification unit can apply a modification algorithm specialized for research. This enables efficient task modification by applying different modification algorithms depending on the task category. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task category data into a generating AI and have the generating AI execute the application of the modification algorithm.

[0121] The editing unit can estimate the user's emotions and determine the priority of tasks to be edited based on the estimated emotions. For example, if the user is stressed, the editing unit will prioritize editing tasks of low importance. For example, if the user is relaxed, the editing unit may also prioritize editing tasks of high importance. Furthermore, if the user is in a hurry, the editing unit may also prioritize editing tasks of high urgency. This enables efficient task editing by determining the priority of tasks to be edited according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editing unit may be performed using AI, or not using AI. For example, the editing unit can input user emotion data into a generative AI and have the generative AI determine the priority of tasks to be edited.

[0122] The revision unit can determine the priority of revisions based on the task submission date when revising tasks. For example, the revision unit can prioritize revising tasks with approaching deadlines. For example, the revision unit can also postpone tasks with distant deadlines. Furthermore, the revision unit can dynamically adjust the revision priority according to the submission deadline. This enables efficient task revision by determining the revision priority based on the task submission date. Some or all of the above processing in the revision unit may be performed using AI, for example, or without AI. For example, the revision unit can input task submission date data into a generating AI and have the generating AI perform the determination of revision priorities.

[0123] The modification unit can adjust the order of modifications based on the relevance of tasks when modifying tasks. For example, the modification unit can group highly relevant tasks together and modify them. For example, the modification unit can also modify unrelated tasks separately. Furthermore, the modification unit can dynamically adjust the order of modifications according to the relevance of tasks. This enables efficient task modification by adjusting the order of modifications based on the relevance of tasks. Some or all of the above processing in the modification unit may be performed using AI, for example, or without AI. For example, the modification unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the modification order.

[0124] The repetition unit can estimate the user's emotions and adjust the repetition method based on the estimated emotions. For example, if the user is stressed, the repetition unit can provide a simple repetition method. For example, if the user is relaxed, the repetition unit can also provide a more detailed repetition method. Furthermore, if the user is in a hurry, the repetition unit can provide a method for rapid repetition. This allows for efficient repetition by adjusting the repetition method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the repetition unit may be performed using AI or not. For example, the repetition unit can input user emotion data into a generative AI and have the generative AI adjust the repetition method.

[0125] The iteration unit can adjust the level of detail of the iteration based on the importance of the task during iteration. For example, the iteration unit can perform detailed iterations for high-importance tasks, and simplified iterations for low-importance tasks. The iteration unit can also adjust the priority of the iterations according to the importance of the tasks. This allows for efficient iteration by adjusting the level of detail of the iteration based on the importance of the tasks. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the iterations.

[0126] The iteration unit can apply different iteration algorithms depending on the task category during iteration. For example, for a marketing task, the iteration unit can apply an iteration algorithm specialized for marketing. For example, for a development task, the iteration unit can apply an iteration algorithm specialized for development. Furthermore, for a research task, the iteration unit can apply an iteration algorithm specialized for research. This enables efficient iteration by applying different iteration algorithms depending on the task category. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task category data into a generating AI and have the generating AI execute the application of the iteration algorithm.

[0127] The repetition unit can estimate the user's emotions and determine the priority of repetitions based on the estimated emotions. For example, if the user is stressed, the repetition unit will prioritize repeating tasks of lower importance. For example, if the user is relaxed, the repetition unit may also prioritize repeating tasks of higher importance. Furthermore, if the user is in a hurry, the repetition unit may also prioritize repeating tasks of higher urgency. This enables efficient repetition by determining the priority of repetitions 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 repetition unit may be performed using AI, for example, or not using AI. For example, the repetition unit can input user emotion data into a generative AI and have the generative AI determine the priority of repetitions.

[0128] The iteration unit can determine the priority of iterations based on the task submission timing during each iteration. For example, the iteration unit may prioritize iterating on tasks with approaching deadlines. For example, it may also postpone tasks with distant deadlines. Furthermore, the iteration unit can dynamically adjust the iteration priority according to the submission deadlines. This enables efficient iteration by determining the iteration priority based on the task submission timing. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task submission timing data into a generating AI and have the generating AI determine the iteration priority.

[0129] The iteration unit can adjust the order of repetitions based on the relevance of tasks during iteration. For example, the iteration unit can group highly relevant tasks together and repeat them. For example, the iteration unit can also repeat unrelated tasks separately. Furthermore, the iteration unit can dynamically adjust the order of repetitions according to the relevance of tasks. This enables efficient iteration by adjusting the order of repetitions based on the relevance of tasks. Some or all of the above processing in the iteration unit may be performed using AI, for example, or without AI. For example, the iteration unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the repetition order.

[0130] The output unit can estimate the user's emotions and adjust the output method based on the estimated emotions. For example, if the user is stressed, the output unit can provide a simple output method. For example, if the user is relaxed, the output unit can provide a detailed output method. Furthermore, if the user is in a hurry, the output unit can provide a quick output method. This allows for efficient output by adjusting the output method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can input user emotion data into a generative AI and have the generative AI adjust the output method.

[0131] The output unit can adjust the level of detail of the output based on the importance of the task. For example, the output unit can provide detailed output for high-importance tasks, and simplified output for low-importance tasks. The output unit can also adjust the priority of the output according to the importance of the task. This allows for efficient output by adjusting the level of detail of the output based on the importance of the task. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task importance data into a generating AI and have the generating AI adjust the level of detail of the output.

[0132] The output unit can apply different output algorithms depending on the task category during output. For example, for a marketing task, the output unit can apply a marketing-specific output algorithm. For example, for a development task, the output unit can apply a development-specific output algorithm. Furthermore, for a research task, the output unit can apply a research-specific output algorithm. This enables efficient output by applying different output algorithms depending on the task category. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task category data into a generating AI and have the generating AI execute the application of the output algorithm.

[0133] The output unit can estimate the user's emotions and determine the priority of outputs based on the estimated emotions. For example, if the user is stressed, the output unit will prioritize outputting low-priority tasks. For example, if the user is relaxed, the output unit can also prioritize outputting high-priority tasks. Furthermore, if the user is in a hurry, the output unit can also prioritize outputting urgent tasks. This enables efficient output by determining the priority of outputs 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 output unit may be performed using AI, for example, or not using AI. For example, the output unit can input user emotion data into a generative AI and have the generative AI determine the priority of outputs.

[0134] The output unit can determine the priority of outputs based on the task submission timing at the time of output. For example, the output unit may prioritize tasks with approaching deadlines. For example, the output unit may also postpone tasks with distant deadlines. Furthermore, the output unit can dynamically adjust the output priority according to the submission deadlines. This enables efficient output by determining the output priority based on the task submission timing. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task submission timing data into a generation AI and have the generation AI determine the output priority.

[0135] The output unit can adjust the order of outputs based on the relevance of the tasks during output. For example, the output unit can group highly relevant tasks together for output. For example, the output unit can also output unrelated tasks separately. Furthermore, the output unit can dynamically adjust the order of outputs according to the relevance of the tasks. This enables efficient output by adjusting the order of outputs based on the relevance of the tasks. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input task relevance data into a generating AI and have the generating AI perform the adjustment of the output order.

[0136] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0137] The reception desk can estimate the user's emotions and change the input method for project details and deadlines based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of project details and deadlines. This reduces the user's burden and enables efficient input by adjusting the input method according to the user's emotions.

[0138] The task organization function can estimate the user's emotions and modify the task organization method based on those estimates. For example, if the user is stressed, it can provide a simple task organization method. If the user is relaxed, it can provide a more detailed method. Furthermore, if the user is in a hurry, it can provide a method for quickly organizing tasks. This allows for efficient task management by adjusting the task organization method according to the user's emotions.

[0139] The scheduling unit can estimate the user's emotions and determine schedule priorities based on those emotions. For example, if the user is stressed, it can prioritize scheduling less important tasks. Conversely, if the user is relaxed, it can prioritize scheduling more important tasks. Furthermore, if the user is in a hurry, it can prioritize scheduling urgent tasks. This enables efficient scheduling by determining schedule priorities according to the user's emotions.

[0140] The response unit can estimate the user's emotions and adjust the task response method based on the estimated emotions. For example, if the user is stressed, it can provide a simple response method. If the user is relaxed, it can provide a more detailed response method. Furthermore, if the user is in a hurry, it can provide a quick response method. In this way, by adjusting the task response method according to the user's emotions, efficient task response is achieved.

[0141] The correction unit can estimate the user's emotions and adjust the task correction method based on those emotions. For example, if the user is stressed, it can provide a simple correction method. If the user is relaxed, it can provide a more detailed correction method. Furthermore, if the user is in a hurry, it can provide a quick correction method. This allows for efficient task correction by adjusting the task correction method according to the user's emotions.

[0142] The reception desk can assist users with inputting project details and completion deadlines by referencing their past project history. For example, it can suggest similar projects based on the details and completion deadlines of projects the user has completed in the past. It can also automatically complete project details entered by the user in the past, saving input effort. Furthermore, it can suggest frequently used completion deadlines based on the user's past project history. In this way, referencing past project history reduces input effort and enables efficient data entry.

[0143] The task organization section allows you to set the level of detail for tasks based on the importance of the project. For example, for high-priority projects, you can organize detailed tasks. Conversely, for low-priority projects, you can organize simplified tasks. Furthermore, you can adjust the priority of tasks according to the importance of the project. This enables efficient task management by adjusting the level of detail of tasks based on the importance of the project.

[0144] The scheduling unit can determine schedule priorities based on the submission deadlines of tasks during scheduling. For example, tasks with approaching deadlines can be prioritized, while tasks with later deadlines can be postponed. Furthermore, schedule priorities can be dynamically adjusted according to the submission deadlines. This enables efficient scheduling by determining schedule priorities based on the submission deadlines.

[0145] The task handling unit can apply different handling algorithms depending on the task category when handling tasks. For example, a marketing-specific handling algorithm can be applied to marketing tasks. Similarly, a development-specific handling algorithm can be applied to development tasks. Furthermore, a research-specific handling algorithm can be applied to research tasks. By applying different handling algorithms according to the task category, efficient task handling is achieved.

[0146] The output unit can adjust the order of outputs based on the relevance of the tasks during the output process. For example, highly related tasks can be grouped together and output. Less relevant tasks can also be output separately. Furthermore, the order of outputs can be dynamically adjusted according to the relevance of the tasks. This allows for efficient output by adjusting the output order based on the relevance of the tasks.

[0147] The following briefly describes the processing flow for example form 2.

[0148] Step 1: The reception desk accepts input for project details and completion deadlines. For example, it provides text boxes or dropdown menus for users to enter project details, and a calendar or date selection tool for entering completion deadlines. Step 2: The organization department organizes the tasks necessary to produce the output based on the information received by the reception department. For example, based on the project's objectives and requirements, they identify tasks such as research, document creation, and presentation preparation, and determine the priority of these tasks. Step 3: The scheduling unit schedules the tasks that have been organized by the organization unit. For example, it determines the execution time and duration of each task and determines the execution order considering the dependencies between tasks. Step 4: The response unit handles tasks scheduled by the scheduling unit. For example, it automatically collects information for research and organizes data for document creation, monitors the progress of tasks, and adjusts tasks as needed. Step 5: The checkpoint organization team organizes the points that users should check for tasks handled by the response team. For example, they clarify the points that users should check, such as the content of the documents or the design of the presentation slides, and provide checklists and guidelines. Step 6: The revision section modifies tasks after the user has checked them, based on the points organized by the checkpoint organization section. For example, it might revise the content of a document based on user feedback or redesign presentation slides. Step 7: The repetition section repeats the task corrected by the revision section until the user is satisfied. For example, revisions to materials or adjustments to the presentation are made until the user is satisfied. Step 8: The output unit ultimately outputs the tasks that have been repeated by the loop unit. For example, it provides the user with completed documents or presentations.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the reception unit, sorting unit, scheduling unit, response unit, checkpoint sorting unit, correction unit, repetition unit, and output unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input the project content and completion deadline. The sorting unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and sorts the necessary tasks based on the project content. The scheduling unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and schedules the sorted tasks. The response unit is implemented, for example, by the control unit 46A of the smart device 14 and corresponds the scheduled tasks. The checkpoint sorting unit is implemented, for example, by the identification processing unit 290 of the data processing device 12 and sorts the points that the user should check. The correction unit is implemented, for example, by the control unit 46A of the smart device 14 and corrects tasks based on user feedback. The repetition section is implemented, for example, by the specific processing unit 290 of the data processing device 12, and repeats the task until the user is satisfied. The output section is implemented, for example, by the control unit 46A of the smart device 14, and finally provides the output. The correspondence between each section and the device or control unit is not limited to the example described above, and various modifications are possible.

[0153] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] Each of the multiple elements described above, including the reception unit, sorting unit, scheduling unit, response unit, checkpoint sorting unit, correction unit, repetition unit, and output unit, is implemented, for example, 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 provides an interface for the user to input the project content and completion deadline. The sorting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and sorts the necessary tasks based on the project content. The scheduling unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and schedules the sorted tasks. The response unit is implemented, for example, by the control unit 46A of the smart glasses 214 and corresponds the scheduled tasks. The checkpoint sorting unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and sorts the points that the user should check. The correction unit is implemented, for example, by the control unit 46A of the smart glasses 214 and corrects tasks based on user feedback. The repetition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and repeats the task until the user is satisfied. The output unit is implemented, for example, by the control unit 46A of the smart glasses 214, and finally provides the output. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0169] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] Each of the multiple elements described above, including the reception unit, sorting unit, scheduling unit, response unit, checkpoint sorting unit, correction unit, repetition unit, and output unit, is implemented by, for example, 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 provides an interface for the user to input the project content and completion deadline. The sorting unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sorts the necessary tasks based on the project content. The scheduling unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and schedules the sorted tasks. The response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and corresponds the scheduled tasks. The checkpoint sorting unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sorts the points that the user should check. The correction unit is implemented by, for example, the control unit 46A of the headset terminal 314 and corrects tasks based on user feedback. The repetition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and repeats the task until the user is satisfied. The output unit is implemented, for example, by the control unit 46A of the headset terminal 314, and finally provides the output. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0185] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.).

[0198] 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.

[0199] 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.

[0200] 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.

[0201] Each of the multiple elements described above, including the reception unit, sorting unit, scheduling unit, response unit, checkpoint sorting unit, correction unit, repetition unit, and output unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input the project content and completion deadline. The sorting unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sorts the necessary tasks based on the project content. The scheduling unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and schedules the sorted tasks. The response unit is implemented by, for example, the control unit 46A of the robot 414 and corresponds the scheduled tasks. The checkpoint sorting unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and sorts the points that the user should check. The correction unit is implemented by, for example, the control unit 46A of the robot 414 and corrects tasks based on user feedback. The repetition unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and repeats the task until the user is satisfied. The output unit is implemented, for example, by the control unit 46A of the robot 414, and finally provides the output. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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."

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] (Note 1) A reception desk that accepts input of project details and completion deadlines, Based on the information received by the aforementioned reception unit, the organization unit organizes the tasks necessary for output, A scheduling unit that schedules the tasks organized by the aforementioned organizing unit, Tasks scheduled by the scheduling unit are assigned to the corresponding corresponding unit, A checkpoint organizing unit organizes the points that the user should check for the task handled by the aforementioned handling unit, A correction unit corrects tasks after they have been checked by the user based on the points organized by the aforementioned checkpoint organization unit. A repeating unit that repeats the task modified by the modification unit until the user is satisfied, The system comprises an output unit that ultimately outputs the tasks repeated by the aforementioned repeating unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates user sentiment and changes the input method for project content and completion deadlines based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is When entering project details and completion deadlines, the system provides input assistance based on the user's past project history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When entering project details and completion deadlines, the system filters the input based on the user's current work status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Estimate user sentiment and prioritize projects based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering project details and completion deadlines, the system prioritizes selecting projects that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When entering project details and completion deadlines, relevant projects are entered based on the user's social media activity. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned editing unit, Estimate the user's emotions and change how tasks are organized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned editing unit, When organizing tasks, set the level of detail for each task based on the importance of the project. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned editing unit, When organizing tasks, apply different organization algorithms based on the project category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned editing unit, It estimates the user's emotions and determines task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned editing unit, When organizing tasks, prioritize them based on the project's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editing unit, When organizing tasks, adjust the order of tasks based on their relevance to the project. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned scheduling unit, It estimates the user's emotions and adjusts the scheduling method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned scheduling unit, When scheduling, adjust the level of detail in the schedule based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned scheduling unit, During scheduling, different scheduling algorithms are applied depending on the task category. 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 determines schedule priorities 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, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned scheduling unit, When scheduling, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The corresponding part is, It estimates the user's emotions and adjusts how the task is handled based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The corresponding part is, When handling tasks, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The corresponding part is, When handling tasks, apply different handling algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The corresponding part is, It estimates the user's emotions and determines the priority of corresponding tasks based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The corresponding part is, When handling tasks, prioritize tasks based on their submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 25) The corresponding part is, When handling tasks, adjust the order of tasks based on their relatedness. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned checkpoint sorting unit, We estimate the user's emotions and adjust the checkpoint organization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned checkpoint sorting unit, When organizing checkpoints, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned checkpoint sorting unit, When organizing checkpoints, apply different organization algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned checkpoint sorting unit, The system estimates the user's emotions and prioritizes checkpoints based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned checkpoint sorting unit, When organizing checkpoints, prioritize them based on the task submission deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned checkpoint sorting unit, When organizing checkpoints, adjust the order of checkpoints based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned modification section is, It estimates the user's emotions and adjusts how the task is modified based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned modification section is, When modifying a task, adjust the level of detail of the modification based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned modification section is, When modifying a task, apply a different modification algorithm depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned modification section is, Estimate user emotions and prioritize tasks to modify based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned modification section is, When revising a task, prioritize revisions based on the task's submission date. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned modification section is, When modifying a task, adjust the order of modification based on the relevance of the task The system according to appendix 1, characterized by the above (Appendix 38) The repeating part is Estimate the user's emotion and adjust the repeating method based on the estimated user's emotion The system according to appendix 1, characterized by the above (Appendix 39) The repeating part is When repeating, adjust the detail level of repetition based on the importance of the task The system according to appendix 1, characterized by the above (Appendix 40) The repeating part is When repeating, apply different repeating algorithms according to the category of the task The system according to appendix 1, characterized by the above (Appendix 41) The repeating part is Estimate the user's emotion and determine the priority of repetition based on the estimated user's emotion The system according to appendix 1, characterized by the above (Appendix 42) The repeating part is When repeating, determine the priority of repetition based on the submission time of the task The system according to appendix 1, characterized by the above (Appendix 43) The repeating part is When repeating, adjust the order of repetition based on the relevance of the task The system according to appendix 1, characterized by the above (Appendix 44) The output part is Estimate the user's emotion and adjust the output method based on the estimated user's emotion The system according to appendix 1, characterized by the above (Appendix 45) The output part is At the time of output, adjust the detail level of the output based on the importance of the task The system according to appended note 1, characterized by this (Appended note 46) The output unit At the time of output, apply different output algorithms according to the category of the task The system according to appended note 1, characterized by this (Appended note 47) The output unit Estimate the user's emotion and determine the priority of the output based on the estimated user's emotion The system according to appended note 1, characterized by this (Appended note 48) The output unit At the time of output, determine the priority of the output based on the submission time of the task The system according to appended note 1, characterized by this (Appended note 49) The output unit At the time of output, adjust the order of the output based on the relevance of the task The system according to appended note 1, characterized by this

Explanation of symbols

[0221] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk that accepts input of project details and completion deadlines, Based on the information received by the aforementioned reception unit, the organization unit organizes the tasks necessary for output, A scheduling unit that schedules the tasks organized by the aforementioned organizing unit, Tasks scheduled by the scheduling unit are assigned to the corresponding corresponding unit, A checkpoint organizing unit organizes the points that the user should check for the task handled by the aforementioned handling unit, A correction unit corrects tasks after they have been checked by the user based on the points organized by the aforementioned checkpoint organization unit. A repeating unit that repeats the task modified by the modification unit until the user is satisfied, The system comprises an output unit that ultimately outputs the tasks repeated by the aforementioned repeating unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates user sentiment and changes the input method for project content and completion deadlines based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned reception unit is When entering project details and completion deadlines, the system provides input assistance based on the user's past project history. The system according to feature 1.

4. The aforementioned reception unit is When entering project details and completion deadlines, the system filters the input based on the user's current work status and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is Estimate user sentiment and prioritize projects based on that estimated sentiment. The system according to feature 1.

6. The aforementioned reception unit is When entering project details and completion deadlines, the system prioritizes selecting projects that are more relevant based on the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When entering project details and completion deadlines, relevant projects are entered based on the user's social media activity. The system according to feature 1.

8. The aforementioned editing unit, Estimate the user's emotions and change how tasks are organized based on those estimated emotions. The system according to feature 1.

9. The aforementioned editing unit, When organizing tasks, set the level of detail for each task based on the importance of the project. The system according to feature 1.

10. The aforementioned editing unit, When organizing tasks, apply different organization algorithms based on the project category. The system according to feature 1.

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