system

Abstract

The system according to this embodiment aims to manage and efficiently execute tasks conceived by the user without fail. [Solution] The system according to the embodiment comprises a learning unit, a generation unit, a priority suggestion unit, an advice unit, a management unit, a reception unit, and a brainstorming unit. The learning unit learns the user's surrounding information. The generation unit automatically generates tasks based on the information learned by the learning unit. The priority suggestion unit suggests priorities for the tasks generated by the generation unit. The advice unit provides advice on task execution proposed by the priority suggestion unit. The management unit manages the progress of tasks advised by the advice unit and provides reminders. The reception unit sends tasks to the inbox via voice chat. The brainstorming unit brainstorms the tasks sent by the reception unit.

Description

Technical Field

[0004] ,

[0006] , , , ,

[0005] , , , , ,

[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 conventional technology, there is a problem that task management is complicated and it is difficult to manage tasks conceived by the user without omission.

[0005] The system according to the embodiment aims to manage tasks conceived by the user without omission and efficiently execute them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a learning unit, a generation unit, a priority suggestion unit, an advice unit, a management unit, a reception unit, and a brainstorming unit. The learning unit learns the user's surrounding information. The generation unit automatically generates tasks based on the information learned by the learning unit. The priority suggestion unit suggests priorities for the tasks generated by the generation unit. The advice unit provides advice on task execution proposed by the priority suggestion unit. The management unit manages the progress of tasks advised by the advice unit and provides reminders. The reception unit receives tasks via voice chat into the inbox. The brainstorming unit brainstorms the tasks received by the reception unit. [Effects of the Invention]

[0007] The system according to this embodiment can manage and efficiently execute tasks conceived by the user without fail. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management system according to an embodiment of the present invention is a system that assists the task management method GTD (GetTaskDone) with a generating AI. This task management system uses AI to learn project information and assists with tasks from generation to prioritization, advice on task execution, progress management, and reminders. Users can invoke the tool via voice chat and throw tasks into the inbox by voice. This allows users to record all their thoughts in the inbox without missing anything, and to clarify tasks even with vague ideas. For example, the AI ​​learns information about the user's work and the projects they are participating in. Next, the AI ​​automatically generates and proposes tasks. Furthermore, it suggests task priorities and provides advice on task execution. For example, it advises on points that should not be forgotten based on past projects. In addition, the AI ​​generates simple tasks (e.g., replying to emails) before the deadline, and automatically generates and manages tasks for user review of the generated products. Furthermore, it also has schedule management and reminder functions. Users clarify tasks by throwing tasks into the inbox via voice chat and brainstorming. This eliminates the common pattern of "I recorded this task, but what was it again?". The system learns the user's surrounding information and supports GTD-based task management, improving task registration omissions, inaccuracies, and hassle. For example, users can easily register tasks by simply voice-dumping their ideas into the inbox. Furthermore, the AI ​​suggests task priorities and provides advice on task execution, enabling efficient task management. This system is designed as an affordable AI tool that anyone can use daily, easily providing tangible benefits. For instance, users can easily register tasks by voice-dumping them into the inbox, and the AI ​​suggests task priorities and provides advice on task execution, enabling efficient task management. In this way, the task management system can efficiently support users in managing their tasks.

[0029] The task management system according to this embodiment comprises a learning unit, a generation unit, a priority suggestion unit, an advice unit, a management unit, a reception unit, and a brainstorming unit. The learning unit learns information about the user's surroundings. For example, the learning unit learns information about the user's work content and projects they are participating in. The learning unit can also analyze the user's behavior patterns and past task history to understand the user's characteristics. The generation unit automatically generates tasks based on the information learned by the learning unit. For example, the generation unit generates tasks based on the user's project information. The generation unit can also refer to the user's past task history to generate similar tasks. The generation unit can also adjust the level of detail of tasks based on the user's current project progress. The priority suggestion unit proposes priorities for tasks generated by the generation unit. For example, the priority suggestion unit proposes priorities based on the importance and urgency of tasks. The priority suggestion unit can also refer to the user's past task completion history to optimize priorities. The priority suggestion unit can also dynamically change priorities based on the user's current project progress. The advice unit provides advice on task execution proposed by the priority suggestion unit. The Advice Department, for example, advises on important points to remember from past cases. The Advice Department can also refer to the user's past task completion history to provide optimal advice. The Advice Department can also adjust the level of detail of the advice based on the user's current project progress. The Management Department manages the progress and provides reminders for tasks advised by the Advice Department. The Management Department, for example, tracks and manages the progress of tasks. The Management Department can also refer to the user's past task completion history to select the optimal management method. The Management Department can also dynamically change the management content based on the user's current project progress. The Reception Department receives tasks in the inbox via voice chat. The Reception Department, for example, allows users to input tasks by voice and register them in the inbox. The Reception Department can also refer to the user's past task reception history to select the optimal reception method. The Reception Department can also prioritize receiving highly relevant tasks by considering the user's geographical location information.The brainstorming unit brainstorms tasks submitted by the reception unit. For example, the brainstorming unit brainstorms ideas conceived by the user, clarifying the task. The brainstorming unit can also refer to the user's past task history and select the optimal brainstorming method. The brainstorming unit can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location information. As a result, the task management system according to this embodiment can efficiently support the user's task management.

[0030] The learning unit learns about the user's surrounding information. Specifically, it collects detailed information about the user's daily tasks and projects they are involved in, and uses this to understand the user's behavior patterns and characteristics. For example, it collects data such as what tasks the user usually performs and at what time of day, and how much time they spend on each project. Furthermore, it analyzes the user's past task history and accumulates information such as what tasks were completed and how long, and which tasks were delayed. This allows the learning unit to understand the user's characteristics in detail and provide the user with the foundational data necessary for optimal task management. The learning unit stores this data in a cloud-based database, making it accessible to other departments. In addition, the learning unit regularly updates the user's behavior patterns and task history, continuously learning based on the latest information. This allows the learning unit to respond to the user's changing needs and circumstances and always support optimal task management.

[0031] The generation unit automatically generates tasks based on information learned by the learning unit. Specifically, it generates new tasks by referring to the user's current projects and similar tasks performed in the past. For example, if a user starts a new project, the generation unit will list the necessary tasks based on the project's goals and deadlines, and automatically generate detailed task descriptions. It can also refer to the user's past task history and generate similar tasks, allowing the user to reuse successful methods and procedures from the past. Furthermore, the generation unit can adjust the level of detail of tasks based on the user's current project progress. For example, it can generate rough tasks in the initial stages of a project and then specify the task details as the project progresses. This allows the generation unit to flexibly generate tasks according to the user's needs and support efficient task management. The generation unit automatically adds the generated tasks to the user's task list, making them ready for immediate use. In addition, the generation unit improves its generation algorithm based on user feedback, achieving more accurate task generation.

[0032] The priority suggestion unit proposes priorities for tasks generated by the generation unit. Specifically, it determines the priority of each task by considering factors such as the importance and urgency of the task, and the user's current project progress. For example, it assigns high priority to tasks of high importance or those with approaching deadlines, enabling the user to complete tasks efficiently. The priority suggestion unit optimizes priorities by referring to the user's past task completion history and analyzing which tasks were completed and at what priority. It can also dynamically change priorities based on the user's current project progress. For example, if a project is behind schedule, it can accelerate project progress by increasing the priority of related tasks. In this way, the priority suggestion unit supports the user in efficiently managing tasks and ensuring that important tasks are not overlooked. Furthermore, the priority suggestion unit improves its priority suggestion algorithm based on user feedback, achieving more accurate priority suggestions.

[0033] The Advice Department provides advice on task execution proposed by the Prioritization Department. Specifically, it offers guidance to help users efficiently complete tasks. For example, it advises users on important points to remember from past projects, ensuring they don't overlook crucial details. The Advice Department provides optimal advice by reviewing the user's past task completion history and analyzing which methods and procedures were effective. It can also adjust the level of detail of the advice based on the user's current project progress. For example, it can provide general advice in the early stages of a project and add more specific advice as the project progresses. In this way, the Advice Department supports users in efficiently completing tasks and leading projects to success. Furthermore, the Advice Department can improve its advice based on user feedback, providing even more effective guidance.

[0034] The Management Department manages the progress and provides reminders for tasks advised by the Advisory Department. Specifically, it monitors the progress and provides timely reminders to ensure users don't forget to complete tasks. For example, it sends notifications as task deadlines approach to encourage users to complete the tasks. The Management Department selects the optimal management method by reviewing the user's past task completion history and analyzing which reminder methods were effective. It can also dynamically change management content based on the user's current project progress. For example, if a project is behind schedule, it increases the frequency of reminders to help users complete tasks quickly. In this way, the Management Department helps users manage tasks efficiently and complete important tasks without missing any. Furthermore, the Management Department improves its management methods based on user feedback to achieve more effective progress management.

[0035] The reception desk receives tasks via voice chat and adds them to the inbox. Specifically, users input tasks by voice, and the system converts the voice into text and registers it in the inbox. For example, if a user gives a voice command saying, "Prepare for tomorrow's meeting," the reception desk analyzes the voice and adds it to the inbox as a task. The reception desk selects the optimal task submission method by referring to the user's past task submission history and analyzing which method was most efficient. It can also prioritize tasks that are highly relevant, taking into account the user's geographical location. For example, if the user is in the office, work-related tasks will be prioritized, and if they are at home, household-related tasks will be prioritized. In this way, the reception desk supports users in efficiently entering and managing tasks. Furthermore, the reception desk can improve the submission method based on user feedback and provide a more user-friendly system.

[0036] The brainstorming team brainstorms tasks submitted by the reception team. Specifically, it brainstorms ideas conceived by users to clarify the tasks. For example, if a user enters a vague task such as "Think of ideas for a new project," the brainstorming team will break down the task into specific steps and clarify the detailed task content. The brainstorming team selects the optimal brainstorming method by referring to the user's past task history and analyzing which methods were most effective. It can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location. For example, if the user is in a meeting room, meeting-related tasks will be prioritized, and if the user is out of the office, tasks that should be done while out of the office will be prioritized. In this way, the brainstorming team supports users in efficiently clarifying and managing tasks. Furthermore, the brainstorming team can improve its brainstorming methods based on user feedback, achieving more effective task clarification.

[0037] The learning unit can learn information about the user's work content and the projects they are participating in. For example, the learning unit can learn the user's work content. The learning unit can also learn the user's project information. The learning unit can also analyze the user's behavior patterns and determine the priority of the information to learn. The learning unit can also analyze the user's past behavior patterns and dynamically adjust the scope of the information to learn. During learning, the learning unit can also optimize the learning content based on the user's current project progress. This allows for more appropriate task generation and management by learning the user's work content and project information. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's work content and project information into AI and have the AI ​​perform the optimization of the learning content.

[0038] The generation unit can automatically generate tasks based on learned information. For example, the generation unit generates tasks based on learned information. The generation unit can also refer to the user's past task history and generate similar tasks. The generation unit can also adjust the level of detail of tasks based on the user's current project progress. The generation unit can also estimate the user's emotions and adjust the content of the tasks generated based on the estimated emotions of the user. The generation unit can also automatically generate similar tasks by referring to the user's past task history during generation. The generation unit can also adjust the level of detail of tasks based on the user's current project progress during generation. This reduces the burden on the user by automatically generating tasks based on learned information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learned information into AI and have the AI ​​perform the automatic task generation.

[0039] The priority suggestion unit can propose priorities for generated tasks. For example, it can suggest priorities based on the importance or urgency of the tasks. The priority suggestion unit can also optimize priorities by referring to the user's past task completion history. It can also dynamically change priorities based on the user's current project progress. Furthermore, it can estimate the user's sentiment and adjust task priorities based on the estimated sentiment. When proposing priorities, the priority suggestion unit can optimize priorities by referring to the user's past task completion history. When proposing priorities, the priority suggestion unit can also dynamically change priorities based on the user's current project progress. This allows important tasks to be processed preferentially by proposing priorities for generated tasks. Some or all of the above processes in the priority suggestion unit may be performed using AI, or not. For example, the priority suggestion unit can input the priorities of generated tasks into an AI and have the AI ​​perform the priority suggestion.

[0040] The advice unit can advise on important points from past cases. For example, it can advise on key points from past cases. The advice unit can also refer to the user's past task completion history to provide optimal advice. The advice unit can also adjust the level of detail of the advice based on the user's current project progress. The advice unit can also estimate the user's emotions and adjust the content of the advice based on the estimated emotions. When providing advice, the advice unit can also refer to the user's past task completion history to provide optimal advice. When providing advice, the advice unit can also adjust the level of detail of the advice based on the user's current project progress. This improves the accuracy of task execution through advice from past cases. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input past case information into AI and have the AI ​​provide advice.

[0041] The management department can perform progress management and reminders. For example, the management department can grasp the progress of tasks and manage their progress. The management department can also refer to the user's past task completion history and select the optimal management method. The management department can also dynamically change the management content based on the user's current project progress. The management department can also estimate the user's emotions and adjust the progress management method based on the estimated user emotions. When managing progress, the management department can also refer to the user's past task completion history and select the optimal management method. When managing progress, the management department can also dynamically change the management content based on the user's current project progress. This makes it easier to grasp the progress of tasks by performing progress management and reminders. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the task progress into AI and have AI execute the progress management method.

[0042] The reception system allows users to submit tasks to their inbox via voice chat. For example, the user can input a task by voice and register it in the inbox. The reception system can also refer to the user's past task submission history and select the most appropriate submission method. The reception system can also prioritize tasks that are highly relevant, taking into account the user's geographical location. The reception system can also estimate the user's emotions and adjust the task submission method based on the estimated emotions. The reception system can also refer to the user's past task submission history to select the most appropriate submission method upon submission. The reception system can also prioritize tasks that are highly relevant, taking into account the user's geographical location. This allows users to record all their thoughts without missing anything by submitting tasks to their inbox via voice chat. Some or all of the above processes in the reception system may be performed using AI, or not. For example, the reception system can input voice-inputted tasks into AI and have the AI ​​execute the task submission process.

[0043] The brainstorming section can conduct brainstorming to clarify tasks even with low-resolution ideas. For example, the brainstorming section can brainstorm the user's ideas to clarify the task. The brainstorming section can also refer to the user's past task history to select the optimal brainstorming method. The brainstorming section can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location. The brainstorming section can also estimate the user's emotions and adjust the brainstorming method based on the estimated emotions. The brainstorming section can also refer to the user's past task history to select the optimal brainstorming method during the brainstorming process. The brainstorming section can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location during the brainstorming process. This eliminates ambiguity in tasks by clarifying them even with low-resolution ideas. Some or all of the above processes in the brainstorming section may be performed using AI, for example, or not. For example, the brainstorming section can input the ideas into AI and have the AI ​​perform the task clarification.

[0044] The learning unit can analyze the user's past behavior patterns and dynamically adjust the scope of information to be learned. For example, the learning unit prioritizes learning information related to tasks the user has frequently performed in the past. The learning unit can also learn information related to tasks the user has avoided in the past and identify areas for improvement. The learning unit can also predict and learn information needed for future tasks based on the user's behavior patterns. This allows the learning unit to learn more appropriate information by adjusting the scope of information to be learned based on the user's past behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past behavior patterns into AI and have the AI ​​adjust the scope of information to be learned.

[0045] The learning unit can optimize the learning content based on the user's current project progress during the learning process. For example, in the early stages of a project, the learning unit can learn basic information. In the middle stages of the project, the learning unit can also learn information related to specific tasks. In the final stages of the project, the learning unit can also learn information necessary for final confirmation and completion. This allows for effective learning of project-related information by optimizing the learning content based on the project progress. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the project progress into AI and have AI perform the optimization of the learning content.

[0046] The learning unit can prioritize learning highly relevant information by considering the user's geographical location during the learning process. For example, if the user is in the office, the learning unit will prioritize learning work-related information. If the user is at home, the learning unit can also prioritize learning information related to home. If the user is on a business trip, the learning unit can also prioritize learning information related to the destination. This allows the learning unit to provide more appropriate information by learning highly relevant information based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the learning of highly relevant information.

[0047] The learning unit can analyze the user's social media activity and learn relevant information during the learning process. For example, the learning unit can learn relevant information based on information shared by the user on social media. The learning unit can also learn relevant information based on information from accounts the user follows. The learning unit can also learn relevant information based on information from groups the user participates in. This allows the learning unit to provide more appropriate information by learning relevant information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into AI and have the AI ​​perform the learning of relevant information.

[0048] The generation unit can automatically generate similar tasks by referring to the user's past task history during the generation process. For example, the generation unit can generate tasks similar to those the user has performed in the past. The generation unit can also generate new tasks based on tasks the user has successfully completed in the past. The generation unit can also generate improved versions of tasks the user has failed at in the past. This makes task generation more efficient by generating similar tasks based on the user's past task history. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past task history into AI and have the AI ​​perform the generation of similar tasks.

[0049] The generation unit can adjust the level of detail of tasks based on the user's current project progress during generation. For example, in the early stages of a project, the generation unit can generate tasks at a high level. In the middle stages of a project, the generation unit can also generate specific tasks. In the final stages of a project, the generation unit can also generate detailed tasks. This allows for the effective generation of project-related tasks by adjusting the level of detail based on the project's progress. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the project's progress into the AI ​​and have the AI ​​perform the task detail adjustment.

[0050] The generation unit can prioritize generating tasks that are highly relevant to the user, taking into account the user's geographical location information during the generation process. For example, if the user is in the office, the generation unit will prioritize generating work-related tasks. If the user is at home, the generation unit can also prioritize generating tasks related to their home. If the user is on a business trip, the generation unit can also prioritize generating tasks related to their destination. This allows for the provision of more appropriate tasks by generating highly relevant tasks based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and have the AI ​​perform the generation of highly relevant tasks.

[0051] The generation unit can analyze the user's social media activity and generate relevant tasks during the generation process. For example, the generation unit can generate relevant tasks based on information shared by the user on social media. The generation unit can also generate relevant tasks based on information about accounts the user follows. The generation unit can also generate relevant tasks based on information about groups the user participates in. This allows for the provision of more appropriate tasks by generating relevant tasks based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI and have the AI ​​perform the generation of relevant tasks.

[0052] The priority suggestion unit can optimize priorities by referring to the user's past task completion history when suggesting priorities. For example, the priority suggestion unit can optimize priorities based on the user's past completed task history. The priority suggestion unit can also optimize priorities based on the user's past failed task history. The priority suggestion unit can also analyze the user's past task completion history and suggest the most efficient priorities. This enables efficient task management by optimizing priorities based on the user's past task completion history. Some or all of the above processes in the priority suggestion unit may be performed using AI, for example, or without AI. For example, the priority suggestion unit can input the user's past task completion history into AI and have the AI ​​perform the priority optimization.

[0053] The priority suggestion unit can dynamically change priorities based on the user's current project progress when suggesting priorities. For example, in the early stages of a project, the priority suggestion unit can prioritize important tasks. In the middle stages of a project, the priority suggestion unit can also prioritize specific tasks. In the final stages of a project, the priority suggestion unit can also prioritize tasks necessary for final review and completion. This allows for effective management of project-related tasks by dynamically changing priorities based on project progress. Some or all of the above processes in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the project progress into AI and have the AI ​​perform the dynamic change of priorities.

[0054] The priority suggestion unit can prioritize suggesting tasks that are highly relevant, taking into account the user's geographical location when suggesting priorities. For example, if the user is in the office, the priority suggestion unit will prioritize suggesting work-related tasks. If the user is at home, the priority suggestion unit can also prioritize suggesting tasks related to their home. If the user is on a business trip, the priority suggestion unit can also prioritize suggesting tasks related to their business trip destination. This enables more appropriate task management by suggesting highly relevant tasks based on the user's geographical location. Some or all of the above processing in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​suggest highly relevant tasks.

[0055] The priority suggestion unit can analyze the user's social media activity and suggest priorities for relevant tasks when suggesting priorities. For example, the priority suggestion unit can suggest priorities for relevant tasks based on information shared by the user on social media. The priority suggestion unit can also suggest priorities for relevant tasks based on information about accounts the user follows. The priority suggestion unit can also suggest priorities for relevant tasks based on information about groups the user participates in. This enables more appropriate task management by suggesting priorities for relevant tasks based on the user's social media activity. Some or all of the above processing in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the user's social media activity into AI and have the AI ​​perform task priority suggestions.

[0056] The advice unit can provide optimal advice by referring to the user's past task completion history when providing advice. For example, the advice unit can provide optimal advice based on the user's past successful task history. The advice unit can also provide advice, including areas for improvement, based on the user's past unsuccessful task history. The advice unit can also analyze the user's past task completion history and provide the most effective advice. This improves the accuracy of task execution by providing optimal advice based on the user's past task completion history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past task completion history into AI and have the AI ​​provide optimal advice.

[0057] The advice unit can adjust the level of detail of its advice based on the user's current project progress. For example, the advice unit provides basic advice in the early stages of a project. In the middle stages of a project, it can provide specific advice. In the final stages of a project, it can provide detailed advice. This allows for the effective provision of project-related advice by adjusting the level of detail based on the project's progress. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the project's progress into the AI ​​and have the AI ​​adjust the level of detail of the advice.

[0058] The advice unit can prioritize providing highly relevant advice by taking into account the user's geographical location. For example, if the user is in the office, the advice unit can prioritize work-related advice. If the user is at home, the advice unit can also prioritize advice related to home life. If the user is on a business trip, the advice unit can also prioritize advice related to their destination. This allows for more appropriate advice to be provided by offering highly relevant advice based on the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into AI and have AI perform the task of providing highly relevant advice.

[0059] The advice unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice unit can provide relevant advice based on information the user has shared on social media. The advice unit can also provide relevant advice based on information about accounts the user follows. The advice unit can also provide relevant advice based on information about groups the user participates in. This allows for more appropriate advice to be provided by providing relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity into AI and have the AI ​​perform the task of providing relevant advice.

[0060] The management department can select the optimal management method by referring to the user's past task completion history when managing progress. For example, the management department can select the optimal management method based on the user's past successful task history. The management department can also select a management method that includes areas for improvement based on the user's past failed task history. The management department can also analyze the user's past task completion history and select the most effective management method. This enables efficient progress management by selecting the optimal management method based on the user's past task completion history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's past task completion history into AI and have the AI ​​select the optimal management method.

[0061] The management unit can dynamically change the management content based on the user's current project progress during progress management. For example, the management unit can provide basic progress management methods in the initial stages of a project. In the middle stages of a project, the management unit can also provide specific progress management methods. In the final stages of a project, the management unit can also provide detailed progress management methods. This allows for effective progress management related to the project by dynamically changing the management content based on the project's progress. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input the project's progress into the AI ​​and have the AI ​​execute the dynamic changes to the management content.

[0062] The management department can prioritize tasks that are highly relevant to the user's geographical location when managing progress. For example, if the user is in the office, the management department can prioritize tasks related to work. If the user is at home, the management department can also prioritize tasks related to home life. If the user is on a business trip, the management department can also prioritize tasks related to the destination. This allows for more appropriate progress management by managing tasks based on the user's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's geographical location into the AI ​​and have the AI ​​manage highly relevant tasks.

[0063] The management department can analyze users' social media activity and manage the progress of related tasks during progress management. For example, the management department can manage the progress of related tasks based on information shared by users on social media. The management department can also manage the progress of related tasks based on information about accounts that users follow. The management department can also manage the progress of related tasks based on information about groups that users participate in. This allows for more appropriate progress management by managing the progress of related tasks based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input users' social media activity into AI and have the AI ​​perform progress management of related tasks.

[0064] The reception unit can select the optimal reception method by referring to the user's past task reception history when a task is received. For example, the reception unit can automatically display tasks that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks to be used during specific time periods based on the user's past reception history. This enables efficient task reception by selecting the optimal reception method based on the user's past task reception history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past task reception history into AI and have the AI ​​select the optimal reception method.

[0065] The reception desk can prioritize accepting tasks that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in the office, the reception desk will prioritize tasks related to work. If the user is at home, the reception desk can also prioritize tasks related to home life. If the user is on a business trip, the reception desk can also prioritize tasks related to their destination. This allows for more appropriate task acceptance by accepting tasks that are highly relevant based on the user's geographical location. 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 geographical location into the AI ​​and have the AI ​​perform the task of accepting highly relevant tasks.

[0066] The brainstorming unit can select the optimal brainstorming method by referring to the user's past task history during the brainstorming session. For example, the brainstorming unit can select the optimal brainstorming method based on the user's past successful task history. The brainstorming unit can also select a brainstorming method that includes areas for improvement based on the user's past unsuccessful task history. The brainstorming unit can also analyze the user's past task history and select the most effective brainstorming method. This enables efficient brainstorming by selecting the optimal brainstorming method based on the user's past task history. Some or all of the above-described processes in the brainstorming unit may be performed using AI, for example, or without AI. For example, the brainstorming unit can input the user's past task history into AI and have the AI ​​select the optimal brainstorming method.

[0067] The brainstorming unit can prioritize tasks that are highly relevant to the user's location during brainstorming, taking into account the user's geographical location. For example, if the user is in the office, the brainstorming unit will prioritize tasks related to work. If the user is at home, the brainstorming unit can also prioritize tasks related to home life. If the user is on a business trip, the brainstorming unit can also prioritize tasks related to their destination. This allows for more appropriate brainstorming by prioritizing tasks that are highly relevant based on the user's geographical location. Some or all of the above processing in the brainstorming unit may be performed using AI, for example, or without AI. For example, the brainstorming unit can input the user's geographical location into the AI ​​and have the AI ​​perform brainstorming on highly relevant tasks.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The task management system can also include a hobby learning section that learns about the user's hobbies and interests. This section learns about the user's past hobbies and interests and reflects this in task suggestions. For example, if the user is interested in music, music-related tasks can be suggested. If the user enjoys traveling, tasks related to travel planning can be suggested. Furthermore, if the user enjoys reading, reading-related tasks can be suggested. This approach, by suggesting tasks based on the user's hobbies and interests, can increase user motivation and make task completion more enjoyable.

[0070] The task management system can also include a communication learning unit that learns the user's communication style. This unit learns the user's past communication patterns and incorporates them into task suggestions. For example, if a user frequently uses email, email-related tasks can be prioritized. Similarly, if a user prefers chat, chat-related tasks can be suggested. Furthermore, if a user prefers phone calls, phone-related tasks can be suggested. This allows for improved user communication efficiency by suggesting tasks based on the user's communication style.

[0071] The task management system can also include a learning style learning unit that learns the user's learning style. This unit learns the user's past learning methods and reflects them in task suggestions. For example, if a user prefers visual learning, visual tasks can be prioritized. Similarly, if a user prefers auditory learning, audio-related tasks can be suggested. Furthermore, if a user prefers experiential learning, practical tasks can be suggested. This allows for improved user learning efficiency by suggesting tasks based on the user's learning style.

[0072] The task management system can also include a lifestyle rhythm learning unit that learns the user's daily routine. This unit learns the user's past daily rhythm and reflects this in task suggestions. For example, if a user has a morning routine, it can suggest important tasks in the morning. Similarly, if a user has a night owl routine, it can suggest important tasks at night. Furthermore, if a user is active on weekends, it can suggest weekend-related tasks. This allows for task management tailored to the user's lifestyle by suggesting tasks based on their daily rhythm.

[0073] The task management system can also include a project management learning unit that learns the user's project management style. This unit learns the user's past project management methods and incorporates them into task suggestions. For example, if the user prefers agile methodologies, it can prioritize suggesting agile-related tasks. Similarly, if the user prefers waterfall methodologies, it can suggest waterfall-related tasks. Furthermore, if the user prefers Kanban methodologies, it can suggest Kanban-related tasks. This allows the system to improve the user's project management efficiency by suggesting tasks based on their project management style.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The learning unit learns about the user's surrounding information. For example, it analyzes information about the user's work, projects they are participating in, behavioral patterns, and past task history to understand the user's characteristics. Step 2: The generation unit automatically generates tasks based on the information learned by the learning unit. For example, it can generate tasks based on the user's project information and generate similar tasks by referring to past task history. It can also adjust the level of detail of tasks based on the current project progress. Step 3: The priority suggestion unit proposes priorities for the tasks generated by the generation unit. For example, it suggests priorities based on the importance and urgency of the tasks, and optimizes priorities by referring to past task completion history. It also dynamically changes priorities based on the current project progress. Step 4: The Advice Department provides advice on task execution proposed by the Prioritization Department. For example, it advises on key points to remember from past projects and provides optimal advice by referring to the user's past task completion history. It also adjusts the level of detail of the advice based on the current project progress. Step 5: The management department manages the progress and provides reminders for tasks advised by the advisory department. For example, they track and manage the progress of tasks. They select the optimal management method by referring to the user's past task completion history and dynamically change the management content based on the current project progress. Step 6: The reception desk submits tasks to the inbox via voice chat. For example, a user inputs a task by voice and registers it in the inbox. The system selects the optimal submission method by referring to past task submission history and prioritizes submission of highly relevant tasks, taking geographical location information into consideration. Step 7: The brainstorming team brainstorms tasks submitted by the reception team. For example, they brainstorm ideas conceived by users to clarify the tasks. They select the optimal brainstorming method by referring to past task history and prioritize brainstorming tasks with high relevance, taking geographical location information into consideration.

[0076] (Example of form 2) The task management system according to an embodiment of the present invention is a system that assists the task management method GTD (GetTaskDone) with a generating AI. This task management system uses AI to learn project information and assists with tasks from generation to prioritization, advice on task execution, progress management, and reminders. Users can invoke the tool via voice chat and throw tasks into the inbox by voice. This allows users to record all their thoughts in the inbox without missing anything, and to clarify tasks even with vague ideas. For example, the AI ​​learns information about the user's work and the projects they are participating in. Next, the AI ​​automatically generates and proposes tasks. Furthermore, it suggests task priorities and provides advice on task execution. For example, it advises on points that should not be forgotten based on past projects. In addition, the AI ​​generates simple tasks (e.g., replying to emails) before the deadline, and automatically generates and manages tasks for user review of the generated products. Furthermore, it also has schedule management and reminder functions. Users clarify tasks by throwing tasks into the inbox via voice chat and brainstorming. This eliminates the common pattern of "I recorded this task, but what was it again?". The system learns the user's surrounding information and supports GTD-based task management, improving task registration omissions, inaccuracies, and hassle. For example, users can easily register tasks by simply voice-dumping their ideas into the inbox. Furthermore, the AI ​​suggests task priorities and provides advice on task execution, enabling efficient task management. This system is designed as an affordable AI tool that anyone can use daily, easily providing tangible benefits. For instance, users can easily register tasks by voice-dumping them into the inbox, and the AI ​​suggests task priorities and provides advice on task execution, enabling efficient task management. In this way, the task management system can efficiently support users in managing their tasks.

[0077] The task management system according to this embodiment comprises a learning unit, a generation unit, a priority suggestion unit, an advice unit, a management unit, a reception unit, and a brainstorming unit. The learning unit learns information about the user's surroundings. For example, the learning unit learns information about the user's work content and projects they are participating in. The learning unit can also analyze the user's behavior patterns and past task history to understand the user's characteristics. The generation unit automatically generates tasks based on the information learned by the learning unit. For example, the generation unit generates tasks based on the user's project information. The generation unit can also refer to the user's past task history to generate similar tasks. The generation unit can also adjust the level of detail of tasks based on the user's current project progress. The priority suggestion unit proposes priorities for tasks generated by the generation unit. For example, the priority suggestion unit proposes priorities based on the importance and urgency of tasks. The priority suggestion unit can also refer to the user's past task completion history to optimize priorities. The priority suggestion unit can also dynamically change priorities based on the user's current project progress. The advice unit provides advice on task execution proposed by the priority suggestion unit. The Advice Department, for example, advises on important points to remember from past cases. The Advice Department can also refer to the user's past task completion history to provide optimal advice. The Advice Department can also adjust the level of detail of the advice based on the user's current project progress. The Management Department manages the progress and provides reminders for tasks advised by the Advice Department. The Management Department, for example, tracks and manages the progress of tasks. The Management Department can also refer to the user's past task completion history to select the optimal management method. The Management Department can also dynamically change the management content based on the user's current project progress. The Reception Department receives tasks in the inbox via voice chat. The Reception Department, for example, allows users to input tasks by voice and register them in the inbox. The Reception Department can also refer to the user's past task reception history to select the optimal reception method. The Reception Department can also prioritize receiving highly relevant tasks by considering the user's geographical location information.The brainstorming unit brainstorms tasks submitted by the reception unit. For example, the brainstorming unit brainstorms ideas conceived by the user, clarifying the task. The brainstorming unit can also refer to the user's past task history and select the optimal brainstorming method. The brainstorming unit can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location information. As a result, the task management system according to this embodiment can efficiently support the user's task management.

[0078] The learning unit learns about the user's surrounding information. Specifically, it collects detailed information about the user's daily tasks and projects they are involved in, and uses this to understand the user's behavior patterns and characteristics. For example, it collects data such as what tasks the user usually performs and at what time of day, and how much time they spend on each project. Furthermore, it analyzes the user's past task history and accumulates information such as what tasks were completed and how long, and which tasks were delayed. This allows the learning unit to understand the user's characteristics in detail and provide the user with the foundational data necessary for optimal task management. The learning unit stores this data in a cloud-based database, making it accessible to other departments. In addition, the learning unit regularly updates the user's behavior patterns and task history, continuously learning based on the latest information. This allows the learning unit to respond to the user's changing needs and circumstances and always support optimal task management.

[0079] The generation unit automatically generates tasks based on information learned by the learning unit. Specifically, it generates new tasks by referring to the user's current projects and similar tasks performed in the past. For example, if a user starts a new project, the generation unit will list the necessary tasks based on the project's goals and deadlines, and automatically generate detailed task descriptions. It can also refer to the user's past task history and generate similar tasks, allowing the user to reuse successful methods and procedures from the past. Furthermore, the generation unit can adjust the level of detail of tasks based on the user's current project progress. For example, it can generate rough tasks in the initial stages of a project and then specify the task details as the project progresses. This allows the generation unit to flexibly generate tasks according to the user's needs and support efficient task management. The generation unit automatically adds the generated tasks to the user's task list, making them ready for immediate use. In addition, the generation unit improves its generation algorithm based on user feedback, achieving more accurate task generation.

[0080] The priority suggestion unit proposes priorities for tasks generated by the generation unit. Specifically, it determines the priority of each task by considering factors such as the importance and urgency of the task, and the user's current project progress. For example, it assigns high priority to tasks of high importance or those with approaching deadlines, enabling the user to complete tasks efficiently. The priority suggestion unit optimizes priorities by referring to the user's past task completion history and analyzing which tasks were completed and at what priority. It can also dynamically change priorities based on the user's current project progress. For example, if a project is behind schedule, it can accelerate project progress by increasing the priority of related tasks. In this way, the priority suggestion unit supports the user in efficiently managing tasks and ensuring that important tasks are not overlooked. Furthermore, the priority suggestion unit improves its priority suggestion algorithm based on user feedback, achieving more accurate priority suggestions.

[0081] The Advice Department provides advice on task execution proposed by the Prioritization Department. Specifically, it offers guidance to help users efficiently complete tasks. For example, it advises users on important points to remember from past projects, ensuring they don't overlook crucial details. The Advice Department provides optimal advice by reviewing the user's past task completion history and analyzing which methods and procedures were effective. It can also adjust the level of detail of the advice based on the user's current project progress. For example, it can provide general advice in the early stages of a project and add more specific advice as the project progresses. In this way, the Advice Department supports users in efficiently completing tasks and leading projects to success. Furthermore, the Advice Department can improve its advice based on user feedback, providing even more effective guidance.

[0082] The Management Department manages the progress and provides reminders for tasks advised by the Advisory Department. Specifically, it monitors the progress and provides timely reminders to ensure users don't forget to complete tasks. For example, it sends notifications as task deadlines approach to encourage users to complete the tasks. The Management Department selects the optimal management method by reviewing the user's past task completion history and analyzing which reminder methods were effective. It can also dynamically change management content based on the user's current project progress. For example, if a project is behind schedule, it increases the frequency of reminders to help users complete tasks quickly. In this way, the Management Department helps users manage tasks efficiently and complete important tasks without missing any. Furthermore, the Management Department improves its management methods based on user feedback to achieve more effective progress management.

[0083] The reception desk receives tasks via voice chat and adds them to the inbox. Specifically, users input tasks by voice, and the system converts the voice into text and registers it in the inbox. For example, if a user gives a voice command saying, "Prepare for tomorrow's meeting," the reception desk analyzes the voice and adds it to the inbox as a task. The reception desk selects the optimal task submission method by referring to the user's past task submission history and analyzing which method was most efficient. It can also prioritize tasks that are highly relevant, taking into account the user's geographical location. For example, if the user is in the office, work-related tasks will be prioritized, and if they are at home, household-related tasks will be prioritized. In this way, the reception desk supports users in efficiently entering and managing tasks. Furthermore, the reception desk can improve the submission method based on user feedback and provide a more user-friendly system.

[0084] The brainstorming team brainstorms tasks submitted by the reception team. Specifically, it brainstorms ideas conceived by users to clarify the tasks. For example, if a user enters a vague task such as "Think of ideas for a new project," the brainstorming team will break down the task into specific steps and clarify the detailed task content. The brainstorming team selects the optimal brainstorming method by referring to the user's past task history and analyzing which methods were most effective. It can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location. For example, if the user is in a meeting room, meeting-related tasks will be prioritized, and if the user is out of the office, tasks that should be done while out of the office will be prioritized. In this way, the brainstorming team supports users in efficiently clarifying and managing tasks. Furthermore, the brainstorming team can improve its brainstorming methods based on user feedback, achieving more effective task clarification.

[0085] The learning unit can learn information about the user's work content and the projects they are participating in. For example, the learning unit can learn the user's work content. The learning unit can also learn the user's project information. The learning unit can also analyze the user's behavior patterns and determine the priority of the information to learn. The learning unit can also analyze the user's past behavior patterns and dynamically adjust the scope of the information to learn. During learning, the learning unit can also optimize the learning content based on the user's current project progress. This allows for more appropriate task generation and management by learning the user's work content and project information. Some or all of the above processes in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the user's work content and project information into AI and have the AI ​​perform the optimization of the learning content.

[0086] The generation unit can automatically generate tasks based on learned information. For example, the generation unit generates tasks based on learned information. The generation unit can also refer to the user's past task history and generate similar tasks. The generation unit can also adjust the level of detail of tasks based on the user's current project progress. The generation unit can also estimate the user's emotions and adjust the content of the tasks generated based on the estimated emotions of the user. The generation unit can also automatically generate similar tasks by referring to the user's past task history during generation. The generation unit can also adjust the level of detail of tasks based on the user's current project progress during generation. This reduces the burden on the user by automatically generating tasks based on learned information. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input learned information into AI and have the AI ​​perform the automatic task generation.

[0087] The priority suggestion unit can propose priorities for generated tasks. For example, it can suggest priorities based on the importance or urgency of the tasks. The priority suggestion unit can also optimize priorities by referring to the user's past task completion history. It can also dynamically change priorities based on the user's current project progress. Furthermore, it can estimate the user's sentiment and adjust task priorities based on the estimated sentiment. When proposing priorities, the priority suggestion unit can optimize priorities by referring to the user's past task completion history. When proposing priorities, the priority suggestion unit can also dynamically change priorities based on the user's current project progress. This allows important tasks to be processed preferentially by proposing priorities for generated tasks. Some or all of the above processes in the priority suggestion unit may be performed using AI, or not. For example, the priority suggestion unit can input the priorities of generated tasks into an AI and have the AI ​​perform the priority suggestion.

[0088] The advice unit can advise on important points from past cases. For example, it can advise on key points from past cases. The advice unit can also refer to the user's past task completion history to provide optimal advice. The advice unit can also adjust the level of detail of the advice based on the user's current project progress. The advice unit can also estimate the user's emotions and adjust the content of the advice based on the estimated emotions. When providing advice, the advice unit can also refer to the user's past task completion history to provide optimal advice. When providing advice, the advice unit can also adjust the level of detail of the advice based on the user's current project progress. This improves the accuracy of task execution through advice from past cases. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input past case information into AI and have the AI ​​provide advice.

[0089] The management department can perform progress management and reminders. For example, the management department can grasp the progress of tasks and manage their progress. The management department can also refer to the user's past task completion history and select the optimal management method. The management department can also dynamically change the management content based on the user's current project progress. The management department can also estimate the user's emotions and adjust the progress management method based on the estimated user emotions. When managing progress, the management department can also refer to the user's past task completion history and select the optimal management method. When managing progress, the management department can also dynamically change the management content based on the user's current project progress. This makes it easier to grasp the progress of tasks by performing progress management and reminders. Some or all of the above processes in the management department may be performed using AI, for example, or not using AI. For example, the management department can input the task progress into AI and have AI execute the progress management method.

[0090] The reception system allows users to submit tasks to their inbox via voice chat. For example, the user can input a task by voice and register it in the inbox. The reception system can also refer to the user's past task submission history and select the most appropriate submission method. The reception system can also prioritize tasks that are highly relevant, taking into account the user's geographical location. The reception system can also estimate the user's emotions and adjust the task submission method based on the estimated emotions. The reception system can also refer to the user's past task submission history to select the most appropriate submission method upon submission. The reception system can also prioritize tasks that are highly relevant, taking into account the user's geographical location. This allows users to record all their thoughts without missing anything by submitting tasks to their inbox via voice chat. Some or all of the above processes in the reception system may be performed using AI, or not. For example, the reception system can input voice-inputted tasks into AI and have the AI ​​execute the task submission process.

[0091] The brainstorming section can conduct brainstorming to clarify tasks even with low-resolution ideas. For example, the brainstorming section can brainstorm the user's ideas to clarify the task. The brainstorming section can also refer to the user's past task history to select the optimal brainstorming method. The brainstorming section can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location. The brainstorming section can also estimate the user's emotions and adjust the brainstorming method based on the estimated emotions. The brainstorming section can also refer to the user's past task history to select the optimal brainstorming method during the brainstorming process. The brainstorming section can also prioritize brainstorming tasks that are highly relevant, taking into account the user's geographical location during the brainstorming process. This eliminates ambiguity in tasks by clarifying them even with low-resolution ideas. Some or all of the above processes in the brainstorming section may be performed using AI, for example, or not. For example, the brainstorming section can input the ideas into AI and have the AI ​​perform the task clarification.

[0092] The learning unit can estimate the user's emotions and determine the priority of information to learn based on the estimated emotions. For example, if the user is stressed, the learning unit will learn high-priority information so that they can concentrate on important tasks. If the user is relaxed, the learning unit can also learn a wide range of information to prepare for future tasks. If the user is tired, the learning unit can also prioritize learning information related to simple tasks. This allows for more effective learning by determining the priority of information to learn based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​determine the priority of information to learn.

[0093] The learning unit can analyze the user's past behavior patterns and dynamically adjust the scope of information to be learned. For example, the learning unit prioritizes learning information related to tasks the user has frequently performed in the past. The learning unit can also learn information related to tasks the user has avoided in the past and identify areas for improvement. The learning unit can also predict and learn information needed for future tasks based on the user's behavior patterns. This allows the learning unit to learn more appropriate information by adjusting the scope of information to be learned based on the user's past behavior patterns. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's past behavior patterns into AI and have the AI ​​adjust the scope of information to be learned.

[0094] The learning unit can optimize the learning content based on the user's current project progress during the learning process. For example, in the early stages of a project, the learning unit can learn basic information. In the middle stages of the project, the learning unit can also learn information related to specific tasks. In the final stages of the project, the learning unit can also learn information necessary for final confirmation and completion. This allows for effective learning of project-related information by optimizing the learning content based on the project progress. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the project progress into AI and have AI perform the optimization of the learning content.

[0095] The learning unit can estimate the user's emotions and adjust the depth of information it learns based on the estimated emotions. For example, if the user is stressed, the learning unit will learn concise and to-the-point information. If the user is relaxed, the learning unit can also learn detailed information. If the user is tired, the learning unit can also learn information that can be understood in a short time. This allows for learning tailored to the user's state by adjusting the depth of information learned based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into an AI and have the AI ​​adjust the depth of information to be learned.

[0096] The learning unit can prioritize learning highly relevant information by considering the user's geographical location during the learning process. For example, if the user is in the office, the learning unit will prioritize learning work-related information. If the user is at home, the learning unit can also prioritize learning information related to home. If the user is on a business trip, the learning unit can also prioritize learning information related to the destination. This allows the learning unit to provide more appropriate information by learning highly relevant information based on the user's geographical location. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the learning of highly relevant information.

[0097] The learning unit can analyze the user's social media activity and learn relevant information during the learning process. For example, the learning unit can learn relevant information based on information shared by the user on social media. The learning unit can also learn relevant information based on information from accounts the user follows. The learning unit can also learn relevant information based on information from groups the user participates in. This allows the learning unit to provide more appropriate information by learning relevant information based on the user's social media activity. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's social media activity into AI and have the AI ​​perform the learning of relevant information.

[0098] The generation unit can estimate the user's emotions and adjust the content of the tasks it generates based on those emotions. For example, if the user is stressed, the generation unit can generate easy and rewarding tasks. If the user is relaxed, the generation unit can also generate challenging tasks. If the user is tired, the generation unit can also generate tasks that can be completed quickly. By adjusting the task content based on the user's emotions, more appropriate tasks can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the task content.

[0099] The generation unit can automatically generate similar tasks by referring to the user's past task history during the generation process. For example, the generation unit can generate tasks similar to those the user has performed in the past. The generation unit can also generate new tasks based on tasks the user has successfully completed in the past. The generation unit can also generate improved versions of tasks the user has failed at in the past. This makes task generation more efficient by generating similar tasks based on the user's past task history. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past task history into AI and have the AI ​​perform the generation of similar tasks.

[0100] The generation unit can adjust the level of detail of tasks based on the user's current project progress during generation. For example, in the early stages of a project, the generation unit can generate tasks at a high level. In the middle stages of a project, the generation unit can also generate specific tasks. In the final stages of a project, the generation unit can also generate detailed tasks. This allows for the effective generation of project-related tasks by adjusting the level of detail based on the project's progress. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can input the project's progress into the AI ​​and have the AI ​​perform the task detail adjustment.

[0101] The generation unit can estimate the user's emotions and determine the priority of tasks to generate based on the estimated emotions. For example, if the user is stressed, the generation unit may prioritize generating important tasks. If the user is relaxed, the generation unit may also prioritize generating future tasks. If the user is tired, the generation unit may also prioritize generating easy tasks. This allows for the prioritization of important tasks by determining task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​determine task priorities.

[0102] The generation unit can prioritize generating tasks that are highly relevant to the user, taking into account the user's geographical location information during the generation process. For example, if the user is in the office, the generation unit will prioritize generating work-related tasks. If the user is at home, the generation unit can also prioritize generating tasks related to their home. If the user is on a business trip, the generation unit can also prioritize generating tasks related to their destination. This allows for the provision of more appropriate tasks by generating highly relevant tasks based on the user's geographical location information. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information into AI and have the AI ​​perform the generation of highly relevant tasks.

[0103] The generation unit can analyze the user's social media activity and generate relevant tasks during the generation process. For example, the generation unit can generate relevant tasks based on information shared by the user on social media. The generation unit can also generate relevant tasks based on information about accounts the user follows. The generation unit can also generate relevant tasks based on information about groups the user participates in. This allows for the provision of more appropriate tasks by generating relevant tasks based on the user's social media activity. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity into AI and have the AI ​​perform the generation of relevant tasks.

[0104] The priority suggestion unit can estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, the priority suggestion unit will prioritize important tasks. If the user is relaxed, the priority suggestion unit can also prioritize future tasks. If the user is tired, the priority suggestion unit can also prioritize easy tasks. This allows for more appropriate task management by adjusting task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the priority suggestion unit may be performed using AI or not. For example, the priority suggestion unit can input user emotion data into an AI and have the AI ​​adjust task priorities.

[0105] The priority suggestion unit can optimize priorities by referring to the user's past task completion history when suggesting priorities. For example, the priority suggestion unit can optimize priorities based on the user's past completed task history. The priority suggestion unit can also optimize priorities based on the user's past failed task history. The priority suggestion unit can also analyze the user's past task completion history and suggest the most efficient priorities. This enables efficient task management by optimizing priorities based on the user's past task completion history. Some or all of the above processes in the priority suggestion unit may be performed using AI, for example, or without AI. For example, the priority suggestion unit can input the user's past task completion history into AI and have the AI ​​perform the priority optimization.

[0106] The priority suggestion unit can dynamically change priorities based on the user's current project progress when suggesting priorities. For example, in the early stages of a project, the priority suggestion unit can prioritize important tasks. In the middle stages of a project, the priority suggestion unit can also prioritize specific tasks. In the final stages of a project, the priority suggestion unit can also prioritize tasks necessary for final review and completion. This allows for effective management of project-related tasks by dynamically changing priorities based on project progress. Some or all of the above processes in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the project progress into AI and have the AI ​​perform the dynamic change of priorities.

[0107] The priority suggestion unit can estimate the user's emotions and adjust the display method of priority suggestions based on the estimated emotions. For example, if the user is stressed, the priority suggestion unit can provide a simple and highly visible display method. If the user is relaxed, the priority suggestion unit can also provide a display method that includes detailed information. If the user is tired, the priority suggestion unit can also provide a concise and to-the-point display method. This allows for highly visible priority suggestions by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 priority suggestion unit may be performed using AI or not. For example, the priority suggestion unit can input user emotion data into AI and have the AI ​​adjust the display method.

[0108] The priority suggestion unit can prioritize suggesting tasks that are highly relevant, taking into account the user's geographical location when suggesting priorities. For example, if the user is in the office, the priority suggestion unit will prioritize suggesting work-related tasks. If the user is at home, the priority suggestion unit can also prioritize suggesting tasks related to their home. If the user is on a business trip, the priority suggestion unit can also prioritize suggesting tasks related to their business trip destination. This enables more appropriate task management by suggesting highly relevant tasks based on the user's geographical location. Some or all of the above processing in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the user's geographical location information into the AI ​​and have the AI ​​suggest highly relevant tasks.

[0109] The priority suggestion unit can analyze the user's social media activity and suggest priorities for relevant tasks when suggesting priorities. For example, the priority suggestion unit can suggest priorities for relevant tasks based on information shared by the user on social media. The priority suggestion unit can also suggest priorities for relevant tasks based on information about accounts the user follows. The priority suggestion unit can also suggest priorities for relevant tasks based on information about groups the user participates in. This enables more appropriate task management by suggesting priorities for relevant tasks based on the user's social media activity. Some or all of the above processing in the priority suggestion unit may be performed using AI, for example, or not. For example, the priority suggestion unit can input the user's social media activity into AI and have the AI ​​perform task priority suggestions.

[0110] The advice unit can estimate the user's emotions and adjust the content of the advice based on the estimated emotions. For example, if the user is feeling stressed, the advice unit can provide relaxing advice. If the user is relaxed, the advice unit can also provide challenging advice. If the user is tired, the advice unit can also provide simple and effective advice. In this way, by adjusting the content of the advice based on the user's emotions, more appropriate advice can be provided. 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 advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input user emotion data into AI and have the AI ​​adjust the content of the advice.

[0111] The advice unit can provide optimal advice by referring to the user's past task completion history when providing advice. For example, the advice unit can provide optimal advice based on the user's past successful task history. The advice unit can also provide advice, including areas for improvement, based on the user's past unsuccessful task history. The advice unit can also analyze the user's past task completion history and provide the most effective advice. This improves the accuracy of task execution by providing optimal advice based on the user's past task completion history. Some or all of the above processes in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's past task completion history into AI and have the AI ​​provide optimal advice.

[0112] The advice unit can adjust the level of detail of its advice based on the user's current project progress. For example, the advice unit provides basic advice in the early stages of a project. In the middle stages of a project, it can provide specific advice. In the final stages of a project, it can provide detailed advice. This allows for the effective provision of project-related advice by adjusting the level of detail based on the project's progress. Some or all of the above processes in the advice unit may be performed using AI, for example, or not. For example, the advice unit can input the project's progress into the AI ​​and have the AI ​​adjust the level of detail of the advice.

[0113] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit will prioritize important advice. If the user is relaxed, the advice unit may also prioritize future advice. If the user is tired, the advice unit may also prioritize simple advice. This ensures that important advice is prioritized by prioritizing advice based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into an AI and have the AI ​​determine the priority of advice.

[0114] The advice unit can prioritize providing highly relevant advice by taking into account the user's geographical location. For example, if the user is in the office, the advice unit can prioritize work-related advice. If the user is at home, the advice unit can also prioritize advice related to home life. If the user is on a business trip, the advice unit can also prioritize advice related to their destination. This allows for more appropriate advice to be provided by offering highly relevant advice based on the user's geographical location. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input the user's geographical location into AI and have AI perform the task of providing highly relevant advice.

[0115] The advice unit can analyze the user's social media activity and provide relevant advice when providing advice. For example, the advice unit can provide relevant advice based on information the user has shared on social media. The advice unit can also provide relevant advice based on information about accounts the user follows. The advice unit can also provide relevant advice based on information about groups the user participates in. This allows for more appropriate advice to be provided by providing relevant advice based on the user's social media activity. Some or all of the above processing in the advice unit may be performed using AI, for example, or not using AI. For example, the advice unit can input the user's social media activity into AI and have the AI ​​perform the task of providing relevant advice.

[0116] The management department can estimate the user's emotions and adjust the progress management method based on the estimated emotions. For example, if the user is stressed, the management department can provide a simple and highly visible progress management method. If the user is relaxed, the management department can also provide a detailed progress management method. If the user is tired, the management department can also provide a concise and to-the-point progress management method. This allows for more appropriate progress management by adjusting the progress management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 management department may be performed using AI, for example, or not using AI. For example, the management department can input user emotion data into AI and have the AI ​​adjust the progress management method.

[0117] The management department can select the optimal management method by referring to the user's past task completion history when managing progress. For example, the management department can select the optimal management method based on the user's past successful task history. The management department can also select a management method that includes areas for improvement based on the user's past failed task history. The management department can also analyze the user's past task completion history and select the most effective management method. This enables efficient progress management by selecting the optimal management method based on the user's past task completion history. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's past task completion history into AI and have the AI ​​select the optimal management method.

[0118] The management unit can dynamically change the management content based on the user's current project progress during progress management. For example, the management unit can provide basic progress management methods in the initial stages of a project. In the middle stages of a project, the management unit can also provide specific progress management methods. In the final stages of a project, the management unit can also provide detailed progress management methods. This allows for effective progress management related to the project by dynamically changing the management content based on the project's progress. Some or all of the above processes in the management unit may be performed using AI, for example, or not. For example, the management unit can input the project's progress into the AI ​​and have the AI ​​execute the dynamic changes to the management content.

[0119] The management unit can estimate the user's emotions and adjust the timing of reminders based on the estimated emotions. For example, if the user is stressed, the management unit can reduce the frequency of reminders. If the user is relaxed, the management unit can also increase the frequency of reminders. If the user is tired, the management unit can also provide only important reminders. This allows for more appropriate reminders by adjusting the timing of reminders based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 management unit may be performed using AI or not. For example, the management unit can input user emotion data into an AI and have the AI ​​adjust the timing of reminders.

[0120] The management department can prioritize tasks that are highly relevant to the user's geographical location when managing progress. For example, if the user is in the office, the management department can prioritize tasks related to work. If the user is at home, the management department can also prioritize tasks related to home life. If the user is on a business trip, the management department can also prioritize tasks related to the destination. This allows for more appropriate progress management by managing tasks based on the user's geographical location. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input the user's geographical location into the AI ​​and have the AI ​​manage highly relevant tasks.

[0121] The management department can analyze users' social media activity and manage the progress of related tasks during progress management. For example, the management department can manage the progress of related tasks based on information shared by users on social media. The management department can also manage the progress of related tasks based on information about accounts that users follow. The management department can also manage the progress of related tasks based on information about groups that users participate in. This allows for more appropriate progress management by managing the progress of related tasks based on users' social media activity. Some or all of the above processes in the management department may be performed using AI, for example, or not. For example, the management department can input users' social media activity into AI and have the AI ​​perform progress management of related tasks.

[0122] The reception desk can estimate the user's emotions and adjust the task acceptance method 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. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick task acceptance. This allows for more appropriate task acceptance by adjusting the task acceptance method based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, 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 an AI and have the AI ​​adjust the task acceptance method.

[0123] The reception unit can select the optimal reception method by referring to the user's past task reception history when a task is received. For example, the reception unit can automatically display tasks that the user has frequently entered in the past as suggestions. The reception unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest tasks to be used during specific time periods based on the user's past reception history. This enables efficient task reception by selecting the optimal reception method based on the user's past task reception history. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's past task reception history into AI and have the AI ​​select the optimal reception method.

[0124] The reception unit can estimate the user's emotions and determine the priority of tasks to accept based on the estimated emotions. For example, if the user is stressed, the reception unit may prioritize important tasks. If the user is relaxed, the reception unit may also prioritize future tasks. If the user is tired, the reception unit may also prioritize easy tasks. This allows for prioritizing important tasks by determining task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input user emotion data into an AI and have the AI ​​determine task priorities.

[0125] The reception desk can prioritize accepting tasks that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in the office, the reception desk will prioritize tasks related to work. If the user is at home, the reception desk can also prioritize tasks related to home life. If the user is on a business trip, the reception desk can also prioritize tasks related to their destination. This allows for more appropriate task acceptance by accepting tasks that are highly relevant based on the user's geographical location. 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 geographical location into the AI ​​and have the AI ​​perform the task of accepting highly relevant tasks.

[0126] The brainstorming unit can estimate the user's emotions and adjust the brainstorming method based on the estimated emotions. For example, if the user is stressed, the brainstorming unit can provide a simple and visually clear method. If the user is relaxed, the brainstorming unit can also provide a detailed method. If the user is tired, the brainstorming unit can also provide a concise and to-the-point method. This allows for more appropriate brainstorming by adjusting the method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. 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 brainstorming unit may be performed using AI, for example, or not using AI. For example, the brainstorming unit can input user emotion data into AI and have the AI ​​adjust the brainstorming method.

[0127] The brainstorming unit can select the optimal brainstorming method by referring to the user's past task history during the brainstorming session. For example, the brainstorming unit can select the optimal brainstorming method based on the user's past successful task history. The brainstorming unit can also select a brainstorming method that includes areas for improvement based on the user's past unsuccessful task history. The brainstorming unit can also analyze the user's past task history and select the most effective brainstorming method. This enables efficient brainstorming by selecting the optimal brainstorming method based on the user's past task history. Some or all of the above-described processes in the brainstorming unit may be performed using AI, for example, or without AI. For example, the brainstorming unit can input the user's past task history into AI and have the AI ​​select the optimal brainstorming method.

[0128] The brainstorming unit can estimate the user's emotions and determine the priority of brainstorming based on the estimated emotions. For example, if the user is stressed, the brainstorming unit can prioritize important tasks. If the user is relaxed, the brainstorming unit can also prioritize future tasks. If the user is tired, the brainstorming unit can also prioritize easy tasks. This allows important tasks to be prioritized by determining the priority of brainstorming based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the brainstorming unit may be performed using AI or not. For example, the brainstorming unit can input user emotion data into an AI and have the AI ​​determine the priority of brainstorming.

[0129] The brainstorming unit can prioritize tasks that are highly relevant to the user's location during brainstorming, taking into account the user's geographical location. For example, if the user is in the office, the brainstorming unit will prioritize tasks related to work. If the user is at home, the brainstorming unit can also prioritize tasks related to home life. If the user is on a business trip, the brainstorming unit can also prioritize tasks related to their destination. This allows for more appropriate brainstorming by prioritizing tasks that are highly relevant based on the user's geographical location. Some or all of the above processing in the brainstorming unit may be performed using AI, for example, or without AI. For example, the brainstorming unit can input the user's geographical location into the AI ​​and have the AI ​​perform brainstorming on highly relevant tasks.

[0130] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0131] The task management system can also include a health management unit that monitors the user's health status. This unit collects data such as the user's heart rate, sleep patterns, and activity level to assess their health. For example, if a user's heart rate is high, it can determine they are stressed and adjust task priorities accordingly. It can also analyze the user's sleep patterns and, if fatigue is accumulating, suggest simpler tasks. Furthermore, if activity levels are low, it can provide advice encouraging adequate rest and exercise. This allows for the optimization of task management based on the user's health status, improving their overall performance.

[0132] The task management system can also include a hobby learning section that learns about the user's hobbies and interests. This section learns about the user's past hobbies and interests and reflects this in task suggestions. For example, if the user is interested in music, music-related tasks can be suggested. If the user enjoys traveling, tasks related to travel planning can be suggested. Furthermore, if the user enjoys reading, reading-related tasks can be suggested. This approach, by suggesting tasks based on the user's hobbies and interests, can increase user motivation and make task completion more enjoyable.

[0133] The task management system can also include a communication learning unit that learns the user's communication style. This unit learns the user's past communication patterns and incorporates them into task suggestions. For example, if a user frequently uses email, email-related tasks can be prioritized. Similarly, if a user prefers chat, chat-related tasks can be suggested. Furthermore, if a user prefers phone calls, phone-related tasks can be suggested. This allows for improved user communication efficiency by suggesting tasks based on the user's communication style.

[0134] Task management systems can further estimate the user's emotions and adjust task notification methods based on those emotions. For example, if a user is stressed, notifications can be kept to a minimum, only for important tasks. If the user is relaxed, detailed notifications can be provided, informing them of task progress in detail. If the user is tired, notifications can be kept concise, conveying only the essentials. By adjusting notification methods based on the user's emotions, this reduces the user's burden and streamlines task management.

[0135] The task management system can also include a learning style learning unit that learns the user's learning style. This unit learns the user's past learning methods and reflects them in task suggestions. For example, if a user prefers visual learning, visual tasks can be prioritized. Similarly, if a user prefers auditory learning, audio-related tasks can be suggested. Furthermore, if a user prefers experiential learning, practical tasks can be suggested. This allows for improved user learning efficiency by suggesting tasks based on the user's learning style.

[0136] The task management system can further estimate the user's emotions and evaluate the task's progress based on those emotions. For example, if the user is stressed, it can determine that the task is behind schedule and offer support. If the user is relaxed, it can determine that the task is progressing well and even suggest additional tasks. If the user is tired, it can determine that the task is stalled and encourage a break. By evaluating task progress based on the user's emotions, the system can provide appropriate support and streamline task management.

[0137] The task management system can also include a lifestyle rhythm learning unit that learns the user's daily routine. This unit learns the user's past daily rhythm and reflects this in task suggestions. For example, if a user has a morning routine, it can suggest important tasks in the morning. Similarly, if a user has a night owl routine, it can suggest important tasks at night. Furthermore, if a user is active on weekends, it can suggest weekend-related tasks. This allows for task management tailored to the user's lifestyle by suggesting tasks based on their daily rhythm.

[0138] Task management systems can further estimate user emotions and adjust task feedback methods based on those emotions. For example, if a user is stressed, feedback can be kept to a minimum, prioritizing positive feedback. If a user is relaxed, detailed feedback can be provided, specifically outlining areas for improvement. If a user is tired, feedback can be concise, focusing only on the essentials. By adjusting feedback methods based on user emotions, this reduces user burden and streamlines task management.

[0139] The task management system can also include a project management learning unit that learns the user's project management style. This unit learns the user's past project management methods and incorporates them into task suggestions. For example, if the user prefers agile methodologies, it can prioritize suggesting agile-related tasks. Similarly, if the user prefers waterfall methodologies, it can suggest waterfall-related tasks. Furthermore, if the user prefers Kanban methodologies, it can suggest Kanban-related tasks. This allows the system to improve the user's project management efficiency by suggesting tasks based on their project management style.

[0140] The task management system can further estimate the user's emotions and adjust how tasks are reported based on those emotions. For example, if a user is stressed, it can provide a concise completion report and prioritize positive feedback. If a user is relaxed, it can provide a detailed completion report to enhance their sense of accomplishment. If a user is tired, it can make the completion report brief and only include the essentials. By adjusting the completion reporting method based on the user's emotions, the system can reduce the user's burden and streamline task management.

[0141] The following briefly describes the processing flow for example form 2.

[0142] Step 1: The learning unit learns about the user's surrounding information. For example, it analyzes information about the user's work, projects they are participating in, behavioral patterns, and past task history to understand the user's characteristics. Step 2: The generation unit automatically generates tasks based on the information learned by the learning unit. For example, it can generate tasks based on the user's project information and generate similar tasks by referring to past task history. It can also adjust the level of detail of tasks based on the current project progress. Step 3: The priority suggestion unit proposes priorities for the tasks generated by the generation unit. For example, it suggests priorities based on the importance and urgency of the tasks, and optimizes priorities by referring to past task completion history. It also dynamically changes priorities based on the current project progress. Step 4: The Advice Department provides advice on task execution proposed by the Prioritization Department. For example, it advises on key points to remember from past projects and provides optimal advice by referring to the user's past task completion history. It also adjusts the level of detail of the advice based on the current project progress. Step 5: The management department manages the progress and provides reminders for tasks advised by the advisory department. For example, they track and manage the progress of tasks. They select the optimal management method by referring to the user's past task completion history and dynamically change the management content based on the current project progress. Step 6: The reception desk submits tasks to the inbox via voice chat. For example, a user inputs a task by voice and registers it in the inbox. The system selects the optimal submission method by referring to past task submission history and prioritizes submission of highly relevant tasks, taking geographical location information into consideration. Step 7: The brainstorming team brainstorms tasks submitted by the reception team. For example, they brainstorm ideas conceived by users to clarify the tasks. They select the optimal brainstorming method by referring to past task history and prioritize brainstorming tasks with high relevance, taking geographical location information into consideration.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the learning unit, generation unit, priority suggestion unit, advice unit, management unit, reception unit, and brainstorming unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the learning unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The generation unit is implemented by the specific processing unit 290 of the data processing device 12. The priority suggestion unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The advice unit is implemented by the specific processing unit 290 of the data processing device 12. The management unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The reception unit is implemented by the control unit 46A of the smart device 14. The brainstorming unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the learning unit, generation unit, priority suggestion unit, advice unit, management unit, reception unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The priority suggestion unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the smart glasses 214. The feedback unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0163] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] Each of the multiple elements described above, including the learning unit, generation unit, priority suggestion unit, advice unit, management unit, reception unit, and feedback unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The priority suggestion unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the headset terminal 314. The feedback unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0179] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.).

[0192] 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.

[0193] 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.

[0194] 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.

[0195] Each of the multiple elements described above, including the learning unit, generation unit, priority suggestion unit, advice unit, management unit, reception unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the learning unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12. The priority suggestion unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The advice unit is implemented by the specific processing unit 290 of the data processing unit 12. The management unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The reception unit is implemented by the control unit 46A of the robot 414. The feedback unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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."

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] (Note 1) A learning unit that learns information about the user's surroundings, A generation unit that automatically generates tasks based on the information learned by the learning unit, A priority suggestion unit proposes the priority of tasks generated by the generation unit, An advice unit that provides advice on task execution proposed by the priority proposal unit, The management unit manages the progress of tasks advised by the aforementioned advisory unit and provides reminders, The reception desk sends tasks to the inbox via voice chat, The system includes a wall-hitting section that handles tasks submitted by the aforementioned reception section. A system characterized by the following features. (Note 2) The aforementioned learning unit, Learn about the user's job responsibilities and the projects they are participating in. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Automatically generate tasks based on learned information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned priority proposal unit, Suggest prioritizing the generated tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned advice section, I will advise you on key points to remember from past cases. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, Track progress and send reminders The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Submit tasks to your inbox via voice chat. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned wall-mounting section is Even with vague ideas, brainstorming can help clarify tasks. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned learning unit, It estimates the user's emotions and determines the priority of information to learn based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned learning unit, Analyze the user's past behavior patterns and dynamically adjust the scope of information to be learned. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned learning unit, During learning, the learning content is optimized based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, It estimates the user's emotions and adjusts the depth of the information learned based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During learning, the system prioritizes learning highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During learning, the system analyzes users' social media activity and learns relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the content of the tasks generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the system automatically generates similar tasks by referencing the user's past task history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the level of detail of tasks is adjusted based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is It estimates the user's emotions and determines the priority of tasks to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system prioritizes generating tasks that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the system analyzes the user's social media activity and generates relevant tasks. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned priority proposal unit, It estimates the user's emotions and adjusts task priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned priority proposal unit, When suggesting priorities, the system optimizes priorities by referencing the user's past task completion history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned priority proposal unit, When suggesting priorities, dynamically change the priority based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned priority proposal unit, It estimates the user's emotions and adjusts how priority suggestions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned priority proposal unit, When suggesting priorities, the system prioritizes tasks that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned priority proposal unit, When suggesting priorities, the system analyzes the user's social media activity and suggests priorities for related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advice section, It estimates the user's emotions and adjusts the content of the advice based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advice section, When providing advice, we refer to the user's past task completion history to provide the most appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advice section, When providing advice, adjust the level of detail based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advice section, It estimates the user's emotions and prioritizes advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned advice section, When providing advice, we prioritize offering highly relevant advice by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned management department, We estimate the user's emotions and adjust the progress management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned management department, When managing progress, the system selects the optimal management method by referring to the user's past task completion history. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned management department, During progress management, the management content is dynamically changed based on the user's current project progress. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned management department, It estimates the user's emotions and adjusts the timing of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned management department, When managing progress, prioritize tasks that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned management department, During progress management, analyze users' social media activity and manage the progress of related tasks. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned reception unit is It estimates the user's emotions and adjusts the task acceptance method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned reception unit is Upon receiving a request, the system will refer to the user's past task request history to select the most suitable method of acceptance. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned reception unit is It estimates the user's emotions and determines the priority of tasks to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned reception unit is When a user submits a request, the system prioritizes accepting tasks that are highly relevant to their location, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned wall-mounting section is The system estimates the user's emotions and adjusts the brainstorming method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned wall-mounting section is During brainstorming sessions, the system selects the optimal brainstorming method by referring to the user's past task history. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned wall-mounting section is The system estimates the user's emotions and determines the priority of brainstorming sessions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned wall-mounting section is During brainstorming sessions, the system prioritizes brainstorming tasks that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0215] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A learning unit that learns information about the user's surroundings, A generation unit that automatically generates tasks based on the information learned by the learning unit, A priority suggestion unit proposes the priority of tasks generated by the generation unit, An advice unit that provides advice on task execution proposed by the priority proposal unit, The management unit manages the progress of tasks advised by the aforementioned advisory unit and provides reminders, The reception desk sends tasks to the inbox via voice chat, The system includes a wall-hitting section that handles tasks submitted by the aforementioned reception section. A system characterized by the following features.

2. The aforementioned learning unit, Learn about the user's job responsibilities and the projects they are participating in. The system according to feature 1.

3. The generating unit is Automatically generate tasks based on learned information. The system according to feature 1.

4. The aforementioned priority proposal unit, Suggest prioritizing the generated tasks. The system according to feature 1.

5. The aforementioned advice section, I will advise you on key points to remember from past cases. The system according to feature 1.

6. The aforementioned management department, Track progress and send reminders The system according to feature 1.

7. The aforementioned reception unit is Submit tasks to your inbox via voice chat. The system according to feature 1.

8. The aforementioned wall-mounting section is, Even with vague ideas, brainstorming can help clarify tasks. The system according to feature 1.

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