system

Abstract

The system according to this embodiment aims to improve productivity by streamlining employee task management and meeting-related tasks. [Solution] The system according to the embodiment comprises a tracking unit, a proxy unit, a monitoring unit, and a follow-up unit. The tracking unit tracks the user's schedule and communications. The proxy unit performs task management, creates meeting minutes, and reserves meeting rooms based on the schedule and communications tracked by the tracking unit. The monitoring unit monitors the progress of tasks and meetings performed by the proxy unit and sends reminders as needed. The follow-up unit follows up on any omissions or errors based on the progress monitored by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, employees may spend a lot of time on task management and meeting-related tasks, which may lead to a decrease in productivity.

[0005] The system according to the embodiment aims to improve the efficiency of employees' task management and meeting-related tasks and enhance productivity.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a tracking unit, a proxy unit, a monitoring unit, and a follow-up unit. The tracking unit tracks the user's schedule and communications. The proxy unit performs task management, creates meeting minutes, and reserves meeting rooms based on the schedule and communications tracked by the tracking unit. The monitoring unit monitors the progress of tasks and meetings performed by the proxy unit and sends reminders as needed. The follow-up unit follows up on any omissions or errors based on the progress monitored by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can streamline employee task management and meeting-related tasks, thereby improving productivity. [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, etc. 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 support system according to an embodiment of the present invention is a multimodal AI-powered system that integrates with internal systems such as email, chat, and groupware to understand users' schedules and communications. This support system improves employee and corporate productivity by having the AI ​​handle tasks such as task management, meeting minute creation, and meeting room reservations, and by following up on any omissions or errors by the user. First, there is a problem where employees' productivity decreases due to the time they spend on task management and meeting-related work. In response to this, the present invention provides an AI-powered personal assistant system that improves productivity by managing employee schedules and handling tasks on their behalf. Specifically, it consists of the following steps: First, it integrates with internal systems such as email, chat, and groupware to understand users' schedules and communications. Next, the AI ​​handles tasks such as task management, meeting minute creation, and meeting room reservations. Furthermore, by following up on any omissions or errors by the user, it provides an environment where employees can concentrate on their work. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. It also monitors the progress of tasks and sends reminders as needed. As a result, employees can reduce the time spent on task management and meeting preparation, and concentrate on their work. This system improves employee productivity and enhances overall business efficiency. For example, by reducing the time spent on task management and meeting preparation, employees can dedicate more time to their core responsibilities. Furthermore, AI helps to correct omissions and errors, reducing human error and improving work accuracy. In addition, AI learning enables the provision of optimal support tailored to individual users. This allows for flexible responses to user needs, leading to increased employee satisfaction. Thus, this invention is a system that utilizes AI to streamline employee task management and meeting-related tasks, thereby improving productivity. This creates an environment where employees can concentrate on their work, improving overall business efficiency. As a result, the support system can streamline employee task management and meeting-related tasks, thereby increasing productivity.

[0029] The support system according to the embodiment comprises a tracking unit, a proxy unit, a monitoring unit, and a follow-up unit. The tracking unit tracks the user's schedule and communications. The tracking unit collects the user's schedule and communications in conjunction with internal systems such as email, chat, and groupware. The tracking unit analyzes the content of emails and adds the user's schedule to a calendar. The tracking unit can also analyze the content of chats, extract important communications, and notify the user. Furthermore, the tracking unit can analyze groupware data to track meeting schedules. For example, the tracking unit analyzes the content of emails using natural language processing technology and automatically adds the user's schedule to a calendar. When analyzing chat content, it extracts important communications using keyword extraction technology and notifies the user. When analyzing groupware data, it automatically tracks meeting schedules and notifies the user. The proxy unit performs task management, creates meeting minutes, and reserves meeting rooms based on the schedule and communications tracked by the tracking unit. The proxy department, for example, automatically reserves a meeting room and creates meeting minutes when a user enters a meeting schedule. The proxy department also checks the availability of meeting rooms and reserves the most suitable one. Furthermore, the proxy department can automatically record the content of meetings and create meeting minutes. In addition, the proxy department can monitor the progress of tasks and send reminders as needed. For example, the proxy department checks the availability of meeting rooms in real time and reserves the most suitable one. When recording the content of a meeting, it uses speech recognition technology to convert the meeting content into text and create meeting minutes. When monitoring the progress of tasks, it integrates with a task management system to grasp the progress in real time and send reminders as needed. The monitoring department monitors the progress of tasks and meetings handled by the proxy department and sends reminders as needed. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. For example, the monitoring department displays the progress of tasks using graphs and charts, allowing users to visually understand the progress. Furthermore, the monitoring unit can also suggest the next steps based on the task's progress. For example, the monitoring unit can periodically check the task's progress and send reminders if it is behind schedule.When displaying task progress using graphs or charts, the progress should be presented in a format that is easy to understand visually. When suggesting the next step, the system should propose the next action to be taken based on the task's progress. The follow-up unit will follow up on any omissions or errors based on the progress monitored by the monitoring unit. For example, the follow-up unit will check the task's progress and follow up if any omissions or errors occur. The follow-up unit can also notify the user of the follow-up and prompt them to take the necessary actions. Furthermore, the follow-up unit can record the results of the follow-up and use them for future follow-ups. For example, the follow-up unit will check the task's progress and follow up if any omissions or errors occur. When notifying the user of the follow-up, specific instructions for necessary actions should be provided. When recording the results of the follow-up, detailed records should be kept so that they can be used for future follow-ups. As a result, the support system according to this embodiment can efficiently grasp the user's schedule and communications, handle task management, create meeting minutes, reserve meeting rooms, monitor progress, and address any omissions, thereby improving employee productivity.

[0030] The tracking unit manages users' schedules and communications. For example, it integrates with internal systems such as email, chat, and groupware to collect user schedules and communications. Specifically, it analyzes email content and adds user appointments to a calendar. It can also analyze chat content, extract important communications, and send notifications. Furthermore, it can analyze groupware data to understand meeting schedules. For example, it uses natural language processing to analyze email content and automatically adds user appointments to a calendar. When analyzing chat content, it uses keyword extraction technology to extract important communications and notifies the user. When analyzing groupware data, it automatically understands meeting schedules and notifies the user. This allows the tracking unit to efficiently manage user schedules and communications, ensuring that important information is not missed. Furthermore, the tracking unit can learn user behavior patterns and past data to predict future schedules and communications. For example, by analyzing past email and chat content and detecting specific patterns, it can predict the information and appointments the user will need next and notify them in advance. This allows users to significantly reduce the time spent managing schedules and communications, enabling them to focus on more important tasks. Furthermore, the system implements strict security policies regarding data handling to protect user privacy and prevent data leaks and unauthorized access. This ensures users can use the system with peace of mind.

[0031] The task management department handles task management, minute-taking, and meeting room reservations based on schedules and communications captured by the information gathering department. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates minutes. Specifically, it checks meeting room availability and reserves the most suitable room. The task management department can also automatically record the meeting content and create minutes. Furthermore, the task management department can monitor task progress and send reminders as needed. For example, the task management department checks meeting room availability in real time and reserves the most suitable room. When recording meeting content, it uses speech recognition technology to convert the meeting content into text and create minutes. When monitoring task progress, it integrates with the task management system to grasp progress in real time and send reminders as needed. This allows the task management department to reduce the burden on users and manage tasks efficiently. Furthermore, the task management department can learn user preferences and past behavior patterns to provide more personalized services. For example, it can suggest the most suitable meeting room based on information about meeting rooms the user has used in the past. Furthermore, it can learn the user's task management style and suggest the optimal timing and method for reminders. This allows the task management unit to respond flexibly to the user's needs, thereby improving the user's productivity.

[0032] The monitoring department monitors the progress of tasks and meetings handled by the delegation department and sends reminders as needed. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. Specifically, it displays task progress using graphs and charts to allow users to visually understand the progress. The monitoring department can also suggest the next steps based on the task progress. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. When displaying task progress using graphs and charts, it displays the progress in a format that is easy to understand visually. When suggesting the next steps, it suggests actions to be taken based on the task progress. In this way, the monitoring department helps users to always be aware of the progress of tasks and take the next actions at the appropriate time. Furthermore, the monitoring department can automatically evaluate the priority of tasks and send reminders preferentially to important tasks. For example, it sends more frequent reminders to tasks with approaching deadlines or high importance to ensure that users do not miss important tasks. Furthermore, the monitoring unit can adjust the content and timing of reminders based on user feedback, providing more effective support. This allows the monitoring unit to efficiently support users' task management and ensure smooth task progress.

[0033] The follow-up unit addresses any omissions or errors based on the progress monitored by the monitoring unit. For example, the follow-up unit checks the task progress and follows up if any omissions or errors occur. Specifically, it checks the task progress and follows up if any omissions or errors occur. The follow-up unit can also notify users of the follow-up content and prompt them to take necessary actions. Furthermore, the follow-up unit can record the results of the follow-up and use them for future follow-ups. For example, the follow-up unit checks the task progress and follows up if any omissions or errors occur. When notifying users of the follow-up content, it provides specific instructions for necessary actions. When recording the results of the follow-up, it records them in detail so they can be used for future follow-ups. This allows the follow-up unit to help users stay informed of task progress and take the next action at the appropriate time. Furthermore, the follow-up unit can adjust the content and timing of follow-ups based on user feedback to provide more effective support. For example, it can understand what kind of support users need for a particular task and provide follow-up accordingly. Furthermore, the follow-up team can analyze the results of past follow-ups and utilize them for future follow-ups, thereby providing more effective support. This allows the follow-up team to efficiently support users' task management and ensure smooth progress on tasks.

[0034] The support unit can provide optimal support tailored to individual users through AI learning. For example, the support unit can analyze users' past behavioral data to provide optimal support to each individual user. For example, the support unit can analyze users' past behavioral data and customize support content based on users' preferences and habits. The support unit can also collect user feedback and improve support content. For example, the support unit can analyze users' past behavioral data and customize support content based on users' preferences and habits. When collecting user feedback, it can use surveys or evaluation systems to collect feedback and improve support content. In this way, by providing optimal support to individual users through AI learning, user satisfaction can be improved. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user behavioral data into a generating AI and have the generating AI execute the generation of optimal support content.

[0035] The proxy system allows users to input meeting schedules, and the AI ​​automatically reserves a meeting room and creates meeting minutes. For example, when a user inputs a meeting schedule, the AI ​​checks the availability of meeting rooms and reserves the most suitable one. The proxy system can also automatically record the meeting content and create meeting minutes. Furthermore, the proxy system can automatically send the meeting minutes to the meeting participants. For example, the proxy system checks the availability of meeting rooms in real time and reserves the most suitable one. When recording the meeting content, speech recognition technology is used to convert the meeting content into text and create the meeting minutes. When automatically sending the meeting minutes, the minutes are sent to meeting participants via email or chat. This reduces the burden on the user by automatically reserving a meeting room and creating meeting minutes when the meeting schedule is entered. Some or all of the above processes in the proxy system may be performed using AI, or not using AI. For example, the proxy system can input the meeting content into a generation AI and have the generation AI create the meeting minutes.

[0036] The monitoring unit can monitor the progress of tasks and send reminders as needed. For example, the monitoring unit can periodically check the progress of tasks and send reminders if progress is behind schedule. For example, the monitoring unit can display the progress of tasks in graphs or charts to allow users to visually understand the progress. The monitoring unit can also suggest the next steps based on the progress of tasks. For example, the monitoring unit can periodically check the progress of tasks and send reminders if progress is behind schedule. When displaying the progress of tasks in graphs or charts, it displays the progress in a format that is easy to understand visually. When suggesting the next steps, it suggests the next action to be taken based on the progress of tasks. In this way, by monitoring the progress of tasks and sending reminders as needed, it is possible to prevent tasks from being missed or overlooked. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the progress of tasks into a generating AI and have the generating AI execute the sending of reminders.

[0037] The tracking unit can analyze the user's past schedules and contact history and select the optimal tracking method. For example, the tracking unit can prioritize displaying people the user has frequently contacted in the past. For example, the tracking unit can prioritize displaying important appointments in specific time slots from the user's past schedule history. Furthermore, the tracking unit can analyze the user's past contact history and prioritize displaying highly relevant contacts. For example, the tracking unit can prioritize displaying people the user has frequently contacted in the past. For example, it can prioritize displaying important appointments in specific time slots from the user's past schedule history. For example, it can analyze the user's past contact history and prioritize displaying highly relevant contacts. In this way, by analyzing past schedules and contact history, the tracking unit can provide the user with the optimal tracking method. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past schedules and contact history into a generating AI and have the generating AI select the optimal tracking method.

[0038] The information gathering unit can filter schedules and communications based on the user's current projects and areas of interest. For example, the information gathering unit can prioritize displaying schedules and communications related to projects the user is currently working on. The information gathering unit can also filter and display relevant schedules and communications based on the user's areas of interest. Furthermore, if the user is focused on a specific project, the information gathering unit can prioritize displaying schedules and communications related to that project. For example, the information gathering unit can prioritize displaying schedules and communications related to projects the user is currently working on. It can filter and display relevant schedules and communications based on the user's areas of interest. If the user is focused on a specific project, it can prioritize displaying schedules and communications related to that project. This allows users to prioritize the identification of highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0039] The information gathering unit can prioritize identifying highly relevant information by considering the user's geographical location when gathering schedules and communications. For example, the information gathering unit can prioritize displaying schedules and communications in locations close to the user's current location. For example, if the user is in a specific region, the information gathering unit can prioritize displaying schedules and communications related to that region. Furthermore, if the user is on the move, the information gathering unit can display the most suitable schedules and communications based on the user's current location. For example, the information gathering unit prioritizes displaying schedules and communications in locations close to the user's current location. If the user is in a specific region, it prioritizes displaying schedules and communications related to that region. If the user is on the move, it displays the most suitable schedules and communications based on the user's current location. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of identifying highly relevant information.

[0040] The tracking unit can analyze a user's social media activity and identify relevant information when tracking schedules and communications. For example, the tracking unit can prioritize displaying events and schedules mentioned by the user on social media. The tracking unit can also analyze the content of a user's social media posts and display relevant schedules and communications. Furthermore, the tracking unit can prioritize displaying schedules and communications related to accounts the user follows on social media. For example, the tracking unit can prioritize displaying events and schedules mentioned by the user on social media. It can analyze the content of a user's social media posts and display relevant schedules and communications. It can prioritize displaying schedules and communications related to accounts the user follows on social media. This allows for the priority identification of relevant information by analyzing social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI and have the generating AI perform the identification of relevant information.

[0041] The task management unit can adjust the level of detail based on the importance of the task when managing tasks and creating meeting minutes. For example, the task management unit can provide detailed explanations and procedures for important tasks. For example, the task management unit can provide concise explanations for less important tasks to enable quick processing. Furthermore, the task management unit can provide detailed procedures for urgent tasks to enable quick response. For example, the task management unit can provide detailed explanations and procedures for important tasks. For less important tasks, it can provide concise explanations to enable quick processing. For urgent tasks, it can provide detailed procedures to enable quick response. This allows for appropriate responses to important tasks by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input task importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0042] The task management unit can apply different task management algorithms depending on the task category when managing tasks or creating meeting minutes. For example, the task management unit can apply a project management-specific algorithm to project management tasks. For example, the task management unit can also apply a routine work-specific algorithm to routine work tasks. Furthermore, the task management unit can apply an emergency response-specific algorithm to emergency response tasks. For example, the task management unit can apply a project management-specific algorithm to project management tasks. For routine work tasks, it can apply a routine work-specific algorithm. For emergency response tasks, it can apply an emergency response-specific algorithm. This enables efficient task management by applying different task management algorithms depending on the task category. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input task category data into a generating AI and have the generating AI execute the application of the task management algorithm.

[0043] The task management unit can prioritize tasks based on their submission dates when managing tasks and creating meeting minutes. For example, the task management unit can prioritize tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the task management unit can respond quickly to tasks that have passed their deadlines. For example, the task management unit prioritizes tasks with approaching deadlines, postpones tasks with distant deadlines, and responds quickly to tasks that have passed their deadlines. By prioritizing tasks based on their submission dates, appropriate responses can be made according to the deadlines. Some or all of the above processes in the task management unit may be performed using AI, for example, or not. For example, the task management unit can input task submission date data into a generating AI and have the generating AI perform the priority determination.

[0044] The task management unit can adjust the order of tasks based on their relevance when managing tasks or creating meeting minutes. For example, the task management unit can group highly relevant tasks and process them efficiently. The task management unit can also process less relevant tasks individually. Furthermore, the task management unit can prioritize highly relevant tasks and postpone less relevant tasks. For example, the task management unit can group highly relevant tasks and process them efficiently. Less relevant tasks can be processed individually. Highly relevant tasks can be prioritized, and less relevant tasks can be postponed. This allows for efficient task management by adjusting the order of tasks based on their relevance. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input task relevance data into a generating AI and have the generating AI perform the order adjustment.

[0045] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between tasks when monitoring progress. For example, the monitoring unit monitors progress by considering the dependencies between tasks. The monitoring unit can also analyze the interrelationships between tasks and provide an efficient monitoring method. Furthermore, the monitoring unit can optimize progress monitoring based on the interrelationships between tasks. For example, the monitoring unit monitors progress by considering the dependencies between tasks. It analyzes the interrelationships between tasks and provides an efficient monitoring method. It optimizes progress monitoring based on the interrelationships between tasks. As a result, the accuracy of progress monitoring is improved by considering the interrelationships between tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task interrelationship data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0046] The monitoring unit can monitor progress while considering the attribute information of the task submitter. For example, the monitoring unit can monitor progress while considering the task submitter's job title and responsibilities. The monitoring unit can also monitor progress based on the task submitter's past performance. Furthermore, the monitoring unit can monitor progress while considering the task submitter's skill set. For example, the monitoring unit can monitor progress while considering the task submitter's job title and responsibilities. For example, it can monitor progress based on the task submitter's past performance. For example, it can monitor progress while considering the skill set of the task submitter. This improves the accuracy of progress monitoring by considering the attribute information of the task submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information of the task submitter into a generating AI and have the generating AI perform the monitoring.

[0047] The monitoring unit can perform monitoring while considering the geographical distribution of tasks when monitoring progress. For example, if tasks are distributed across different regions, the monitoring unit can monitor the progress for each region. The monitoring unit can also provide an efficient monitoring method by considering the geographical distribution of tasks. Furthermore, the monitoring unit can optimize progress monitoring based on the geographical distribution of tasks. For example, if tasks are distributed across different regions, the monitoring unit can monitor the progress for each region. It can provide an efficient monitoring method by considering the geographical distribution of tasks. It can optimize progress monitoring based on the geographical distribution of tasks. As a result, the accuracy of progress monitoring is improved by considering the geographical distribution of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of tasks into a generating AI and have the generating AI perform the monitoring.

[0048] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the task when monitoring progress. For example, the monitoring unit can refer to relevant literature for the task and monitor progress. The monitoring unit can also provide an efficient monitoring method based on relevant literature for the task. Furthermore, the monitoring unit can analyze relevant literature for the task and optimize progress monitoring. For example, the monitoring unit can refer to relevant literature for the task and monitor progress. It can provide an efficient monitoring method based on relevant literature for the task. It can analyze relevant literature for the task and optimize progress monitoring. As a result, the accuracy of progress monitoring is improved by referring to relevant literature for the task. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data for the task into a generating AI and have the generating AI perform the monitoring.

[0049] The follow-up unit can select the optimal follow-up method by referring to past follow-up data when addressing omissions or errors. For example, the follow-up unit can select the optimal follow-up method based on follow-up methods that were effective in the past. The follow-up unit can also analyze past follow-up data and provide the user with the optimal follow-up method. Furthermore, the follow-up unit can select an efficient follow-up method by referring to past follow-up data. For example, the follow-up unit can select the optimal follow-up method based on follow-up methods that were effective in the past. It can analyze past follow-up data and provide the user with the optimal follow-up method. It can select an efficient follow-up method by referring to past follow-up data. In this way, the optimal follow-up method can be provided by referring to past follow-up data. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without using AI. For example, the follow-up unit can input past follow-up data into a generating AI and have the generating AI select the optimal follow-up method.

[0050] The follow-up unit can apply different follow-up methods to each task category when addressing omissions or errors. For example, the follow-up unit can apply a follow-up method specifically for project management tasks. Similarly, it can apply a follow-up method specifically for daily tasks. Furthermore, it can apply a follow-up method specifically for emergency response tasks. This allows for efficient follow-up by applying different follow-up methods depending on the task category. Some or all of the above processing in the follow-up unit may be performed using AI, or without AI. For example, the follow-up unit can input task category data into a generating AI and have the generating AI apply the follow-up method.

[0051] The follow-up unit can analyze changes in follow-up based on the task submission timing when addressing omissions or errors. For example, the follow-up unit can quickly follow up on tasks with approaching deadlines. For example, the follow-up unit can reduce the frequency of follow-up on tasks with distant deadlines. Furthermore, the follow-up unit can quickly follow up on tasks that have passed their deadlines and prompt action. For example, the follow-up unit can quickly follow up on tasks with approaching deadlines. For tasks with distant deadlines, it can reduce the frequency of follow-up. For tasks that have passed their deadlines, it can quickly follow up and prompt action. This allows for appropriate follow-up by analyzing changes in follow-up based on the task submission timing. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input task submission timing data into a generating AI and have the generating AI perform an analysis of changes in follow-up.

[0052] The follow-up unit can analyze the follow-up process by referring to relevant market data for the task when addressing any omissions or errors. For example, the follow-up unit can refer to relevant market data for the task and provide the optimal follow-up method. The follow-up unit can also select an efficient follow-up method based on the market data. Furthermore, the follow-up unit can analyze the market data to optimize the task follow-up. For example, the follow-up unit can refer to relevant market data for the task and provide the optimal follow-up method. It can select an efficient follow-up method based on the market data. It can analyze the market data to optimize the task follow-up. This allows the follow-up unit to provide the optimal follow-up method by referring to relevant market data for the task. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input relevant market data for the task into a generating AI and have the generating AI perform the follow-up analysis.

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

[0054] The support system can further include a learning support unit that analyzes the user's learning history and provides learning content for skill improvement. For example, the learning support unit analyzes what the user has learned in the past and suggests what they should learn next. The learning support unit can also provide appropriate learning content according to the user's skill level. Furthermore, the learning support unit can monitor the user's learning progress and provide feedback as needed. For example, the learning support unit suggests steps for the user to acquire a specific skill. If the user encounters difficulties in their learning, it provides solutions. This supports the user's skill development and improves work efficiency.

[0055] The support system can also include a communication support unit that analyzes the user's communication style and proposes the optimal communication method. For example, if the user prefers email or chat, the communication support unit will prioritize suggesting those methods. If the user prefers face-to-face communication, the communication support unit can also suggest setting up a meeting. Furthermore, based on the user's communication style, the communication support unit can also suggest how to write effective messages. For example, if the user prefers short messages, the communication support unit will suggest how to write concise messages. If the user prefers detailed explanations, it will suggest how to write detailed messages. This improves communication efficiency by providing the optimal method according to the user's communication style.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The information gathering unit gathers user schedules and communications. For example, it collects user schedules and communications by linking with internal systems such as email, chat, and groupware. It analyzes email content and adds user appointments to the calendar. It analyzes chat content, extracts important communications, and sends notifications. It analyzes groupware data to understand meeting schedules. Step 2: The task management unit handles task management, meeting minute creation, and meeting room reservations based on the schedules and communications gathered by the information gathering unit. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. It checks meeting room availability and reserves the most suitable room. It automatically records the meeting content and creates meeting minutes. It monitors task progress and sends reminders as needed. Step 3: The monitoring unit monitors the progress of tasks and meetings handled by the delegation unit and sends reminders as needed. For example, it periodically checks the progress of tasks and sends reminders if progress is behind schedule. It displays the task progress using graphs and charts to allow users to visually understand the progress. Based on the task progress, it suggests the next steps. Step 4: The follow-up team will address any omissions or errors based on the progress monitored by the monitoring team. For example, they will check the progress of tasks and follow up if any omissions or errors occur. They will notify the user of the follow-up and prompt them to take the necessary action. They will record the results of the follow-up and use them for future follow-ups.

[0058] (Example of form 2) The support system according to an embodiment of the present invention is a multimodal AI-powered system that integrates with internal systems such as email, chat, and groupware to understand users' schedules and communications. This support system improves employee and corporate productivity by having the AI ​​handle tasks such as task management, meeting minute creation, and meeting room reservations, and by following up on any omissions or errors by the user. First, there is a problem where employees' productivity decreases due to the time they spend on task management and meeting-related work. In response to this, the present invention provides an AI-powered personal assistant system that improves productivity by managing employee schedules and handling tasks on their behalf. Specifically, it consists of the following steps: First, it integrates with internal systems such as email, chat, and groupware to understand users' schedules and communications. Next, the AI ​​handles tasks such as task management, meeting minute creation, and meeting room reservations. Furthermore, by following up on any omissions or errors by the user, it provides an environment where employees can concentrate on their work. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. It also monitors the progress of tasks and sends reminders as needed. As a result, employees can reduce the time spent on task management and meeting preparation, and concentrate on their work. This system improves employee productivity and enhances overall business efficiency. For example, by reducing the time spent on task management and meeting preparation, employees can dedicate more time to their core responsibilities. Furthermore, AI helps to correct omissions and errors, reducing human error and improving work accuracy. In addition, AI learning enables the provision of optimal support tailored to individual users. This allows for flexible responses to user needs, leading to increased employee satisfaction. Thus, this invention is a system that utilizes AI to streamline employee task management and meeting-related tasks, thereby improving productivity. This creates an environment where employees can concentrate on their work, improving overall business efficiency. As a result, the support system can streamline employee task management and meeting-related tasks, thereby increasing productivity.

[0059] The support system according to the embodiment comprises a tracking unit, a proxy unit, a monitoring unit, and a follow-up unit. The tracking unit tracks the user's schedule and communications. The tracking unit collects the user's schedule and communications in conjunction with internal systems such as email, chat, and groupware. The tracking unit analyzes the content of emails and adds the user's schedule to a calendar. The tracking unit can also analyze the content of chats, extract important communications, and notify the user. Furthermore, the tracking unit can analyze groupware data to track meeting schedules. For example, the tracking unit analyzes the content of emails using natural language processing technology and automatically adds the user's schedule to a calendar. When analyzing chat content, it extracts important communications using keyword extraction technology and notifies the user. When analyzing groupware data, it automatically tracks meeting schedules and notifies the user. The proxy unit performs task management, creates meeting minutes, and reserves meeting rooms based on the schedule and communications tracked by the tracking unit. The proxy department, for example, automatically reserves a meeting room and creates meeting minutes when a user enters a meeting schedule. The proxy department also checks the availability of meeting rooms and reserves the most suitable one. Furthermore, the proxy department can automatically record the content of meetings and create meeting minutes. In addition, the proxy department can monitor the progress of tasks and send reminders as needed. For example, the proxy department checks the availability of meeting rooms in real time and reserves the most suitable one. When recording the content of a meeting, it uses speech recognition technology to convert the meeting content into text and create meeting minutes. When monitoring the progress of tasks, it integrates with a task management system to grasp the progress in real time and send reminders as needed. The monitoring department monitors the progress of tasks and meetings handled by the proxy department and sends reminders as needed. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. For example, the monitoring department displays the progress of tasks using graphs and charts, allowing users to visually understand the progress. Furthermore, the monitoring unit can also suggest the next steps based on the task's progress. For example, the monitoring unit can periodically check the task's progress and send reminders if it is behind schedule.When displaying task progress using graphs or charts, the progress should be presented in a format that is easy to understand visually. When suggesting the next step, the system should propose the next action to be taken based on the task's progress. The follow-up unit will follow up on any omissions or errors based on the progress monitored by the monitoring unit. For example, the follow-up unit will check the task's progress and follow up if any omissions or errors occur. The follow-up unit can also notify the user of the follow-up and prompt them to take the necessary actions. Furthermore, the follow-up unit can record the results of the follow-up and use them for future follow-ups. For example, the follow-up unit will check the task's progress and follow up if any omissions or errors occur. When notifying the user of the follow-up, specific instructions for necessary actions should be provided. When recording the results of the follow-up, detailed records should be kept so that they can be used for future follow-ups. As a result, the support system according to this embodiment can efficiently grasp the user's schedule and communications, handle task management, create meeting minutes, reserve meeting rooms, monitor progress, and address any omissions, thereby improving employee productivity.

[0060] The tracking unit manages users' schedules and communications. For example, it integrates with internal systems such as email, chat, and groupware to collect user schedules and communications. Specifically, it analyzes email content and adds user appointments to a calendar. It can also analyze chat content, extract important communications, and send notifications. Furthermore, it can analyze groupware data to understand meeting schedules. For example, it uses natural language processing to analyze email content and automatically adds user appointments to a calendar. When analyzing chat content, it uses keyword extraction technology to extract important communications and notifies the user. When analyzing groupware data, it automatically understands meeting schedules and notifies the user. This allows the tracking unit to efficiently manage user schedules and communications, ensuring that important information is not missed. Furthermore, the tracking unit can learn user behavior patterns and past data to predict future schedules and communications. For example, by analyzing past email and chat content and detecting specific patterns, it can predict the information and appointments the user will need next and notify them in advance. This allows users to significantly reduce the time spent managing schedules and communications, enabling them to focus on more important tasks. Furthermore, the system implements strict security policies regarding data handling to protect user privacy and prevent data leaks and unauthorized access. This ensures users can use the system with peace of mind.

[0061] The task management department handles task management, minute-taking, and meeting room reservations based on schedules and communications captured by the information gathering department. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates minutes. Specifically, it checks meeting room availability and reserves the most suitable room. The task management department can also automatically record the meeting content and create minutes. Furthermore, the task management department can monitor task progress and send reminders as needed. For example, the task management department checks meeting room availability in real time and reserves the most suitable room. When recording meeting content, it uses speech recognition technology to convert the meeting content into text and create minutes. When monitoring task progress, it integrates with the task management system to grasp progress in real time and send reminders as needed. This allows the task management department to reduce the burden on users and manage tasks efficiently. Furthermore, the task management department can learn user preferences and past behavior patterns to provide more personalized services. For example, it can suggest the most suitable meeting room based on information about meeting rooms the user has used in the past. Furthermore, it can learn the user's task management style and suggest the optimal timing and method for reminders. This allows the task management unit to respond flexibly to the user's needs, thereby improving the user's productivity.

[0062] The monitoring department monitors the progress of tasks and meetings handled by the delegation department and sends reminders as needed. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. Specifically, it displays task progress using graphs and charts to allow users to visually understand the progress. The monitoring department can also suggest the next steps based on the task progress. For example, the monitoring department periodically checks the progress of tasks and sends reminders if progress is behind schedule. When displaying task progress using graphs and charts, it displays the progress in a format that is easy to understand visually. When suggesting the next steps, it suggests actions to be taken based on the task progress. In this way, the monitoring department helps users to always be aware of the progress of tasks and take the next actions at the appropriate time. Furthermore, the monitoring department can automatically evaluate the priority of tasks and send reminders preferentially to important tasks. For example, it sends more frequent reminders to tasks with approaching deadlines or high importance to ensure that users do not miss important tasks. Furthermore, the monitoring unit can adjust the content and timing of reminders based on user feedback, providing more effective support. This allows the monitoring unit to efficiently support users' task management and ensure smooth task progress.

[0063] The follow-up unit addresses any omissions or errors based on the progress monitored by the monitoring unit. For example, the follow-up unit checks the task progress and follows up if any omissions or errors occur. Specifically, it checks the task progress and follows up if any omissions or errors occur. The follow-up unit can also notify users of the follow-up content and prompt them to take necessary actions. Furthermore, the follow-up unit can record the results of the follow-up and use them for future follow-ups. For example, the follow-up unit checks the task progress and follows up if any omissions or errors occur. When notifying users of the follow-up content, it provides specific instructions for necessary actions. When recording the results of the follow-up, it records them in detail so they can be used for future follow-ups. This allows the follow-up unit to help users stay informed of task progress and take the next action at the appropriate time. Furthermore, the follow-up unit can adjust the content and timing of follow-ups based on user feedback to provide more effective support. For example, it can understand what kind of support users need for a particular task and provide follow-up accordingly. Furthermore, the follow-up team can analyze the results of past follow-ups and utilize them for future follow-ups, thereby providing more effective support. This allows the follow-up team to efficiently support users' task management and ensure smooth progress on tasks.

[0064] The support unit can provide optimal support tailored to individual users through AI learning. For example, the support unit can analyze users' past behavioral data to provide optimal support to each individual user. For example, the support unit can analyze users' past behavioral data and customize support content based on users' preferences and habits. The support unit can also collect user feedback and improve support content. For example, the support unit can analyze users' past behavioral data and customize support content based on users' preferences and habits. When collecting user feedback, it can use surveys or evaluation systems to collect feedback and improve support content. In this way, by providing optimal support to individual users through AI learning, user satisfaction can be improved. Some or all of the above processes in the support unit may be performed using AI, for example, or not using AI. For example, the support unit can input user behavioral data into a generating AI and have the generating AI execute the generation of optimal support content.

[0065] The proxy system allows users to input meeting schedules, and the AI ​​automatically reserves a meeting room and creates meeting minutes. For example, when a user inputs a meeting schedule, the AI ​​checks the availability of meeting rooms and reserves the most suitable one. The proxy system can also automatically record the meeting content and create meeting minutes. Furthermore, the proxy system can automatically send the meeting minutes to the meeting participants. For example, the proxy system checks the availability of meeting rooms in real time and reserves the most suitable one. When recording the meeting content, speech recognition technology is used to convert the meeting content into text and create the meeting minutes. When automatically sending the meeting minutes, the minutes are sent to meeting participants via email or chat. This reduces the burden on the user by automatically reserving a meeting room and creating meeting minutes when the meeting schedule is entered. Some or all of the above processes in the proxy system may be performed using AI, or not using AI. For example, the proxy system can input the meeting content into a generation AI and have the generation AI create the meeting minutes.

[0066] The monitoring unit can monitor the progress of tasks and send reminders as needed. For example, the monitoring unit can periodically check the progress of tasks and send reminders if progress is behind schedule. For example, the monitoring unit can display the progress of tasks in graphs or charts to allow users to visually understand the progress. The monitoring unit can also suggest the next steps based on the progress of tasks. For example, the monitoring unit can periodically check the progress of tasks and send reminders if progress is behind schedule. When displaying the progress of tasks in graphs or charts, it displays the progress in a format that is easy to understand visually. When suggesting the next steps, it suggests the next action to be taken based on the progress of tasks. In this way, by monitoring the progress of tasks and sending reminders as needed, it is possible to prevent tasks from being missed or overlooked. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or not using AI. For example, the monitoring unit can input the progress of tasks into a generating AI and have the generating AI execute the sending of reminders.

[0067] The sensing unit can estimate the user's emotions and adjust how schedules and communications are handled based on the estimated emotions. For example, if the user is stressed, the sensing unit can provide a simple interface and quickly handle schedules and communications. For example, if the user is relaxed, the sensing unit can provide detailed information and carefully handle schedules and communications. Furthermore, if the user is in a hurry, the sensing unit can prioritize and display important appointments and communications for quick access. For example, if the user is stressed, the sensing unit can provide a simple interface and quickly handle schedules and communications. If the user is relaxed, it can provide detailed information and carefully handle schedules and communications. If the user is in a hurry, it can prioritize and display important appointments and communications for quick access. This reduces user stress by adjusting how schedules and communications are handled according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine 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-described processing in the grasping unit may be performed using AI, for example, or without AI. For example, the grasping unit can input user emotion data into a generating AI and cause the generating AI to adjust the grasping method based on emotion.

[0068] The tracking unit can analyze the user's past schedules and contact history and select the optimal tracking method. For example, the tracking unit can prioritize displaying people the user has frequently contacted in the past. For example, the tracking unit can prioritize displaying important appointments in specific time slots from the user's past schedule history. Furthermore, the tracking unit can analyze the user's past contact history and prioritize displaying highly relevant contacts. For example, the tracking unit can prioritize displaying people the user has frequently contacted in the past. For example, it can prioritize displaying important appointments in specific time slots from the user's past schedule history. For example, it can analyze the user's past contact history and prioritize displaying highly relevant contacts. In this way, by analyzing past schedules and contact history, the tracking unit can provide the user with the optimal tracking method. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's past schedules and contact history into a generating AI and have the generating AI select the optimal tracking method.

[0069] The information gathering unit can filter schedules and communications based on the user's current projects and areas of interest. For example, the information gathering unit can prioritize displaying schedules and communications related to projects the user is currently working on. The information gathering unit can also filter and display relevant schedules and communications based on the user's areas of interest. Furthermore, if the user is focused on a specific project, the information gathering unit can prioritize displaying schedules and communications related to that project. For example, the information gathering unit can prioritize displaying schedules and communications related to projects the user is currently working on. It can filter and display relevant schedules and communications based on the user's areas of interest. If the user is focused on a specific project, it can prioritize displaying schedules and communications related to that project. This allows users to prioritize the identification of highly relevant information by filtering based on their current projects and areas of interest. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input data on the user's projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0070] The emotional intelligence unit can estimate the user's emotions and determine the priority of appointments and communications based on the estimated emotions. For example, if the user is stressed, the unit will prioritize important appointments and communications, delaying other information. If the user is relaxed, the unit can also display all appointments and communications equally, allowing the user to choose freely. Furthermore, if the user is in a hurry, the unit can prioritize urgent appointments and communications, enabling quick responses. For example, if the user is stressed, the unit will prioritize important appointments and communications, delaying other information. If the user is relaxed, all appointments and communications will be displayed equally, allowing the user to choose freely. If the user is in a hurry, urgent appointments and communications will be prioritized, enabling quick responses. This allows users to quickly grasp important information by determining the priority of appointments and communications according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative 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 processing described above in the grasping unit may be performed using AI, or not using AI. For example, the grasping unit can input user emotion data into the generation AI and have the generation AI perform priority determination.

[0071] The information gathering unit can prioritize identifying highly relevant information by considering the user's geographical location when gathering schedules and communications. For example, the information gathering unit can prioritize displaying schedules and communications in locations close to the user's current location. For example, if the user is in a specific region, the information gathering unit can prioritize displaying schedules and communications related to that region. Furthermore, if the user is on the move, the information gathering unit can display the most suitable schedules and communications based on the user's current location. For example, the information gathering unit prioritizes displaying schedules and communications in locations close to the user's current location. If the user is in a specific region, it prioritizes displaying schedules and communications related to that region. If the user is on the move, it displays the most suitable schedules and communications based on the user's current location. In this way, by considering geographical location information, highly relevant information can be prioritized. Some or all of the above processing in the information gathering unit may be performed using AI, for example, or without AI. For example, the information gathering unit can input the user's geographical location information into a generating AI and have the generating AI perform the task of identifying highly relevant information.

[0072] The tracking unit can analyze a user's social media activity and identify relevant information when tracking schedules and communications. For example, the tracking unit can prioritize displaying events and schedules mentioned by the user on social media. The tracking unit can also analyze the content of a user's social media posts and display relevant schedules and communications. Furthermore, the tracking unit can prioritize displaying schedules and communications related to accounts the user follows on social media. For example, the tracking unit can prioritize displaying events and schedules mentioned by the user on social media. It can analyze the content of a user's social media posts and display relevant schedules and communications. It can prioritize displaying schedules and communications related to accounts the user follows on social media. This allows for the priority identification of relevant information by analyzing social media activity. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the user's social media activity data into a generating AI and have the generating AI perform the identification of relevant information.

[0073] The proxy system can estimate the user's emotions and adjust task management and meeting minute creation methods based on the estimated emotions. For example, if the user is stressed, the proxy system can provide a simple task management interface and simplify task input. If the user is relaxed, the proxy system can also provide detailed task management options and suggest customizable task management methods. Furthermore, if the user is in a hurry, the proxy system can prioritize voice input to allow for quick task input. For example, if the user is stressed, the proxy system can provide a simple task management interface and simplify task input. If the user is relaxed, it can provide detailed task management options and suggest customizable task management methods. If the user is in a hurry, it can prioritize voice input to allow for quick task input. This reduces user stress by adjusting task management and meeting minute creation methods according to 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-mentioned processes in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input user emotion data into a generating AI and have the generating AI perform tasks and adjust the methods for creating meeting minutes.

[0074] The task management unit can adjust the level of detail based on the importance of the task when managing tasks and creating meeting minutes. For example, the task management unit can provide detailed explanations and procedures for important tasks. For example, the task management unit can provide concise explanations for less important tasks to enable quick processing. Furthermore, the task management unit can provide detailed procedures for urgent tasks to enable quick response. For example, the task management unit can provide detailed explanations and procedures for important tasks. For less important tasks, it can provide concise explanations to enable quick processing. For urgent tasks, it can provide detailed procedures to enable quick response. This allows for appropriate responses to important tasks by adjusting the level of detail based on the importance of the task. Some or all of the above processing in the task management unit may be performed using AI, for example, or not using AI. For example, the task management unit can input task importance data into a generating AI and have the generating AI perform the level of detail adjustment.

[0075] The task management unit can apply different task management algorithms depending on the task category when managing tasks or creating meeting minutes. For example, the task management unit can apply a project management-specific algorithm to project management tasks. For example, the task management unit can also apply a routine work-specific algorithm to routine work tasks. Furthermore, the task management unit can apply an emergency response-specific algorithm to emergency response tasks. For example, the task management unit can apply a project management-specific algorithm to project management tasks. For routine work tasks, it can apply a routine work-specific algorithm. For emergency response tasks, it can apply an emergency response-specific algorithm. This enables efficient task management by applying different task management algorithms depending on the task category. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input task category data into a generating AI and have the generating AI execute the application of the task management algorithm.

[0076] The task management system can estimate the user's emotions and prioritize task management and meeting minute creation based on those emotions. For example, if the user is stressed, the system will prioritize important tasks and meeting minutes. If the user is relaxed, the system can also process all tasks and meeting minutes equally. Furthermore, if the user is in a hurry, the system can prioritize urgent tasks and meeting minutes. For example, if the user is stressed, the system will prioritize important tasks and meeting minutes. If the user is relaxed, it will process all tasks and meeting minutes equally. If the user is in a hurry, it will prioritize urgent tasks and meeting minutes. This allows for the rapid processing of important tasks by prioritizing task management and meeting minute creation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine 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-described processes in the proxy unit may be performed using AI, for example, or without AI. For example, the proxy unit can input user emotion data into a generating AI and have the generating AI perform priority determination.

[0077] The task management unit can prioritize tasks based on their submission dates when managing tasks and creating meeting minutes. For example, the task management unit can prioritize tasks with approaching deadlines. It can also postpone tasks with distant deadlines. Furthermore, the task management unit can respond quickly to tasks that have passed their deadlines. For example, the task management unit prioritizes tasks with approaching deadlines, postpones tasks with distant deadlines, and responds quickly to tasks that have passed their deadlines. By prioritizing tasks based on their submission dates, appropriate responses can be made according to the deadlines. Some or all of the above processes in the task management unit may be performed using AI, for example, or not. For example, the task management unit can input task submission date data into a generating AI and have the generating AI perform the priority determination.

[0078] The task management unit can adjust the order of tasks based on their relevance when managing tasks or creating meeting minutes. For example, the task management unit can group highly relevant tasks and process them efficiently. The task management unit can also process less relevant tasks individually. Furthermore, the task management unit can prioritize highly relevant tasks and postpone less relevant tasks. For example, the task management unit can group highly relevant tasks and process them efficiently. Less relevant tasks can be processed individually. Highly relevant tasks can be prioritized, and less relevant tasks can be postponed. This allows for efficient task management by adjusting the order of tasks based on their relevance. Some or all of the above processing in the task management unit may be performed using AI, for example, or without AI. For example, the task management unit can input task relevance data into a generating AI and have the generating AI perform the order adjustment.

[0079] The monitoring unit can estimate the user's emotions and adjust the progress monitoring method based on the estimated user emotions. For example, if the user is stressed, the monitoring unit can provide a simple progress display method. For example, if the user is relaxed, the monitoring unit can also provide a detailed progress display method. Furthermore, if the user is in a hurry, the monitoring unit can provide a concise progress display method. For example, if the user is stressed, the monitoring unit can provide a simple progress display method. If the user is relaxed, it can provide a detailed progress display method. If the user is in a hurry, it can provide a concise progress display method. This allows for reducing user stress by adjusting the progress monitoring method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI adjust the monitoring method.

[0080] The monitoring unit can improve the accuracy of monitoring by considering the interrelationships between tasks when monitoring progress. For example, the monitoring unit monitors progress by considering the dependencies between tasks. The monitoring unit can also analyze the interrelationships between tasks and provide an efficient monitoring method. Furthermore, the monitoring unit can optimize progress monitoring based on the interrelationships between tasks. For example, the monitoring unit monitors progress by considering the dependencies between tasks. It analyzes the interrelationships between tasks and provides an efficient monitoring method. It optimizes progress monitoring based on the interrelationships between tasks. As a result, the accuracy of progress monitoring is improved by considering the interrelationships between tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input task interrelationship data into a generating AI and have the generating AI perform the improvement of monitoring accuracy.

[0081] The monitoring unit can monitor progress while considering the attribute information of the task submitter. For example, the monitoring unit can monitor progress while considering the task submitter's job title and responsibilities. The monitoring unit can also monitor progress based on the task submitter's past performance. Furthermore, the monitoring unit can monitor progress while considering the task submitter's skill set. For example, the monitoring unit can monitor progress while considering the task submitter's job title and responsibilities. For example, it can monitor progress based on the task submitter's past performance. For example, it can monitor progress while considering the skill set of the task submitter. This improves the accuracy of progress monitoring by considering the attribute information of the task submitter. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the attribute information of the task submitter into a generating AI and have the generating AI perform the monitoring.

[0082] The monitoring unit can estimate the user's emotions and adjust the timing of reminder sending based on the estimated emotions. For example, if the user is stressed, the monitoring unit can reduce the frequency of reminder sending. For example, if the user is relaxed, the monitoring unit can increase the frequency of reminder sending. Furthermore, if the user is in a hurry, the monitoring unit can prioritize sending important reminders. For example, if the user is stressed, the monitoring unit reduces the frequency of reminder sending. If the user is relaxed, it increases the frequency of reminder sending. If the user is in a hurry, it prioritizes sending important reminders. This reduces user stress by adjusting the timing of reminder sending according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into the generating AI and have the generating AI adjust the timing of reminder sending.

[0083] The monitoring unit can perform monitoring while considering the geographical distribution of tasks when monitoring progress. For example, if tasks are distributed across different regions, the monitoring unit can monitor the progress for each region. The monitoring unit can also provide an efficient monitoring method by considering the geographical distribution of tasks. Furthermore, the monitoring unit can optimize progress monitoring based on the geographical distribution of tasks. For example, if tasks are distributed across different regions, the monitoring unit can monitor the progress for each region. It can provide an efficient monitoring method by considering the geographical distribution of tasks. It can optimize progress monitoring based on the geographical distribution of tasks. As a result, the accuracy of progress monitoring is improved by considering the geographical distribution of tasks. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of tasks into a generating AI and have the generating AI perform the monitoring.

[0084] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature for the task when monitoring progress. For example, the monitoring unit can refer to relevant literature for the task and monitor progress. The monitoring unit can also provide an efficient monitoring method based on relevant literature for the task. Furthermore, the monitoring unit can analyze relevant literature for the task and optimize progress monitoring. For example, the monitoring unit can refer to relevant literature for the task and monitor progress. It can provide an efficient monitoring method based on relevant literature for the task. It can analyze relevant literature for the task and optimize progress monitoring. As a result, the accuracy of progress monitoring is improved by referring to relevant literature for the task. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data for the task into a generating AI and have the generating AI perform the monitoring.

[0085] The follow-up unit can estimate the user's emotions and adjust the follow-up method to address any omissions or oversights based on the estimated emotions. For example, if the user is stressed, the follow-up unit can provide a simple follow-up method and respond quickly. If the user is relaxed, the follow-up unit can also provide a detailed follow-up method and respond carefully. Furthermore, if the user is in a hurry, the follow-up unit can prioritize important follow-ups and respond quickly. For example, if the user is stressed, the follow-up unit can provide a simple follow-up method and respond quickly. If the user is relaxed, it can provide a detailed follow-up method and respond carefully. If the user is in a hurry, it can prioritize important follow-ups and respond quickly. This reduces user stress by adjusting the follow-up method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up function can input user emotion data into a generating AI and have the AI ​​adjust the follow-up method.

[0086] The follow-up unit can select the optimal follow-up method by referring to past follow-up data when addressing omissions or errors. For example, the follow-up unit can select the optimal follow-up method based on follow-up methods that were effective in the past. The follow-up unit can also analyze past follow-up data and provide the user with the optimal follow-up method. Furthermore, the follow-up unit can select an efficient follow-up method by referring to past follow-up data. For example, the follow-up unit can select the optimal follow-up method based on follow-up methods that were effective in the past. It can analyze past follow-up data and provide the user with the optimal follow-up method. It can select an efficient follow-up method by referring to past follow-up data. In this way, the optimal follow-up method can be provided by referring to past follow-up data. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without using AI. For example, the follow-up unit can input past follow-up data into a generating AI and have the generating AI select the optimal follow-up method.

[0087] The follow-up unit can apply different follow-up methods to each task category when addressing omissions or errors. For example, the follow-up unit can apply a follow-up method specifically for project management tasks. Similarly, it can apply a follow-up method specifically for daily tasks. Furthermore, it can apply a follow-up method specifically for emergency response tasks. This allows for efficient follow-up by applying different follow-up methods depending on the task category. Some or all of the above processing in the follow-up unit may be performed using AI, or without AI. For example, the follow-up unit can input task category data into a generating AI and have the generating AI apply the follow-up method.

[0088] The follow-up unit can estimate the user's emotions and determine the priority of follow-ups based on the estimated emotions. For example, if the user is stressed, the follow-up unit will prioritize important follow-ups. If the user is relaxed, the follow-up unit can also distribute all follow-ups evenly. Furthermore, if the user is in a hurry, the follow-up unit can prioritize urgent follow-ups. For example, if the user is stressed, the follow-up unit will prioritize important follow-ups. If the user is relaxed, it will distribute all follow-ups evenly. If the user is in a hurry, it will prioritize urgent follow-ups. This allows important follow-ups to be delivered quickly by determining the priority of follow-ups according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input user emotion data into a generative AI and have the generative AI perform the priority determination.

[0089] The follow-up unit can analyze changes in follow-up based on the task submission timing when addressing omissions or errors. For example, the follow-up unit can quickly follow up on tasks with approaching deadlines. For example, the follow-up unit can reduce the frequency of follow-up on tasks with distant deadlines. Furthermore, the follow-up unit can quickly follow up on tasks that have passed their deadlines and prompt action. For example, the follow-up unit can quickly follow up on tasks with approaching deadlines. For tasks with distant deadlines, it can reduce the frequency of follow-up. For tasks that have passed their deadlines, it can quickly follow up and prompt action. This allows for appropriate follow-up by analyzing changes in follow-up based on the task submission timing. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input task submission timing data into a generating AI and have the generating AI perform an analysis of changes in follow-up.

[0090] The follow-up unit can analyze the follow-up process by referring to relevant market data for the task when addressing any omissions or errors. For example, the follow-up unit can refer to relevant market data for the task and provide the optimal follow-up method. The follow-up unit can also select an efficient follow-up method based on the market data. Furthermore, the follow-up unit can analyze the market data to optimize the task follow-up. For example, the follow-up unit can refer to relevant market data for the task and provide the optimal follow-up method. It can select an efficient follow-up method based on the market data. It can analyze the market data to optimize the task follow-up. This allows the follow-up unit to provide the optimal follow-up method by referring to relevant market data for the task. Some or all of the above processing in the follow-up unit may be performed using AI, for example, or without AI. For example, the follow-up unit can input relevant market data for the task into a generating AI and have the generating AI perform the follow-up analysis.

[0091] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0092] The support system can also include a health management unit that monitors the user's health status. This unit can, for example, monitor the user's heart rate and sleep patterns to understand their health condition. It can also provide advice on relaxation if the user is experiencing stress. Furthermore, the health management unit can adjust task priorities based on the user's health status. For example, if the user is tired, it can suggest postponing important tasks and prioritizing rest. This enables task management that considers the user's health, improving productivity while maintaining the user's well-being.

[0093] The support system can also include a refresh suggestion unit that proposes refresh time based on the user's hobbies and interests. For example, if the user has been working for a long time, the refresh suggestion unit will suggest a break at an appropriate time. The refresh suggestion unit can also suggest methods of refreshment based on the user's hobbies. Furthermore, the refresh suggestion unit can provide content suitable for refreshment based on the user's interests. For example, if the user likes music, the refresh suggestion unit will suggest relaxing music. If the user likes reading, it will suggest books that may interest them. By providing refresh time based on the user's hobbies and interests, work efficiency can be improved.

[0094] The support system can further include a learning support unit that analyzes the user's learning history and provides learning content for skill improvement. For example, the learning support unit analyzes what the user has learned in the past and suggests what they should learn next. The learning support unit can also provide appropriate learning content according to the user's skill level. Furthermore, the learning support unit can monitor the user's learning progress and provide feedback as needed. For example, the learning support unit suggests steps for the user to acquire a specific skill. If the user encounters difficulties in their learning, it provides solutions. This supports the user's skill development and improves work efficiency.

[0095] The support system can also include a communication support unit that analyzes the user's communication style and proposes the optimal communication method. For example, if the user prefers email or chat, the communication support unit will prioritize suggesting those methods. If the user prefers face-to-face communication, the communication support unit can also suggest setting up a meeting. Furthermore, based on the user's communication style, the communication support unit can also suggest how to write effective messages. For example, if the user prefers short messages, the communication support unit will suggest how to write concise messages. If the user prefers detailed explanations, it will suggest how to write detailed messages. This improves communication efficiency by providing the optimal method according to the user's communication style.

[0096] The support system can further estimate the user's emotions and adjust the content of reminders based on those emotions. For example, if the user is stressed, the reminder content can be simplified and only essential information can be provided. If the user is relaxed, more detailed information can be provided and the reminder content can be enriched. Furthermore, if the user is in a hurry, the reminder content can be made concise to allow for quick action. By adjusting the content of reminders according to the user's emotions, it is possible to reduce user stress and enable efficient task management.

[0097] The support system can further estimate the user's emotions and adjust the meeting's pace based on those emotions. For example, if a user is stressed, the meeting can be accelerated and focused on important topics. If a user is relaxed, the meeting can be slowed down and detailed discussions can be held. Furthermore, if a user is in a hurry, the meeting can be made more efficient and conclusions can be reached quickly. In this way, the efficiency of meetings can be improved by adjusting the meeting's pace according to the user's emotions.

[0098] The support system can further estimate the user's emotions and adjust task assignments based on those emotions. For example, if a user is stressed, it can prioritize assigning easy tasks. If a user is relaxed, it can assign more complex tasks. Furthermore, if a user is in a hurry, it can prioritize assigning more urgent tasks. This allows for efficient task management by adjusting task assignments according to the user's emotions.

[0099] The support system can further estimate the user's emotions and adjust how task progress is reported based on those emotions. For example, if the user is stressed, a concise progress report can be provided. If the user is relaxed, a detailed progress report can be provided. Furthermore, if the user is in a hurry, a to-the-point progress report can be provided. By adjusting the progress reporting method according to the user's emotions, it is possible to reduce user stress and enable efficient task management.

[0100] The support system can further estimate the user's emotions and adjust task priorities based on those emotions. For example, if the user is stressed, important tasks will be prioritized. If the user is relaxed, all tasks may be distributed evenly. Furthermore, if the user is in a hurry, urgent tasks may be prioritized. This allows important tasks to be handled quickly by adjusting task priorities according to the user's emotions.

[0101] The support system can further estimate the user's emotions and adjust how task progress is monitored based on those emotions. For example, if the user is stressed, it can provide a simple progress display. If the user is relaxed, it can provide a detailed progress display. Furthermore, if the user is in a hurry, it can provide a concise progress display. By adjusting the progress monitoring method according to the user's emotions, it is possible to reduce user stress and enable efficient task management.

[0102] The following briefly describes the processing flow for example form 2.

[0103] Step 1: The information gathering unit gathers user schedules and communications. For example, it collects user schedules and communications by linking with internal systems such as email, chat, and groupware. It analyzes email content and adds user appointments to the calendar. It analyzes chat content, extracts important communications, and sends notifications. It analyzes groupware data to understand meeting schedules. Step 2: The task management unit handles task management, meeting minute creation, and meeting room reservations based on the schedules and communications gathered by the information gathering unit. For example, when a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. It checks meeting room availability and reserves the most suitable room. It automatically records the meeting content and creates meeting minutes. It monitors task progress and sends reminders as needed. Step 3: The monitoring unit monitors the progress of tasks and meetings handled by the delegation unit and sends reminders as needed. For example, it periodically checks the progress of tasks and sends reminders if progress is behind schedule. It displays the task progress using graphs and charts to allow users to visually understand the progress. Based on the task progress, it suggests the next steps. Step 4: The follow-up team will address any omissions or errors based on the progress monitored by the monitoring team. For example, they will check the progress of tasks and follow up if any omissions or errors occur. They will notify the user of the follow-up and prompt them to take the necessary action. They will record the results of the follow-up and use them for future follow-ups.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] Each of the multiple elements described above, including the information gathering unit, the proxy unit, the monitoring unit, and the follow-up unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the information gathering unit is implemented by the computer 36 of the smart device 14 and collects user schedules and communications in cooperation with internal systems such as email, chat, and groupware. The proxy unit is implemented by the specific processing unit 290 of the data processing device 12 and performs tasks such as task management, meeting minute creation, and meeting room reservation. The monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of tasks and sends reminders as needed. The follow-up unit is implemented by the specific processing unit 290 of the data processing device 12 and follows up on any omissions or errors based on the progress. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0108] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] Each of the multiple elements described above, including the grasping unit, the proxy unit, the monitoring unit, and the follow-up unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the grasping unit is implemented by the computer 36 of the smart glasses 214 and collects the user's schedule and communications in cooperation with internal systems such as email, chat, and groupware. The proxy unit is implemented by the specific processing unit 290 of the data processing device 12 and performs tasks such as task management, meeting minute creation, and meeting room reservation. The monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of tasks and sends reminders as needed. The follow-up unit is implemented by the specific processing unit 290 of the data processing device 12 and follows up on any omissions or errors based on the progress. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0124] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] Each of the multiple elements described above, including the information gathering unit, the proxy unit, the monitoring unit, and the follow-up unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the information gathering unit is implemented by the computer 36 of the headset terminal 314 and collects user schedules and communications in cooperation with internal systems such as email, chat, and groupware. The proxy unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs tasks such as task management, minute-taking, and meeting room reservations. The monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of tasks and sends reminders as needed. The follow-up unit is implemented by the specific processing unit 290 of the data processing unit 12 and follows up on any omissions or errors based on the progress. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0140] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the grasping unit, the delegation unit, the monitoring unit, and the follow-up unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the grasping unit is implemented by the computer 36 of the robot 414 and collects user schedules and communications in cooperation with internal systems such as email, chat, and groupware. The delegation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs tasks such as task management, minute-taking, and meeting room reservations. The monitoring unit is implemented by, for example, the control unit 46A of the robot 414 and monitors the progress of tasks and sends reminders as needed. The follow-up unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and follows up on any omissions or errors based on the progress. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] (Note 1) A tracking unit that keeps track of the user's schedule and communications, Based on the schedules and communications gathered by the aforementioned information gathering unit, the agency unit will handle task management, meeting minute creation, and meeting room reservations. A monitoring unit monitors the progress of tasks and meetings performed by the aforementioned proxy unit and sends reminders as needed. The system includes a follow-up unit that follows up on any omissions or errors based on the progress status monitored by the aforementioned monitoring unit. A system characterized by the following features. (Note 2) The aforementioned follow-up section is, Through AI learning, we provide optimal support tailored to each individual user. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned agency unit, When a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, Monitor task progress and send reminders as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The gripping part is, It estimates the user's emotions and adjusts how schedules and communications are tracked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The gripping part is, Analyze the user's past schedule and contact history to select the most effective method for understanding their needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The gripping part is, When reviewing schedules and communications, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The gripping part is, It estimates the user's emotions and determines the priority of appointments and communications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The gripping part is, When managing schedules and communications, the system prioritizes identifying highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 10) The gripping part is, When tracking schedules and communications, analyze users' social media activity to identify relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned agency unit, It estimates user emotions and adjusts task management and meeting minute creation methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned agency unit, When managing tasks or creating meeting minutes, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned agency unit, When managing tasks or creating meeting minutes, different automation algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned agency unit, It estimates the user's emotions and prioritizes task management and meeting minute creation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned agency unit, When managing tasks or creating meeting minutes, prioritize tasks based on their submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned agency unit, When managing tasks or creating meeting minutes, adjust the order of tasks based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned monitoring unit, We estimate user sentiment and adjust progress monitoring methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned monitoring unit, When monitoring progress, improve the accuracy of monitoring by considering the interrelationships between tasks. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned monitoring unit, When monitoring progress, the monitoring process should take into account the attribute information of the task submitter. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the timing of sending reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned monitoring unit, When monitoring progress, the geographical distribution of tasks should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned monitoring unit, When monitoring progress, refer to relevant literature for the task to improve the accuracy of monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned follow-up section is, The system estimates the user's emotions and adjusts the follow-up methods to address any omissions or oversights based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned follow-up section is, When addressing any omissions or errors, refer to past follow-up data to select the most appropriate follow-up method. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned follow-up section is, When addressing omissions or errors, apply different follow-up methods to each task category. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned follow-up section is, It estimates the user's emotions and determines the priority of follow-up based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned follow-up section is, When following up on omissions and errors, analyze how follow-up changes based on the task submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned follow-up section is, When addressing any omissions or errors, analyze the follow-up process by referring to relevant market data for the task. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0176] 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 tracking unit that keeps track of the user's schedule and communications, Based on the schedules and communications gathered by the aforementioned information gathering unit, the agency unit will handle task management, meeting minute creation, and meeting room reservations. A monitoring unit monitors the progress of tasks and meetings performed by the aforementioned proxy unit and sends reminders as needed. The system includes a follow-up unit that follows up on any omissions or errors based on the progress status monitored by the aforementioned monitoring unit. A system characterized by the following features.

2. The aforementioned follow-up section is AI learning provides optimal support tailored to each individual user. The system according to feature 1.

3. The aforementioned agency unit, When a user enters a meeting schedule, the AI ​​automatically reserves a meeting room and creates meeting minutes. The system according to feature 1.

4. The aforementioned monitoring unit, Monitor task progress and send reminders as needed. The system according to feature 1.

5. The gripping part is, It estimates the user's emotions and adjusts how schedules and communications are tracked based on those estimated emotions. The system according to feature 1.

6. The gripping part is, Analyze the user's past schedule and contact history to select the most effective method for understanding their needs. The system according to feature 1.

7. The gripping part is, When reviewing schedules and communications, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

8. The gripping part is, It estimates the user's emotions and determines the priority of appointments and communications based on those estimated emotions. The system according to feature 1.

9. The gripping part is, When managing schedules and communications, the system prioritizes identifying highly relevant information by considering the user's geographical location. The system according to feature 1.

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