system

Abstract

The system according to this embodiment aims to organize internal company information and efficiently manage tasks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a management unit, a reminder unit, and a learning unit. The collection unit collects information from the user's "email system," "communication tools," and "calendar system." The analysis unit analyzes the information collected by the collection unit. The management unit performs task management based on the information analyzed by the analysis unit. The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. The learning unit learns the user's behavior patterns.

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 the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the information within the company is in a mess and it is difficult to efficiently manage important information.

[0005] The system according to the embodiment aims to organize the information within the company and perform task management efficiently.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a management unit, a reminder unit, and a learning unit. The collection unit collects information from the user's "email system," "communication tools," and "calendar system." The analysis unit analyzes the information collected by the collection unit. The management unit performs task management based on the information analyzed by the analysis unit. The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. The learning unit learns the user's behavior patterns. [Effects of the Invention]

[0007] The system according to this embodiment can organize internal company information and efficiently manage tasks. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The task management system according to an embodiment of the present invention is a system for centrally managing internal company information and efficiently managing important tasks. This task management system collects information from the user's "email system," "communication tools," and "calendar system," and uses AI to analyze it and perform task management. This allows the user to efficiently manage tasks without overlooking important information. For example, the task management system collects information from the user's "email system," "communication tools," and "calendar system." In this process, it obtains information using the API of each tool. For example, it collects email content from the email system, message history from communication tools, and schedule information from the calendar system. Next, the AI ​​analyzes the collected information. Based on the collected information, the AI ​​analyzes the user's past behavior history and task priorities. For example, if the user has overlooked important emails or messages in the past, the AI ​​learns that pattern and can issue an alert when a similar situation occurs. Furthermore, the AI ​​performs task management based on the analysis results. Specifically, it organizes the user's tasks in order of priority and displays important tasks in an easy-to-understand manner. For example, it prioritizes displaying tasks with approaching deadlines and important meeting schedules. It can also monitor the progress of tasks in real time and send reminders as needed. This system allows users to efficiently manage tasks without overlooking important information. For example, users will no longer forget meetings due to being overwhelmed with email correspondence or miss important emails due to being busy with communication tools. Furthermore, real-time tracking of task progress enables more efficient work. In addition, the AI ​​learns user behavior patterns and can provide task management tailored to individual needs. For instance, it can prioritize displaying functions frequently used by specific users or tasks concentrated during certain time periods, thereby improving user efficiency. Thus, this invention is a system for centrally managing internal company information and efficiently managing important tasks, thereby improving user efficiency.This allows the task management system to centrally manage information from the user's email system, communication tools, and calendar system, enabling efficient management of important tasks.

[0029] The task management system according to this embodiment comprises a collection unit, an analysis unit, a management unit, a reminder unit, and a learning unit. The collection unit collects information from the user's "email system," "communication tool," and "calendar system." For example, the collection unit collects email content from the email system, message history from the communication tool, and schedule information from the calendar system. The collection unit can obtain information using the APIs of each tool. For example, the collection unit can obtain email content using the email system's API. The collection unit can also obtain message history using the communication tool's API. Furthermore, the collection unit can obtain schedule information using the calendar system's API. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past behavior history and task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. The management unit performs task management based on the information analyzed by the analysis unit. The management unit, for example, organizes user tasks by priority and clearly displays important tasks. The management unit can prioritize tasks and display important tasks preferentially. For example, the management unit can prioritize displaying tasks with approaching deadlines and important meeting schedules. The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders as needed. The reminder unit can send reminders using email notifications or push notifications. The learning unit learns user behavior patterns and provides task management tailored to individual needs. For example, the learning unit can learn user behavior patterns and prioritize displaying features frequently used by specific users or tasks concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms.As a result, the task management system according to this embodiment can centrally manage information from the user's "email system," "communication tool," and "calendar system," and efficiently manage important tasks.

[0030] The data collection unit collects information from the user's "email system," "communication tools," and "calendar system." For example, the unit collects email content from the email system, message history from communication tools, and appointment information from the calendar system. The unit can obtain information using the APIs of each tool. For instance, the unit retrieves email content using the email system's API. It can also retrieve message history using the communication tool's API. Furthermore, it can retrieve appointment information using the calendar system's API. Specifically, the unit retrieves detailed information such as email subject, sender, recipient, date and time sent, and body from the inbox and sent email folders via the email system's API. This allows the unit to identify important emails received by the user and unread emails, and register them as tasks. For communication tools, it retrieves chat message history and analyzes specific keywords and phrases to extract important conversations and messages related to tasks. For example, it can collect messages regarding project progress and meeting schedules and register them as tasks. For the calendar system, it retrieves the user's appointment information and registers important events such as meetings and deadlines as tasks. This allows for centralized management of user schedules and appropriate task prioritization. The data collection unit collects this information in real time and stores it in a central database. This enables other departments to quickly access the information they need. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, during the progress of an important project, increasing the data collection frequency allows for real-time situation monitoring. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past behavior history and task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. Specifically, the analysis unit uses natural language processing technology to analyze the content of emails and messages and extract important keywords and phrases. This allows it to identify important information that users tend to overlook and register it as a task. The analysis unit also analyzes task priorities based on the user's past behavior history. For example, it can prioritize displaying tasks that the user has frequently dealt with in the past or tasks related to important projects. Furthermore, the analysis unit can use machine learning algorithms to learn the user's behavior patterns and dynamically adjust task priorities. This allows the analysis unit to achieve flexible task management tailored to the user's needs. The analysis unit not only analyzes user behavior patterns and task priorities based on collected information, but can also detect unusual patterns and abnormal data using anomaly detection algorithms. For example, it can issue alerts for important emails sent outside of normal business hours or for sudden schedule changes. This allows the analysis unit to not only grasp the situation in real time, but also to handle anomaly detection and risk management, thereby improving the reliability and security of the entire system.

[0032] The management department manages tasks based on information analyzed by the analysis department. For example, the management department organizes user tasks by priority and displays important tasks clearly. The management department can prioritize tasks and display important tasks first. For example, the management department can prioritize tasks with approaching deadlines and important meeting schedules. Specifically, the management department organizes user tasks by category and displays them in a visually easy-to-understand interface. This allows users to grasp important tasks at a glance and manage them efficiently. The management department can also dynamically adjust task priorities. For example, if a new task is added or the priority of an existing task is changed, the management department immediately recalculates the task priorities and displays tasks based on the latest information. Furthermore, the management department can monitor task progress in real time and provide appropriate feedback to users. For example, it can send reminders to users or notify them of task completion status depending on the task progress. This allows the management department to enable users to manage tasks efficiently and address important tasks without overlooking them. The management department can also collect user feedback and continuously improve the accuracy and effectiveness of task management. For example, by having users provide feedback on task prioritization, the management department can improve the task prioritization algorithm, resulting in more accurate task management. This allows the management department to provide flexible task management tailored to user needs and improve the overall performance of the system.

[0033] The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders as necessary. The reminder unit can send reminders using email notifications and push notifications. Specifically, the reminder unit sends reminders to users when task deadlines are approaching or when task progress is behind schedule. This allows users to efficiently manage tasks without overlooking important tasks. The reminder unit can dynamically adjust the timing and content of reminder notifications based on the user's behavior patterns and task priorities. For example, if a user concentrates on work during a specific time period, sending reminders to coincide with that time can improve the user's work efficiency. Furthermore, the reminder unit can reliably transmit information using multiple notification methods. For example, it can reliably deliver important information by using not only email notifications but also smartphone push notifications, voice calls, and SMS in combination. This allows the reminder unit to provide users with quick and reliable reminders and efficiently manage task progress. The reminder unit can also collect user feedback and continuously improve the accuracy and effectiveness of reminders. For example, by providing user feedback on the timing and content of reminders, the reminder unit can improve its reminder sending algorithm and provide more accurate reminders. This allows the reminder unit to provide flexible reminder functions tailored to user needs and improve the overall system performance.

[0034] The learning unit learns user behavior patterns and provides task management tailored to individual needs. For example, by learning user behavior patterns, the learning unit can prioritize displaying features frequently used by a particular user or tasks that are concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms. Specifically, the learning unit analyzes the user's past behavior history and task completion status to identify user behavior patterns. This allows the learning unit to display important tasks according to the user's tendency to concentrate on work during specific time periods. Furthermore, the learning unit can learn the features frequently used by the user and the priority given to specific tasks, providing task management tailored to the user's needs. For example, by prioritizing the display of features frequently used by the user or tasks related to specific projects, the user's work efficiency can be improved. In addition, the learning unit can collect user feedback and continuously improve the accuracy and effectiveness of its learning algorithms. For example, by providing user feedback on task priorities and display content, the learning unit can improve its learning algorithms and provide more accurate task management. This allows the learning unit to provide flexible task management tailored to user needs and improve overall system performance. The learning unit can not only learn user behavior patterns but also draw inspiration from other users' behavior patterns and industry best practices. This enables the learning unit to provide users with the optimal task management method and improve overall system efficiency.

[0035] The data collection unit can acquire information using the APIs of various tools. For example, the data collection unit can acquire email content using the API of an email system. The data collection unit can also acquire message history using the API of a communication tool. The data collection unit can also acquire schedule information using the API of a calendar system. This allows for efficient information collection by utilizing the APIs of various tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the information acquired using the APIs of various tools into an AI and have the AI ​​perform the analysis of the information.

[0036] The analysis unit can analyze the user's past behavior history and task priorities based on the collected information. For example, the analysis unit can analyze the user's past behavior history based on the collected information. The analysis unit can also analyze task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. This enables more appropriate task management by analyzing the user's past behavior history and task priorities. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the analysis of the user's past behavior history and task priorities.

[0037] The management department can organize users' tasks in order of priority and display important tasks clearly. For example, the management department can organize users' tasks in order of priority. The management department can also display important tasks clearly. The management department can prioritize tasks and display important tasks preferentially. For example, the management department can prioritize tasks with approaching deadlines and important meeting schedules. This allows users to efficiently manage tasks by organizing them in order of priority and displaying important tasks clearly. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user task information into AI and have the AI ​​perform task prioritization.

[0038] The reminder unit can monitor the task's progress in real time and send reminders as needed. For example, the reminder unit can monitor the task's progress in real time. The reminder unit can also send reminders as needed. The reminder unit can send reminders using email notifications or push notifications. For example, the reminder unit monitors the task's progress in real time and sends reminders as needed. This makes it easier for users to understand the task's progress by monitoring it in real time and sending reminders as needed. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the task's progress into AI and have the AI ​​send reminders.

[0039] The learning unit can learn user behavior patterns and perform task management tailored to individual needs. For example, the learning unit learns user behavior patterns. The learning unit can also prioritize displaying functions frequently used by a particular user or tasks concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms. This enables task management tailored to individual needs by learning user behavior patterns. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user behavior data into AI and have the AI ​​perform behavior pattern learning.

[0040] The data collection unit can select the optimal data acquisition method by referring to the user's past behavior history when acquiring information using the APIs of each tool. For example, the data collection unit can prioritize acquiring information from tools that the user has frequently used in the past. For example, the data collection unit can also prioritize acquiring information from tools used during specific time periods based on the user's past behavior history. For example, the data collection unit can analyze the user's past behavior history and select the most efficient data acquisition method. This allows the optimal data acquisition method to be selected by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior history into AI and have the AI ​​select the optimal data acquisition method.

[0041] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the project the user is currently working on. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. The data collection unit can also dynamically filter necessary information according to the progress of the user's project. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project information and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0042] The data collection unit can prioritize acquiring highly relevant information based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can dynamically collect necessary information based on the user's current location. This allows for efficient collection of necessary information by prioritizing the acquisition of highly relevant information based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the acquisition of highly relevant information.

[0043] The data collection unit can analyze the user's social media activity and obtain relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can also analyze the user's social media activity and prioritize the collection of information related to their areas of interest. For example, the data collection unit can collect relevant information based on information from accounts that the user follows on social media. This allows for the collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the acquisition of relevant information.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history when analyzing the collected information. For example, the analysis unit can improve the accuracy of its analysis based on the user's past behavior history. The analysis unit can also, for example, incorporate important information that the user may have overlooked in the past into its analysis. The analysis unit can also, for example, learn the user's past behavior patterns to improve the accuracy of its analysis. This allows the analysis to improve accuracy by referring to the user's past behavior history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past behavior history into AI and have AI perform the task of improving the accuracy of its analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to email information to extract important content. For example, the analysis unit can apply a sentiment analysis algorithm to messages from communication tools to evaluate their importance. For example, the analysis unit can apply a schedule analysis algorithm to calendar information to identify important appointments. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have AI select the appropriate analysis algorithm according to the category of information.

[0046] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of information with approaching deadlines. The analysis unit may also prioritize the analysis of information that users have previously overlooked. The analysis unit may also prioritize the analysis of important information based on the user's schedule. This allows for the prioritization of important information by determining the analysis priority based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of information submission into the AI ​​and have the AI ​​determine the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information related to the user's current project. The analysis unit may also prioritize the analysis of information related to the user's areas of interest. For example, the analysis unit may prioritize the analysis of highly relevant information based on the user's past behavioral history. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0048] The management department can select the optimal task management method by referring to the user's past task history during task management. For example, the management department can select the optimal management method based on the user's past successful task management methods. For example, the management department can also analyze the user's past task history and propose an efficient management method. For example, the management department can select the optimal management method by considering tasks the user has overlooked in the past. In this way, the optimal task management method can be selected by referring to the user's past task history. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's past task history into AI and have the AI ​​select the optimal management method.

[0049] The management unit can customize task priorities based on the user's current life circumstances when managing tasks. For example, if the user is busy, the management unit will prioritize displaying only important tasks. For example, if the user has more free time, the management unit can also provide detailed task management methods. The management unit can also dynamically adjust task priorities based on the user's life circumstances. This allows for more efficient task management by customizing task priorities based on the user's current life circumstances. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input user life circumstances data into the AI ​​and have the AI ​​perform the customization of task priorities.

[0050] The management unit can select the optimal task management method based on the user's geographical location information when managing tasks. For example, if the user is in a specific region, the management unit will prioritize displaying tasks related to that region. The management unit can also filter and display highly relevant tasks based on the user's geographical location information. For example, if the user is on the move, the management unit can dynamically display necessary tasks based on their current location. This enables efficient task management by selecting the optimal task management method based on the user's geographical location information. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal task management method.

[0051] The management department can analyze users' social media activity to determine task priorities during task management. For example, the management department can prioritize displaying relevant tasks based on information shared by users on social media. The management department can also prioritize displaying tasks related to users' areas of interest by analyzing users' social media activity. For example, the management department can prioritize displaying relevant tasks based on information about accounts that users follow on social media. This allows for the priority management of highly relevant tasks by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media data into AI and have the AI ​​determine task priorities.

[0052] The reminder unit can select the optimal sending method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal sending method based on the user's past effective reminder sending methods. The reminder unit can also, for example, analyze the user's past reminder history and suggest an efficient sending method. The reminder unit can also, for example, consider reminders the user has overlooked in the past when selecting the optimal sending method. In this way, the optimal reminder sending method can be selected by referring to the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the user's past reminder history into AI and have the AI ​​select the optimal sending method.

[0053] The reminder unit can select the optimal sending method based on the user's device information when sending a reminder. For example, if the user is using a smartphone, the reminder unit can send the reminder using push notifications. For example, if the user is using a tablet, the reminder unit can also send a reminder optimized for a larger screen. For example, if the user is using a smartwatch, the reminder unit can also send a concise and highly visible reminder. This allows for efficient reminder delivery by selecting the optimal reminder sending method based on the user's device information. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the user's device information into AI and have the AI ​​select the optimal sending method.

[0054] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can improve the accuracy of the learning algorithm based on past learning data. The learning unit can also incorporate data that the user has previously overlooked into the learning process. For example, the learning unit can learn the user's past learning patterns and optimize the algorithm. This allows the accuracy of the learning algorithm to be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0055] The learning unit can weight the training data based on the submission timing of the collected information during training. For example, the learning unit can prioritize learning information with approaching deadlines. The learning unit can also prioritize learning information that the user has previously overlooked. For example, the learning unit can prioritize learning important information based on the user's schedule. This allows for prioritizing the learning of important information by weighting the training data based on the submission timing of the collected information. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the submission timing of the collected information into the AI ​​and have the AI ​​perform the weighting of the training data.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The task management system can also include a health management section that collects user health data and adjusts task priorities accordingly. For example, it can collect user sleep data and reduce task load if the user is sleep-deprived. It can also collect user exercise data and send reminders if the user is not getting enough exercise. Furthermore, it can monitor the user's stress level and suggest relaxation tasks if stress levels are high. This allows for more balanced work performance by managing tasks based on the user's health status.

[0058] A task management system can also include a hobby management section that customizes tasks based on the user's hobbies and interests. For example, if a user enjoys music, music-related tasks will be prioritized. If a user enjoys reading, reading-related tasks can be sent as reminders. Furthermore, if a user enjoys traveling, tasks related to travel planning can be suggested. This allows for improved work motivation by customizing tasks based on the user's hobbies and interests.

[0059] The task management system can also include a location information management unit that suggests tasks based on the user's geographical location. For example, if a user is in a specific region, tasks related to that region will be displayed preferentially. If the user is on the move, tasks related to their destination can also be suggested. Furthermore, if the user is at home, tasks to be done at home can be sent as reminders. This allows for efficient task management by suggesting tasks based on the user's geographical location.

[0060] The task management system can also include a social media management section that analyzes users' social media activity and suggests relevant tasks. For example, it can suggest relevant tasks based on information shared by users on social media. It can also prioritize tasks related to areas of interest based on information about accounts that users follow. Furthermore, it can analyze users' social media activity and suggest tasks related to trends. This allows for efficient management of highly relevant tasks by suggesting tasks based on users' social media activity.

[0061] A task management system can also include a history management unit that refers to the user's past task history to suggest the optimal task management method. For example, it can suggest the optimal management method based on the user's past successful task management methods. It can also analyze the user's past task history to suggest efficient management methods. Furthermore, it can suggest the optimal management method by considering tasks the user may have overlooked in the past. In this way, the system can suggest the optimal task management method by referring to the user's past task history.

[0062] The task management system can also include a location-based prioritization section that adjusts task priorities based on the user's geographical location. For example, if a user is in a specific region, tasks related to that region can be displayed preferentially. Similarly, if a user is traveling, tasks related to their destination can be displayed preferentially. Furthermore, if a user is at home, tasks that should be done at home can be displayed preferentially. This allows for efficient task management by adjusting task priorities based on the user's geographical location.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects information from the user's "email system," "communication tools," and "calendar system." For example, the collection unit uses the email system's API to retrieve email content, the communication tool's API to retrieve message history, and the calendar system's API to retrieve schedule information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, based on the collected information, the analysis unit can analyze the user's past behavior history and task priorities, and use text analysis technology to issue alerts to ensure that important emails and messages are not overlooked. Step 3: The management department manages tasks based on the information analyzed by the analysis department. For example, the management department organizes user tasks by priority and displays important tasks clearly. Tasks with approaching deadlines and important meeting schedules can be displayed preferentially. Step 4: The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders using email or push notifications. Step 5: The learning unit learns the user's behavior patterns and provides task management tailored to individual needs. For example, the learning unit can use machine learning algorithms to learn the user's behavior patterns and prioritize displaying features that a particular user frequently uses or tasks that they concentrate on during specific time periods.

[0065] (Example of form 2) The task management system according to an embodiment of the present invention is a system for centrally managing internal company information and efficiently managing important tasks. This task management system collects information from the user's "email system," "communication tools," and "calendar system," and uses AI to analyze it and perform task management. This allows the user to efficiently manage tasks without overlooking important information. For example, the task management system collects information from the user's "email system," "communication tools," and "calendar system." In this process, it obtains information using the API of each tool. For example, it collects email content from the email system, message history from communication tools, and schedule information from the calendar system. Next, the AI ​​analyzes the collected information. Based on the collected information, the AI ​​analyzes the user's past behavior history and task priorities. For example, if the user has overlooked important emails or messages in the past, the AI ​​learns that pattern and can issue an alert when a similar situation occurs. Furthermore, the AI ​​performs task management based on the analysis results. Specifically, it organizes the user's tasks in order of priority and displays important tasks in an easy-to-understand manner. For example, it prioritizes displaying tasks with approaching deadlines and important meeting schedules. It can also monitor the progress of tasks in real time and send reminders as needed. This system allows users to efficiently manage tasks without overlooking important information. For example, users will no longer forget meetings due to being overwhelmed with email correspondence or miss important emails due to being busy with communication tools. Furthermore, real-time tracking of task progress enables more efficient work. In addition, the AI ​​learns user behavior patterns and can provide task management tailored to individual needs. For instance, it can prioritize displaying functions frequently used by specific users or tasks concentrated during certain time periods, thereby improving user efficiency. Thus, this invention is a system for centrally managing internal company information and efficiently managing important tasks, thereby improving user efficiency.This allows the task management system to centrally manage information from the user's email system, communication tools, and calendar system, enabling efficient management of important tasks.

[0066] The task management system according to this embodiment comprises a collection unit, an analysis unit, a management unit, a reminder unit, and a learning unit. The collection unit collects information from the user's "email system," "communication tool," and "calendar system." For example, the collection unit collects email content from the email system, message history from the communication tool, and schedule information from the calendar system. The collection unit can obtain information using the APIs of each tool. For example, the collection unit can obtain email content using the email system's API. The collection unit can also obtain message history using the communication tool's API. Furthermore, the collection unit can obtain schedule information using the calendar system's API. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past behavior history and task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. The management unit performs task management based on the information analyzed by the analysis unit. The management unit, for example, organizes user tasks by priority and clearly displays important tasks. The management unit can prioritize tasks and display important tasks preferentially. For example, the management unit can prioritize displaying tasks with approaching deadlines and important meeting schedules. The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders as needed. The reminder unit can send reminders using email notifications or push notifications. The learning unit learns user behavior patterns and provides task management tailored to individual needs. For example, the learning unit can learn user behavior patterns and prioritize displaying features frequently used by specific users or tasks concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms.As a result, the task management system according to this embodiment can centrally manage information from the user's "email system," "communication tool," and "calendar system," and efficiently manage important tasks.

[0067] The data collection unit collects information from the user's "email system," "communication tools," and "calendar system." For example, the unit collects email content from the email system, message history from communication tools, and appointment information from the calendar system. The unit can obtain information using the APIs of each tool. For instance, the unit retrieves email content using the email system's API. It can also retrieve message history using the communication tool's API. Furthermore, it can retrieve appointment information using the calendar system's API. Specifically, the unit retrieves detailed information such as email subject, sender, recipient, date and time sent, and body from the inbox and sent email folders via the email system's API. This allows the unit to identify important emails received by the user and unread emails, and register them as tasks. For communication tools, it retrieves chat message history and analyzes specific keywords and phrases to extract important conversations and messages related to tasks. For example, it can collect messages regarding project progress and meeting schedules and register them as tasks. For the calendar system, it retrieves the user's appointment information and registers important events such as meetings and deadlines as tasks. This allows for centralized management of user schedules and appropriate task prioritization. The data collection unit collects this information in real time and stores it in a central database. This enables other departments to quickly access the information they need. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. For example, during the progress of an important project, increasing the data collection frequency allows for real-time situation monitoring. This enables the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0068] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the user's past behavior history and task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. Specifically, the analysis unit uses natural language processing technology to analyze the content of emails and messages and extract important keywords and phrases. This allows it to identify important information that users tend to overlook and register it as a task. The analysis unit also analyzes task priorities based on the user's past behavior history. For example, it can prioritize displaying tasks that the user has frequently dealt with in the past or tasks related to important projects. Furthermore, the analysis unit can use machine learning algorithms to learn the user's behavior patterns and dynamically adjust task priorities. This allows the analysis unit to achieve flexible task management tailored to the user's needs. The analysis unit not only analyzes user behavior patterns and task priorities based on collected information, but can also detect unusual patterns and abnormal data using anomaly detection algorithms. For example, it can issue alerts for important emails sent outside of normal business hours or for sudden schedule changes. This allows the analysis unit to not only grasp the situation in real time, but also to handle anomaly detection and risk management, thereby improving the reliability and security of the entire system.

[0069] The management department manages tasks based on information analyzed by the analysis department. For example, the management department organizes user tasks by priority and displays important tasks clearly. The management department can prioritize tasks and display important tasks first. For example, the management department can prioritize tasks with approaching deadlines and important meeting schedules. Specifically, the management department organizes user tasks by category and displays them in a visually easy-to-understand interface. This allows users to grasp important tasks at a glance and manage them efficiently. The management department can also dynamically adjust task priorities. For example, if a new task is added or the priority of an existing task is changed, the management department immediately recalculates the task priorities and displays tasks based on the latest information. Furthermore, the management department can monitor task progress in real time and provide appropriate feedback to users. For example, it can send reminders to users or notify them of task completion status depending on the task progress. This allows the management department to enable users to manage tasks efficiently and address important tasks without overlooking them. The management department can also collect user feedback and continuously improve the accuracy and effectiveness of task management. For example, by having users provide feedback on task prioritization, the management department can improve the task prioritization algorithm, resulting in more accurate task management. This allows the management department to provide flexible task management tailored to user needs and improve the overall performance of the system.

[0070] The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders as necessary. The reminder unit can send reminders using email notifications and push notifications. Specifically, the reminder unit sends reminders to users when task deadlines are approaching or when task progress is behind schedule. This allows users to efficiently manage tasks without overlooking important tasks. The reminder unit can dynamically adjust the timing and content of reminder notifications based on the user's behavior patterns and task priorities. For example, if a user concentrates on work during a specific time period, sending reminders to coincide with that time can improve the user's work efficiency. Furthermore, the reminder unit can reliably transmit information using multiple notification methods. For example, it can reliably deliver important information by using not only email notifications but also smartphone push notifications, voice calls, and SMS in combination. This allows the reminder unit to provide users with quick and reliable reminders and efficiently manage task progress. The reminder unit can also collect user feedback and continuously improve the accuracy and effectiveness of reminders. For example, by providing user feedback on the timing and content of reminders, the reminder unit can improve its reminder sending algorithm and provide more accurate reminders. This allows the reminder unit to provide flexible reminder functions tailored to user needs and improve the overall system performance.

[0071] The learning unit learns user behavior patterns and provides task management tailored to individual needs. For example, by learning user behavior patterns, the learning unit can prioritize displaying features frequently used by a particular user or tasks that are concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms. Specifically, the learning unit analyzes the user's past behavior history and task completion status to identify user behavior patterns. This allows the learning unit to display important tasks according to the user's tendency to concentrate on work during specific time periods. Furthermore, the learning unit can learn the features frequently used by the user and the priority given to specific tasks, providing task management tailored to the user's needs. For example, by prioritizing the display of features frequently used by the user or tasks related to specific projects, the user's work efficiency can be improved. In addition, the learning unit can collect user feedback and continuously improve the accuracy and effectiveness of its learning algorithms. For example, by providing user feedback on task priorities and display content, the learning unit can improve its learning algorithms and provide more accurate task management. This allows the learning unit to provide flexible task management tailored to user needs and improve overall system performance. The learning unit can not only learn user behavior patterns but also draw inspiration from other users' behavior patterns and industry best practices. This enables the learning unit to provide users with the optimal task management method and improve overall system efficiency.

[0072] The data collection unit can acquire information using the APIs of various tools. For example, the data collection unit can acquire email content using the API of an email system. The data collection unit can also acquire message history using the API of a communication tool. The data collection unit can also acquire schedule information using the API of a calendar system. This allows for efficient information collection by utilizing the APIs of various tools. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the information acquired using the APIs of various tools into an AI and have the AI ​​perform the analysis of the information.

[0073] The analysis unit can analyze the user's past behavior history and task priorities based on the collected information. For example, the analysis unit can analyze the user's past behavior history based on the collected information. The analysis unit can also analyze task priorities based on the collected information. The analysis unit can analyze the collected information using text analysis technology. For example, if the analysis unit has previously overlooked important emails or messages, it can learn that pattern and issue an alert when a similar situation occurs. This enables more appropriate task management by analyzing the user's past behavior history and task priorities. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected information into AI and have the AI ​​perform the analysis of the user's past behavior history and task priorities.

[0074] The management department can organize users' tasks in order of priority and display important tasks clearly. For example, the management department can organize users' tasks in order of priority. The management department can also display important tasks clearly. The management department can prioritize tasks and display important tasks preferentially. For example, the management department can prioritize tasks with approaching deadlines and important meeting schedules. This allows users to efficiently manage tasks by organizing them in order of priority and displaying important tasks clearly. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user task information into AI and have the AI ​​perform task prioritization.

[0075] The reminder unit can monitor the task's progress in real time and send reminders as needed. For example, the reminder unit can monitor the task's progress in real time. The reminder unit can also send reminders as needed. The reminder unit can send reminders using email notifications or push notifications. For example, the reminder unit monitors the task's progress in real time and sends reminders as needed. This makes it easier for users to understand the task's progress by monitoring it in real time and sending reminders as needed. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the task's progress into AI and have the AI ​​send reminders.

[0076] The learning unit can learn user behavior patterns and perform task management tailored to individual needs. For example, the learning unit learns user behavior patterns. The learning unit can also prioritize displaying functions frequently used by a particular user or tasks concentrated during specific time periods. The learning unit can learn user behavior patterns using machine learning algorithms. This enables task management tailored to individual needs by learning user behavior patterns. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user behavior data into AI and have the AI ​​perform behavior pattern learning.

[0077] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect only important information. For example, if the user is relaxed, the data collection unit can collect information at a normal frequency and include detailed information. For example, if the user is in a hurry, the data collection unit can collect information in real time and provide it immediately. This allows for information to be collected at a more appropriate time by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of information collection.

[0078] The data collection unit can select the optimal data acquisition method by referring to the user's past behavior history when acquiring information using the APIs of each tool. For example, the data collection unit can prioritize acquiring information from tools that the user has frequently used in the past. For example, the data collection unit can also prioritize acquiring information from tools used during specific time periods based on the user's past behavior history. For example, the data collection unit can analyze the user's past behavior history and select the most efficient data acquisition method. This allows the optimal data acquisition method to be selected by referring to the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's past behavior history into AI and have the AI ​​select the optimal data acquisition method.

[0079] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting information related to the project the user is currently working on. The data collection unit can also filter and collect highly relevant information based on the user's areas of interest. The data collection unit can also dynamically filter necessary information according to the progress of the user's project. This allows for the collection of highly relevant information by filtering information based on the user's current projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's project information and areas of interest into the AI ​​and have the AI ​​perform the information filtering.

[0080] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important information. If the user is relaxed, the data collection unit can collect information with normal priority. If the user is in a hurry, the data collection unit can prioritize collecting information of high urgency. This allows for the priority collection of important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the priority of information.

[0081] The data collection unit can prioritize acquiring highly relevant information based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize collecting information related to that region. The data collection unit can also filter and collect highly relevant information based on the user's geographical location information. For example, if the user is on the move, the data collection unit can dynamically collect necessary information based on the user's current location. This allows for efficient collection of necessary information by prioritizing the acquisition of highly relevant information based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI ​​and have the AI ​​perform the acquisition of highly relevant information.

[0082] The data collection unit can analyze the user's social media activity and obtain relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. The data collection unit can also analyze the user's social media activity and prioritize the collection of information related to their areas of interest. For example, the data collection unit can collect relevant information based on information from accounts that the user follows on social media. This allows for the collection of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's social media data into an AI and have the AI ​​perform the acquisition of relevant information.

[0083] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0084] The analysis unit can improve the accuracy of its analysis by referring to the user's past behavior history when analyzing the collected information. For example, the analysis unit can improve the accuracy of its analysis based on the user's past behavior history. The analysis unit can also, for example, incorporate important information that the user may have overlooked in the past into its analysis. The analysis unit can also, for example, learn the user's past behavior patterns to improve the accuracy of its analysis. This allows the analysis to improve accuracy by referring to the user's past behavior history. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past behavior history into AI and have AI perform the task of improving the accuracy of its analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a natural language processing algorithm to email information to extract important content. For example, the analysis unit can apply a sentiment analysis algorithm to messages from communication tools to evaluate their importance. For example, the analysis unit can apply a schedule analysis algorithm to calendar information to identify important appointments. By applying different analysis algorithms depending on the category of information, more accurate analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can have AI select the appropriate analysis algorithm according to the category of information.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. By adjusting the display method of the analysis results based on the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the display method of the analysis results.

[0087] The analysis unit can determine the priority of analysis based on the timing of information submission during the analysis process. For example, the analysis unit may prioritize the analysis of information with approaching deadlines. The analysis unit may also prioritize the analysis of information that users have previously overlooked. The analysis unit may also prioritize the analysis of important information based on the user's schedule. This allows for the prioritization of important information by determining the analysis priority based on the timing of information submission. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the timing of information submission into the AI ​​and have the AI ​​determine the analysis priority.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis process. For example, the analysis unit may prioritize the analysis of information related to the user's current project. The analysis unit may also prioritize the analysis of information related to the user's areas of interest. For example, the analysis unit may prioritize the analysis of highly relevant information based on the user's past behavioral history. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the analysis order.

[0089] The management unit can estimate the user's emotions and adjust the task management method based on the estimated emotions. For example, if the user is stressed, the management unit can simplify tasks and display only important tasks. For example, if the user is relaxed, the management unit can provide a detailed task management method. For example, if the user is in a hurry, the management unit can provide a method for managing tasks quickly. This allows for more appropriate task management by adjusting the task management method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into AI and have the AI ​​adjust the task management method.

[0090] The management department can select the optimal task management method by referring to the user's past task history during task management. For example, the management department can select the optimal management method based on the user's past successful task management methods. For example, the management department can also analyze the user's past task history and propose an efficient management method. For example, the management department can select the optimal management method by considering tasks the user has overlooked in the past. In this way, the optimal task management method can be selected by referring to the user's past task history. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input the user's past task history into AI and have the AI ​​select the optimal management method.

[0091] The management unit can customize task priorities based on the user's current life circumstances when managing tasks. For example, if the user is busy, the management unit will prioritize displaying only important tasks. For example, if the user has more free time, the management unit can also provide detailed task management methods. The management unit can also dynamically adjust task priorities based on the user's life circumstances. This allows for more efficient task management by customizing task priorities based on the user's current life circumstances. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input user life circumstances data into the AI ​​and have the AI ​​perform the customization of task priorities.

[0092] The management unit can estimate the user's emotions and adjust the task display method based on the estimated emotions. For example, if the user is stressed, the management unit can provide a simple and highly visible display method. For example, if the user is relaxed, the management unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the management unit can also provide a display method that gets straight to the point. By adjusting the task display method based on the user's emotions, a more appropriate task display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can input user emotion data into AI and have the AI ​​adjust the task display method.

[0093] The management unit can select the optimal task management method based on the user's geographical location information when managing tasks. For example, if the user is in a specific region, the management unit will prioritize displaying tasks related to that region. The management unit can also filter and display highly relevant tasks based on the user's geographical location information. For example, if the user is on the move, the management unit can dynamically display necessary tasks based on their current location. This enables efficient task management by selecting the optimal task management method based on the user's geographical location information. Some or all of the above processes in the management unit may be performed using AI or not. For example, the management unit can input the user's geographical location information into the AI ​​and have the AI ​​select the optimal task management method.

[0094] The management department can analyze users' social media activity to determine task priorities during task management. For example, the management department can prioritize displaying relevant tasks based on information shared by users on social media. The management department can also prioritize displaying tasks related to users' areas of interest by analyzing users' social media activity. For example, the management department can prioritize displaying relevant tasks based on information about accounts that users follow on social media. This allows for the priority management of highly relevant tasks by analyzing users' social media activity. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input user social media data into AI and have the AI ​​determine task priorities.

[0095] The reminder 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 reminder unit can reduce the frequency of reminders and send only important reminders. For example, if the user is relaxed, the reminder unit can send reminders at the normal frequency. For example, if the user is in a hurry, the reminder unit can send reminders in real time to prompt immediate action. This allows reminders to be sent at a more appropriate time by adjusting the timing of reminder sending based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder function can input user emotion data into the AI ​​and have the AI ​​adjust the timing of reminder sending.

[0096] The reminder unit can select the optimal sending method by referring to the user's past reminder history when sending a reminder. For example, the reminder unit can select the optimal sending method based on the user's past effective reminder sending methods. The reminder unit can also, for example, analyze the user's past reminder history and suggest an efficient sending method. The reminder unit can also, for example, consider reminders the user has overlooked in the past when selecting the optimal sending method. In this way, the optimal reminder sending method can be selected by referring to the user's past reminder history. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the user's past reminder history into AI and have the AI ​​select the optimal sending method.

[0097] The reminder unit can estimate the user's emotions and adjust the content of the reminder based on the estimated emotions. For example, if the user is stressed, the reminder unit can provide a simple and highly visible reminder. For example, if the user is relaxed, the reminder unit can also provide a reminder with more detailed information. For example, if the user is in a hurry, the reminder unit can provide a concise reminder. By adjusting the content of the reminder based on the user's emotions, more appropriate reminders can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input user emotion data into AI and have the AI ​​adjust the content of the reminder.

[0098] The reminder unit can select the optimal sending method based on the user's device information when sending a reminder. For example, if the user is using a smartphone, the reminder unit can send the reminder using push notifications. For example, if the user is using a tablet, the reminder unit can also send a reminder optimized for a larger screen. For example, if the user is using a smartwatch, the reminder unit can also send a concise and highly visible reminder. This allows for efficient reminder delivery by selecting the optimal reminder sending method based on the user's device information. Some or all of the above processing in the reminder unit may be performed using AI or not. For example, the reminder unit can input the user's device information into AI and have the AI ​​select the optimal sending method.

[0099] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit can select only important data as training data. If the user is relaxed, the learning unit can also select detailed data as training data. If the user is in a hurry, the learning unit can also select data that can be learned quickly. This allows for learning more appropriate data by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​perform the selection of training data.

[0100] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can improve the accuracy of the learning algorithm based on past learning data. The learning unit can also incorporate data that the user has previously overlooked into the learning process. For example, the learning unit can learn the user's past learning patterns and optimize the algorithm. This allows the accuracy of the learning algorithm to be improved by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI or not. For example, the learning unit can input past learning data into AI and have the AI ​​perform the optimization of the learning algorithm.

[0101] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can reduce the learning frequency and learn only important data. For example, if the user is relaxed, the learning unit can learn at a normal frequency. For example, if the user is in a hurry, the learning unit can learn in real time and respond immediately. This allows for learning at a more appropriate frequency by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into an AI and have the AI ​​adjust the learning frequency.

[0102] The learning unit can weight the training data based on the submission timing of the collected information during training. For example, the learning unit can prioritize learning information with approaching deadlines. The learning unit can also prioritize learning information that the user has previously overlooked. For example, the learning unit can prioritize learning important information based on the user's schedule. This allows for prioritizing the learning of important information by weighting the training data based on the submission timing of the collected information. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input the submission timing of the collected information into the AI ​​and have the AI ​​perform the weighting of the training data.

[0103] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0104] The task management system can also include a health management section that collects user health data and adjusts task priorities accordingly. For example, it can collect user sleep data and reduce task load if the user is sleep-deprived. It can also collect user exercise data and send reminders if the user is not getting enough exercise. Furthermore, it can monitor the user's stress level and suggest relaxation tasks if stress levels are high. This allows for more balanced work performance by managing tasks based on the user's health status.

[0105] A task management system can also include a hobby management section that customizes tasks based on the user's hobbies and interests. For example, if a user enjoys music, music-related tasks will be prioritized. If a user enjoys reading, reading-related tasks can be sent as reminders. Furthermore, if a user enjoys traveling, tasks related to travel planning can be suggested. This allows for improved work motivation by customizing tasks based on the user's hobbies and interests.

[0106] The task management system can further estimate the user's emotions and adjust the difficulty of tasks based on those emotions. For example, if the user is tired, it will prioritize displaying easier tasks. Conversely, if the user is focused, it can suggest more difficult tasks. Furthermore, if the user is stressed, it can suggest relaxing tasks. By adjusting the difficulty of tasks based on the user's emotions, tasks can be completed more efficiently.

[0107] The task management system can also include a location information management unit that suggests tasks based on the user's geographical location. For example, if a user is in a specific region, tasks related to that region will be displayed preferentially. If the user is on the move, tasks related to their destination can also be suggested. Furthermore, if the user is at home, tasks to be done at home can be sent as reminders. This allows for efficient task management by suggesting tasks based on the user's geographical location.

[0108] The task management system can further estimate the user's emotions and adjust the way tasks are notified based on those emotions. For example, if the user is stressed, the frequency of notifications can be reduced, and only important notifications can be sent. Conversely, if the user is relaxed, notifications can be sent at the normal frequency. Furthermore, if the user is in a hurry, real-time notifications can be sent to prompt immediate action. This allows notifications to be sent at a more appropriate time by adjusting the notification method based on the user's emotions.

[0109] The task management system can also include a social media management section that analyzes users' social media activity and suggests relevant tasks. For example, it can suggest relevant tasks based on information shared by users on social media. It can also prioritize tasks related to areas of interest based on information about accounts that users follow. Furthermore, it can analyze users' social media activity and suggest tasks related to trends. This allows for efficient management of highly relevant tasks by suggesting tasks based on users' social media activity.

[0110] The task management system can further estimate the user's emotions and adjust how tasks are displayed based on those emotions. For example, if the user is stressed, it can provide a simple and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how tasks are displayed based on the user's emotions, a more appropriate task display becomes possible.

[0111] A task management system can also include a history management unit that refers to the user's past task history to suggest the optimal task management method. For example, it can suggest the optimal management method based on the user's past successful task management methods. It can also analyze the user's past task history to suggest efficient management methods. Furthermore, it can suggest the optimal management method by considering tasks the user may have overlooked in the past. In this way, the system can suggest the optimal task management method by referring to the user's past task history.

[0112] The task management system can further estimate the user's emotions and adjust the task progress based on those emotions. For example, if the user is stressed, the task progress can be set to a slower pace. If the user is relaxed, the task can be managed at a normal pace. Furthermore, if the user is in a hurry, the progress can be set to expedite the task. This allows for more appropriate task management by adjusting the task progress based on the user's emotions.

[0113] The task management system can also include a location-based prioritization section that adjusts task priorities based on the user's geographical location. For example, if a user is in a specific region, tasks related to that region can be displayed preferentially. Similarly, if a user is traveling, tasks related to their destination can be displayed preferentially. Furthermore, if a user is at home, tasks that should be done at home can be displayed preferentially. This allows for efficient task management by adjusting task priorities based on the user's geographical location.

[0114] The following briefly describes the processing flow for example form 2.

[0115] Step 1: The collection unit collects information from the user's "email system," "communication tools," and "calendar system." For example, the collection unit uses the email system's API to retrieve email content, the communication tool's API to retrieve message history, and the calendar system's API to retrieve schedule information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, based on the collected information, the analysis unit can analyze the user's past behavior history and task priorities, and use text analysis technology to issue alerts to ensure that important emails and messages are not overlooked. Step 3: The management department manages tasks based on the information analyzed by the analysis department. For example, the management department organizes user tasks by priority and displays important tasks clearly. Tasks with approaching deadlines and important meeting schedules can be displayed preferentially. Step 4: The reminder unit monitors the progress of tasks managed by the management unit and sends reminders as needed. For example, the reminder unit can monitor task progress in real time and send reminders using email or push notifications. Step 5: The learning unit learns the user's behavior patterns and provides task management tailored to individual needs. For example, the learning unit can use machine learning algorithms to learn the user's behavior patterns and prioritize displaying features that a particular user frequently uses or tasks that they concentrate on during specific time periods.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, reminder unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information from the email system, communication tools, and calendar system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes tasks in order of priority. The reminder unit is implemented by the control unit 46A of the smart device 14 and monitors the progress of tasks and sends reminders. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, reminder unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from the email system, communication tools, and calendar system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes tasks in order of priority. The reminder unit is implemented by the control unit 46A of the smart glasses 214 and monitors the progress of tasks and sends reminders. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, reminder unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from the email system, communication tools, and calendar system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes tasks in order of priority. The reminder unit is implemented by the control unit 46A of the headset terminal 314 and monitors the progress of tasks and sends reminders. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0162] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0163] In 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.

[0164] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0165] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0166] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0167] The data processing system 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.

[0168] Each of the multiple elements described above, including the collection unit, analysis unit, management unit, reminder unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from the mail system, communication tools, and calendar system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected information. The management unit is implemented by the specific processing unit 290 of the data processing unit 12 and organizes tasks in order of priority. The reminder unit is implemented by the control unit 46A of the robot 414 and monitors the progress of tasks and sends reminders. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns the user's behavior patterns. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] (Note 1) A collection unit that collects information on the user's "email system," "communication tools," and "calendar system," An analysis unit analyzes the information collected by the aforementioned collection unit, A management unit that performs task management based on the information analyzed by the aforementioned analysis unit, A reminder unit monitors the progress of tasks managed by the aforementioned management unit and sends reminders as needed. It comprises a learning unit that learns user behavior patterns, A system characterized by the following features. (Note 2) The aforementioned collection unit is Information is retrieved using the APIs of each tool. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, the system analyzes the user's past behavior history and task priorities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, Organize user tasks by priority and clearly display important tasks. The system described in Appendix 1, characterized by the features described herein. (Note 5) The reminder unit is, Monitor task progress in real time and send reminders as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned learning unit, Learn user behavior patterns and perform task management tailored to individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When retrieving information using the APIs of each tool, the system selects the optimal retrieval method by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, 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 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, the system prioritizes obtaining highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During information gathering, we analyze users' social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, When analyzing collected information, we improve the accuracy of the analysis by referring to the user's past behavior history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned management department, It estimates the user's emotions and adjusts the task management method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned management department, When managing tasks, the system selects the optimal management method by referring to the user's past task history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned management department, When managing tasks, customize task priorities based on the user's current life circumstances. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned management department, It estimates the user's emotions and adjusts how tasks are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned management department, When managing tasks, the optimal task management method is selected based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned management department, When managing tasks, analyze users' social media activity to determine task priorities. The system described in Appendix 1, characterized by the features described herein. (Note 25) The reminder unit is, 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 26) The reminder unit is, When sending a reminder, the system will refer to the user's past reminder history to select the most suitable sending method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The reminder unit is, It estimates the user's emotions and adjusts the content of reminders based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The reminder unit is, When sending reminders, the system selects the optimal sending method based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned learning unit, During training, the training data is weighted based on when the collected information was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0188] 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 collection unit that collects information from the user's email system, communication tools, and calendar system, An analysis unit analyzes the information collected by the aforementioned collection unit, A management unit that performs task management based on the information analyzed by the aforementioned analysis unit, A reminder unit monitors the progress of tasks managed by the aforementioned management unit and sends reminders as needed. It comprises a learning unit that learns user behavior patterns. A system characterized by the following features.

2. The aforementioned collection unit is Information is retrieved using the APIs of each tool. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, the system analyzes the user's past behavior history and task priorities. The system according to feature 1.

4. The aforementioned management department, Organize user tasks by priority and clearly display important tasks. The system according to feature 1.

5. The reminder unit is, Monitor task progress in real time and send reminders as needed. The system according to feature 1.

6. The aforementioned learning unit, Learn user behavior patterns and perform task management tailored to individual needs. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is When retrieving information using the APIs of each tool, the system selects the optimal retrieval method by referring to the user's past behavior history. The system according to feature 1.

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