system

Abstract

The system according to this embodiment aims to achieve efficient and effective email processing. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a management unit, and a tracking unit. The collection unit receives emails. The analysis unit analyzes the content of the emails received by the collection unit. The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The management unit adds the tasks generated by the generation unit to a management tool. The tracking unit tracks the progress of the tasks managed by the management unit and sends reminders.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that email management is burdensome in terms of labor and time, and efficient processing is difficult.

[0005] The system according to the embodiment aims to achieve efficient and effective email processing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a management unit, and a tracking unit. The collection unit receives emails. The analysis unit analyzes the content of the emails received by the collection unit. The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The management unit adds the tasks generated by the generation unit to a management tool. The tracking unit tracks the progress of the tasks managed by the management unit and sends reminders. [Effects of the Invention]

[0007] The system according to this embodiment can achieve efficient and effective email processing. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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 email management system according to an embodiment of the present invention is a system that utilizes a generating AI agent to achieve efficient and effective email processing in the modern business environment. This email management system has a function to analyze the content of emails and automatically set priorities based on urgency and importance. As a result, important emails are notified immediately, and low-priority emails are listed. For example, emails regarding urgent matters are notified immediately, and routine communication emails can be checked later. Next, it has an information gathering function that automatically searches for relevant past emails and documents based on the content of the email and provides links. As a result, when a project-related email is received, relevant materials are immediately presented, enabling efficient information gathering. For example, past meeting records and related documents are automatically searched and links are provided. Furthermore, it has a database linkage function that links with internal databases and CRM systems and automatically displays customer purchase history and inquiry history. As a result, customer support is provided quickly and accurately. For example, when an inquiry email is received from a customer, past purchase history and inquiry history are automatically displayed, enabling a quick response. It also has a task management function that automatically generates tasks based on the content of emails and adds them to a task management tool. As a result, the progress of tasks can be tracked and reminders can be sent. For example, tasks requested via email are automatically generated, added to the task management tool, and reminders are sent as deadlines approach. Finally, it includes an information sharing function to share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination. This makes email response status and task progress visible and shareable. For example, it enables smooth information sharing within project teams and efficient work execution. In this way, an email management system utilizing a generation AI agent can dramatically improve business productivity through its functions of email prioritization, information gathering, database integration, task management, and information sharing. As a result, the email management system can efficiently handle everything from receiving emails to generating, managing, and tracking tasks.

[0029] The email management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a management unit, and a tracking unit. The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. The collection unit retrieves emails from a mail server and stores them in the system. The collection unit can also estimate the user's emotions when receiving emails and adjust the timing of email collection based on the estimated emotions. For example, if a user is feeling stressed, the collection frequency of emails is reduced, and only important emails are prioritized. The analysis unit analyzes the content of emails received by the collection unit. The analysis unit analyzes the content of emails using methods such as text analysis, emotion analysis, and keyword extraction. The analysis unit can automatically set priorities based on the urgency and importance of emails. For example, emails concerning urgent matters are notified immediately, while routine communication emails can be reviewed later. The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. The generation unit can automatically generate tasks based on email content and add them to the task management tool. The management unit adds the tasks generated by the generation unit to the management tool. The management unit manages tasks using methods such as task prioritization, progress tracking, and resource allocation. The management unit can integrate with internal databases and CRM systems to automatically display customer purchase and inquiry history. The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks task progress using methods such as progress monitoring and reminder sending timing. The tracking unit tracks task progress and can send reminders for tasks nearing their deadlines. This allows the email management system to efficiently handle everything from receiving emails to task generation, management, and tracking.

[0030] The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. Specifically, it retrieves emails from mail servers using protocols such as POP3 and IMAP and stores them within the system. This allows users to manage their emails centrally. Furthermore, the collection unit can estimate the user's emotions upon receiving emails and adjust the timing of email collection based on the estimated emotions. For example, if a user is stressed, the collection frequency is reduced, prioritizing the collection of only important emails. Emotion estimation involves analyzing data such as the user's past email content, reply speed, and email reading time, and using AI to estimate the user's emotional state. This reduces the user's burden and enables efficient email management. In addition, the collection unit has a spam filtering function that can automatically filter out unwanted emails. Spam filtering utilizes technologies such as Bayesian filtering, blacklists, and whitelists. This allows users to focus on important emails. The collection unit can also analyze email metadata (sender, recipient, subject, date and time, etc.) to classify and tag emails. This allows users to efficiently search and organize their emails.

[0031] The analysis unit analyzes the content of emails received by the collection unit. The analysis unit uses methods such as text analysis, sentiment analysis, and keyword extraction to analyze email content. Specifically, it uses natural language processing (NLP) techniques to analyze email text and extract important information. Sentiment analysis uses algorithms to identify positive, negative, and neutral sentiment in the text. This allows for automatic prioritization based on the urgency and importance of emails. For example, emails concerning urgent matters are notified immediately, while routine communication emails can be reviewed later. The analysis unit can analyze not only email content but also the content of attachments. For example, it can convert the content of PDF and Word documents into text and extract important information. Furthermore, the analysis unit can evaluate the relevance of emails based on sender and recipient information and group related emails. This allows users to quickly see relevant emails. The analysis unit can learn from email content using AI and improve analysis accuracy based on user preferences and behavioral patterns. This enables the analysis unit to provide users with the most important information quickly and accurately.

[0032] The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. Specifically, it automatically extracts information such as task title, deadline, assignee, and priority from the email content and adds it to the task management tool. The generation unit can automatically generate tasks based on email content and add them to the task management tool. For example, if an email related to a project is received, the generation unit analyzes the email content and automatically generates project tasks. If an email related to daily work is received, it generates daily work tasks, and if an email related to an urgent matter is received, it generates urgent tasks. The generation unit can use AI to analyze email content and improve the accuracy of task generation. This eliminates the need for users to manually create tasks, allowing for efficient task management. Furthermore, the generation unit can automatically set task dependencies and priorities. This allows users to grasp the progress of tasks at a glance and proceed with tasks efficiently. The generation unit can continuously improve its task generation algorithm based on user feedback, achieving more accurate task generation.

[0033] The management department adds tasks generated by the generation department to management tools. The management department manages tasks using methods such as task prioritization, progress tracking, and resource allocation. Specifically, it sets task priorities to ensure that important tasks are processed first. For progress tracking, it uses tools such as Gantt charts and Kanban boards to visually understand the progress of tasks. For resource allocation, it assigns the appropriate person to each task and ensures optimal resource distribution. The management department can integrate with internal databases and CRM systems to automatically display customer purchase history and inquiry history. This allows for understanding the background information of tasks and enabling more appropriate responses. Furthermore, the management department can monitor task progress in real time and reassign tasks or change priorities as needed. This prevents task delays and enables efficient task management. Based on user feedback, the management department can continuously improve the task management algorithm to achieve more effective task management. As a result, the management department can efficiently manage the entire process from task generation to completion and improve the overall system performance.

[0034] The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks task progress using methods such as monitoring progress and timing reminder sending. Specifically, it monitors task progress in real time and sends reminders for tasks approaching their deadlines. Reminders are sent via methods such as email, SMS, and push notifications to inform users of task deadlines. The tracking unit can track task progress and send reminders for tasks approaching their deadlines. This allows users to always be aware of task progress and meet deadlines. Furthermore, the tracking unit can adjust the content and timing of reminders according to the task progress. For example, it can send frequent reminders for important tasks and reminders at appropriate intervals for routine tasks. Based on user feedback, the tracking unit can continuously improve its reminder sending algorithm to achieve more effective reminder delivery. This allows the tracking unit to efficiently track the progress of tasks and support users in completing tasks on time.

[0035] The analysis unit can automatically set priorities based on the urgency and importance of emails. For example, the analysis unit can evaluate urgency and set priorities based on the importance of the email content and sender. For example, emails regarding urgent matters are notified immediately, while routine communication emails can be reviewed later. The analysis unit can also evaluate importance and set priorities based on the email content and the sender's job title. For example, emails from superiors are given a high priority, while emails from colleagues are given a low priority. This allows for the rapid processing of important emails by automatically setting email priorities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can set priorities using an AI model that takes email content as input and outputs urgency and importance.

[0036] The analysis unit can automatically search for relevant past emails and documents based on the email content and provide links. For example, the analysis unit can search for relevant past emails and documents based on keyword matching or content similarity. For example, if a project-related email is received, past meeting records and related documents will be automatically searched and links will be provided. The analysis unit can also automatically search for relevant documents based on the email content and provide links. For example, technical literature and research papers related to the email content will be automatically searched and links will be provided. This enables efficient information gathering by automatically searching for relevant past emails and documents and providing links. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can collect information using a generative AI model that takes email content as input and outputs links to relevant past emails and documents.

[0037] The management department can integrate with internal databases and CRM systems to automatically display customer purchase and inquiry history. For example, the management department can integrate with internal databases such as customer databases and product databases to automatically display customer purchase history. For instance, when a customer sends an inquiry email, past purchase history is automatically displayed, enabling a quick response. The management department can also integrate with CRM systems to automatically display customer inquiry history. For example, when a customer sends an inquiry email, past inquiry history is automatically displayed, enabling an accurate response. This allows for quick and accurate customer service by automatically displaying customer purchase and inquiry 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 use an AI model that takes customer emails as input and outputs purchase and inquiry history to handle customer inquiries.

[0038] The generation unit can automatically generate tasks based on email content and add them to a task management tool. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. For example, tasks requested via email are automatically generated and added to the task management tool. The generation unit can also track task progress and send reminders. For example, reminders are sent for tasks nearing their deadlines. This streamlines task management by automatically generating tasks based on email content and adding them to the task management tool. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate tasks using a generation AI model that takes email content as input and outputs tasks.

[0039] The tracking unit can track the progress of tasks and send reminders for tasks nearing their deadlines. The tracking unit tracks task progress using methods such as monitoring progress and timing reminder sending. For example, the tracking unit can monitor task progress in real time and send reminders according to the progress. The tracking unit can also generate a dashboard to visually display task progress. For example, it can visually display task progress using graphs and charts. This allows for tracking task progress and sending reminders for tasks nearing their deadlines, thereby preventing task delays. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can send reminders using an AI model that takes task progress as input and outputs reminders.

[0040] The management department can generate dashboards to visually display task progress. For example, the management department can visually display task progress using graphs and charts. For example, it can display progress using color coding based on task priority. The management department can also update task progress in real time to provide the latest information. For example, it can monitor task progress in real time and update the dashboard according to the progress. This allows for a quick grasp of the task status by visually displaying task progress. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not. For example, the management department can generate dashboards using a generative AI model that takes task progress as input and outputs dashboards.

[0041] The management department can share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination. For example, the management department can automatically notify stakeholders of the progress of tasks and share information. For example, it can notify stakeholders of the progress of tasks via email or chat tools. The management department can also automatically generate proposals and materials for internal coordination and provide them to stakeholders. For example, it can automatically extract the necessary information based on the proposal format and generate the proposal. Furthermore, the management department can propose the next steps based on the progress of tasks and notify stakeholders. For example, it can analyze the progress of tasks and propose the next actions to be taken. This improves the efficiency of operations by sharing information with stakeholders and carrying out preliminary preparations such as proposals and internal coordination. Some or all of the above processes in the management department may be carried out using, for example, generative AI, or not using generative AI. For example, the management department can generate materials using a generative AI model that takes the progress of tasks as input and outputs proposals and materials for internal coordination.

[0042] The collection unit can analyze the user's past email reception history and select the optimal collection method. For example, the collection unit can analyze the time periods in which the user frequently received emails in the past and prioritize collecting emails from those times. For example, it can analyze the characteristics of emails that the user previously deemed important and prioritize collecting emails with similar characteristics. The collection unit can also prioritize collecting emails from specific senders based on the user's past email reception history. In this way, the optimal collection method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can select a collection method using a generative AI model that takes the user's past email reception history as input and outputs the optimal collection method.

[0043] The collection unit can filter emails based on the user's current projects and areas of interest during collection. For example, the collection unit can prioritize collecting emails related to projects the user is currently working on. For example, it can prioritize collecting emails containing keywords related to the user's areas of interest. The collection unit can also filter emails based on specific project tags set by the user and prioritize collecting related emails. This allows for the priority collection of highly relevant emails by filtering emails based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can filter emails using a generative AI model that takes the user's projects and areas of interest as input and outputs filtered emails.

[0044] The collection unit can prioritize collecting emails that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit will prioritize collecting emails related to that region. For example, if the user is on a business trip, the collection unit will prioritize collecting emails related to the destination. The collection unit can also prioritize collecting emails related to home if the user is at home. By prioritizing the collection of highly relevant emails while considering the user's geographical location information, important information for the user can be obtained quickly. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0045] The collection unit can analyze a user's social media activity when collecting emails and collect relevant emails. For example, the collection unit can prioritize collecting emails related to topics mentioned by the user on social media. For example, it can prioritize collecting emails from accounts that the user follows. The collection unit can also prioritize collecting emails related to groups or events that the user participates in. In this way, relevant emails can be prioritized by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's social media activity as input and outputs relevant emails.

[0046] The analysis unit can adjust the level of detail in its email analysis based on the importance of the email. For example, it can analyze high-importance emails in detail and provide all the information, while analyzing low-importance emails concisely and providing only the key points. It can also analyze urgent emails immediately and provide prompt notifications. This allows for quick identification of important information by adjusting the level of detail in the analysis based on the importance of the email. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can adjust the level of detail in its analysis using a generative AI model that takes the importance of the email as input and outputs the level of detail in the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the email category during email analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business emails to extract important information. For example, it can apply a project-specific analysis algorithm to project-related emails to extract relevant tasks. The analysis unit can also apply a personal-specific analysis algorithm to personal emails to protect privacy during analysis. By applying different analysis algorithms depending on the email category, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes the email category as input and outputs an analysis algorithm.

[0048] The analysis unit can perform email analysis while considering the sender's attribute information. For example, if the sender is a supervisor, the analysis unit will set the importance level high and perform a detailed analysis. For example, if the sender is a customer, the analysis will consider the customer's attribute information to encourage a quick response. The analysis unit can also prioritize analyzing project-related information if the sender is a colleague. By considering the sender's attribute information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes the sender's attribute information as input and outputs analysis results.

[0049] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during email analysis. For example, the analysis unit can refer to past emails related to the email content and reflect this in the analysis results. For example, it can automatically search for documents related to the email content and provide links to them in the analysis results. The analysis unit can also refer to external literature related to the email content and reflect this in the analysis results. In this way, the accuracy of the analysis can be improved by referring to relevant literature for emails. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can refer to relevant literature using a generative AI model that takes email content as input and outputs relevant literature.

[0050] The generation unit can adjust the level of detail of tasks based on the content of emails when generating tasks. For example, the generation unit can generate detailed tasks based on important email content, or concise tasks based on simple email content. It can also generate tasks that require immediate attention based on urgent email content. By adjusting the level of detail of tasks based on the content of emails, appropriate tasks can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes email content as input and outputs the level of detail of tasks.

[0051] The generation unit can apply different generation algorithms depending on the email category when generating tasks. For example, the generation unit can apply a business-oriented generation algorithm to business emails to generate important tasks. For example, it can apply a project-oriented generation algorithm to project-related emails to generate relevant tasks. The generation unit can also apply a personal-oriented generation algorithm to personal emails to generate tasks while protecting privacy. This allows for the generation of more appropriate tasks by applying different generation algorithms depending on the email category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the email category as input and outputs a generation algorithm.

[0052] The generation unit can generate tasks while considering the attribute information of the email sender. For example, if the sender is a supervisor, the generation unit will set the importance level high and generate a detailed task. For example, if the sender is a customer, it will generate a task considering the customer's attribute information to encourage a quick response. The generation unit can also prioritize generating project-related tasks if the sender is a colleague. In this way, more appropriate tasks can be generated by considering the attribute information of the email sender. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate tasks using a generation AI model that takes the sender's attribute information as input and outputs tasks.

[0053] The generation unit can improve the accuracy of tasks by referring to relevant literature in emails when generating tasks. For example, the generation unit can refer to past emails related to the content of the email and reflect them in the task. For example, it can automatically search for documents related to the content of the email and provide links to them in the task. The generation unit can also refer to external literature related to the content of the email and reflect it in the task. In this way, the accuracy of tasks can be improved by referring to relevant literature in emails. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can refer to relevant literature using a generation AI model that takes email content as input and outputs relevant literature.

[0054] The management department can adjust the level of detail in task management based on the importance of the task. For example, the management department can manage high-importance tasks in detail and closely track their progress. For example, it can manage low-importance tasks concisely and track only the essentials. The management department can also manage high-urgency tasks immediately and respond quickly. In this way, important tasks can be properly managed by adjusting the level of detail in management based on the importance of the task. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can adjust the level of detail using a generative AI model that takes the importance of a task as input and outputs the level of detail in management.

[0055] The management department can apply different management algorithms to tasks depending on the task category. For example, the management department can apply a business-specific management algorithm to business tasks to extract important information. For example, it can apply a project-specific management algorithm to project-related tasks to extract relevant information. The management department can also apply a personal-specific management algorithm to personal tasks to manage them while protecting privacy. This allows for more appropriate task management by applying different management algorithms depending on the task category. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can apply a management algorithm using a generative AI model that takes the task category as input and outputs a management algorithm.

[0056] The management department can generate a dashboard to visually display the progress of tasks during task management. For example, the management department can visually display the progress of tasks using graphs and charts. For example, it can display the progress of tasks using different colors based on their priority. The management department can also update the progress of tasks in real time to provide the latest information. For example, it can monitor the progress of tasks in real time and update the dashboard according to the progress. This allows for a quick grasp of the task status by visually displaying the progress of tasks. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can generate a dashboard using a generative AI model that takes the progress of tasks as input and outputs a dashboard.

[0057] The management department can share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination when managing tasks. For example, the management department can automatically notify stakeholders of the task's progress and share information. For example, it can notify stakeholders of the task's progress via email or chat tools. The management department can also automatically generate proposals and materials for internal coordination and provide them to stakeholders. For example, it can automatically extract the necessary information based on the proposal format and generate the proposal. Furthermore, the management department can propose the next steps based on the task's progress and notify stakeholders. For example, it can analyze the task's progress and propose the next actions to be taken. This improves work efficiency by sharing information with stakeholders and carrying out preliminary preparations such as proposals and internal coordination. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can generate materials using a generative AI model that takes the task's progress as input and outputs proposals and materials for internal coordination.

[0058] The tracking unit can adjust the level of detail in tracking tasks based on their importance. For example, it can track high-importance tasks in detail and report their progress closely. For example, it can track low-importance tasks concisely and report only the key points. The tracking unit can also track urgent tasks immediately and respond quickly. This allows for proper tracking of important tasks by adjusting the level of detail based on their importance. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can adjust the level of detail using a generative AI model that takes task importance as input and outputs the level of detail of the tracking.

[0059] The tracking unit can apply different tracking algorithms depending on the task category when tracking the progress of a task. For example, the tracking unit can apply a business tracking algorithm to business tasks and extract important information. For example, it can apply a project tracking algorithm to project-related tasks and extract relevant information. The tracking unit can also apply a personal tracking algorithm to personal tasks, tracking them while protecting privacy. This allows for more appropriate task tracking by applying different tracking algorithms depending on the task category. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can apply a tracking algorithm using a generative AI model that takes the task category as input and outputs a tracking algorithm.

[0060] The tracking unit can track the progress of tasks while considering the attribute information of the task sender. For example, if the sender is a supervisor, the tracking unit will set the importance level high and track it in detail. For example, if the sender is a customer, the tracking unit will consider the customer's attribute information to encourage a quick response. The tracking unit can also prioritize tracking project-related tasks if the sender is a colleague. This allows for more appropriate task tracking by considering the attribute information of the task sender. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can perform tracking using a generative AI model that takes the sender's attribute information as input and outputs tracking results.

[0061] The tracking unit can improve the accuracy of tracking by referring to relevant literature when tracking the progress of a task. For example, the tracking unit can refer to past tasks related to the task content and reflect them in the progress status. For example, it can automatically search for documents related to the task content and provide links to them in the progress status. The tracking unit can also refer to external literature related to the task content and reflect it in the progress status. In this way, the accuracy of tracking can be improved by referring to relevant literature for the task. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can refer to relevant literature using a generative AI model that takes task content as input and outputs relevant literature.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The collection unit can analyze the user's past email reception history and select the optimal collection method. For example, it can analyze the time periods when the user frequently received emails in the past and prioritize collecting emails from those times. It can also analyze the characteristics of emails that the user previously deemed important and prioritize collecting emails with similar characteristics. Furthermore, it can prioritize collecting emails from specific senders based on the user's past email reception history. In this way, the optimal collection method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can select a collection method using a generative AI model that takes the user's past email reception history as input and outputs the optimal collection method.

[0064] The generation unit can adjust the level of detail of tasks based on the content of emails when generating tasks. For example, it can generate detailed tasks based on important email content, or concise tasks based on simple email content. It can also generate tasks that require immediate attention based on urgent email content. By adjusting the level of detail of tasks based on the content of emails, it is possible to generate appropriate tasks. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes email content as input and outputs the level of detail of tasks.

[0065] The management department can adjust the level of detail in task management based on the importance of the task. For example, high-importance tasks can be managed in detail, with their progress closely tracked. Low-importance tasks can be managed concisely, with only the essentials tracked. High-urgency tasks can also be managed immediately and addressed quickly. By adjusting the level of detail in management based on the importance of the task, important tasks can be managed appropriately. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not. For example, the management department can adjust the level of detail using a generative AI model that takes the importance of a task as input and outputs the level of detail in management.

[0066] The analysis unit can apply different analysis algorithms depending on the email category during email analysis. For example, a business analysis algorithm can be applied to business emails to extract important information. A project analysis algorithm can be applied to project-related emails to extract relevant tasks. Furthermore, a personal analysis algorithm can be applied to personal emails to protect privacy during analysis. By applying different analysis algorithms depending on the email category, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes the email category as input and outputs an analysis algorithm.

[0067] The collection unit can prioritize the collection of highly relevant emails by considering the user's geographical location information when collecting emails. For example, if the user is in a specific region, it can prioritize the collection of emails related to that region. If the user is on a business trip, it can prioritize the collection of emails related to the business trip destination. It can also prioritize the collection of emails related to home if the user is at home. By prioritizing the collection of highly relevant emails while considering the user's geographical location information, the system can quickly obtain information that is important to the user. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0068] The tracking unit can track the progress of tasks while considering the attribute information of the task sender. For example, if the sender is a supervisor, the importance level can be set high and the task can be tracked in detail. If the sender is a customer, the tracking can be conducted while considering the customer's attribute information to encourage a quick response. If the sender is a colleague, project-related tasks can be prioritized for tracking. This allows for more appropriate task tracking by considering the attribute information of the task sender. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can perform tracking using a generative AI model that takes the sender's attribute information as input and outputs tracking results.

[0069] The following briefly describes the processing flow for example form 1.

[0070] Step 1: The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. The collection unit retrieves emails from the mail server and stores them in the system. The collection unit can also estimate the user's mood when receiving emails and adjust the timing of email collection based on the estimated mood. For example, if the user is feeling stressed, the collection unit can reduce the frequency of email collection and prioritize collecting only important emails. Step 2: The analysis unit analyzes the content of emails received by the collection unit. The analysis unit analyzes the content of emails using methods such as text analysis, sentiment analysis, and keyword extraction. The analysis unit can automatically set priorities based on the urgency and importance of the emails. For example, emails regarding urgent matters are notified immediately, while routine communication emails can be reviewed later. Step 3: The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. The generation unit can automatically generate tasks based on email content and add them to a task management tool. Step 4: The management department adds the tasks generated by the generation department to the management tool. The management department manages the tasks using methods such as task prioritization, progress tracking, and resource allocation. The management department can also integrate with internal databases and CRM systems to automatically display customer purchase history and inquiry history. Step 5: The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks the progress of tasks using methods such as monitoring progress and timing reminder sending. The tracking unit can track the progress of tasks and send reminders for tasks that are nearing their deadline.

[0071] (Example of form 2) The email management system according to an embodiment of the present invention is a system that utilizes a generating AI agent to achieve efficient and effective email processing in the modern business environment. This email management system has a function to analyze the content of emails and automatically set priorities based on urgency and importance. As a result, important emails are notified immediately, and low-priority emails are listed. For example, emails regarding urgent matters are notified immediately, and routine communication emails can be checked later. Next, it has an information gathering function that automatically searches for relevant past emails and documents based on the content of the email and provides links. As a result, when a project-related email is received, relevant materials are immediately presented, enabling efficient information gathering. For example, past meeting records and related documents are automatically searched and links are provided. Furthermore, it has a database linkage function that links with internal databases and CRM systems and automatically displays customer purchase history and inquiry history. As a result, customer support is provided quickly and accurately. For example, when an inquiry email is received from a customer, past purchase history and inquiry history are automatically displayed, enabling a quick response. It also has a task management function that automatically generates tasks based on the content of emails and adds them to a task management tool. As a result, the progress of tasks can be tracked and reminders can be sent. For example, tasks requested via email are automatically generated, added to the task management tool, and reminders are sent as deadlines approach. Finally, it includes an information sharing function to share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination. This makes email response status and task progress visible and shareable. For example, it enables smooth information sharing within project teams and efficient work execution. In this way, an email management system utilizing a generation AI agent can dramatically improve business productivity through its functions of email prioritization, information gathering, database integration, task management, and information sharing. As a result, the email management system can efficiently handle everything from receiving emails to generating, managing, and tracking tasks.

[0072] The email management system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a management unit, and a tracking unit. The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. The collection unit retrieves emails from a mail server and stores them in the system. The collection unit can also estimate the user's emotions when receiving emails and adjust the timing of email collection based on the estimated emotions. For example, if a user is feeling stressed, the collection frequency of emails is reduced, and only important emails are prioritized. The analysis unit analyzes the content of emails received by the collection unit. The analysis unit analyzes the content of emails using methods such as text analysis, emotion analysis, and keyword extraction. The analysis unit can automatically set priorities based on the urgency and importance of emails. For example, emails concerning urgent matters are notified immediately, while routine communication emails can be reviewed later. The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. The generation unit can automatically generate tasks based on email content and add them to the task management tool. The management unit adds the tasks generated by the generation unit to the management tool. The management unit manages tasks using methods such as task prioritization, progress tracking, and resource allocation. The management unit can integrate with internal databases and CRM systems to automatically display customer purchase and inquiry history. The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks task progress using methods such as progress monitoring and reminder sending timing. The tracking unit tracks task progress and can send reminders for tasks nearing their deadlines. This allows the email management system to efficiently handle everything from receiving emails to task generation, management, and tracking.

[0073] The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. Specifically, it retrieves emails from mail servers using protocols such as POP3 and IMAP and stores them within the system. This allows users to manage their emails centrally. Furthermore, the collection unit can estimate the user's emotions upon receiving emails and adjust the timing of email collection based on the estimated emotions. For example, if a user is stressed, the collection frequency is reduced, prioritizing the collection of only important emails. Emotion estimation involves analyzing data such as the user's past email content, reply speed, and email reading time, and using AI to estimate the user's emotional state. This reduces the user's burden and enables efficient email management. In addition, the collection unit has a spam filtering function that can automatically filter out unwanted emails. Spam filtering utilizes technologies such as Bayesian filtering, blacklists, and whitelists. This allows users to focus on important emails. The collection unit can also analyze email metadata (sender, recipient, subject, date and time, etc.) to classify and tag emails. This allows users to efficiently search and organize their emails.

[0074] The analysis unit analyzes the content of emails received by the collection unit. The analysis unit uses methods such as text analysis, sentiment analysis, and keyword extraction to analyze email content. Specifically, it uses natural language processing (NLP) techniques to analyze email text and extract important information. Sentiment analysis uses algorithms to identify positive, negative, and neutral sentiment in the text. This allows for automatic prioritization based on the urgency and importance of emails. For example, emails concerning urgent matters are notified immediately, while routine communication emails can be reviewed later. The analysis unit can analyze not only email content but also the content of attachments. For example, it can convert the content of PDF and Word documents into text and extract important information. Furthermore, the analysis unit can evaluate the relevance of emails based on sender and recipient information and group related emails. This allows users to quickly see relevant emails. The analysis unit can learn from email content using AI and improve analysis accuracy based on user preferences and behavioral patterns. This enables the analysis unit to provide users with the most important information quickly and accurately.

[0075] The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. Specifically, it automatically extracts information such as task title, deadline, assignee, and priority from the email content and adds it to the task management tool. The generation unit can automatically generate tasks based on email content and add them to the task management tool. For example, if an email related to a project is received, the generation unit analyzes the email content and automatically generates project tasks. If an email related to daily work is received, it generates daily work tasks, and if an email related to an urgent matter is received, it generates urgent tasks. The generation unit can use AI to analyze email content and improve the accuracy of task generation. This eliminates the need for users to manually create tasks, allowing for efficient task management. Furthermore, the generation unit can automatically set task dependencies and priorities. This allows users to grasp the progress of tasks at a glance and proceed with tasks efficiently. The generation unit can continuously improve its task generation algorithm based on user feedback, achieving more accurate task generation.

[0076] The management department adds tasks generated by the generation department to management tools. The management department manages tasks using methods such as task prioritization, progress tracking, and resource allocation. Specifically, it sets task priorities to ensure that important tasks are processed first. For progress tracking, it uses tools such as Gantt charts and Kanban boards to visually understand the progress of tasks. For resource allocation, it assigns the appropriate person to each task and ensures optimal resource distribution. The management department can integrate with internal databases and CRM systems to automatically display customer purchase history and inquiry history. This allows for understanding the background information of tasks and enabling more appropriate responses. Furthermore, the management department can monitor task progress in real time and reassign tasks or change priorities as needed. This prevents task delays and enables efficient task management. Based on user feedback, the management department can continuously improve the task management algorithm to achieve more effective task management. As a result, the management department can efficiently manage the entire process from task generation to completion and improve the overall system performance.

[0077] The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks task progress using methods such as monitoring progress and timing reminder sending. Specifically, it monitors task progress in real time and sends reminders for tasks approaching their deadlines. Reminders are sent via methods such as email, SMS, and push notifications to inform users of task deadlines. The tracking unit can track task progress and send reminders for tasks approaching their deadlines. This allows users to always be aware of task progress and meet deadlines. Furthermore, the tracking unit can adjust the content and timing of reminders according to the task progress. For example, it can send frequent reminders for important tasks and reminders at appropriate intervals for routine tasks. Based on user feedback, the tracking unit can continuously improve its reminder sending algorithm to achieve more effective reminder delivery. This allows the tracking unit to efficiently track the progress of tasks and support users in completing tasks on time.

[0078] The analysis unit can automatically set priorities based on the urgency and importance of emails. For example, the analysis unit can evaluate urgency and set priorities based on the importance of the email content and sender. For example, emails regarding urgent matters are notified immediately, while routine communication emails can be reviewed later. The analysis unit can also evaluate importance and set priorities based on the email content and the sender's job title. For example, emails from superiors are given a high priority, while emails from colleagues are given a low priority. This allows for the rapid processing of important emails by automatically setting email priorities. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can set priorities using an AI model that takes email content as input and outputs urgency and importance.

[0079] The analysis unit can automatically search for relevant past emails and documents based on the email content and provide links. For example, the analysis unit can search for relevant past emails and documents based on keyword matching or content similarity. For example, if a project-related email is received, past meeting records and related documents will be automatically searched and links will be provided. The analysis unit can also automatically search for relevant documents based on the email content and provide links. For example, technical literature and research papers related to the email content will be automatically searched and links will be provided. This enables efficient information gathering by automatically searching for relevant past emails and documents and providing links. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can collect information using a generative AI model that takes email content as input and outputs links to relevant past emails and documents.

[0080] The management department can integrate with internal databases and CRM systems to automatically display customer purchase and inquiry history. For example, the management department can integrate with internal databases such as customer databases and product databases to automatically display customer purchase history. For instance, when a customer sends an inquiry email, past purchase history is automatically displayed, enabling a quick response. The management department can also integrate with CRM systems to automatically display customer inquiry history. For example, when a customer sends an inquiry email, past inquiry history is automatically displayed, enabling an accurate response. This allows for quick and accurate customer service by automatically displaying customer purchase and inquiry 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 use an AI model that takes customer emails as input and outputs purchase and inquiry history to handle customer inquiries.

[0081] The generation unit can automatically generate tasks based on email content and add them to a task management tool. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. For example, tasks requested via email are automatically generated and added to the task management tool. The generation unit can also track task progress and send reminders. For example, reminders are sent for tasks nearing their deadlines. This streamlines task management by automatically generating tasks based on email content and adding them to the task management tool. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate tasks using a generation AI model that takes email content as input and outputs tasks.

[0082] The tracking unit can track the progress of tasks and send reminders for tasks nearing their deadlines. The tracking unit tracks task progress using methods such as monitoring progress and timing reminder sending. For example, the tracking unit can monitor task progress in real time and send reminders according to the progress. The tracking unit can also generate a dashboard to visually display task progress. For example, it can visually display task progress using graphs and charts. This allows for tracking task progress and sending reminders for tasks nearing their deadlines, thereby preventing task delays. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can send reminders using an AI model that takes task progress as input and outputs reminders.

[0083] The management department can generate dashboards to visually display task progress. For example, the management department can visually display task progress using graphs and charts. For example, it can display progress using color coding based on task priority. The management department can also update task progress in real time to provide the latest information. For example, it can monitor task progress in real time and update the dashboard according to the progress. This allows for a quick grasp of the task status by visually displaying task progress. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not. For example, the management department can generate dashboards using a generative AI model that takes task progress as input and outputs dashboards.

[0084] The management department can share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination. For example, the management department can automatically notify stakeholders of the progress of tasks and share information. For example, it can notify stakeholders of the progress of tasks via email or chat tools. The management department can also automatically generate proposals and materials for internal coordination and provide them to stakeholders. For example, it can automatically extract the necessary information based on the proposal format and generate the proposal. Furthermore, the management department can propose the next steps based on the progress of tasks and notify stakeholders. For example, it can analyze the progress of tasks and propose the next actions to be taken. This improves the efficiency of operations by sharing information with stakeholders and carrying out preliminary preparations such as proposals and internal coordination. Some or all of the above processes in the management department may be carried out using, for example, generative AI, or not using generative AI. For example, the management department can generate materials using a generative AI model that takes the progress of tasks as input and outputs proposals and materials for internal coordination.

[0085] The collection unit can estimate the user's emotions and adjust the timing of email collection based on the estimated emotions. For example, if the user is stressed, the collection unit will reduce the frequency of email collection and prioritize collecting only important emails. For example, if the user is relaxed, it will collect emails at the normal frequency and list all emails. The collection unit can also immediately collect all emails and prioritize notifications for high-priority emails if the user is in a hurry. This reduces the user's burden by adjusting the timing of email collection based on their 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 collection unit may be performed using AI or not. For example, the collection unit can adjust the collection timing using an AI model that takes user emotion data as input and outputs the timing of email collection.

[0086] The collection unit can analyze the user's past email reception history and select the optimal collection method. For example, the collection unit can analyze the time periods in which the user frequently received emails in the past and prioritize collecting emails from those times. For example, it can analyze the characteristics of emails that the user previously deemed important and prioritize collecting emails with similar characteristics. The collection unit can also prioritize collecting emails from specific senders based on the user's past email reception history. In this way, the optimal collection method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can select a collection method using a generative AI model that takes the user's past email reception history as input and outputs the optimal collection method.

[0087] The collection unit can filter emails based on the user's current projects and areas of interest during collection. For example, the collection unit can prioritize collecting emails related to projects the user is currently working on. For example, it can prioritize collecting emails containing keywords related to the user's areas of interest. The collection unit can also filter emails based on specific project tags set by the user and prioritize collecting related emails. This allows for the priority collection of highly relevant emails by filtering emails based on the user's current projects and areas of interest. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can filter emails using a generative AI model that takes the user's projects and areas of interest as input and outputs filtered emails.

[0088] The collection unit can estimate the user's emotions and determine the priority of emails to collect based on the estimated emotions. For example, if the user is stressed, the collection unit will prioritize collecting only important emails and postpone other emails. For example, if the user is relaxed, it will collect all emails equally and list them. The collection unit can also prioritize collecting high-priority emails and immediately notify the user if the user is in a hurry. This allows important emails to be processed preferentially by determining the priority of emails to collect 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 collection unit may be performed using AI or not. For example, the collection unit can determine priorities using an AI model that takes user emotion data as input and outputs email priorities.

[0089] The collection unit can prioritize collecting emails that are highly relevant, taking into account the user's geographical location information. For example, if the user is in a specific region, the collection unit will prioritize collecting emails related to that region. For example, if the user is on a business trip, the collection unit will prioritize collecting emails related to the destination. The collection unit can also prioritize collecting emails related to home if the user is at home. By prioritizing the collection of highly relevant emails while considering the user's geographical location information, important information for the user can be obtained quickly. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0090] The collection unit can analyze a user's social media activity when collecting emails and collect relevant emails. For example, the collection unit can prioritize collecting emails related to topics mentioned by the user on social media. For example, it can prioritize collecting emails from accounts that the user follows. The collection unit can also prioritize collecting emails related to groups or events that the user participates in. In this way, relevant emails can be prioritized by analyzing the user's social media activity. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or not using generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's social media activity as input and outputs relevant emails.

[0091] The analysis unit can estimate the user's emotions and adjust the email analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a concise analysis result, highlighting only the important points. For example, if the user is relaxed, it provides a detailed analysis result, covering all information. The analysis unit can also prioritize analyzing high-priority information and provide immediate notification if the user is in a hurry. In this way, by adjusting the email analysis method based on the user's emotions, the system can provide the user with the most optimal analysis result. 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can adjust the analysis method using an AI model that takes user emotion data as input and outputs an analysis method.

[0092] The analysis unit can adjust the level of detail in its email analysis based on the importance of the email. For example, it can analyze high-importance emails in detail and provide all the information, while analyzing low-importance emails concisely and providing only the key points. It can also analyze urgent emails immediately and provide prompt notifications. This allows for quick identification of important information by adjusting the level of detail in the analysis based on the importance of the email. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can adjust the level of detail in its analysis using a generative AI model that takes the importance of the email as input and outputs the level of detail in the analysis.

[0093] The analysis unit can apply different analysis algorithms depending on the email category during email analysis. For example, the analysis unit can apply a business-specific analysis algorithm to business emails to extract important information. For example, it can apply a project-specific analysis algorithm to project-related emails to extract relevant tasks. The analysis unit can also apply a personal-specific analysis algorithm to personal emails to protect privacy during analysis. By applying different analysis algorithms depending on the email category, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes the email category as input and outputs an analysis algorithm.

[0094] 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 provides a simple and highly visible display method. For example, if the user is relaxed, it provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the user is in a hurry. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0095] The analysis unit can perform email analysis while considering the sender's attribute information. For example, if the sender is a supervisor, the analysis unit will set the importance level high and perform a detailed analysis. For example, if the sender is a customer, the analysis will consider the customer's attribute information to encourage a quick response. The analysis unit can also prioritize analyzing project-related information if the sender is a colleague. By considering the sender's attribute information, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can perform analysis using a generative AI model that takes the sender's attribute information as input and outputs analysis results.

[0096] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during email analysis. For example, the analysis unit can refer to past emails related to the email content and reflect this in the analysis results. For example, it can automatically search for documents related to the email content and provide links to them in the analysis results. The analysis unit can also refer to external literature related to the email content and reflect this in the analysis results. In this way, the accuracy of the analysis can be improved by referring to relevant literature for emails. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can refer to relevant literature using a generative AI model that takes email content as input and outputs relevant literature.

[0097] The generation unit can estimate the user's emotions and determine the priority of tasks to generate based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize generating only important tasks and postpone other tasks. For example, if the user is relaxed, it will generate and list all tasks evenly. The generation unit can also prioritize generating high-priority tasks and immediately notify the user if the user is in a hurry. This allows important tasks to be processed preferentially by determining task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can determine priorities using an AI model that takes user emotion data as input and outputs task priorities.

[0098] The generation unit can adjust the level of detail of tasks based on the content of emails when generating tasks. For example, the generation unit can generate detailed tasks based on important email content, or concise tasks based on simple email content. It can also generate tasks that require immediate attention based on urgent email content. By adjusting the level of detail of tasks based on the content of emails, appropriate tasks can be generated. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes email content as input and outputs the level of detail of tasks.

[0099] The generation unit can apply different generation algorithms depending on the email category when generating tasks. For example, the generation unit can apply a business-oriented generation algorithm to business emails to generate important tasks. For example, it can apply a project-oriented generation algorithm to project-related emails to generate relevant tasks. The generation unit can also apply a personal-oriented generation algorithm to personal emails to generate tasks while protecting privacy. This allows for the generation of more appropriate tasks by applying different generation algorithms depending on the email category. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can apply a generation algorithm using a generation AI model that takes the email category as input and outputs a generation algorithm.

[0100] The generation unit can estimate the user's emotions and adjust the display method of the generated tasks based on the estimated user emotions. For example, if the user is nervous, the generation unit provides a simple and highly visible display method. For example, if the user is relaxed, it provides a display method that includes detailed information. The generation unit can also provide a concise display method if the user is in a hurry. In this way, by adjusting the display method of tasks based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0101] The generation unit can generate tasks while considering the attribute information of the email sender. For example, if the sender is a supervisor, the generation unit will set the importance level high and generate a detailed task. For example, if the sender is a customer, it will generate a task considering the customer's attribute information to encourage a quick response. The generation unit can also prioritize generating project-related tasks if the sender is a colleague. In this way, more appropriate tasks can be generated by considering the attribute information of the email sender. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit can generate tasks using a generation AI model that takes the sender's attribute information as input and outputs tasks.

[0102] The generation unit can improve the accuracy of tasks by referring to relevant literature in emails when generating tasks. For example, the generation unit can refer to past emails related to the content of the email and reflect them in the task. For example, it can automatically search for documents related to the content of the email and provide links to them in the task. The generation unit can also refer to external literature related to the content of the email and reflect it in the task. In this way, the accuracy of tasks can be improved by referring to relevant literature in emails. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can refer to relevant literature using a generation AI model that takes email content as input and outputs relevant literature.

[0103] The management unit can estimate the user's emotions and adjust task management methods based on the estimated emotions. For example, if the user is stressed, the management unit will prioritize managing only important tasks and postpone other tasks. For example, if the user is relaxed, it will manage and list all tasks equally. The management unit can also prioritize managing high-priority tasks and immediately notify the user if the user is in a hurry. This allows for prioritizing important tasks by adjusting task management methods 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 management unit may be performed using AI or not. For example, the management unit can adjust management methods using an AI model that takes user emotion data as input and outputs management methods.

[0104] The management department can adjust the level of detail in task management based on the importance of the task. For example, the management department can manage high-importance tasks in detail and closely track their progress. For example, it can manage low-importance tasks concisely and track only the essentials. The management department can also manage high-urgency tasks immediately and respond quickly. In this way, important tasks can be properly managed by adjusting the level of detail in management based on the importance of the task. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can adjust the level of detail using a generative AI model that takes the importance of a task as input and outputs the level of detail in management.

[0105] The management department can apply different management algorithms to tasks depending on the task category. For example, the management department can apply a business-specific management algorithm to business tasks to extract important information. For example, it can apply a project-specific management algorithm to project-related tasks to extract relevant information. The management department can also apply a personal-specific management algorithm to personal tasks to manage them while protecting privacy. This allows for more appropriate task management by applying different management algorithms depending on the task category. Some or all of the above processing in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can apply a management algorithm using a generative AI model that takes the task category as input and outputs a management algorithm.

[0106] 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, it can provide a display method that includes detailed information. The management unit can also provide a concise display method if the user is in a hurry. In this way, by adjusting the task display method based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI, for example, or not using AI. For example, the management unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0107] The management department can generate a dashboard to visually display the progress of tasks during task management. For example, the management department can visually display the progress of tasks using graphs and charts. For example, it can display the progress of tasks using different colors based on their priority. The management department can also update the progress of tasks in real time to provide the latest information. For example, it can monitor the progress of tasks in real time and update the dashboard according to the progress. This allows for a quick grasp of the task status by visually displaying the progress of tasks. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can generate a dashboard using a generative AI model that takes the progress of tasks as input and outputs a dashboard.

[0108] The management department can share information with stakeholders and carry out preliminary preparations such as proposals and internal coordination when managing tasks. For example, the management department can automatically notify stakeholders of the task's progress and share information. For example, it can notify stakeholders of the task's progress via email or chat tools. The management department can also automatically generate proposals and materials for internal coordination and provide them to stakeholders. For example, it can automatically extract the necessary information based on the proposal format and generate the proposal. Furthermore, the management department can propose the next steps based on the task's progress and notify stakeholders. For example, it can analyze the task's progress and propose the next actions to be taken. This improves work efficiency by sharing information with stakeholders and carrying out preliminary preparations such as proposals and internal coordination. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not using generative AI. For example, the management department can generate materials using a generative AI model that takes the task's progress as input and outputs proposals and materials for internal coordination.

[0109] The tracking 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 tracking unit can reduce the frequency of reminders and send only important reminders. For example, if the user is relaxed, it can send reminders at the normal frequency. The tracking unit can also send reminders immediately if the user is in a hurry, prioritizing notifications for high-priority tasks. This reduces the user's burden by adjusting the timing of reminder sending based on their 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 tracking unit may be performed using AI or not. For example, the tracking unit can adjust the sending timing using an AI model that takes user emotion data as input and outputs the timing of reminder sending.

[0110] The tracking unit can adjust the level of detail in tracking tasks based on their importance. For example, it can track high-importance tasks in detail and report their progress closely. For example, it can track low-importance tasks concisely and report only the key points. The tracking unit can also track urgent tasks immediately and respond quickly. This allows for proper tracking of important tasks by adjusting the level of detail based on their importance. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can adjust the level of detail using a generative AI model that takes task importance as input and outputs the level of detail of the tracking.

[0111] The tracking unit can apply different tracking algorithms depending on the task category when tracking the progress of a task. For example, the tracking unit can apply a business tracking algorithm to business tasks and extract important information. For example, it can apply a project tracking algorithm to project-related tasks and extract relevant information. The tracking unit can also apply a personal tracking algorithm to personal tasks, tracking them while protecting privacy. This allows for more appropriate task tracking by applying different tracking algorithms depending on the task category. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can apply a tracking algorithm using a generative AI model that takes the task category as input and outputs a tracking algorithm.

[0112] The tracking unit can estimate the user's emotions and adjust the way reminders are displayed based on the estimated emotions. For example, if the user is stressed, the tracking unit provides a simple and highly visible display method. For example, if the user is relaxed, it provides a display method that includes detailed information. The tracking unit can also provide a concise display method if the user is in a hurry. In this way, by adjusting the way reminders are displayed based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can adjust the display method using an AI model that takes user emotion data as input and outputs a way to display reminders.

[0113] The tracking unit can track the progress of tasks while considering the attribute information of the task sender. For example, if the sender is a supervisor, the tracking unit will set the importance level high and track it in detail. For example, if the sender is a customer, the tracking unit will consider the customer's attribute information to encourage a quick response. The tracking unit can also prioritize tracking project-related tasks if the sender is a colleague. This allows for more appropriate task tracking by considering the attribute information of the task sender. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can perform tracking using a generative AI model that takes the sender's attribute information as input and outputs tracking results.

[0114] The tracking unit can improve the accuracy of tracking by referring to relevant literature when tracking the progress of a task. For example, the tracking unit can refer to past tasks related to the task content and reflect them in the progress status. For example, it can automatically search for documents related to the task content and provide links to them in the progress status. The tracking unit can also refer to external literature related to the task content and reflect it in the progress status. In this way, the accuracy of tracking can be improved by referring to relevant literature for the task. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can refer to relevant literature using a generative AI model that takes task content as input and outputs relevant literature.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The analysis unit can estimate the user's emotions and adjust the email analysis method based on the estimated emotions. For example, if the user is stressed, it can provide a concise analysis result, highlighting only the important points. If the user is relaxed, it can provide a detailed analysis result, covering all information. If the user is in a hurry, it can prioritize analyzing high-priority information and provide immediate notification. In this way, by adjusting the email analysis method based on the user's emotions, the system can provide the optimal analysis result for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can adjust the analysis method using an AI model that takes user emotion data as input and outputs an analysis method.

[0117] The collection unit can analyze the user's past email reception history and select the optimal collection method. For example, it can analyze the time periods when the user frequently received emails in the past and prioritize collecting emails from those times. It can also analyze the characteristics of emails that the user previously deemed important and prioritize collecting emails with similar characteristics. Furthermore, it can prioritize collecting emails from specific senders based on the user's past email reception history. In this way, the optimal collection method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the collection unit may be performed using, for example, generative AI, or without generative AI. For example, the collection unit can select a collection method using a generative AI model that takes the user's past email reception history as input and outputs the optimal collection method.

[0118] The management unit can estimate the user's emotions and adjust task management methods based on those estimates. For example, if the user is stressed, only important tasks will be prioritized, and other tasks will be postponed. If the user is relaxed, all tasks will be managed and listed equally. If the user is in a hurry, high-priority tasks can be prioritized and notifications can be sent immediately. This allows for prioritizing important tasks by adjusting task management methods 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 includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the management unit may be performed using AI or not. For example, the management unit can adjust management methods using an AI model that takes user emotion data as input and outputs management methods.

[0119] The generation unit can adjust the level of detail of tasks based on the content of emails when generating tasks. For example, it can generate detailed tasks based on important email content, or concise tasks based on simple email content. It can also generate tasks that require immediate attention based on urgent email content. By adjusting the level of detail of tasks based on the content of emails, it is possible to generate appropriate tasks. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes email content as input and outputs the level of detail of tasks.

[0120] The tracking 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 frequency of reminders sent will be reduced, and only important reminders will be sent. If the user is relaxed, reminders will be sent at the normal frequency. If the user is in a hurry, reminders can be sent immediately, prioritizing notifications for high-priority tasks. This reduces the user's burden by adjusting the timing of reminder sending based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI, for example, or not using AI. For example, the tracking unit can adjust the sending timing using an AI model that takes user emotion data as input and outputs the timing of reminder sending.

[0121] The management department can adjust the level of detail in task management based on the importance of the task. For example, high-importance tasks can be managed in detail, with their progress closely tracked. Low-importance tasks can be managed concisely, with only the essentials tracked. High-urgency tasks can also be managed immediately and addressed quickly. By adjusting the level of detail in management based on the importance of the task, important tasks can be managed appropriately. Some or all of the above processes in the management department may be performed using, for example, generative AI, or not. For example, the management department can adjust the level of detail using a generative AI model that takes the importance of a task as input and outputs the level of detail in management.

[0122] The analysis unit can apply different analysis algorithms depending on the email category during email analysis. For example, a business analysis algorithm can be applied to business emails to extract important information. A project analysis algorithm can be applied to project-related emails to extract relevant tasks. Furthermore, a personal analysis algorithm can be applied to personal emails to protect privacy during analysis. By applying different analysis algorithms depending on the email category, more accurate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can apply an analysis algorithm using a generative AI model that takes the email category as input and outputs an analysis algorithm.

[0123] The collection unit can prioritize the collection of highly relevant emails by considering the user's geographical location information when collecting emails. For example, if the user is in a specific region, it can prioritize the collection of emails related to that region. If the user is on a business trip, it can prioritize the collection of emails related to the business trip destination. It can also prioritize the collection of emails related to home if the user is at home. By prioritizing the collection of highly relevant emails while considering the user's geographical location information, the system can quickly obtain information that is important to the user. Some or all of the above processing in the collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the collection unit can collect emails using a generative AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0124] The generation unit can estimate the user's emotions and determine the priority of tasks to generate based on the estimated emotions. For example, if the user is stressed, it can prioritize generating only important tasks and postpone other tasks. If the user is relaxed, it can generate and list all tasks evenly. If the user is in a hurry, it can prioritize generating high-priority tasks and immediately notify the user. This allows important tasks to be processed preferentially by determining task priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can determine priorities using an AI model that takes user emotion data as input and outputs task priorities.

[0125] The tracking unit can track the progress of tasks while considering the attribute information of the task sender. For example, if the sender is a supervisor, the importance level can be set high and the task can be tracked in detail. If the sender is a customer, the tracking can be conducted while considering the customer's attribute information to encourage a quick response. If the sender is a colleague, project-related tasks can be prioritized for tracking. This allows for more appropriate task tracking by considering the attribute information of the task sender. Some or all of the above processing in the tracking unit may be performed using, for example, generative AI, or without generative AI. For example, the tracking unit can perform tracking using a generative AI model that takes the sender's attribute information as input and outputs tracking results.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The collection unit receives emails. The collection unit can receive emails in various formats, such as text emails, HTML emails, and emails with attachments. The collection unit retrieves emails from the mail server and stores them in the system. The collection unit can also estimate the user's mood when receiving emails and adjust the timing of email collection based on the estimated mood. For example, if the user is feeling stressed, the collection unit can reduce the frequency of email collection and prioritize collecting only important emails. Step 2: The analysis unit analyzes the content of emails received by the collection unit. The analysis unit analyzes the content of emails using methods such as text analysis, sentiment analysis, and keyword extraction. The analysis unit can automatically set priorities based on the urgency and importance of the emails. For example, emails regarding urgent matters are notified immediately, while routine communication emails can be reviewed later. Step 3: The generation unit automatically generates tasks based on the email content analyzed by the analysis unit. The generation unit can generate various types of tasks, such as project tasks, daily work tasks, and urgent tasks. The generation unit can automatically generate tasks based on email content and add them to a task management tool. Step 4: The management department adds the tasks generated by the generation department to the management tool. The management department manages the tasks using methods such as task prioritization, progress tracking, and resource allocation. The management department can also integrate with internal databases and CRM systems to automatically display customer purchase history and inquiry history. Step 5: The tracking unit tracks the progress of tasks managed by the management unit and sends reminders. The tracking unit tracks the progress of tasks using methods such as monitoring progress and timing reminder sending. The tracking unit can track the progress of tasks and send reminders for tasks that are nearing their deadline.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, management unit, and tracking 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 computer 36 of the smart device 14, which retrieves emails from the mail server and stores them in the system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the content of emails and automatically sets priorities based on urgency and importance. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically generates tasks based on the analyzed email content. The management unit is implemented by the control unit 46A of the smart device 14, which adds the generated tasks to a management tool and performs prioritization and progress management. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12, which tracks the progress of tasks and sends reminders. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, management unit, and tracking 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 computer 36 of the smart glasses 214, which retrieves emails from the mail server and stores them in the system. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the content of emails and automatically sets priorities based on urgency and importance. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12, which automatically generates tasks based on the analyzed email content. The management unit is implemented by the control unit 46A of the smart glasses 214, which adds the generated tasks to a management tool and performs prioritization and progress management. The tracking unit is implemented by the identification processing unit 290 of the data processing unit 12, which tracks the progress of tasks and sends reminders. 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.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, management unit, and tracking 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 computer 36 of the headset terminal 314, which retrieves emails from the mail server and stores them in the system. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the content of emails and automatically sets priorities based on urgency and importance. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically generates tasks based on the analyzed email content. The management unit is implemented by the control unit 46A of the headset terminal 314, which adds the generated tasks to a management tool and performs prioritization and progress management. The tracking unit is implemented by the specific processing unit 290 of the data processing unit 12, which tracks the progress of tasks and sends reminders. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, management unit, and tracking unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414, which retrieves emails from the mail server and stores them in the system. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of emails and automatically sets priorities based on urgency and importance. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically generates tasks based on the analyzed email content. The management unit is implemented by, for example, the control unit 46A of the robot 414, which adds the generated tasks to a management tool and performs prioritization and progress management. The tracking unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which tracks the progress of tasks and sends reminders. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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."

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] (Note 1) The collection unit that receives emails, An analysis unit analyzes the content of emails received by the collection unit, A generation unit that automatically generates tasks based on the email content analyzed by the analysis unit, A management unit adds the tasks generated by the generation unit to a management tool, The system includes a tracking unit that tracks the progress of tasks managed by the aforementioned management unit and sends reminders. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Automatically prioritize emails based on their urgency and importance. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The system automatically searches for relevant past emails and documents based on the email content and provides links to them. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned management department, It integrates with internal databases and CRM systems to automatically display customer purchase history and inquiry history. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Automatically generate tasks based on email content and add them to the task management tool. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned tracking unit is Track task progress and send reminders for tasks nearing their deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, Generate a dashboard to visually display the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned management department, We will share information with relevant parties and carry out preliminary preparations such as preparing proposals and coordinating internally. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of email collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the user's past email receiving history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting emails, filter them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and determines the priority of emails to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting emails, the system prioritizes collecting emails that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting emails, the system analyzes users' social media activity and collects relevant emails. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, We estimate the user's emotions and adjust the email analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, When analyzing emails, adjust the level of detail based on the importance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing emails, different analysis algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, When analyzing emails, the sender's attribute information is taken into consideration during the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When analyzing emails, we improve the accuracy of the analysis by referring to related literature. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and determines the priority of tasks to be generated based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When creating a task, adjust the level of detail based on the content of the email. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating tasks, different generation algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is 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 25) The generating unit is When creating a task, consider the attribute information of the email sender when generating the task. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating tasks, refer to relevant literature in emails to improve task accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, It estimates the user's emotions and adjusts how tasks are managed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, When managing tasks, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, When managing tasks, apply different management algorithms depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 30) 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 31) The aforementioned management department, Generate a dashboard to visually display the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned management department, When managing tasks, share information with relevant parties and carry out preliminary preparations such as preparing proposals and coordinating internally. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned tracking 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 34) The aforementioned tracking unit is When tracking task progress, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned tracking unit is When tracking task progress, different tracking algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned tracking unit is It estimates the user's emotions and adjusts how reminders are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned tracking unit is When tracking the progress of a task, the attribute information of the task submitter should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned tracking unit is When tracking task progress, referencing relevant literature improves the accuracy of the tracking. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0200] 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. The collection unit that receives emails, An analysis unit analyzes the content of emails received by the collection unit, A generation unit that automatically generates tasks based on the email content analyzed by the analysis unit, A management unit adds the tasks generated by the generation unit to a management tool, The system includes a tracking unit that tracks the progress of tasks managed by the aforementioned management unit and sends reminders. A system characterized by the following features.

2. The aforementioned analysis unit, Automatically prioritize emails based on their urgency and importance. The system according to feature 1.

3. The aforementioned analysis unit, The system automatically searches for relevant past emails and documents based on the email content and provides links to them. The system according to feature 1.

4. The aforementioned management department, It integrates with internal databases and CRM systems to automatically display customer purchase history and inquiry history. The system according to feature 1.

5. The generating unit is Automatically generate tasks based on email content and add them to the task management tool. The system according to feature 1.

6. The aforementioned tracking unit is Track task progress and send reminders for tasks nearing their deadlines. The system according to feature 1.

7. The aforementioned management department, Generate a dashboard to visually display the progress of tasks. The system according to feature 1.

8. The aforementioned management department, We will share information with relevant parties and carry out preliminary preparations such as preparing proposals and coordinating internally. The system according to feature 1.

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