system

Abstract

The system according to this embodiment aims to efficiently organize the contents of business emails and clarify the tasks that need to be performed. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a listing unit, and a management unit. The collection unit collects emails. The analysis unit analyzes the emails collected by the collection unit. The listing unit lists tasks based on the results of the analysis performed by the analysis unit. The management unit manages the tasks listed by the listing unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently organize the content of increasing business emails and clarify the tasks to be executed.

[0005] The system according to the embodiment aims to efficiently organize the content of business emails and clarify the tasks to be executed.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a management unit. The collection unit collects emails. The analysis unit analyzes the emails collected by the collection unit. The listing unit lists tasks based on the results of the analysis performed by the analysis unit. The management unit manages the tasks listed by the listing unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently organize the contents of business emails and clarify the tasks that need to be performed. [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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The email management system according to an embodiment of the present invention is a system that uses a generation AI to automatically create a summary of all emails received at the end of the day and a list of tasks to be performed. The email management system helps to clarify the content of emails and the next actions to be taken, supporting a thorough and complete response. For example, the email management system automatically collects all emails that have arrived in the inbox at the end of the workday. Next, the generation AI analyzes the content of the emails and classifies them into important work instructions, customer communications, reference information, company-wide announcements, advertisements, etc. A summary of each email is automatically generated, allowing for quick understanding of the content. Furthermore, the email management system automatically creates a list of tasks to be performed based on important work instructions and customer communications. Priorities and deadlines are set for each task, clarifying the action plan for the next day. The email management system provides a dashboard that allows for centralized management of summaries and task lists, and the summarized email content and tasks can be easily viewed on the dashboard. In addition, the email management system allows for setting reminder notifications for important emails and tasks, and notifications can be received via email or chat tools. The email management system streamlines information sharing and task management across the entire team by sharing an email summary and action list as a daily report with supervisors and the team at the end of the day. In this way, the email management system helps to efficiently review the day's email content, clarify next actions, and ensure that tasks are handled without omissions. This allows the email management system to efficiently collect, analyze, list, and manage emails.

[0029] The email management system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a management unit. The collection unit collects emails. For example, the collection unit automatically collects all emails that have arrived in the inbox at the end of the workday. The collection unit can also use AI to collect emails. The analysis unit analyzes the emails collected by the collection unit. The analysis unit uses a generation AI to analyze the content of the emails and classifies them into important work instructions, communications from customers, reference information, company-wide announcements, advertisements, etc. For example, the analysis unit uses a generation AI to analyze the content of the emails and automatically generates a summary of each email. The listing unit lists tasks based on the results of the analysis by the analysis unit. The listing unit can also use AI to list tasks. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions and communications from customers. The management unit manages the tasks listed by the listing unit. The management unit can also use AI to manage tasks. The management department provides, for example, a dashboard that allows for centralized management of summaries and task lists. This enables the email management system according to the embodiment to efficiently collect, analyze, list, and manage emails.

[0030] The collection unit collects emails. For example, it automatically collects all emails that have arrived in the inbox at the end of the workday. Specifically, the collection unit works with the mail server and retrieves emails using protocols such as IMAP and POP3. This eliminates the need for users to manually check their emails and allows for efficient email collection. Furthermore, the collection unit can also use AI to collect emails. The AI ​​analyzes the timing of email receipt and the importance of the sender to identify emails that should be collected with priority. For example, it can be set to collect emails from superiors or important business partners urgently, while collecting other emails at regular intervals. The collection unit also has a spam filtering function that automatically excludes unnecessary and junk emails. This allows the collection unit to efficiently collect only the necessary emails and improve the overall system performance. In addition, the collection unit has a function to analyze email metadata (sender, recipient, subject, date and time, etc.) and automatically determine the priority and category of emails. This allows the collection unit to provide basic data for appropriate processing based on the content of the emails.

[0031] The analysis unit analyzes emails collected by the collection unit. Using generative AI, the analysis unit analyzes the content of emails and classifies them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. Specifically, the generative AI uses natural language processing technology to analyze the email body and understand the content of each email. For example, the generative AI analyzes keywords and context to identify emails containing important business instructions. It can also automatically classify emails containing customer communications or inquiries. Furthermore, the generative AI has the function of summarizing email content and extracting key points. This saves users the trouble of reading long emails and allows them to quickly grasp only the essentials. The analysis unit automatically generates summaries for each email based on the generative AI's analysis. For example, the generative AI extracts important information from the email body and generates a concise summary. This allows users to quickly understand the email content and take necessary action. Furthermore, the analysis unit can learn from past email data to perform more accurate analyses. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, thereby improving the user's work efficiency.

[0032] The listing unit creates a list of tasks based on the results analyzed by the analysis unit. The listing unit can also use AI to create task lists. Specifically, the listing unit automatically creates a list of tasks to be performed based on the classification results of emails provided by the analysis unit. For example, it automatically creates a list of tasks to be performed based on important work instructions or communications from customers. The listing unit can efficiently manage tasks by considering their priority and deadlines. Furthermore, the listing unit also has a function to track task progress in real time and provide appropriate reminders to the user. This allows the user to always be aware of task progress and complete tasks within the deadline. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions or communications from customers. Specifically, the listing unit automatically identifies and lists the content and priority of tasks based on the classification results of emails provided by the analysis unit. This allows the user to quickly grasp the overall picture of tasks and proceed with work efficiently. Furthermore, the listing unit also has a function to track task progress in real time and provide appropriate reminders to the user. This allows users to stay informed about task progress and complete tasks within deadlines.

[0033] The Management Department manages tasks listed by the Listing Department. The Management Department can also use AI to manage tasks. Specifically, the Management Department centrally manages the progress of tasks based on the task list provided by the Listing Department. For example, the Management Department provides a dashboard that allows for centralized management of summaries and execution lists. The dashboard is designed to make it easy for users to grasp the overall picture of tasks, and visually displays task priority, deadlines, and progress. This allows users to quickly grasp the progress of tasks and proceed with their work efficiently. Furthermore, the Management Department also has a function to track task progress in real time and provide users with appropriate reminders. This allows users to always know the progress of tasks and complete tasks within the deadline. The Management Department can also use AI to manage tasks. The AI ​​analyzes the progress and priority of tasks and supports efficient task management. For example, the AI ​​provides users with appropriate reminders based on the progress of tasks to encourage task completion. In addition, the AI ​​learns from past task data and analyzes users' work patterns and trends to achieve more efficient task management. This allows the management department to improve users' work efficiency and increase task completion rates.

[0034] The analysis unit can analyze the content of emails and classify them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. For example, the analysis unit can use a generation AI to analyze the content of emails and automatically generate summaries for each email. The generation AI analyzes the content of emails and classifies them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. This allows for efficient classification of email content. Specific criteria and methods for classification include, for example, classification categories and classification algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the content of emails into the generation AI, which then analyzes the content and outputs the classification results.

[0035] The listing unit can automatically create a list of tasks to be performed based on important work instructions and communications from customers. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions and communications from customers. The listing unit can also use AI to create task lists. This allows for the automatic creation of a list of tasks to be performed. Specific methods and criteria for automatic creation include, for example, the algorithm used and the timing of creation. Some or all of the above-described processes in the listing unit may be performed using AI or not. For example, the listing unit inputs important work instructions and communications from customers into a generating AI, which then automatically creates a list of tasks.

[0036] The listing unit can set priorities and deadlines for each task, clarifying the action plan for the next day. The listing unit can also use AI to set task priorities and deadlines. This clarifies the action plan for the next day. Criteria and methods for setting priorities include, for example, evaluation criteria and setting methods for priorities. Criteria and methods for setting deadlines include, for example, evaluation criteria and setting methods for deadlines. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs each task into a generating AI, and the generating AI sets the priorities and deadlines.

[0037] The management department can provide a dashboard that allows for centralized management of summaries and execution lists. For example, the management department can provide a dashboard that allows for centralized management of summaries and execution lists. The management department can also manage the dashboard using AI. This allows for centralized management of summaries and execution lists. The specific functions and display content of the dashboard include, for example, the types of information to display and the design of the interface. Some or all of the above-mentioned processes in the management department may be performed using AI or not. For example, the management department inputs summaries and execution lists into a generating AI, and the generating AI provides the dashboard.

[0038] The management department can set reminder notifications for important emails and tasks, and these notifications can be received via email or chat tools. The management department can also use AI to set reminder notifications. This allows for setting reminder notifications for important emails and tasks. The method and means of setting reminder notifications include, for example, the timing of notifications and the notification method (email, chat tool, etc.). Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs important emails and tasks into a generating AI, which then sets reminder notifications.

[0039] The management department can share an email summary and an action list as a daily report with their supervisor and team at the end of the day. For example, the management department can share an email summary and an action list as a daily report with their supervisor and team at the end of the day. The management department can also use AI to share the daily report. This allows the email summary and action list to be shared as a daily report. The specific content and sharing method of the daily report include, for example, the information to be included in the daily report and the means of sharing. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs the email summary and action list into a generating AI, the generating AI creates a daily report, and shares it with their supervisor and team.

[0040] The collection unit can analyze the user's past email collection history and select the optimal collection method. For example, the collection unit can suggest the optimal collection time based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, automatic, etc.). The collection unit can also suggest the optimal collection method for specific days of the week or time slots based on the user's past collection history. This allows for the selection of the optimal collection method based on past collection history. The selection criteria and specific methods for the collection method include, for example, the collection algorithm and selection criteria. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the user's past collection history data into a generating AI, and the generating AI selects the optimal collection method.

[0041] The collection unit can filter emails based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting emails related to projects the user is currently working on. The collection unit can also filter and collect relevant emails based on the user's areas of interest. The collection unit can also filter and collect important emails based on keywords set by the user. This allows emails to be filtered based on projects and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input data on the user's projects and areas of interest into a generating AI, which then performs the filtering.

[0042] 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 on a business trip, the collection unit will prioritize collecting emails related to the business trip location. If the user is in the office, the collection unit can also prioritize collecting office-related emails. If the user is at home, the collection unit can also prioritize collecting home-related emails. This allows for the priority collection of highly relevant emails based on geographical location information. The method of acquiring and using geographical location information includes, for example, the means of acquiring location information and the algorithm used. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI, and the generating AI will prioritize collecting highly relevant emails.

[0043] 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. The collection unit can also prioritize collecting emails related to accounts the user follows. The collection unit can also prioritize collecting emails related to groups or events the user participates in. This allows for the collection of relevant emails based on social media activity. The methods and criteria for analyzing social media activity include, for example, the data used and the analysis algorithms. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity data into a generating AI, which then collects relevant emails.

[0044] The analysis unit can adjust the level of detail in its analysis based on the importance of the emails. For example, the analysis unit can perform a detailed analysis for important work instructions. It can also perform a concise analysis for advertising and promotional materials. It can also perform a moderate level of detail analysis for company-wide announcements. This allows the level of detail in the analysis to be adjusted based on the importance of the emails. The criteria and specific methods for adjusting the level of detail in the analysis include, for example, the criteria for evaluating the level of detail and the adjustment method. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs email importance data into a generating AI, and the generating AI adjusts the level of detail in the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, the analysis unit can apply a task extraction algorithm to business instruction emails. The analysis unit can also apply a customer response algorithm to customer contact emails. The analysis unit can also apply an information summarization algorithm to reference information emails. This allows the appropriate analysis algorithm to be applied according to the email category. Specific types of analysis algorithms and application methods include, for example, the type of algorithm used and the application criteria. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email category data into a generation AI, and the generation AI applies an appropriate analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on when the emails were received. For example, the analysis unit may prioritize the analysis of the most recent emails. The analysis unit may also prioritize the analysis of important business instruction emails. The analysis unit may also prioritize the analysis of customer communication emails. This allows the analysis priority to be determined based on when the emails were received. The criteria for evaluating the reception time and the method for determining the analysis priority include, for example, the criteria for evaluating the reception time and the method for determining the priority. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email reception time data into a generation AI, and the generation AI determines the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit might analyze important business instruction emails first. It could also analyze customer contact emails next. It could also analyze promotional emails last. This allows the order of analysis to be adjusted based on the relevance of the emails. Criteria for evaluating relevance and methods for adjusting the order of analysis include, for example, criteria for evaluating relevance and methods for adjusting the order. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email relevance data into a generation AI, and the generation AI adjusts the order of analysis.

[0048] The listing unit can adjust the level of detail in the list based on the importance of the tasks during the listing process. For example, the listing unit can add detailed descriptions to important tasks. It can also add concise descriptions to low-priority tasks. It can also add descriptions of moderate importance to tasks with a moderate level of detail. This allows the level of detail in the list to be adjusted based on the importance of the tasks. The criteria and specific methods for adjusting the level of detail in the list include, for example, the criteria for evaluating the level of detail and the adjustment method. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task importance data into a generating AI, and the generating AI adjusts the level of detail in the list.

[0049] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a detailed listing algorithm to work instruction tasks. The listing unit can also apply a customer service listing algorithm to customer service tasks. The listing unit can also apply an information summary listing algorithm to reference information tasks. This allows for the application of an appropriate listing algorithm depending on the task category. Specific types and application methods of the listing algorithms include, for example, the type of algorithm used and the application criteria. Some or all of the above-described processes in the listing unit may be performed using AI or not. For example, the listing unit inputs task category data into a generating AI, and the generating AI applies an appropriate listing algorithm.

[0050] The listing unit can adjust the order of tasks in the list based on their deadlines. For example, the listing unit can prioritize listing tasks with approaching deadlines. The listing unit can also postpone long-term tasks. The listing unit can also list tasks with medium deadlines with medium priority. This allows the order of the list to be adjusted based on the task deadlines. The criteria and specific methods for adjusting the order of the list include, for example, evaluation criteria and adjustment methods for the order. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task deadline data into a generating AI, and the generating AI adjusts the order of the list.

[0051] The listing unit can adjust how the list is displayed based on the relevance of the tasks during the listing process. For example, the listing unit can display important tasks first. It can also display customer service tasks next. It can also display advertising tasks last. This allows the listing unit to adjust how the list is displayed based on the relevance of the tasks. The criteria and specific methods for adjusting how the list is displayed include, for example, the display format and adjustment method. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task relevance data into a generating AI, and the generating AI adjusts how the list is displayed.

[0052] The management department can optimize management algorithms by referring to past management data during management. For example, the management department can propose the optimal management method based on past management data. The management department can also derive efficient management algorithms from past management data. The management department can also analyze past management data to improve the accuracy of management. This allows for the optimization of management algorithms based on past management data. The optimization criteria and specific methods for management algorithms include, for example, the optimization criteria and the algorithms used. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs past management data into a generating AI, and the generating AI optimizes the management algorithm.

[0053] The management department can customize management methods based on the progress of tasks during management. For example, the management department can adjust management methods according to the progress of tasks. The management department can also monitor task progress in real time and optimize management methods. The management department can also set reminder notifications based on task progress. This allows for customization of management methods based on task progress. The criteria and specific methods for customizing management methods include, for example, the criteria for customization and the type of method. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs task progress data into a generating AI, and the generating AI customizes the management methods.

[0054] The management department can select the optimal management method when managing tasks, taking into account their geographical distribution. For example, if tasks are scattered across multiple locations, the management department can group geographically close tasks together for management. If tasks are concentrated in a specific region, the management department can also provide a management method tailored to that region. If tasks are internationally distributed, the management department can also manage them while considering the time zones of each region. This allows for the selection of the optimal management method based on the geographical distribution of tasks. Criteria for evaluating geographical distribution and selecting management methods include, for example, distribution evaluation criteria and management method selection criteria. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input geographical distribution data of tasks into a generating AI, and the generating AI can select the optimal management method.

[0055] The management department can improve the accuracy of its management by referring to relevant literature for tasks during the management process. For example, the management department can automatically collect relevant literature for tasks and use it to aid in management. The management department can also optimize its management methods by referring to relevant literature according to the progress of tasks. The management department can also improve the accuracy of its management by referring to relevant literature based on the content of tasks. This improves the accuracy of management by referring to relevant literature for tasks. Criteria for referring to relevant literature and improving the accuracy of management include, for example, the type of literature and the method of reference. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input relevant literature data for tasks into a generating AI, and the generating AI can improve the accuracy of management.

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

[0057] The email management system can also include a function to analyze the user's past email processing history and suggest the optimal email processing method. For example, it can suggest the optimal processing time based on the time slots the user frequently processed emails in the past. It can also prioritize suggesting processing methods the user has used in the past (manual, automatic, etc.). It can also suggest the optimal processing method for specific days of the week or time slots based on the user's past processing history. This allows for the suggestion of the optimal processing method based on past processing history. The selection criteria and specific methods for processing methods include, for example, processing algorithms and selection criteria. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs the user's past processing history data into a generating AI, and the generating AI suggests the optimal processing method.

[0058] The email management system can also include a function to prioritize the processing of highly relevant emails by taking into account the user's geographical location. For example, if the user is on a business trip, emails related to the business trip destination can be prioritized. If the user is in the office, emails related to the office can be prioritized. If the user is at home, emails related to home can be prioritized. This allows for the prioritization of highly relevant emails based on geographical location. The method of acquiring and using geographical location information includes, for example, the means of acquiring location information and the algorithms used. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system could input the user's geographical location information into a generating AI, which would then prioritize the processing of highly relevant emails.

[0059] The email management system can further analyze the user's social media activity and prioritize the processing of relevant emails. For example, it can prioritize emails related to topics the user has mentioned on social media. It can also prioritize emails related to accounts the user follows. It can also prioritize emails related to groups or events the user participates in. This allows for the prioritization of relevant emails based on social media activity. The methods and criteria for analyzing social media activity include, for example, the data used and the analysis algorithms. Some or all of the processing described above in the email management system may be performed using AI or not. For example, the email management system could input the user's social media activity data into a generating AI, which would then prioritize the processing of relevant emails.

[0060] The email management system can also include a function to filter emails based on the user's current projects and areas of interest. For example, it can prioritize emails related to projects the user is currently working on. It can also filter and process relevant emails based on the user's areas of interest. It can also filter and process important emails based on keywords set by the user. This allows emails to be filtered based on projects and areas of interest. Specific methods and criteria for filtering include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system can input data on the user's projects and areas of interest into a generating AI, which then performs the filtering.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection unit collects emails. For example, it automatically collects all emails that have arrived in the inbox at the end of the workday. The collection unit can also use AI to collect emails. Step 2: The analysis unit analyzes the emails collected by the collection unit. The analysis unit uses a generation AI to analyze the content of the emails and classify them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. For example, the generation AI analyzes the content of the emails and automatically generates a summary of each email. Step 3: The listing unit creates a list of tasks based on the results analyzed by the analysis unit. The listing unit can also use AI to create task lists. For example, it can automatically create a list of tasks to be performed based on important work instructions or communications from customers. Step 4: The management department manages the tasks listed by the listing department. The management department can also use AI to manage tasks. For example, it can provide a dashboard that allows for centralized management of summaries and action lists.

[0063] (Example of form 2) The email management system according to an embodiment of the present invention is a system that uses a generation AI to automatically create a summary of all emails received at the end of the day and a list of tasks to be performed. The email management system helps to clarify the content of emails and the next actions to be taken, supporting a thorough and complete response. For example, the email management system automatically collects all emails that have arrived in the inbox at the end of the workday. Next, the generation AI analyzes the content of the emails and classifies them into important work instructions, customer communications, reference information, company-wide announcements, advertisements, etc. A summary of each email is automatically generated, allowing for quick understanding of the content. Furthermore, the email management system automatically creates a list of tasks to be performed based on important work instructions and customer communications. Priorities and deadlines are set for each task, clarifying the action plan for the next day. The email management system provides a dashboard that allows for centralized management of summaries and task lists, and the summarized email content and tasks can be easily viewed on the dashboard. In addition, the email management system allows for setting reminder notifications for important emails and tasks, and notifications can be received via email or chat tools. The email management system streamlines information sharing and task management across the entire team by sharing an email summary and action list as a daily report with supervisors and the team at the end of the day. In this way, the email management system helps to efficiently review the day's email content, clarify next actions, and ensure that tasks are handled without omissions. This allows the email management system to efficiently collect, analyze, list, and manage emails.

[0064] The email management system according to this embodiment comprises a collection unit, an analysis unit, a listing unit, and a management unit. The collection unit collects emails. For example, the collection unit automatically collects all emails that have arrived in the inbox at the end of the workday. The collection unit can also use AI to collect emails. The analysis unit analyzes the emails collected by the collection unit. The analysis unit uses a generation AI to analyze the content of the emails and classifies them into important work instructions, communications from customers, reference information, company-wide announcements, advertisements, etc. For example, the analysis unit uses a generation AI to analyze the content of the emails and automatically generates a summary of each email. The listing unit lists tasks based on the results of the analysis by the analysis unit. The listing unit can also use AI to list tasks. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions and communications from customers. The management unit manages the tasks listed by the listing unit. The management unit can also use AI to manage tasks. The management department provides, for example, a dashboard that allows for centralized management of summaries and task lists. This enables the email management system according to the embodiment to efficiently collect, analyze, list, and manage emails.

[0065] The collection unit collects emails. For example, it automatically collects all emails that have arrived in the inbox at the end of the workday. Specifically, the collection unit works with the mail server and retrieves emails using protocols such as IMAP and POP3. This eliminates the need for users to manually check their emails and allows for efficient email collection. Furthermore, the collection unit can also use AI to collect emails. The AI ​​analyzes the timing of email receipt and the importance of the sender to identify emails that should be collected with priority. For example, it can be set to collect emails from superiors or important business partners urgently, while collecting other emails at regular intervals. The collection unit also has a spam filtering function that automatically excludes unnecessary and junk emails. This allows the collection unit to efficiently collect only the necessary emails and improve the overall system performance. In addition, the collection unit has a function to analyze email metadata (sender, recipient, subject, date and time, etc.) and automatically determine the priority and category of emails. This allows the collection unit to provide basic data for appropriate processing based on the content of the emails.

[0066] The analysis unit analyzes emails collected by the collection unit. Using generative AI, the analysis unit analyzes the content of emails and classifies them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. Specifically, the generative AI uses natural language processing technology to analyze the email body and understand the content of each email. For example, the generative AI analyzes keywords and context to identify emails containing important business instructions. It can also automatically classify emails containing customer communications or inquiries. Furthermore, the generative AI has the function of summarizing email content and extracting key points. This saves users the trouble of reading long emails and allows them to quickly grasp only the essentials. The analysis unit automatically generates summaries for each email based on the generative AI's analysis. For example, the generative AI extracts important information from the email body and generates a concise summary. This allows users to quickly understand the email content and take necessary action. Furthermore, the analysis unit can learn from past email data to perform more accurate analyses. This allows the analysis unit to provide highly accurate analysis based on the latest information at all times, thereby improving the user's work efficiency.

[0067] The listing unit creates a list of tasks based on the results analyzed by the analysis unit. The listing unit can also use AI to create task lists. Specifically, the listing unit automatically creates a list of tasks to be performed based on the classification results of emails provided by the analysis unit. For example, it automatically creates a list of tasks to be performed based on important work instructions or communications from customers. The listing unit can efficiently manage tasks by considering their priority and deadlines. Furthermore, the listing unit also has a function to track task progress in real time and provide appropriate reminders to the user. This allows the user to always be aware of task progress and complete tasks within the deadline. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions or communications from customers. Specifically, the listing unit automatically identifies and lists the content and priority of tasks based on the classification results of emails provided by the analysis unit. This allows the user to quickly grasp the overall picture of tasks and proceed with work efficiently. Furthermore, the listing unit also has a function to track task progress in real time and provide appropriate reminders to the user. This allows users to stay informed about task progress and complete tasks within deadlines.

[0068] The Management Department manages tasks listed by the Listing Department. The Management Department can also use AI to manage tasks. Specifically, the Management Department centrally manages the progress of tasks based on the task list provided by the Listing Department. For example, the Management Department provides a dashboard that allows for centralized management of summaries and execution lists. The dashboard is designed to make it easy for users to grasp the overall picture of tasks, and visually displays task priority, deadlines, and progress. This allows users to quickly grasp the progress of tasks and proceed with their work efficiently. Furthermore, the Management Department also has a function to track task progress in real time and provide users with appropriate reminders. This allows users to always know the progress of tasks and complete tasks within the deadline. The Management Department can also use AI to manage tasks. The AI ​​analyzes the progress and priority of tasks and supports efficient task management. For example, the AI ​​provides users with appropriate reminders based on the progress of tasks to encourage task completion. In addition, the AI ​​learns from past task data and analyzes users' work patterns and trends to achieve more efficient task management. This allows the management department to improve users' work efficiency and increase task completion rates.

[0069] The analysis unit can analyze the content of emails and classify them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. For example, the analysis unit can use a generation AI to analyze the content of emails and automatically generate summaries for each email. The generation AI analyzes the content of emails and classifies them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. This allows for efficient classification of email content. Specific criteria and methods for classification include, for example, classification categories and classification algorithms. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the content of emails into the generation AI, which then analyzes the content and outputs the classification results.

[0070] The listing unit can automatically create a list of tasks to be performed based on important work instructions and communications from customers. For example, the listing unit automatically creates a list of tasks to be performed based on important work instructions and communications from customers. The listing unit can also use AI to create task lists. This allows for the automatic creation of a list of tasks to be performed. Specific methods and criteria for automatic creation include, for example, the algorithm used and the timing of creation. Some or all of the above-described processes in the listing unit may be performed using AI or not. For example, the listing unit inputs important work instructions and communications from customers into a generating AI, which then automatically creates a list of tasks.

[0071] The listing unit can set priorities and deadlines for each task, clarifying the action plan for the next day. The listing unit can also use AI to set task priorities and deadlines. This clarifies the action plan for the next day. Criteria and methods for setting priorities include, for example, evaluation criteria and setting methods for priorities. Criteria and methods for setting deadlines include, for example, evaluation criteria and setting methods for deadlines. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs each task into a generating AI, and the generating AI sets the priorities and deadlines.

[0072] The management department can provide a dashboard that allows for centralized management of summaries and execution lists. For example, the management department can provide a dashboard that allows for centralized management of summaries and execution lists. The management department can also manage the dashboard using AI. This allows for centralized management of summaries and execution lists. The specific functions and display content of the dashboard include, for example, the types of information to display and the design of the interface. Some or all of the above-mentioned processes in the management department may be performed using AI or not. For example, the management department inputs summaries and execution lists into a generating AI, and the generating AI provides the dashboard.

[0073] The management department can set reminder notifications for important emails and tasks, and these notifications can be received via email or chat tools. The management department can also use AI to set reminder notifications. This allows for setting reminder notifications for important emails and tasks. The method and means of setting reminder notifications include, for example, the timing of notifications and the notification method (email, chat tool, etc.). Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs important emails and tasks into a generating AI, which then sets reminder notifications.

[0074] The management department can share an email summary and an action list as a daily report with their supervisor and team at the end of the day. For example, the management department can share an email summary and an action list as a daily report with their supervisor and team at the end of the day. The management department can also use AI to share the daily report. This allows the email summary and action list to be shared as a daily report. The specific content and sharing method of the daily report include, for example, the information to be included in the daily report and the means of sharing. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs the email summary and action list into a generating AI, the generating AI creates a daily report, and shares it with their supervisor and team.

[0075] 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 can delay email collection until the user is relaxed. If the user is focused, the collection unit can also collect all emails at once at the end of the workday. If the user is tired, the collection unit can collect emails the following morning. This allows the timing of email collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Methods and criteria for adjusting collection timing include, for example, collection frequency and timing evaluation criteria. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs user emotion data into the generative AI, and the generative AI adjusts the collection timing.

[0076] The collection unit can analyze the user's past email collection history and select the optimal collection method. For example, the collection unit can suggest the optimal collection time based on the time slots the user frequently collected in the past. The collection unit can also prioritize suggesting collection methods the user has used in the past (manual, automatic, etc.). The collection unit can also suggest the optimal collection method for specific days of the week or time slots based on the user's past collection history. This allows for the selection of the optimal collection method based on past collection history. The selection criteria and specific methods for the collection method include, for example, the collection algorithm and selection criteria. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs the user's past collection history data into a generating AI, and the generating AI selects the optimal collection method.

[0077] The collection unit can filter emails based on the user's current projects and areas of interest. For example, the collection unit can prioritize collecting emails related to projects the user is currently working on. The collection unit can also filter and collect relevant emails based on the user's areas of interest. The collection unit can also filter and collect important emails based on keywords set by the user. This allows emails to be filtered based on projects and areas of interest. Specific filtering methods and criteria include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input data on the user's projects and areas of interest into a generating AI, which then performs the filtering.

[0078] 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 may postpone collecting less important emails. If the user is relaxed, the collection unit may collect all emails at once. If the user is in a hurry, the collection unit may prioritize collecting important emails. This allows for the prioritization of emails according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Criteria and methods for determining priority include, for example, evaluation criteria and determination methods for priority. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit inputs user emotion data into a generative AI, and the generative AI determines the priority.

[0079] 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 on a business trip, the collection unit will prioritize collecting emails related to the business trip location. If the user is in the office, the collection unit can also prioritize collecting office-related emails. If the user is at home, the collection unit can also prioritize collecting home-related emails. This allows for the priority collection of highly relevant emails based on geographical location information. The method of acquiring and using geographical location information includes, for example, the means of acquiring location information and the algorithm used. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's geographical location information into a generating AI, and the generating AI will prioritize collecting highly relevant emails.

[0080] 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. The collection unit can also prioritize collecting emails related to accounts the user follows. The collection unit can also prioritize collecting emails related to groups or events the user participates in. This allows for the collection of relevant emails based on social media activity. The methods and criteria for analyzing social media activity include, for example, the data used and the analysis algorithms. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the user's social media activity data into a generating AI, which then collects relevant emails.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results. If the user is stressed, the analysis unit can also provide visually easy-to-understand analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The criteria and specific methods for adjusting the presentation of the analysis include, for example, the format of the presentation and the adjustment method. 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 inputs user emotion data into the generative AI, and the generative AI adjusts the presentation of the analysis.

[0082] The analysis unit can adjust the level of detail in its analysis based on the importance of the emails. For example, the analysis unit can perform a detailed analysis for important work instructions. It can also perform a concise analysis for advertising and promotional materials. It can also perform a moderate level of detail analysis for company-wide announcements. This allows the level of detail in the analysis to be adjusted based on the importance of the emails. The criteria and specific methods for adjusting the level of detail in the analysis include, for example, the criteria for evaluating the level of detail and the adjustment method. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs email importance data into a generating AI, and the generating AI adjusts the level of detail in the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, the analysis unit can apply a task extraction algorithm to business instruction emails. The analysis unit can also apply a customer response algorithm to customer contact emails. The analysis unit can also apply an information summarization algorithm to reference information emails. This allows the appropriate analysis algorithm to be applied according to the email category. Specific types of analysis algorithms and application methods include, for example, the type of algorithm used and the application criteria. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email category data into a generation AI, and the generation AI applies an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can perform a short, concise analysis. If the user is relaxed, the analysis unit can also perform a detailed analysis. If the user is stressed, the analysis unit can also perform a visually easy-to-understand analysis. This allows the length of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The criteria and specific methods for adjusting the length of the analysis include, for example, length evaluation criteria and adjustment methods. Some or all of the above processing in the analysis unit may be performed using a generative AI or not. For example, the analysis unit inputs user emotion data into a generative AI, and the generative AI adjusts the length of the analysis.

[0085] The analysis unit can determine the priority of analysis based on when the emails were received. For example, the analysis unit may prioritize the analysis of the most recent emails. The analysis unit may also prioritize the analysis of important business instruction emails. The analysis unit may also prioritize the analysis of customer communication emails. This allows the analysis priority to be determined based on when the emails were received. The criteria for evaluating the reception time and the method for determining the analysis priority include, for example, the criteria for evaluating the reception time and the method for determining the priority. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email reception time data into a generation AI, and the generation AI determines the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit might analyze important business instruction emails first. It could also analyze customer contact emails next. It could also analyze promotional emails last. This allows the order of analysis to be adjusted based on the relevance of the emails. Criteria for evaluating relevance and methods for adjusting the order of analysis include, for example, criteria for evaluating relevance and methods for adjusting the order. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs email relevance data into a generation AI, and the generation AI adjusts the order of analysis.

[0087] The listing unit can estimate the user's emotions and adjust the listing method based on the estimated emotions. For example, if the user is relaxed, the listing unit will create a detailed list. If the user is in a hurry, the listing unit can also create a concise list. If the user is stressed, the listing unit can also create a visually easy-to-understand list. This allows the listing method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The criteria and specific methods for adjusting the listing method include, for example, the format of the listing and the adjustment method. Some or all of the above processing in the listing unit may be performed using a generative AI or not. For example, the listing unit inputs user emotion data into a generative AI, and the generative AI adjusts the listing method.

[0088] The listing unit can adjust the level of detail in the list based on the importance of the tasks during the listing process. For example, the listing unit can add detailed descriptions to important tasks. It can also add concise descriptions to low-priority tasks. It can also add descriptions of moderate importance to tasks with a moderate level of detail. This allows the level of detail in the list to be adjusted based on the importance of the tasks. The criteria and specific methods for adjusting the level of detail in the list include, for example, the criteria for evaluating the level of detail and the adjustment method. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task importance data into a generating AI, and the generating AI adjusts the level of detail in the list.

[0089] The listing unit can apply different listing algorithms depending on the task category during the listing process. For example, the listing unit can apply a detailed listing algorithm to work instruction tasks. The listing unit can also apply a customer service listing algorithm to customer service tasks. The listing unit can also apply an information summary listing algorithm to reference information tasks. This allows for the application of an appropriate listing algorithm depending on the task category. Specific types and application methods of the listing algorithms include, for example, the type of algorithm used and the application criteria. Some or all of the above-described processes in the listing unit may be performed using AI or not. For example, the listing unit inputs task category data into a generating AI, and the generating AI applies an appropriate listing algorithm.

[0090] The listing unit can estimate the user's emotions and determine the priority of the listing based on the estimated emotions. For example, if the user is stressed, the listing unit will postpone less important tasks. If the user is relaxed, the listing unit can list all tasks at once. If the user is in a hurry, the listing unit can prioritize listing important tasks. This allows the listing priority to be determined according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. The criteria and methods for determining the listing priority include, for example, the evaluation criteria and determination method for priority. Some or all of the above processing in the listing unit may be performed using a generative AI or not. For example, the listing unit inputs user emotion data into a generative AI, and the generative AI determines the listing priority.

[0091] The listing unit can adjust the order of tasks in the list based on their deadlines. For example, the listing unit can prioritize listing tasks with approaching deadlines. The listing unit can also postpone long-term tasks. The listing unit can also list tasks with medium deadlines with medium priority. This allows the order of the list to be adjusted based on the task deadlines. The criteria and specific methods for adjusting the order of the list include, for example, evaluation criteria and adjustment methods for the order. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task deadline data into a generating AI, and the generating AI adjusts the order of the list.

[0092] The listing unit can adjust how the list is displayed based on the relevance of the tasks during the listing process. For example, the listing unit can display important tasks first. It can also display customer service tasks next. It can also display advertising tasks last. This allows the listing unit to adjust how the list is displayed based on the relevance of the tasks. The criteria and specific methods for adjusting how the list is displayed include, for example, the display format and adjustment method. Some or all of the above processing in the listing unit may be performed using AI or not. For example, the listing unit inputs task relevance data into a generating AI, and the generating AI adjusts how the list is displayed.

[0093] The management unit can estimate the user's emotions and adjust the management method based on the estimated emotions. For example, if the user is relaxed, the management unit can provide a detailed management method. If the user is in a hurry, the management unit can also provide a concise management method. If the user is stressed, the management unit can also provide a visually easy-to-understand management method. This allows the management method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. The criteria and specific methods for adjusting the management method include, for example, the format of management and the adjustment method. Some or all of the above processing in the management unit may be performed using generative AI or not using generative AI. For example, the management unit inputs user emotion data into the generative AI, and the generative AI adjusts the management method.

[0094] The management department can optimize management algorithms by referring to past management data during management. For example, the management department can propose the optimal management method based on past management data. The management department can also derive efficient management algorithms from past management data. The management department can also analyze past management data to improve the accuracy of management. This allows for the optimization of management algorithms based on past management data. The optimization criteria and specific methods for management algorithms include, for example, the optimization criteria and the algorithms used. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs past management data into a generating AI, and the generating AI optimizes the management algorithm.

[0095] The management department can customize management methods based on the progress of tasks during management. For example, the management department can adjust management methods according to the progress of tasks. The management department can also monitor task progress in real time and optimize management methods. The management department can also set reminder notifications based on task progress. This allows for customization of management methods based on task progress. The criteria and specific methods for customizing management methods include, for example, the criteria for customization and the type of method. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department inputs task progress data into a generating AI, and the generating AI customizes the management methods.

[0096] The management department can estimate the user's emotions and determine management priorities based on those estimated emotions. For example, if the user is stressed, the management department may postpone less important tasks. If the user is relaxed, the management department may manage all tasks at once. If the user is in a hurry, the management department may prioritize important tasks. This allows for the determination of management priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Criteria and methods for determining management priorities include, for example, evaluation criteria and determination methods for prioritizing. Some or all of the above processes in the management department may be performed using generative AI or not. For example, the management department inputs user emotion data into a generative AI, and the generative AI determines the management priorities.

[0097] The management department can select the optimal management method when managing tasks, taking into account their geographical distribution. For example, if tasks are scattered across multiple locations, the management department can group geographically close tasks together for management. If tasks are concentrated in a specific region, the management department can also provide a management method tailored to that region. If tasks are internationally distributed, the management department can also manage them while considering the time zones of each region. This allows for the selection of the optimal management method based on the geographical distribution of tasks. Criteria for evaluating geographical distribution and selecting management methods include, for example, distribution evaluation criteria and management method selection criteria. Some or all of the above processes in the management department may be performed using AI, or they may not. For example, the management department can input geographical distribution data of tasks into a generating AI, and the generating AI can select the optimal management method.

[0098] The management department can improve the accuracy of its management by referring to relevant literature for tasks during the management process. For example, the management department can automatically collect relevant literature for tasks and use it to aid in management. The management department can also optimize its management methods by referring to relevant literature according to the progress of tasks. The management department can also improve the accuracy of its management by referring to relevant literature based on the content of tasks. This improves the accuracy of management by referring to relevant literature for tasks. Criteria for referring to relevant literature and improving the accuracy of management include, for example, the type of literature and the method of reference. Some or all of the above processes in the management department may be performed using AI or not. For example, the management department can input relevant literature data for tasks into a generating AI, and the generating AI can improve the accuracy of management.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The email management system can also be equipped with the ability to estimate the user's emotions and dynamically change the priority of emails based on those emotions. For example, if the user is stressed, less important emails can be postponed, while if they are relaxed, all emails can be processed in a batch. Conversely, if the user is in a hurry, important emails can be prioritized. This allows for dynamic changes in email priority according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Criteria and methods for changing priority include, for example, priority evaluation criteria and methods for changing priority. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system could input user emotion data into a generative AI, which would then dynamically change the priority.

[0101] The email management system can also include a function to analyze the user's past email processing history and suggest the optimal email processing method. For example, it can suggest the optimal processing time based on the time slots the user frequently processed emails in the past. It can also prioritize suggesting processing methods the user has used in the past (manual, automatic, etc.). It can also suggest the optimal processing method for specific days of the week or time slots based on the user's past processing history. This allows for the suggestion of the optimal processing method based on past processing history. The selection criteria and specific methods for processing methods include, for example, processing algorithms and selection criteria. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs the user's past processing history data into a generating AI, and the generating AI suggests the optimal processing method.

[0102] The email management system can further estimate the user's emotions and adjust how it summarizes the email content based on those emotions. For example, if the user is relaxed, it can provide a detailed summary. If the user is in a hurry, it can provide a concise summary. If the user is stressed, it can provide a visually easy-to-understand summary. This allows the summarization method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. The criteria and specific methods for adjusting the summarization method include, for example, the format of the summary and the adjustment method. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs user emotion data into a generative AI, and the generative AI adjusts the summarization method.

[0103] The email management system can also include a function to prioritize the processing of highly relevant emails by taking into account the user's geographical location. For example, if the user is on a business trip, emails related to the business trip destination can be prioritized. If the user is in the office, emails related to the office can be prioritized. If the user is at home, emails related to home can be prioritized. This allows for the prioritization of highly relevant emails based on geographical location. The method of acquiring and using geographical location information includes, for example, the means of acquiring location information and the algorithms used. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system could input the user's geographical location information into a generating AI, which would then prioritize the processing of highly relevant emails.

[0104] The email management system can further estimate the user's emotions and adjust the task listing method based on the estimated emotions. For example, if the user is relaxed, a detailed list may be created. If the user is in a hurry, a concise list may be created. If the user is stressed, a visually easy-to-understand list may be created. This allows the listing method to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. The criteria and specific methods for adjusting the listing method include, for example, the format of the list and the adjustment method. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs user emotion data into a generative AI, and the generative AI adjusts the listing method.

[0105] The email management system can further analyze the user's social media activity and prioritize the processing of relevant emails. For example, it can prioritize emails related to topics the user has mentioned on social media. It can also prioritize emails related to accounts the user follows. It can also prioritize emails related to groups or events the user participates in. This allows for the prioritization of relevant emails based on social media activity. The methods and criteria for analyzing social media activity include, for example, the data used and the analysis algorithms. Some or all of the processing described above in the email management system may be performed using AI or not. For example, the email management system could input the user's social media activity data into a generating AI, which would then prioritize the processing of relevant emails.

[0106] The email management system can also include a function to estimate the user's emotions and determine task priorities based on those emotions. For example, if the user is stressed, less important tasks can be postponed. If the user is relaxed, all tasks can be processed at once. If the user is in a hurry, important tasks can be prioritized. This allows tasks to be prioritized according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Criteria and methods for determining priorities include, for example, evaluation criteria and determination methods for prioritizing. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs user emotion data into a generative AI, and the generative AI determines the priorities.

[0107] The email management system can also include a function to filter emails based on the user's current projects and areas of interest. For example, it can prioritize emails related to projects the user is currently working on. It can also filter and process relevant emails based on the user's areas of interest. It can also filter and process important emails based on keywords set by the user. This allows emails to be filtered based on projects and areas of interest. Specific methods and criteria for filtering include, for example, filtering conditions and the algorithms used. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system can input data on the user's projects and areas of interest into a generating AI, which then performs the filtering.

[0108] The email management system can also include a function to estimate the user's emotions and adjust the timing of email collection based on those emotions. For example, if the user is stressed, email collection can be delayed until they are relaxed. If the user is focused, emails can be collected all at once at the end of the workday. If the user is tired, emails can be collected the next morning. This allows the timing of email collection to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is not limited to, but may include, text generation AI or multimodal generation AI. Methods and criteria for adjusting collection timing include, for example, collection frequency and timing evaluation criteria. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs user emotion data into a generative AI, which then adjusts the collection timing.

[0109] The email management system can further estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results. If the user is stressed, it can provide visually easy-to-understand analysis results. This allows the presentation of the analysis to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. The criteria and specific methods for adjusting the presentation of the analysis include, for example, the format of the presentation and the method of adjustment. Some or all of the above processing in the email management system may be performed using AI or not. For example, the email management system inputs user emotion data into a generative AI, and the generative AI adjusts the presentation of the analysis.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects emails. For example, it automatically collects all emails that have arrived in the inbox at the end of the workday. The collection unit can also use AI to collect emails. Step 2: The analysis unit analyzes the emails collected by the collection unit. The analysis unit uses a generation AI to analyze the content of the emails and classify them into categories such as important business instructions, customer communications, reference information, company-wide announcements, and advertisements. For example, the generation AI analyzes the content of the emails and automatically generates a summary of each email. Step 3: The listing unit creates a list of tasks based on the results analyzed by the analysis unit. The listing unit can also use AI to create task lists. For example, it can automatically create a list of tasks to be performed based on important work instructions or communications from customers. Step 4: The management department manages the tasks listed by the listing department. The management department can also use AI to manage tasks. For example, it can provide a dashboard that allows for centralized management of summaries and action lists.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and management unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically collects all emails received in the inbox at the end of the workday. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generation AI to analyze the content of emails and classify them into important work instructions, customer communications, etc. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists tasks based on the analysis results. The management unit is implemented by the control unit 46A of the smart device 14 and provides a dashboard that allows for centralized management of summaries and execution lists. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and management unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically collects all emails received in the inbox at the end of the workday. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generation AI to analyze the content of emails and classify them into important work instructions, customer communications, etc. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists tasks based on the analysis results. The management unit is implemented by the control unit 46A of the smart glasses 214 and provides a dashboard that allows for centralized management of summaries and execution lists. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[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 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.

[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 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.

[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 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.

[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 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.

[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 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and management unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically collects all emails received in the inbox at the end of the workday. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the content of emails and classify them into important work instructions, customer communications, etc. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists tasks based on the analysis results. The management unit is implemented by the control unit 46A of the headset terminal 314 and provides a dashboard that allows for centralized management of summaries and execution lists. 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] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[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 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.

[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 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).

[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] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, listing unit, and management 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 control unit 46A of the robot 414 and automatically collects all emails that have arrived in the inbox at the end of the workday. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses a generation AI to analyze the content of emails and classify them into important work instructions, customer communications, etc. The listing unit is implemented by the identification processing unit 290 of the data processing unit 12 and lists tasks based on the analysis results. The management unit is implemented by the control unit 46A of the robot 414 and provides a dashboard that allows for centralized management of summaries and execution lists. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The collection department that collects emails, An analysis unit analyzes the emails collected by the aforementioned collection unit, A listing unit that lists tasks based on the results analyzed by the aforementioned analysis unit, The system comprises a management unit that manages the tasks listed by the listing unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The content of emails is analyzed and categorized into important work instructions, customer communications, reference information, company-wide announcements, and advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The listing unit, Automatically create a list of tasks to be performed based on important work instructions and customer communications. The system described in Appendix 1, characterized by the features described herein. (Note 4) The listing unit, Set priorities and deadlines for each task, and clearly define your action plan for the next day. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned management department, Provides a dashboard that allows for centralized management of summaries and action lists. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned management department, You can set up reminder notifications for important emails and tasks, and receive notifications via email or chat tools. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned management department, At the end of the day, share an email summary and a to-do list with your boss and team as a daily report. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned collection unit is Analyze the user's past email collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) 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 11) 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 12) 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 13) 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 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the email was received. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 20) The listing unit, It estimates the user's sentiment and adjusts the listing method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The listing unit, When creating a list, adjust the level of detail in the list based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 22) The listing unit, When creating lists, different listing algorithms are applied depending on the task category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The listing unit, The system estimates the user's emotions and determines the priority of the list based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The listing unit, When creating a list, adjust the order of the list based on the task deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 25) The listing unit, When creating a list, adjust how the list is displayed based on the relevance of the tasks. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned management department, It estimates user sentiment and adjusts management methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned management department, During management, the management algorithm is optimized by referring to past management data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned management department, During management, customize management methods based on the progress of tasks. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned management department, It estimates user sentiment and determines management priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned management department, When managing tasks, select the optimal management method considering the geographical distribution of those tasks. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned management department, During management, refer to relevant literature for the task to improve the accuracy of management. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 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 department that collects emails, An analysis unit analyzes the emails collected by the aforementioned collection unit, A listing unit that lists tasks based on the results analyzed by the aforementioned analysis unit, The system comprises a management unit that manages the tasks listed by the listing unit. A system characterized by the following features.

2. The aforementioned analysis unit, The content of emails is analyzed and categorized into important work instructions, customer communications, reference information, company-wide announcements, and advertisements. The system according to feature 1.

3. The listing unit, Automatically create a list of tasks to be performed based on important work instructions and customer communications. The system according to feature 1.

4. The listing unit, Set priorities and deadlines for each task, and clearly define your action plan for the next day. The system according to feature 1.

5. The aforementioned management department, Provides a dashboard that allows for centralized management of summaries and action lists. The system according to feature 1.

6. The aforementioned management department, You can set up reminder notifications for important emails and tasks, and receive notifications via email or chat tools. The system according to feature 1.

7. The aforementioned management department, At the end of the day, share an email summary and a to-do list with your boss and team as a daily report. The system according to feature 1.

8. 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 according to feature 1.

9. The aforementioned collection unit is Analyze the user's past email collection history and select the optimal collection method. The system according to feature 1.

10. The aforementioned collection unit is When collecting emails, filter them based on the user's current projects and areas of interest. The system according to feature 1.

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