system

Abstract

The system according to this embodiment aims to efficiently extract and organize tasks from the content of emails. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, an extraction unit, a creation unit, and a presentation unit. The reading unit reads the contents of the email. The analysis unit analyzes the contents read by the reading unit. The extraction unit extracts tasks from the contents analyzed by the analysis unit. The creation unit creates a task list based on the tasks extracted by the extraction unit. The presentation unit presents the task list created by the creation unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to efficiently extract and organize tasks from the content of emails.

[0005] The system according to the embodiment aims to efficiently extract and organize tasks from the content of emails.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, an extraction unit, a creation unit, and a presentation unit. The reading unit reads the contents of an email. The analysis unit analyzes the contents read by the reading unit. The extraction unit extracts tasks from the contents analyzed by the analysis unit. The creation unit creates a task list based on the tasks extracted by the extraction unit. The presentation unit presents the task list created by the creation unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently extract and organize tasks from the content of emails. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that reads emails, organizes tasks, and presents a simple summary of tasks. The AI ​​agent system reads emails, analyzes the text, extracts necessary tasks, creates a task list, and presents it to the user. This system allows the user to instantly grasp the content of emails and efficiently organize tasks. For example, when reading an email, the AI ​​agent system analyzes the content of the email in detail and extracts important information. For example, it identifies information important to the user, such as meeting schedules and tasks with deadlines. Next, the AI ​​agent system extracts the necessary tasks from the analyzed text. The AI ​​agent creates a task list based on the extracted information. For example, if a meeting is scheduled, it adds information such as the date, time, location, and participants of the meeting to the task list. Finally, the AI ​​agent system presents the created task list to the user. By checking the task list, the user can instantly grasp the content of emails and efficiently organize tasks. For example, by checking the task list, the user can prepare for meetings and decide on task priorities. This system allows the user to instantly grasp the content of emails and efficiently organize tasks. This minimizes administrative time, allowing users to dedicate more time to creative work. It also enables the AI ​​agent system to efficiently organize user emails and quickly grasp tasks.

[0029] The AI ​​agent system according to this embodiment comprises a reading unit, an analysis unit, an extraction unit, a creation unit, and a presentation unit. The reading unit reads the content of an email. The reading unit reads the content of an email using, for example, text analysis technology. The reading unit can also convert an image-formatted email into text data using OCR technology. For example, the reading unit analyzes the body of the email and extracts important information. The analysis unit analyzes the content read by the reading unit. The analysis unit analyzes the content of the email using, for example, natural language processing technology. The analysis unit can also identify important information using keyword extraction technology. For example, the analysis unit analyzes the content of the email and identifies meeting schedules, tasks with deadlines, etc. The extraction unit extracts tasks from the content analyzed by the analysis unit. The extraction unit extracts tasks based on, for example, importance or deadlines. The extraction unit can also extract tasks based on highly relevant information. For example, the extraction unit extracts meeting schedules and tasks with deadlines and creates a task list. The creation unit creates a task list based on the tasks extracted by the extraction unit. The creation unit organizes the tasks in a list format, for example. The creation unit can also set task priorities. For example, the creation unit sets task priorities based on importance and deadlines and creates a task list. The presentation unit presents the task list created by the creation unit to the user. For example, the presentation unit displays the task list on the screen. The presentation unit can also notify the user of the task list using a notification function. For example, the presentation unit displays the task list as a pop-up to notify the user. As a result, the AI ​​agent system according to the embodiment can efficiently read and analyze the contents of emails, extract tasks, create task lists, and present them to the user.

[0030] The reading unit reads the content of emails. For example, it uses text analysis technology to read the email content. Specifically, it utilizes natural language processing technology to analyze the grammatical structure and meaning of the email body and extract important information. The reading unit can also convert image-formatted emails into text data using OCR technology. For example, it converts the content of scanned documents or emails sent as images into text data using OCR technology, and then performs text analysis. This allows for accurate reading of the content even in image-formatted emails. Furthermore, the reading unit can analyze not only the email body but also the content of attached files. For example, it can open attached files such as PDFs and Word documents, extract their content as text data, and perform analysis. This allows for a comprehensive understanding of the entire email. By combining these technologies, the reading unit can efficiently and accurately read the content of emails.

[0031] The analysis unit analyzes the content read by the reading unit. For example, the analysis unit analyzes the content of the email using natural language processing techniques. Specifically, it performs morphological and grammatical analysis to understand the structure of the email body. The analysis unit can also identify important information using keyword extraction techniques. For example, it detects specific keywords and phrases to extract important information such as meeting schedules or tasks with deadlines from the email content. Furthermore, the analysis unit performs contextual analysis to understand the intent and purpose of the email content. This allows it to grasp the meaning of the entire email and accurately identify important information, rather than simply extracting keywords. The analysis unit utilizes these techniques to analyze the content of the email in detail and extract information that is important to the user.

[0032] The extraction unit extracts tasks from the information analyzed by the analysis unit. For example, the extraction unit extracts tasks based on importance or deadlines. Specifically, it evaluates the importance and deadlines of each task and prioritizes them based on meeting schedules and tasks with deadlines identified by the analysis unit. The extraction unit can also extract tasks based on highly relevant information. For example, it can extract common tasks from multiple emails related to the same project and combine them into a single task list. Furthermore, the extraction unit can dynamically adjust task priorities by referring to the user's past activity history and task completion status. This helps users focus on the most important tasks. Through these functions, the extraction unit efficiently extracts the most important tasks for the user and creates a task list.

[0033] The creation unit creates a task list based on the tasks extracted by the extraction unit. The creation unit organizes tasks in a list format, for example. Specifically, it classifies the extracted tasks based on importance and deadlines and displays them in a visually easy-to-understand list format. The creation unit can also set task priorities. For example, it can set task priorities based on importance and deadlines, allowing users to tackle the most important tasks first. Furthermore, the creation unit includes a function to manage task progress. For example, it can automatically remove tasks from the list upon completion and update progress, ensuring users always have access to the latest task list. Through these functions, the creation unit supports users in efficiently managing their tasks.

[0034] The presentation unit presents the task list created by the creation unit to the user. For example, the presentation unit displays the task list on the screen. Specifically, it displays the task list on the user's device screen, providing it in a visually easy-to-understand format. The presentation unit can also notify the user of the task list using a notification function. For example, it notifies the user via pop-up notifications or audio notifications when the task list is updated or new tasks are added. Furthermore, the presentation unit provides customizable display options according to the user's preferences. For example, it provides a more user-friendly interface by allowing the user to select the display format of the task list and the notification method. Through these functions, the presentation unit supports the user in always checking the latest task list and efficiently managing tasks.

[0035] The creation unit can set the priority of tasks in the task list. For example, the creation unit can set the priority of tasks based on importance. For example, the creation unit can also set the priority of tasks based on deadlines. For example, the creation unit can also set the priority of tasks based on urgency. This allows important tasks to be processed preferentially by setting the priority of the task list. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can set the priority of tasks in the task list using an AI model that takes the importance and deadline of tasks as input and outputs a priority.

[0036] The notification unit can monitor the progress of tasks and send reminders as needed. For example, the notification unit can monitor the completion status of tasks. The notification unit can also monitor the progress of tasks. The notification unit can also send reminders based on the progress of tasks. This makes task management easier by monitoring task progress and sending reminders. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send reminders using an AI model that takes task progress as input and outputs reminders.

[0037] The reading unit can analyze the content of emails in detail and extract important information. For example, the reading unit can analyze the body of the email in detail. The reading unit can also analyze the content of emails using natural language processing techniques, for example. The reading unit can also identify important information using keyword extraction techniques, for example. This allows users to efficiently grasp important information by analyzing the content of emails in detail and extracting important information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can extract important information using an AI model that takes the body of an email as input and outputs important information.

[0038] The extraction unit can identify information that is important to the user, such as meeting schedules and tasks with deadlines. For example, the extraction unit can identify meeting schedules. For example, the extraction unit can also identify tasks with deadlines. For example, the extraction unit can identify tasks based on information that is important to the user. This allows users to manage important tasks without overlooking them by identifying information that is important to them. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can identify important information using an AI model that takes meeting schedules and tasks with deadlines as input and outputs important information.

[0039] The presentation unit can present a task list to the user, allowing them to instantly grasp the contents of an email. The presentation unit can, for example, display the task list on the screen. The presentation unit can also, for example, notify the user of the task list using a notification function. The presentation unit can also, for example, notify the user by displaying the task list as a pop-up. This allows the user to instantly grasp the contents of an email and efficiently organize tasks by presenting the task list to them. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can present the task list to the user using an AI model that takes the task list as input and outputs a display method.

[0040] The reading unit can analyze the user's past email reading history and select the optimal reading method. For example, the reading unit can analyze patterns of emails that the user has frequently opened in the past and prioritize reading similar emails. For example, if the user tends to read emails at a specific time of day, the reading unit can also read emails according to that time of day. For example, if the user prioritizes emails from a specific sender, the reading unit can also prioritize reading emails from that sender. In this way, the optimal reading method can be selected by analyzing the user's past email reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can select the optimal reading method using an AI model that takes the user's past email reading history as input and outputs the optimal reading method.

[0041] The reading unit can filter emails based on the user's current projects and areas of interest when reading them. For example, the reading unit can prioritize reading emails related to projects the user is currently working on. The reading unit can also filter and read emails containing keywords related to the user's areas of interest. For example, if the user has shown interest in a particular topic, the reading unit can prioritize reading emails related to that topic. This allows the reading unit to prioritize reading highly relevant emails by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can perform filtering using an AI model that takes the user's current projects and areas of interest as input and outputs the filtered results.

[0042] The reading unit can prioritize reading emails that are highly relevant based on the user's geographical location information when reading emails. For example, if the user is on a business trip, the reading unit will prioritize reading emails related to the destination. For example, if the user is in a specific location, the reading unit can also prioritize reading emails containing information related to that location. For example, if the user is traveling, the reading unit can also prioritize reading emails related to the travel destination. This allows the user to efficiently grasp information that is important to them by prioritizing the reading of highly relevant emails based on their geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read emails using an AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0043] The reading unit can analyze the user's social media activity when reading emails and read relevant emails. For example, the reading unit can prioritize reading emails related to topics the user has mentioned on social media. The reading unit can also prioritize reading emails from people the user follows on social media. The reading unit can also prioritize reading emails related to events the user has participated in on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize reading relevant emails. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read emails using an AI model that takes the user's social media activity as input and outputs 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 on high-importance emails. For example, the analysis unit can perform a simplified analysis on low-importance emails. For example, the analysis unit can perform a rapid analysis on urgent emails. This allows for detailed analysis of important emails by adjusting the level of detail based on the importance of the emails. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail using an AI model that takes the importance of an email as input and outputs the level of detail of 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 business analysis algorithm to business emails. For example, the analysis unit can also apply a private analysis algorithm to private emails. For example, the analysis unit can apply a spam filtering analysis algorithm to spam emails. By applying different analysis algorithms depending on the email category, the analysis unit can perform the most optimal analysis for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that takes the email category as input and outputs an 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 recently received emails. The analysis unit may also prioritize the analysis of emails of high urgency. The analysis unit may also prioritize the analysis of emails related to important events. This allows for the prioritization of high-urgency emails by determining the priority of analysis based on when the emails were received. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority using an AI model that takes the email reception date as input and outputs 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 may prioritize analyzing emails related to the user's current project. The analysis unit may also prioritize analyzing emails related to the user's areas of interest. The analysis unit may also prioritize analyzing emails from senders with whom the user has frequently exchanged emails in the past. By adjusting the order of analysis based on the relevance of the emails, highly relevant emails can be prioritized. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order using an AI model that takes the relevance of emails as input and outputs the order of analysis.

[0048] The extraction unit can improve the accuracy of its extraction by considering the interrelationships between emails during the extraction process. For example, the extraction unit can group related emails and extract tasks. The extraction unit can also analyze the history of email exchanges and extract related tasks. For example, the extraction unit can cross-reference the content of emails and extract related tasks. This improves the accuracy of the extraction by considering the interrelationships between emails. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can improve the accuracy of its extraction by using an AI model that takes the interrelationships between emails as input and outputs the extraction results.

[0049] The extraction unit can perform extraction while considering the attribute information of the email sender. For example, the extraction unit can prioritize the extraction of emails from important senders. For example, the extraction unit can also extract tasks based on the sender's job title or relationship. For example, the extraction unit can also extract tasks while considering the sender's past communication history. This allows for the priority extraction of emails from important senders by considering the attribute information of the email sender. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can perform extraction using an AI model that takes sender attribute information as input and outputs extraction results.

[0050] The extraction unit can perform extraction while considering the geographical distribution of emails. For example, if the user is on a business trip, the extraction unit will prioritize extracting tasks related to the business trip location. For example, if the user is in a specific location, the extraction unit can also prioritize extracting tasks related to that location. For example, if the user is traveling, the extraction unit can also prioritize extracting tasks related to the travel destination. In this way, relevant tasks can be prioritized by considering the geographical distribution of emails. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can perform extraction using an AI model that takes the geographical distribution of emails as input and outputs the extraction results.

[0051] The extraction unit can improve the accuracy of its extraction by referring to related literature in the email during the extraction process. For example, the extraction unit can refer to literature related to the email and extract tasks. The extraction unit can also, for example, compare the content of the email with related literature and extract tasks. The extraction unit can also, for example, cross-reference the content of the email with related literature and extract tasks. This improves the accuracy of the extraction by referring to related literature in the email. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can improve the accuracy of its extraction by using an AI model that takes related literature as input and outputs extraction results.

[0052] The creation unit can select the optimal creation method when creating a task list by referring to the user's past task management history. For example, the creation unit can create an optimal task list based on the format of task lists previously used by the user. The creation unit can also create an efficient task list by, for example, analyzing the user's past task management history. The creation unit can also, for example, prioritize suggesting the format of task lists that the user has preferred to use in the past. This allows the creation of an optimal task list by referring to the user's past task management history. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can select a creation method using an AI model that takes the user's past task management history as input and outputs the optimal creation method.

[0053] The creation unit can customize the contents of a task list based on the user's current project status when creating the task list. For example, the creation unit can prioritize adding tasks related to the user's current project to the task list. The creation unit can also add necessary tasks to the task list based on the user's project progress. For example, if the user is working on multiple projects simultaneously, the creation unit can add tasks related to each project to separate task lists. This allows the creation unit to prioritize listing relevant tasks by customizing the contents of the task list based on the user's current project status. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can customize the contents using an AI model that takes the user's project status as input and outputs the contents of the task list.

[0054] The task creation unit can create an optimal task list by considering the user's geographical location information when creating a task list. For example, if the user is on a business trip, the unit will prioritize listing tasks that should be done at the destination. For example, if the user is in a specific location, the unit can also list tasks that should be done at that location. For example, if the user is traveling, the unit can also list tasks that should be done at the travel destination. This allows the unit to prioritize listing relevant tasks by considering the user's geographical location information. Some or all of the above processing in the task creation unit may be performed using AI, for example, or without AI. For example, the task creation unit can create a task list using an AI model that takes the user's geographical location information as input and outputs an optimal task list.

[0055] The creation unit can analyze the user's social media activity and suggest task list content when creating a task list. For example, the creation unit can list tasks that the user has mentioned on social media. For example, the creation unit can list tasks from people the user follows on social media. For example, the creation unit can list tasks related to events the user is participating in on social media. This allows the creation unit to prioritize listing relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can suggest content using an AI model that takes the user's social media activity as input and outputs task list content.

[0056] The display unit can select the optimal display method when displaying the task list by referring to the user's past operation history. For example, the display unit can provide the optimal display method based on the display format the user has previously preferred to use. The display unit can also, for example, analyze the user's past operation history and provide an efficient display method. The display unit can also, for example, prioritize suggesting display formats the user has previously used. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0057] The display unit can customize the displayed content based on the user's current work status when displaying the task list. For example, the display unit can prioritize displaying tasks related to the work the user is currently working on. The display unit can also display necessary tasks based on the user's work progress. For example, if the user is working on multiple tasks simultaneously, the display unit can display tasks related to each task separately. This allows for the priority display of relevant tasks by customizing the displayed content based on the user's current work status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the content using an AI model that takes the user's work status as input and outputs the displayed content.

[0058] The display unit can select the optimal display method when displaying a task list, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

[0059] The presentation unit can analyze the user's social media activity and suggest content to display when displaying the task list. For example, the presentation unit can display tasks that the user has mentioned on social media. For example, the presentation unit can also display tasks from people the user follows on social media. For example, the presentation unit can display tasks related to events the user is participating in on social media. This allows relevant tasks to be displayed preferentially by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can suggest content using an AI model that takes the user's social media activity as input and outputs content to display.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The AI ​​agent system can also recognize user voice commands and add tasks by voice. For example, if a user says, "Add the next meeting preparation to the list," the system will recognize the instruction and add the meeting preparation task to the task list. The system can also read out today's tasks if the user says, "Read out today's tasks." Furthermore, if the user says, "Mark this task as complete," the system can mark that task as completed. This allows users to manage tasks using only their voice, without using their hands, improving work efficiency.

[0062] The AI ​​agent system can also retrieve the user's calendar information and link it to the task list. For example, it can automatically add information about meetings and events registered in the user's calendar to the task list. It can also analyze the calendar's free time and suggest suitable times for completing tasks. Furthermore, if a calendar event changes, the changes can be reflected in the task list. This allows users to centrally manage their calendar and task list, making schedule management easier.

[0063] The AI ​​agent system can further analyze a user's past task completion history and predict task completion. For example, it can analyze how long it took a user to complete tasks in the past and provide an estimated completion time for the current task. It can also predict the likelihood of completing the current task based on past task completion rates. Furthermore, it can analyze past task completion patterns and optimize task priorities. This allows users to manage tasks efficiently while referring to task completion predictions.

[0064] The AI ​​agent system can also acquire user health data and incorporate it into task management. For example, based on the user's sleep data, it can reduce the workload of tasks if the user is highly fatigued. It can also schedule tasks considering the refresh time after exercise based on the user's exercise data. Furthermore, it can adjust tasks considering the rest time after meals based on the user's meal data. This allows for the optimization of task management according to the user's health condition.

[0065] The AI ​​agent system can also be equipped with the ability to synchronize task lists across the user's devices. For example, tasks added by the user on their smartphone can be viewed on their PC or tablet. Furthermore, task progress can be synchronized in real time across devices. It can also provide an optimal display method for each device, allowing users to comfortably manage tasks on any device. This enables users to efficiently manage tasks while using multiple devices.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reading unit reads the content of the email. For example, it can read the content of the email using text analysis technology. It can also convert image-formatted emails into text data using OCR technology. Step 2: The analysis unit analyzes the content read by the reading unit. For example, it analyzes the content of the email using natural language processing technology and identifies important information using keyword extraction technology. Step 3: The extraction unit extracts tasks from the information analyzed by the analysis unit. For example, tasks can be extracted based on importance or deadline, or tasks can be extracted based on highly relevant information. Step 4: The creation unit creates a task list based on the tasks extracted by the extraction unit. For example, it organizes the tasks in a list format and sets task priorities based on importance and deadlines. Step 5: The presentation unit presents the task list created by the creation unit to the user. For example, it displays the task list on the screen and notifies the user of the task list using a notification function.

[0068] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that reads emails, organizes tasks, and presents a simple summary of tasks. The AI ​​agent system reads emails, analyzes the text, extracts necessary tasks, creates a task list, and presents it to the user. This system allows the user to instantly grasp the content of emails and efficiently organize tasks. For example, when reading an email, the AI ​​agent system analyzes the content of the email in detail and extracts important information. For example, it identifies information important to the user, such as meeting schedules and tasks with deadlines. Next, the AI ​​agent system extracts the necessary tasks from the analyzed text. The AI ​​agent creates a task list based on the extracted information. For example, if a meeting is scheduled, it adds information such as the date, time, location, and participants of the meeting to the task list. Finally, the AI ​​agent system presents the created task list to the user. By checking the task list, the user can instantly grasp the content of emails and efficiently organize tasks. For example, by checking the task list, the user can prepare for meetings and decide on task priorities. This system allows the user to instantly grasp the content of emails and efficiently organize tasks. This minimizes administrative time, allowing users to dedicate more time to creative work. It also enables the AI ​​agent system to efficiently organize user emails and quickly grasp tasks.

[0069] The AI ​​agent system according to this embodiment comprises a reading unit, an analysis unit, an extraction unit, a creation unit, and a presentation unit. The reading unit reads the content of an email. The reading unit reads the content of an email using, for example, text analysis technology. The reading unit can also convert an image-formatted email into text data using OCR technology. For example, the reading unit analyzes the body of the email and extracts important information. The analysis unit analyzes the content read by the reading unit. The analysis unit analyzes the content of the email using, for example, natural language processing technology. The analysis unit can also identify important information using keyword extraction technology. For example, the analysis unit analyzes the content of the email and identifies meeting schedules, tasks with deadlines, etc. The extraction unit extracts tasks from the content analyzed by the analysis unit. The extraction unit extracts tasks based on, for example, importance or deadlines. The extraction unit can also extract tasks based on highly relevant information. For example, the extraction unit extracts meeting schedules and tasks with deadlines and creates a task list. The creation unit creates a task list based on the tasks extracted by the extraction unit. The creation unit organizes the tasks in a list format, for example. The creation unit can also set task priorities. For example, the creation unit sets task priorities based on importance and deadlines and creates a task list. The presentation unit presents the task list created by the creation unit to the user. For example, the presentation unit displays the task list on the screen. The presentation unit can also notify the user of the task list using a notification function. For example, the presentation unit displays the task list as a pop-up to notify the user. As a result, the AI ​​agent system according to the embodiment can efficiently read and analyze the contents of emails, extract tasks, create task lists, and present them to the user.

[0070] The reading unit reads the content of emails. For example, it uses text analysis technology to read the email content. Specifically, it utilizes natural language processing technology to analyze the grammatical structure and meaning of the email body and extract important information. The reading unit can also convert image-formatted emails into text data using OCR technology. For example, it converts the content of scanned documents or emails sent as images into text data using OCR technology, and then performs text analysis. This allows for accurate reading of the content even in image-formatted emails. Furthermore, the reading unit can analyze not only the email body but also the content of attached files. For example, it can open attached files such as PDFs and Word documents, extract their content as text data, and perform analysis. This allows for a comprehensive understanding of the entire email. By combining these technologies, the reading unit can efficiently and accurately read the content of emails.

[0071] The analysis unit analyzes the content read by the reading unit. For example, the analysis unit analyzes the content of the email using natural language processing techniques. Specifically, it performs morphological and grammatical analysis to understand the structure of the email body. The analysis unit can also identify important information using keyword extraction techniques. For example, it detects specific keywords and phrases to extract important information such as meeting schedules or tasks with deadlines from the email content. Furthermore, the analysis unit performs contextual analysis to understand the intent and purpose of the email content. This allows it to grasp the meaning of the entire email and accurately identify important information, rather than simply extracting keywords. The analysis unit utilizes these techniques to analyze the content of the email in detail and extract information that is important to the user.

[0072] The extraction unit extracts tasks from the information analyzed by the analysis unit. For example, the extraction unit extracts tasks based on importance or deadlines. Specifically, it evaluates the importance and deadlines of each task and prioritizes them based on meeting schedules and tasks with deadlines identified by the analysis unit. The extraction unit can also extract tasks based on highly relevant information. For example, it can extract common tasks from multiple emails related to the same project and combine them into a single task list. Furthermore, the extraction unit can dynamically adjust task priorities by referring to the user's past activity history and task completion status. This helps users focus on the most important tasks. Through these functions, the extraction unit efficiently extracts the most important tasks for the user and creates a task list.

[0073] The creation unit creates a task list based on the tasks extracted by the extraction unit. The creation unit organizes tasks in a list format, for example. Specifically, it classifies the extracted tasks based on importance and deadlines and displays them in a visually easy-to-understand list format. The creation unit can also set task priorities. For example, it can set task priorities based on importance and deadlines, allowing users to tackle the most important tasks first. Furthermore, the creation unit includes a function to manage task progress. For example, it can automatically remove tasks from the list upon completion and update progress, ensuring users always have access to the latest task list. Through these functions, the creation unit supports users in efficiently managing their tasks.

[0074] The presentation unit presents the task list created by the creation unit to the user. For example, the presentation unit displays the task list on the screen. Specifically, it displays the task list on the user's device screen, providing it in a visually easy-to-understand format. The presentation unit can also notify the user of the task list using a notification function. For example, it notifies the user via pop-up notifications or audio notifications when the task list is updated or new tasks are added. Furthermore, the presentation unit provides customizable display options according to the user's preferences. For example, it provides a more user-friendly interface by allowing the user to select the display format of the task list and the notification method. Through these functions, the presentation unit supports the user in always checking the latest task list and efficiently managing tasks.

[0075] The creation unit can set the priority of tasks in the task list. For example, the creation unit can set the priority of tasks based on importance. For example, the creation unit can also set the priority of tasks based on deadlines. For example, the creation unit can also set the priority of tasks based on urgency. This allows important tasks to be processed preferentially by setting the priority of the task list. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can set the priority of tasks in the task list using an AI model that takes the importance and deadline of tasks as input and outputs a priority.

[0076] The notification unit can monitor the progress of tasks and send reminders as needed. For example, the notification unit can monitor the completion status of tasks. The notification unit can also monitor the progress of tasks. The notification unit can also send reminders based on the progress of tasks. This makes task management easier by monitoring task progress and sending reminders. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send reminders using an AI model that takes task progress as input and outputs reminders.

[0077] The reading unit can analyze the content of emails in detail and extract important information. For example, the reading unit can analyze the body of the email in detail. The reading unit can also analyze the content of emails using natural language processing techniques, for example. The reading unit can also identify important information using keyword extraction techniques, for example. This allows users to efficiently grasp important information by analyzing the content of emails in detail and extracting important information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can extract important information using an AI model that takes the body of an email as input and outputs important information.

[0078] The extraction unit can identify information that is important to the user, such as meeting schedules and tasks with deadlines. For example, the extraction unit can identify meeting schedules. For example, the extraction unit can also identify tasks with deadlines. For example, the extraction unit can identify tasks based on information that is important to the user. This allows users to manage important tasks without overlooking them by identifying information that is important to them. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can identify important information using an AI model that takes meeting schedules and tasks with deadlines as input and outputs important information.

[0079] The presentation unit can present a task list to the user, allowing them to instantly grasp the contents of an email. The presentation unit can, for example, display the task list on the screen. The presentation unit can also, for example, notify the user of the task list using a notification function. The presentation unit can also, for example, notify the user by displaying the task list as a pop-up. This allows the user to instantly grasp the contents of an email and efficiently organize tasks by presenting the task list to them. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can present the task list to the user using an AI model that takes the task list as input and outputs a display method.

[0080] The reading unit can estimate the user's emotions and adjust the timing of email reading based on the estimated emotions. For example, if the user is stressed, the reading unit can adjust the timing of email reading to a time when the user can relax. For example, if the user is concentrating, the reading unit can postpone reading emails so as not to interrupt their concentration. For example, if the user is in a hurry, the reading unit can read emails immediately and quickly provide important information. In this way, the user's burden can be reduced by adjusting the timing of email reading 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. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can adjust the timing of email reading using an AI model that takes user emotion data as input and outputs the reading timing.

[0081] The reading unit can analyze the user's past email reading history and select the optimal reading method. For example, the reading unit can analyze patterns of emails that the user has frequently opened in the past and prioritize reading similar emails. For example, if the user tends to read emails at a specific time of day, the reading unit can also read emails according to that time of day. For example, if the user prioritizes emails from a specific sender, the reading unit can also prioritize reading emails from that sender. In this way, the optimal reading method can be selected by analyzing the user's past email reading history. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can select the optimal reading method using an AI model that takes the user's past email reading history as input and outputs the optimal reading method.

[0082] The reading unit can filter emails based on the user's current projects and areas of interest when reading them. For example, the reading unit can prioritize reading emails related to projects the user is currently working on. The reading unit can also filter and read emails containing keywords related to the user's areas of interest. For example, if the user has shown interest in a particular topic, the reading unit can prioritize reading emails related to that topic. This allows the reading unit to prioritize reading highly relevant emails by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can perform filtering using an AI model that takes the user's current projects and areas of interest as input and outputs the filtered results.

[0083] The reading unit can estimate the user's emotions and determine the priority of emails to read based on the estimated emotions. For example, if the user is stressed, the reading unit may postpone reading less important emails. For example, if the user is relaxed, the reading unit may prioritize reading more important emails. For example, if the user is in a hurry, the reading unit may prioritize reading urgent emails. This allows important emails to be processed preferentially by determining the priority of emails to read 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. Some or all of the above processing in the reading unit may be performed using AI or not. For example, the reading unit can determine priorities using an AI model that takes user emotion data as input and outputs email priorities.

[0084] The reading unit can prioritize reading emails that are highly relevant based on the user's geographical location information when reading emails. For example, if the user is on a business trip, the reading unit will prioritize reading emails related to the destination. For example, if the user is in a specific location, the reading unit can also prioritize reading emails containing information related to that location. For example, if the user is traveling, the reading unit can also prioritize reading emails related to the travel destination. This allows the user to efficiently grasp information that is important to them by prioritizing the reading of highly relevant emails based on their geographical location information. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read emails using an AI model that takes the user's geographical location information as input and outputs highly relevant emails.

[0085] The reading unit can analyze the user's social media activity when reading emails and read relevant emails. For example, the reading unit can prioritize reading emails related to topics the user has mentioned on social media. The reading unit can also prioritize reading emails from people the user follows on social media. The reading unit can also prioritize reading emails related to events the user has participated in on social media. In this way, by analyzing the user's social media activity, it is possible to prioritize reading relevant emails. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can read emails using an AI model that takes the user's social media activity as input and outputs relevant emails.

[0086] 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 tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the presentation using an AI model that takes user emotion data as input and outputs a presentation of the analysis.

[0087] 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 on high-importance emails. For example, the analysis unit can perform a simplified analysis on low-importance emails. For example, the analysis unit can perform a rapid analysis on urgent emails. This allows for detailed analysis of important emails by adjusting the level of detail based on the importance of the emails. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail using an AI model that takes the importance of an email as input and outputs the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, the analysis unit can apply a business analysis algorithm to business emails. For example, the analysis unit can also apply a private analysis algorithm to private emails. For example, the analysis unit can apply a spam filtering analysis algorithm to spam emails. By applying different analysis algorithms depending on the email category, the analysis unit can perform the most optimal analysis for each category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an analysis algorithm using an AI model that takes the email category as input and outputs an analysis algorithm.

[0089] 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 provide a short, concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the length using an AI model that takes user emotion data as input and outputs the length of the analysis.

[0090] 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 recently received emails. The analysis unit may also prioritize the analysis of emails of high urgency. The analysis unit may also prioritize the analysis of emails related to important events. This allows for the prioritization of high-urgency emails by determining the priority of analysis based on when the emails were received. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority using an AI model that takes the email reception date as input and outputs the analysis priority.

[0091] 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 may prioritize analyzing emails related to the user's current project. The analysis unit may also prioritize analyzing emails related to the user's areas of interest. The analysis unit may also prioritize analyzing emails from senders with whom the user has frequently exchanged emails in the past. By adjusting the order of analysis based on the relevance of the emails, highly relevant emails can be prioritized. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order using an AI model that takes the relevance of emails as input and outputs the order of analysis.

[0092] The extraction unit can estimate the user's emotions and determine the priority of tasks to extract based on the estimated emotions. For example, if the user is stressed, the extraction unit may postpone less important tasks. For example, if the user is relaxed, the extraction unit may prioritize extracting more important tasks. For example, if the user is in a hurry, the extraction unit may prioritize extracting urgent tasks. This allows important tasks to be processed preferentially by determining the priority of tasks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can determine priorities using an AI model that takes user emotion data as input and outputs task priorities.

[0093] The extraction unit can improve the accuracy of its extraction by considering the interrelationships between emails during the extraction process. For example, the extraction unit can group related emails and extract tasks. The extraction unit can also analyze the history of email exchanges and extract related tasks. For example, the extraction unit can cross-reference the content of emails and extract related tasks. This improves the accuracy of the extraction by considering the interrelationships between emails. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can improve the accuracy of its extraction by using an AI model that takes the interrelationships between emails as input and outputs the extraction results.

[0094] The extraction unit can perform extraction while considering the attribute information of the email sender. For example, the extraction unit can prioritize the extraction of emails from important senders. For example, the extraction unit can also extract tasks based on the sender's job title or relationship. For example, the extraction unit can also extract tasks while considering the sender's past communication history. This allows for the priority extraction of emails from important senders by considering the attribute information of the email sender. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can perform extraction using an AI model that takes sender attribute information as input and outputs extraction results.

[0095] The extraction unit can estimate the user's emotions and adjust the display method of the extracted tasks based on the estimated user emotions. For example, if the user is nervous, the extraction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the extraction unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the extraction unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of tasks according to the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0096] The extraction unit can perform extraction while considering the geographical distribution of emails. For example, if the user is on a business trip, the extraction unit will prioritize extracting tasks related to the business trip location. For example, if the user is in a specific location, the extraction unit can also prioritize extracting tasks related to that location. For example, if the user is traveling, the extraction unit can also prioritize extracting tasks related to the travel destination. In this way, relevant tasks can be prioritized by considering the geographical distribution of emails. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can perform extraction using an AI model that takes the geographical distribution of emails as input and outputs the extraction results.

[0097] The extraction unit can improve the accuracy of its extraction by referring to related literature in the email during the extraction process. For example, the extraction unit can refer to literature related to the email and extract tasks. The extraction unit can also, for example, compare the content of the email with related literature and extract tasks. The extraction unit can also, for example, cross-reference the content of the email with related literature and extract tasks. This improves the accuracy of the extraction by referring to related literature in the email. Some or all of the above processing in the extraction unit may be performed using AI, for example, or without AI. For example, the extraction unit can improve the accuracy of its extraction by using an AI model that takes related literature as input and outputs extraction results.

[0098] The creation unit can estimate the user's emotions and adjust the task list creation method based on the estimated emotions. For example, if the user is stressed, the creation unit can create a simple and highly visual task list. If the user is relaxed, the creation unit can also create a task list with detailed information. If the user is in a hurry, the creation unit can also create a concise task list that gets straight to the point. By adjusting the task list creation method according to the user's emotions, a highly visual task list can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the creation unit may be performed using AI, for example, or not using AI. For example, the creation unit can adjust the creation method using an AI model that takes user emotion data as input and outputs a task list creation method.

[0099] The creation unit can select the optimal creation method when creating a task list by referring to the user's past task management history. For example, the creation unit can create an optimal task list based on the format of task lists previously used by the user. The creation unit can also create an efficient task list by, for example, analyzing the user's past task management history. The creation unit can also, for example, prioritize suggesting the format of task lists that the user has preferred to use in the past. This allows the creation of an optimal task list by referring to the user's past task management history. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can select a creation method using an AI model that takes the user's past task management history as input and outputs the optimal creation method.

[0100] The creation unit can customize the contents of a task list based on the user's current project status when creating the task list. For example, the creation unit can prioritize adding tasks related to the user's current project to the task list. The creation unit can also add necessary tasks to the task list based on the user's project progress. For example, if the user is working on multiple projects simultaneously, the creation unit can add tasks related to each project to separate task lists. This allows the creation unit to prioritize listing relevant tasks by customizing the contents of the task list based on the user's current project status. Some or all of the above processes in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can customize the contents using an AI model that takes the user's project status as input and outputs the contents of the task list.

[0101] The creation unit can estimate the user's emotions and determine the priority of the task list based on the estimated emotions. For example, if the user is stressed, the creation unit may postpone less important tasks. For example, if the user is relaxed, the creation unit may prioritize listing high-importance tasks. For example, if the user is in a hurry, the creation unit may prioritize listing urgent tasks. This allows important tasks to be processed preferentially by determining the priority of the task list 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. Some or all of the above processing in the creation unit may be performed using AI or not. For example, the creation unit can determine priorities using an AI model that takes user emotion data as input and outputs the priority of the task list.

[0102] The task creation unit can create an optimal task list by considering the user's geographical location information when creating a task list. For example, if the user is on a business trip, the unit will prioritize listing tasks that should be done at the destination. For example, if the user is in a specific location, the unit can also list tasks that should be done at that location. For example, if the user is traveling, the unit can also list tasks that should be done at the travel destination. This allows the unit to prioritize listing relevant tasks by considering the user's geographical location information. Some or all of the above processing in the task creation unit may be performed using AI, for example, or without AI. For example, the task creation unit can create a task list using an AI model that takes the user's geographical location information as input and outputs an optimal task list.

[0103] The creation unit can analyze the user's social media activity and suggest task list content when creating a task list. For example, the creation unit can list tasks that the user has mentioned on social media. For example, the creation unit can list tasks from people the user follows on social media. For example, the creation unit can list tasks related to events the user is participating in on social media. This allows the creation unit to prioritize listing relevant tasks by analyzing the user's social media activity. Some or all of the above processing in the creation unit may be performed using AI, for example, or without AI. For example, the creation unit can suggest content using an AI model that takes the user's social media activity as input and outputs task list content.

[0104] The presentation unit can estimate the user's emotions and adjust the display method of the task list based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide a simple and highly visible display method. For example, if the user is relaxed, the presentation unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the presentation unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the task list according to the user's emotions, a highly visible display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0105] The display unit can select the optimal display method when displaying the task list by referring to the user's past operation history. For example, the display unit can provide the optimal display method based on the display format the user has previously preferred to use. The display unit can also, for example, analyze the user's past operation history and provide an efficient display method. The display unit can also, for example, prioritize suggesting display formats the user has previously used. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can select a display method using an AI model that takes the user's past operation history as input and outputs the optimal display method.

[0106] The display unit can customize the displayed content based on the user's current work status when displaying the task list. For example, the display unit can prioritize displaying tasks related to the work the user is currently working on. The display unit can also display necessary tasks based on the user's work progress. For example, if the user is working on multiple tasks simultaneously, the display unit can display tasks related to each task separately. This allows for the priority display of relevant tasks by customizing the displayed content based on the user's current work status. Some or all of the above processing in the display unit may be performed using AI, for example, or without AI. For example, the display unit can customize the content using an AI model that takes the user's work status as input and outputs the displayed content.

[0107] The presentation unit can estimate the user's emotions and adjust the operation procedure of the task list based on the estimated user emotions. For example, if the user is nervous, the presentation unit can provide simple and intuitive operation procedures. For example, if the user is relaxed, the presentation unit can also provide detailed operation procedures. For example, if the user is in a hurry, the presentation unit can also provide procedures that can be operated quickly. In this way, by adjusting the operation procedure of the task list according to the user's emotions, an intuitive operation procedure can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can adjust the procedures using an AI model that takes user emotion data as input and outputs operation procedures.

[0108] The display unit can select the optimal display method when displaying a task list, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. In this way, the optimal display method can be provided by taking the user's device information into consideration. Some or all of the above processing in the display unit may be performed using AI, for example, or without using AI. For example, the display unit can select a display method using an AI model that takes the user's device information as input and outputs the optimal display method.

[0109] The presentation unit can analyze the user's social media activity and suggest content to display when displaying the task list. For example, the presentation unit can display tasks that the user has mentioned on social media. For example, the presentation unit can also display tasks from people the user follows on social media. For example, the presentation unit can display tasks related to events the user is participating in on social media. This allows relevant tasks to be displayed preferentially by analyzing the user's social media activity. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can suggest content using an AI model that takes the user's social media activity as input and outputs content to display.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The AI ​​agent system can also recognize user voice commands and add tasks by voice. For example, if a user says, "Add the next meeting preparation to the list," the system will recognize the instruction and add the meeting preparation task to the task list. The system can also read out today's tasks if the user says, "Read out today's tasks." Furthermore, if the user says, "Mark this task as complete," the system can mark that task as completed. This allows users to manage tasks using only their voice, without using their hands, improving work efficiency.

[0112] The AI ​​agent system can further estimate the user's emotions and adjust task reminders based on those emotions. For example, if the user is stressed, the frequency of reminders can be reduced to provide time for relaxation. If the user is focused, reminders can be kept to a minimum to maintain focus. Furthermore, if the user is in a hurry, reminders for important tasks can be sent more frequently to facilitate task completion. In this way, task management becomes more effective by adjusting reminders according to the user's emotions.

[0113] The AI ​​agent system can also retrieve the user's calendar information and link it to the task list. For example, it can automatically add information about meetings and events registered in the user's calendar to the task list. It can also analyze the calendar's free time and suggest suitable times for completing tasks. Furthermore, if a calendar event changes, the changes can be reflected in the task list. This allows users to centrally manage their calendar and task list, making schedule management easier.

[0114] The AI ​​agent system can further estimate the user's emotions and dynamically change task priorities based on those emotions. For example, if the user is tired, it can postpone less important tasks and prioritize easier ones. Conversely, if the user is highly motivated, it can prioritize more important tasks. Furthermore, if the user is anxious, it can prioritize urgent tasks. This allows for efficient task management by dynamically changing task priorities according to the user's emotions.

[0115] The AI ​​agent system can further analyze a user's past task completion history and predict task completion. For example, it can analyze how long it took a user to complete tasks in the past and provide an estimated completion time for the current task. It can also predict the likelihood of completing the current task based on past task completion rates. Furthermore, it can analyze past task completion patterns and optimize task priorities. This allows users to manage tasks efficiently while referring to task completion predictions.

[0116] The AI ​​agent system can further estimate the user's emotions and adjust the way tasks are notified based on those emotions. For example, if the user is relaxed, notifications will be made with a gentle sound. If the user is focused, visual notifications can be downplayed and audio notifications can be prioritized. Furthermore, if the user is in a hurry, urgent tasks can be highlighted and notified. This makes task management more effective by adjusting notification methods according to the user's emotions.

[0117] The AI ​​agent system can also acquire user health data and incorporate it into task management. For example, based on the user's sleep data, it can reduce the workload of tasks if the user is highly fatigued. It can also schedule tasks considering the refresh time after exercise based on the user's exercise data. Furthermore, it can adjust tasks considering the rest time after meals based on the user's meal data. This allows for the optimization of task management according to the user's health condition.

[0118] The AI ​​agent system can further estimate the user's emotions and provide task feedback based on those emotions. For example, if the user is feeling a sense of accomplishment, it can provide positive feedback. If the user is feeling stressed, it can provide encouraging messages. Furthermore, if the user is feeling down, it can provide motivational feedback. In this way, it can support task completion by providing appropriate feedback according to the user's emotions.

[0119] The AI ​​agent system can also be equipped with the ability to synchronize task lists across the user's devices. For example, tasks added by the user on their smartphone can be viewed on their PC or tablet. Furthermore, task progress can be synchronized in real time across devices. It can also provide an optimal display method for each device, allowing users to comfortably manage tasks on any device. This enables users to efficiently manage tasks while using multiple devices.

[0120] The AI ​​agent system can further estimate the user's emotions and report task completion based on those emotions. For example, if the user feels a sense of accomplishment, it can display a congratulatory message upon task completion. If the user is tired, it can display a relaxing message upon task completion. Furthermore, if the user is anxious, it can display a message encouraging a smooth transition to the next task upon task completion. This makes task management more effective by reporting task completion according to the user's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reading unit reads the content of the email. For example, it can read the content of the email using text analysis technology. It can also convert image-formatted emails into text data using OCR technology. Step 2: The analysis unit analyzes the content read by the reading unit. For example, it analyzes the content of the email using natural language processing technology and identifies important information using keyword extraction technology. Step 3: The extraction unit extracts tasks from the information analyzed by the analysis unit. For example, tasks can be extracted based on importance or deadline, or tasks can be extracted based on highly relevant information. Step 4: The creation unit creates a task list based on the tasks extracted by the extraction unit. For example, it organizes the tasks in a list format and sets task priorities based on importance and deadlines. Step 5: The presentation unit presents the task list created by the creation unit to the user. For example, it displays the task list on the screen and notifies the user of the task list using a notification function.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reading unit, analysis unit, extraction unit, creation unit, and presentation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart device 14 and reads the contents of the email. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the read contents. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts tasks from the analyzed contents. The creation unit is implemented by the control unit 46A of the smart device 14 and creates a task list based on the extracted tasks. The presentation unit is implemented by the output device 40 of the smart device 14 and presents the created task list to the user. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reading unit, analysis unit, extraction unit, creation unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the smart glasses 214 and reads the contents of an email. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the read content. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts tasks from the analyzed content. The creation unit is implemented by the control unit 46A of the smart glasses 214 and creates a task list based on the extracted tasks. The presentation unit is implemented by the speaker 240 of the smart glasses 214 and presents the created task list to the user. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reading unit, analysis unit, extraction unit, creation unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the headset terminal 314 and reads the contents of the email. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the read contents. The extraction unit is implemented by the identification processing unit 290 of the data processing unit 12 and extracts tasks from the analyzed contents. The creation unit is implemented by the control unit 46A of the headset terminal 314 and creates a task list based on the extracted tasks. The presentation unit is implemented by the display 343 of the headset terminal 314 and presents the created task list to the user. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reading unit, analysis unit, extraction unit, creation unit, and presentation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the computer 36 of the robot 414 and reads the contents of the email. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes the read contents. The extraction unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and extracts tasks from the analyzed contents. The creation unit is implemented by, for example, the control unit 46A of the robot 414 and creates a task list based on the extracted tasks. The presentation unit is implemented by, for example, the speaker 240 of the robot 414 and presents the created task list to the user. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reading unit that reads the contents of the email, An analysis unit analyzes the contents read by the reading unit, An extraction unit extracts tasks from the content analyzed by the aforementioned analysis unit, A creation unit creates a task list based on the tasks extracted by the extraction unit, The system includes a presentation unit that presents the task list created by the creation unit to the user. A system characterized by the following features. (Note 2) The aforementioned creation unit, Set priority for your task list. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned display unit is, Monitor task progress and send reminders as needed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The reading unit is Analyze the email content in detail and extract important information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The extraction unit is Identify information that is important to the user, such as meeting schedules and tasks with deadlines. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Present a task list to the user and allow them to instantly grasp the content of the email. The system described in Appendix 1, characterized by the features described herein. (Note 7) The reading unit is It estimates the user's emotions and adjusts the timing of email delivery based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The reading unit is Analyze the user's past email reading history and select the optimal reading method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The reading unit is When reading 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 10) The reading unit is It estimates the user's emotions and determines the priority of emails to read based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The reading unit is When reading emails, the system prioritizes reading emails that are more relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reading unit is When reading emails, the system analyzes the user's social media activity and reads relevant emails. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, 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 15) 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 16) 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 17) 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 18) 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 19) The extraction unit is Estimate the user's emotions and determine the priority of tasks to extract based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is During extraction, the accuracy of the extraction is improved by considering the relationships between emails. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is During extraction, the sender's attribute information of the email will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is We estimate user sentiment and adjust how tasks are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is During extraction, the geographical distribution of emails is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The extraction unit is During extraction, we refer to related literature in emails to improve the accuracy of the extraction. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned creation unit, It estimates the user's emotions and adjusts how the task list is created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned creation unit, When creating a task list, the system will refer to the user's past task management history to select the most suitable creation method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned creation unit, When creating a task list, customize the task list content based on the user's current project status. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned creation unit, It estimates the user's emotions and determines the priority of the task list based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned creation unit, When creating a task list, the system takes the user's geographical location into consideration to create the optimal task list. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned creation unit, When creating a task list, the system analyzes the user's social media activity and suggests content for the task list. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned display unit is, It estimates the user's emotions and adjusts how the task list is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is, When displaying the task list, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is, When displaying the task list, customize the displayed content based on the user's current work status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is, It estimates the user's emotions and adjusts the steps in the task list based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is, When displaying the task list, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned display unit is, When displaying the task list, the system analyzes the user's social media activity and suggests content to display. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reading unit that reads the contents of the email, An analysis unit analyzes the contents read by the reading unit, An extraction unit extracts tasks from the content analyzed by the aforementioned analysis unit, A creation unit creates a task list based on the tasks extracted by the extraction unit, The system includes a presentation unit that presents the task list created by the creation unit to the user. A system characterized by the following features.

2. The aforementioned creation unit, Set priority for your task list. The system according to feature 1.

3. The aforementioned display unit is, Monitor task progress and send reminders as needed. The system according to feature 1.

4. The reading unit is Analyze the email content in detail and extract important information. The system according to feature 1.

5. The extraction unit is Identify information that is important to the user, such as meeting schedules and tasks with deadlines. The system according to feature 1.

6. The aforementioned display unit is, Present a task list to the user and allow them to instantly grasp the content of the email. The system according to feature 1.

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

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

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

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