system

Abstract

The system according to the embodiment aims to analyze the contents of emails and efficiently classify them by displaying them as icons. [Solution] A system according to an embodiment includes an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the content of an email. The selection unit selects an icon based on the content analyzed by the analysis unit. The display unit displays the icon selected by the selection unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional techniques have had the problem that it is difficult to efficiently classify the contents of emails, making management of unread emails cumbersome.

[0005] The system according to the embodiment aims to analyze the contents of emails and efficiently classify them by displaying them as icons. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the content of the email. The selection unit selects an icon based on the content analyzed by the analysis unit. The display unit displays the icon selected by the selection unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the contents of emails and efficiently classify them by displaying icons. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple 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), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An email management system according to an embodiment of the present invention automatically reads unread emails in a mailbox and classifies them by icon display. In this email management system, when new unread emails arrive in a mailbox, an AI automatically reads the email. Next, the AI ​​analyzes the email content and selects an appropriate icon based on the email's type and importance. For example, work-related emails can be marked with a "Work" icon, personal emails with a "Private" icon, and urgent emails with an "Urgent" icon. This allows users to grasp the type and importance of emails at a glance and efficiently manage their emails. First, when new unread emails arrive in a mailbox, the AI ​​automatically reads the email. The AI ​​analyzes the email's subject, body, sender information, and other information to understand the email's content. For example, if the subject contains keywords such as "important" or "urgent," the AI ​​classifies the email as an urgent email. Next, the AI ​​analyzes the email content and selects an appropriate icon based on the email's type and importance. For example, work-related emails can be marked with a "Work" icon, personal emails with a "Private" icon, and urgent emails with an "Urgent" icon. This allows users to understand the type and importance of emails at a glance. Furthermore, AI can also determine the priority of emails based on their content. For example, emails from superiors or emails about project progress are classified as high priority, while casual emails from friends are classified as low priority. This allows users to prioritize important emails and manage their emails more efficiently. This system also allows users to efficiently manage unread emails in their mailboxes. For example, if an urgent email arrives while at work, the AI ​​will display it with an "urgent" icon, allowing the user to respond to it immediately. Private emails will be displayed with a "private" icon, allowing the user to review them at their leisure after work. In this way, AI analyzes the content of emails and classifies them by icon display, allowing users to understand the type and importance of emails at a glance and manage their emails more efficiently.This allows the email management system to allow the user to grasp the type and importance of email at a glance, and to manage email efficiently.

[0029] An email management system according to an embodiment includes an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the content of an email. For example, the analysis unit analyzes the subject, body, sender information, and the like of the email to understand the content of the email. For example, if the subject contains keywords such as "important" or "urgent," the analysis unit classifies the email as an urgent email. The analysis unit can also analyze the body of the email to determine the type and importance of the email. For example, the analysis unit can analyze the content of the email using natural language processing technology and extract important information. The selection unit selects an icon based on the content analyzed by the analysis unit. For example, the selection unit can select a "work" icon for work-related email, a "private" icon for personal email, and an "urgent" icon for urgent email. The selection unit can select an appropriate icon depending on the type and importance of the email. The display unit displays the icon selected by the selection unit. For example, the display unit can display the icon on an email list screen. The display unit can display icons so that the user can understand the type and importance of email at a glance. This allows the email management system according to the embodiment to allow the user to understand the type and importance of email at a glance and manage email efficiently.

[0030] The email management system includes a determination unit that determines the priority of emails. The determination unit determines the priority of emails. The determination unit can determine the priority based on, for example, the sender, content, and urgency of the email. For example, the determination unit can classify emails from a boss or emails related to project progress as high priority. The determination unit can also classify casual emails from friends as low priority. This allows the determination unit to determine the priority of emails so that the user can prioritize checking important emails. This allows the email management system to prioritize processing important emails for the user.

[0031] The email management system includes a classification unit that classifies emails based on the determination result. The classification unit classifies emails based on the determination result. The classification unit can classify emails, for example, based on the priority of the emails. For example, the classification unit can classify high-priority emails into an "important" category and low-priority emails into a "general" category. The classification unit can also classify emails based on the type of email. For example, the classification unit can classify work-related emails into a "work" category and private emails into a "private" category. In this way, the classification unit can classify emails so that the user can efficiently manage their emails. In this way, the email management system allows the user to efficiently manage their emails.

[0032] The email management system includes a notification unit that notifies the user of classified emails. The notification unit notifies the user of classified emails. The notification unit can notify the user by, for example, a pop-up notification, an email notification, an in-app notification, or the like. For example, when an urgent email arrives, the notification unit can display a pop-up notification to notify the user that an urgent email has arrived. Furthermore, when an important email arrives, the notification unit can send an email notification to notify the user that an important email has arrived. Furthermore, the notification unit can use an in-app notification to notify the user that an important email has arrived when the user opens the app. In this way, the notification unit can notify the user of classified emails so that the user does not miss important emails. In this way, the email management system can prevent the user from missing important emails.

[0033] The analysis unit can optimize the analysis algorithm by referring to past analysis results of emails. The analysis unit, for example, can optimize the analysis algorithm by referring to past analysis results of emails. For example, the analysis unit can improve the analysis accuracy of emails with similar patterns based on the results of emails analyzed in the past. The analysis unit can also learn from past analysis results the patterns of emails that frequently resulted in false positives and modify the algorithm. Furthermore, the analysis unit can analyze past analysis results and improve the analysis accuracy for emails from specific senders. As a result, the analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis results. As a result, the email management system can improve the accuracy of the analysis algorithm by referring to past analysis results.

[0034] The analysis unit can perform analysis based on attribute information of the email sender. The analysis unit performs analysis based on, for example, attribute information of the email sender. For example, if the sender is a boss, the analysis unit can set the importance of the email high and analyze it in detail. Furthermore, if the sender is a friend, the analysis unit can analyze the content of the email lightly and set the importance low. Furthermore, if the sender is a customer, the analysis unit can analyze the content of the email in detail and set the importance high. This allows the analysis unit to perform analysis based on attribute information of the sender. This allows the email management system to improve the accuracy of analysis by taking the attribute information of the sender into consideration.

[0035] The analysis unit can perform analysis based on the geographical information of the email. The analysis unit performs analysis based on, for example, the geographical information of the email. For example, if the sender's location is close, the analysis unit can set the importance of the email high and analyze it in detail. Also, if the sender's location is far away, the analysis unit can set the importance of the email low and analyze it lightly. Furthermore, if the sender's location is in a specific area, the analysis unit can prioritize analysis of information related to that area. This allows the analysis unit to perform analysis taking geographical information into consideration. This allows the email management system to improve the accuracy of analysis by taking geographical information into consideration.

[0036] The analysis unit can improve the accuracy of the analysis by referring to literature related to the email. The analysis unit can improve the accuracy of the analysis by referring to literature related to the email, for example. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the content of the email. The analysis unit can also improve the accuracy of the analysis by referring to past emails related to the content of the email. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to websites related to the content of the email. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature. In this way, the email management system can improve the accuracy of the analysis by referring to related literature.

[0037] The selection unit can select an icon based on the importance of the email. The selection unit selects an icon based on, for example, the importance of the email. For example, the selection unit can select an "important" icon for important email. The selection unit can also select an "urgent" icon for urgent email. The selection unit can also select a "private" icon for private email. This allows the selection unit to select an icon based on the importance of the email. This allows the email management system to identify important emails at a glance by selecting an icon based on the importance of the email.

[0038] The selection unit can apply different icon selection algorithms depending on the category of email. For example, the selection unit can apply an algorithm that selects a "work" icon to work-related emails. The selection unit can also apply an algorithm that selects a "private" icon to private emails. The selection unit can also apply an algorithm that selects an "advertising" icon to advertising emails. This allows the selection unit to apply different icon selection algorithms depending on the category of email. This allows the email management system to understand the type of email at a glance by applying an icon selection algorithm depending on the category.

[0039] The selection unit can select an icon based on the time when the email was sent. The selection unit selects an icon based on, for example, the time when the email was sent. For example, the selection unit can select a "New" icon for emails sent recently. The selection unit can also select a "Past" icon for emails sent in the past. Furthermore, the selection unit can select an icon corresponding to a specific time period for emails sent within that time period. This allows the selection unit to select an icon based on the time when the email was sent. By selecting an icon based on the time when the email was sent, the email management system can grasp the recency of emails at a glance.

[0040] The selection unit can select an icon based on the relevance of the emails. The selection unit selects an icon based on, for example, the relevance of the emails. For example, the selection unit can select the same icon for related emails. The selection unit can also select a "related" icon for highly related emails. The selection unit can also select an "unrelated" icon for less related emails. This allows the selection unit to select an icon based on the relevance of the emails. This allows the email management system to grasp related emails at a glance by selecting icons based on relevance.

[0041] The display unit can adjust the display size of an icon based on the importance of the email. The display unit adjusts the display size of an icon based on, for example, the importance of the email. For example, the display unit can display a large icon for important email. Furthermore, the display unit can display a prominent icon for urgent email. Furthermore, the display unit can display a normal-sized icon for private email. In this way, the display unit can adjust the display size of an icon based on the importance of the email. In this way, the email management system can make important emails stand out by adjusting the display size based on the importance.

[0042] The display unit can apply different display methods depending on the category of email. For example, the display unit can display a "work" icon for work-related email. The display unit can also display a "private" icon for private email. The display unit can also display an "advertising" icon for advertising email. This allows the display unit to apply different display methods depending on the category of email. By applying a display method depending on the category, the email management system can identify the type of email at a glance.

[0043] The display unit can display an icon based on the time when the email was sent. The display unit displays an icon based on, for example, the time when the email was sent. For example, the display unit can display a "New" icon for emails sent recently. The display unit can also display a "Past" icon for emails sent in the past. Furthermore, the display unit can display an icon corresponding to a specific time period for emails sent during that time period. This allows the display unit to display an icon based on the time when the email was sent. By displaying an icon based on the time when the email was sent, the email management system can grasp the recency of emails at a glance.

[0044] The display unit can display icons based on the relevance of emails. The display unit displays icons based on, for example, the relevance of emails. For example, the display unit can display the same icon for related emails. The display unit can also display a "related" icon for highly related emails. Furthermore, the display unit can display an "unrelated" icon for less related emails. This allows the display unit to display icons based on the relevance of emails. This allows the email management system to grasp related emails at a glance by displaying icons based on relevance.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The email management system may include a schedule linking unit that acquires a user's schedule information and adjusts the priority of emails. For example, the schedule linking unit may link with the user's calendar app and set high priority to emails that arrive during a meeting or an important task. The schedule linking unit may also set low priority to emails that arrive while the user is on vacation, and notify the user only of emails with high urgency. Furthermore, the schedule linking unit may automatically classify emails that arrive during specific time periods based on the user's schedule and select an appropriate icon. This allows users to manage their emails according to their schedule and process them efficiently.

[0047] The email management system can include a learning unit that learns a user's past email processing history and automatically adjusts email priorities. For example, the learning unit can analyze what emails the user has prioritized in the past and set a higher priority for emails that share a similar pattern. The learning unit can also prioritize emails from senders with whom the user frequently communicates. Furthermore, if the user tends to prioritize emails containing specific keywords, the learning unit can set a higher priority for emails containing those keywords. This enables email management based on the user's past behavior, resulting in more efficient email processing.

[0048] The email management system can include a location linking unit that acquires user location information and adjusts email priority based on the location information. For example, when the user is in the office, the location linking unit can set work-related emails to a high priority. When the user is at home, the location linking unit can also set personal emails to a high priority. Furthermore, when the user is on the move, the location linking unit can notify only emails with a high level of urgency. This enables email management based on the user's location information, allowing for efficient email processing.

[0049] The email management system may include a voice operation unit that accepts voice commands from the user and performs email operations based on the voice commands. For example, when the user instructs the voice operation unit to "display important emails," the voice operation unit can display important emails with priority. When the user instructs the voice operation unit to "open all unread emails," the voice operation unit can open all unread emails at once. Furthermore, when the user instructs the voice operation unit to "notify me of urgent emails," the voice operation unit can notify me of urgent emails with a pop-up notification. This allows the user to efficiently operate emails using voice commands.

[0050] The email management system can include a history analysis unit that analyzes a user's email browsing history and adjusts the priority of emails based on the browsing frequency. For example, the history analysis unit can set a high priority for emails from senders that the user frequently browses. The history analysis unit can also set a high priority for emails that contain keywords that the user has considered important in the past. Furthermore, the history analysis unit can set a low priority for emails from senders that the user does not browse often. This enables email management based on the user's browsing history, allowing for efficient email processing.

[0051] The email management system can include a transmission analysis unit that analyzes a user's email sending history and adjusts the priority of emails based on sending frequency. For example, the transmission analysis unit can set a high priority for emails to senders to which the user frequently sends. The transmission analysis unit can also set a high priority for emails that contain content that the user has previously considered important. Furthermore, the transmission analysis unit can set a low priority for emails to senders to which the user does not often send. This enables email management based on the user's sending history and allows for efficient email processing.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The analysis unit analyzes the content of the email. The analysis unit analyzes, for example, the subject, body, and sender information of the email to understand the content of the email. For example, if the subject contains keywords such as "important" or "urgent," the analysis unit classifies the email as an urgent email. The analysis unit can also analyze the body of the email to determine the type and importance of the email. For example, the analysis unit can use natural language processing technology to analyze the content of the email and extract important information. Step 2: The selection unit selects an icon based on the content analyzed by the analysis unit. For example, the selection unit can select a "Work" icon for work-related emails, a "Private" icon for private emails, and an "Urgent" icon for urgent emails. The selection unit can select an appropriate icon depending on the type and importance of the email. Step 3: The display unit displays the icon selected by the selection unit. The display unit can display the icon on an email list screen, for example. The display unit can display the icon so that the user can understand the type and importance of the email at a glance.

[0054] (Example 2) An email management system according to an embodiment of the present invention automatically reads unread emails in a mailbox and classifies them by icon display. In this email management system, when new unread emails arrive in a mailbox, an AI automatically reads the email. Next, the AI ​​analyzes the email content and selects an appropriate icon based on the email's type and importance. For example, work-related emails can be marked with a "Work" icon, personal emails with a "Private" icon, and urgent emails with an "Urgent" icon. This allows users to grasp the type and importance of emails at a glance and efficiently manage their emails. First, when new unread emails arrive in a mailbox, the AI ​​automatically reads the email. The AI ​​analyzes the email's subject, body, sender information, and other information to understand the email's content. For example, if the subject contains keywords such as "important" or "urgent," the AI ​​classifies the email as an urgent email. Next, the AI ​​analyzes the email content and selects an appropriate icon based on the email's type and importance. For example, work-related emails can be marked with a "Work" icon, personal emails with a "Private" icon, and urgent emails with an "Urgent" icon. This allows users to understand the type and importance of emails at a glance. Furthermore, AI can also determine the priority of emails based on their content. For example, emails from superiors or emails about project progress are classified as high priority, while casual emails from friends are classified as low priority. This allows users to prioritize important emails and manage their emails more efficiently. This system also allows users to efficiently manage unread emails in their mailboxes. For example, if an urgent email arrives while at work, the AI ​​will display it with an "urgent" icon, allowing the user to respond to it immediately. Private emails will be displayed with a "private" icon, allowing the user to review them at their leisure after work. In this way, AI analyzes the content of emails and classifies them by icon display, allowing users to understand the type and importance of emails at a glance and manage their emails more efficiently.This allows the email management system to allow the user to grasp the type and importance of email at a glance, and to manage email efficiently.

[0055] An email management system according to an embodiment includes an analysis unit, a selection unit, and a display unit. The analysis unit analyzes the content of an email. For example, the analysis unit analyzes the subject, body, sender information, and the like of the email to understand the content of the email. For example, if the subject contains keywords such as "important" or "urgent," the analysis unit classifies the email as an urgent email. The analysis unit can also analyze the body of the email to determine the type and importance of the email. For example, the analysis unit can analyze the content of the email using natural language processing technology and extract important information. The selection unit selects an icon based on the content analyzed by the analysis unit. For example, the selection unit can select a "work" icon for work-related email, a "private" icon for personal email, and an "urgent" icon for urgent email. The selection unit can select an appropriate icon depending on the type and importance of the email. The display unit displays the icon selected by the selection unit. For example, the display unit can display the icon on an email list screen. The display unit can display icons so that the user can understand the type and importance of email at a glance. This allows the email management system according to the embodiment to allow the user to understand the type and importance of email at a glance and manage email efficiently.

[0056] The email management system includes a determination unit that determines the priority of emails. The determination unit determines the priority of emails. The determination unit can determine the priority based on, for example, the sender, content, and urgency of the email. For example, the determination unit can classify emails from a boss or emails related to project progress as high priority. The determination unit can also classify casual emails from friends as low priority. This allows the determination unit to determine the priority of emails so that the user can prioritize checking important emails. This allows the email management system to prioritize processing important emails for the user.

[0057] The email management system includes a classification unit that classifies emails based on the determination result. The classification unit classifies emails based on the determination result. The classification unit can classify emails, for example, based on the priority of the emails. For example, the classification unit can classify high-priority emails into an "important" category and low-priority emails into a "general" category. The classification unit can also classify emails based on the type of email. For example, the classification unit can classify work-related emails into a "work" category and private emails into a "private" category. In this way, the classification unit can classify emails so that the user can efficiently manage their emails. In this way, the email management system allows the user to efficiently manage their emails.

[0058] The email management system includes a notification unit that notifies the user of classified emails. The notification unit notifies the user of classified emails. The notification unit can notify the user by, for example, a pop-up notification, an email notification, an in-app notification, or the like. For example, when an urgent email arrives, the notification unit can display a pop-up notification to notify the user that an urgent email has arrived. Furthermore, when an important email arrives, the notification unit can send an email notification to notify the user that an important email has arrived. Furthermore, the notification unit can use an in-app notification to notify the user that an important email has arrived when the user opens the app. In this way, the notification unit can notify the user of classified emails so that the user does not miss important emails. In this way, the email management system can prevent the user from missing important emails.

[0059] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, when the user is feeling stressed, the analysis unit can increase the analysis accuracy and prioritize analyzing important emails. Furthermore, when the user is relaxed, the analysis unit can return the analysis accuracy to normal and analyze all emails equally. Furthermore, when the user is in a hurry, the analysis unit can increase the analysis accuracy and prioritize analyzing urgent emails. This allows the analysis unit to adjust the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the email management system to adjust the accuracy of the analysis according to the user's emotions.

[0060] The analysis unit can optimize the analysis algorithm by referring to past analysis results of emails. The analysis unit, for example, can optimize the analysis algorithm by referring to past analysis results of emails. For example, the analysis unit can improve the analysis accuracy of emails with similar patterns based on the results of emails analyzed in the past. The analysis unit can also learn from past analysis results the patterns of emails that frequently resulted in false positives and modify the algorithm. Furthermore, the analysis unit can analyze past analysis results and improve the analysis accuracy for emails from specific senders. As a result, the analysis unit can improve the accuracy of the analysis algorithm by referring to past analysis results. As a result, the email management system can improve the accuracy of the analysis algorithm by referring to past analysis results.

[0061] The analysis unit can perform analysis based on attribute information of the email sender. The analysis unit performs analysis based on, for example, attribute information of the email sender. For example, if the sender is a boss, the analysis unit can set the importance of the email high and analyze it in detail. Furthermore, if the sender is a friend, the analysis unit can analyze the content of the email lightly and set the importance low. Furthermore, if the sender is a customer, the analysis unit can analyze the content of the email in detail and set the importance high. This allows the analysis unit to perform analysis based on attribute information of the sender. This allows the email management system to improve the accuracy of analysis by taking the attribute information of the sender into consideration.

[0062] The analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and determine the analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can prioritize analyzing important emails. Furthermore, if the user is relaxed, the analysis unit can analyze all emails equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing urgent emails. This allows the analysis unit to determine the analysis priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the email management system to determine the analysis priorities based on the user's emotions.

[0063] The analysis unit can perform analysis based on the geographical information of the email. The analysis unit performs analysis based on, for example, the geographical information of the email. For example, if the sender's location is close, the analysis unit can set the importance of the email high and analyze it in detail. Also, if the sender's location is far away, the analysis unit can set the importance of the email low and analyze it lightly. Furthermore, if the sender's location is in a specific area, the analysis unit can prioritize analysis of information related to that area. This allows the analysis unit to perform analysis taking geographical information into consideration. This allows the email management system to improve the accuracy of analysis by taking geographical information into consideration.

[0064] The analysis unit can improve the accuracy of the analysis by referring to literature related to the email. The analysis unit can improve the accuracy of the analysis by referring to literature related to the email, for example. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to the content of the email. The analysis unit can also improve the accuracy of the analysis by referring to past emails related to the content of the email. Furthermore, the analysis unit can improve the accuracy of the analysis by referring to websites related to the content of the email. In this way, the analysis unit can improve the accuracy of the analysis by referring to related literature. In this way, the email management system can improve the accuracy of the analysis by referring to related literature.

[0065] The selection unit can estimate the user's emotion and adjust the icon selection criteria based on the estimated user's emotion. For example, the selection unit can estimate the user's emotion and adjust the icon selection criteria based on the estimated user's emotion. For example, the selection unit can select a simple icon when the user is stressed. Furthermore, the selection unit can select a detailed icon when the user is relaxed. Furthermore, the selection unit can preferentially select an urgent icon when the user is in a hurry. This allows the selection unit to adjust the icon selection criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the email management system to adjust the icon selection criteria according to the user's emotion.

[0066] The selection unit can select an icon based on the importance of the email. The selection unit selects an icon based on, for example, the importance of the email. For example, the selection unit can select an "important" icon for important email. The selection unit can also select an "urgent" icon for urgent email. The selection unit can also select a "private" icon for private email. This allows the selection unit to select an icon based on the importance of the email. This allows the email management system to identify important emails at a glance by selecting an icon based on the importance of the email.

[0067] The selection unit can apply different icon selection algorithms depending on the category of email. For example, the selection unit can apply an algorithm that selects a "work" icon to work-related emails. The selection unit can also apply an algorithm that selects a "private" icon to private emails. The selection unit can also apply an algorithm that selects an "advertising" icon to advertising emails. This allows the selection unit to apply different icon selection algorithms depending on the category of email. This allows the email management system to understand the type of email at a glance by applying an icon selection algorithm depending on the category.

[0068] The selection unit can estimate the user's emotion and adjust the display order of icons based on the estimated user's emotion. The selection unit, for example, estimates the user's emotion and adjusts the display order of icons based on the estimated user's emotion. For example, when the user is stressed, the selection unit can prioritize displaying important icons. Furthermore, when the user is relaxed, the selection unit can display all icons evenly. Furthermore, when the user is in a hurry, the selection unit can prioritize displaying urgent icons. This allows the selection unit to adjust the display order of icons according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the email management system to adjust the display order of icons according to the user's emotion.

[0069] The selection unit can select an icon based on the time when the email was sent. The selection unit selects an icon based on, for example, the time when the email was sent. For example, the selection unit can select a "New" icon for emails sent recently. The selection unit can also select a "Past" icon for emails sent in the past. Furthermore, the selection unit can select an icon corresponding to a specific time period for emails sent within that time period. This allows the selection unit to select an icon based on the time when the email was sent. By selecting an icon based on the time when the email was sent, the email management system can grasp the recency of emails at a glance.

[0070] The selection unit can select an icon based on the relevance of the emails. The selection unit selects an icon based on, for example, the relevance of the emails. For example, the selection unit can select the same icon for related emails. The selection unit can also select a "related" icon for highly related emails. The selection unit can also select an "unrelated" icon for less related emails. This allows the selection unit to select an icon based on the relevance of the emails. This allows the email management system to grasp related emails at a glance by selecting icons based on relevance.

[0071] The display unit can estimate the user's emotion and adjust the icon display method based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the icon display method based on the estimated user's emotion. For example, the display unit can provide a simple display method when the user is stressed. Furthermore, the display unit can provide a detailed display method when the user is relaxed. Furthermore, the display unit can provide a display method that focuses on the main points when the user is in a hurry. This allows the display unit to adjust the display method according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. This allows the email management system to adjust the display method according to the user's emotion.

[0072] The display unit can adjust the display size of an icon based on the importance of the email. The display unit adjusts the display size of an icon based on, for example, the importance of the email. For example, the display unit can display a large icon for important email. Furthermore, the display unit can display a prominent icon for urgent email. Furthermore, the display unit can display a normal-sized icon for private email. In this way, the display unit can adjust the display size of an icon based on the importance of the email. In this way, the email management system can make important emails stand out by adjusting the display size based on the importance.

[0073] The display unit can apply different display methods depending on the category of email. For example, the display unit can display a "work" icon for work-related email. The display unit can also display a "private" icon for private email. The display unit can also display an "advertising" icon for advertising email. This allows the display unit to apply different display methods depending on the category of email. By applying a display method depending on the category, the email management system can identify the type of email at a glance.

[0074] The display unit can estimate the user's emotion and adjust the display order of icons based on the estimated user's emotion. For example, the display unit can estimate the user's emotion and adjust the display order of icons based on the estimated user's emotion. For example, if the user is feeling stressed, the display unit can prioritize displaying important icons. Furthermore, if the user is relaxed, the display unit can evenly display all icons. Furthermore, if the user is in a hurry, the display unit can prioritize displaying urgent icons. This allows the display unit to adjust the display order of icons according to the user's emotion. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. This allows the email management system to adjust the display order of icons according to the user's emotion.

[0075] The display unit can display an icon based on the time when the email was sent. The display unit displays an icon based on, for example, the time when the email was sent. For example, the display unit can display a "New" icon for emails sent recently. The display unit can also display a "Past" icon for emails sent in the past. Furthermore, the display unit can display an icon corresponding to a specific time period for emails sent during that time period. This allows the display unit to display an icon based on the time when the email was sent. By displaying an icon based on the time when the email was sent, the email management system can grasp the recency of emails at a glance.

[0076] The display unit can display icons based on the relevance of emails. The display unit displays icons based on, for example, the relevance of emails. For example, the display unit can display the same icon for related emails. The display unit can also display a "related" icon for highly related emails. Furthermore, the display unit can display an "unrelated" icon for less related emails. This allows the display unit to display icons based on the relevance of emails. This allows the email management system to grasp related emails at a glance by displaying icons based on relevance. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned analysis unit, selection unit, display unit, determination unit, classification unit, and notification unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart device 14 and analyzes the subject, body, and sender information of an email. The selection unit is realized by the specific processing unit 290 of the data processing device 12 and selects an icon based on the analyzed content. The display unit is realized by the display 40A of the smart device 14 and displays the selected icon. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the priority of the email. The classification unit is realized by the control unit 46A of the smart device 14 and classifies the email based on the determination result. The notification unit is realized by the output device 40 of the smart device 14 and notifies the user of the classified email. === Hard Collateral 1-2 === Each of the multiple elements, including the above-described analysis unit, selection unit, display unit, determination unit, classification unit, and notification unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the smart glasses 214 and analyzes the subject, body, and sender information of an email. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects an icon based on the analyzed content. The display unit is realized, for example, by the display of the smart glasses 214 and displays the selected icon. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority of the email. The classification unit is realized, for example, by the control unit 46A of the smart glasses 214 and classifies the email based on the determination result. The notification unit is realized, for example, by the output device of the smart glasses 214 and notifies the user of the classified email. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, selection unit, display unit, determination unit, classification unit, and notification unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the headset type terminal 314 and analyzes the subject, body, and sender information of an email. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects an icon based on the analyzed content. The display unit is realized, for example, by the display 343 of the headset type terminal 314 and displays the selected icon. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority of the email. The classification unit is realized, for example, by the control unit 46A of the headset type terminal 314 and classifies the email based on the determination result. The notification unit is realized, for example, by the output device of the headset type terminal 314 and notifies the user of the classified email. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned analysis unit, selection unit, display unit, determination unit, classification unit, and notification unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the processor 46 of the robot 414 and analyzes the subject, body, and sender information of an email. The selection unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and selects an icon based on the analyzed content. The display unit is realized, for example, by the display of the robot 414 and displays the selected icon. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the priority of the email. The classification unit is realized, for example, by the control unit 46A of the robot 414 and classifies the email based on the determination result. The notification unit is realized, for example, by the output device of the robot 414 and notifies the user of the classified email.

[0077] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0078] The email management system may include a schedule linking unit that acquires a user's schedule information and adjusts the priority of emails. For example, the schedule linking unit may link with the user's calendar app and set high priority to emails that arrive during a meeting or an important task. The schedule linking unit may also set low priority to emails that arrive while the user is on vacation, and notify the user only of emails with high urgency. Furthermore, the schedule linking unit may automatically classify emails that arrive during specific time periods based on the user's schedule and select an appropriate icon. This allows users to manage their emails according to their schedule and process them efficiently.

[0079] The email management system can include a learning unit that learns a user's past email processing history and automatically adjusts email priorities. For example, the learning unit can analyze what emails the user has prioritized in the past and set a higher priority for emails that share a similar pattern. The learning unit can also prioritize emails from senders with whom the user frequently communicates. Furthermore, if the user tends to prioritize emails containing specific keywords, the learning unit can set a higher priority for emails containing those keywords. This enables email management based on the user's past behavior, resulting in more efficient email processing.

[0080] The email management system may include a notification adjustment unit that estimates a user's emotions and adjusts the email notification method based on the estimated emotions. For example, if the user is feeling stressed, the notification adjustment unit may suppress pop-up notifications and select a quieter notification method. If the user is relaxed, the notification adjustment unit may use a normal notification method. Furthermore, if the user is in a hurry, the notification adjustment unit may notify only urgent emails via pop-up notifications. This provides a flexible notification method according to the user's emotions, reducing stress while preventing important emails from being missed.

[0081] The email management system can include a location linking unit that acquires user location information and adjusts email priority based on the location information. For example, when the user is in the office, the location linking unit can set work-related emails to a high priority. When the user is at home, the location linking unit can also set personal emails to a high priority. Furthermore, when the user is on the move, the location linking unit can notify only emails with a high level of urgency. This enables email management based on the user's location information, allowing for efficient email processing.

[0082] The email management system may include a layout adjustment unit that estimates a user's emotions and adjusts the display layout of emails based on the estimated emotions. For example, if the user is feeling stressed, the layout adjustment unit may provide a simple layout that highlights only important emails. If the user is relaxed, the layout adjustment unit may provide a detailed layout that evenly displays all emails. Furthermore, if the user is in a hurry, the layout adjustment unit may prioritize the display of urgent emails. This provides a flexible display layout that corresponds to the user's emotions, allowing for efficient email processing.

[0083] The email management system may include a voice operation unit that accepts voice commands from the user and performs email operations based on the voice commands. For example, when the user instructs the voice operation unit to "display important emails," the voice operation unit can display important emails with priority. When the user instructs the voice operation unit to "open all unread emails," the voice operation unit can open all unread emails at once. Furthermore, when the user instructs the voice operation unit to "notify me of urgent emails," the voice operation unit can notify me of urgent emails with a pop-up notification. This allows the user to efficiently operate emails using voice commands.

[0084] The email management system may include an automatic reply unit that estimates a user's emotions and adjusts the content of an automatic reply to an email based on the estimated emotions. For example, the automatic reply unit may generate a concise automatic reply if the user is stressed. Alternatively, the automatic reply unit may generate a detailed automatic reply if the user is relaxed. Furthermore, the automatic reply unit may generate a quick automatic reply to an urgent email if the user is in a hurry. This provides flexible automatic replies that correspond to the user's emotions, enabling efficient email processing.

[0085] The email management system can include a history analysis unit that analyzes a user's email browsing history and adjusts the priority of emails based on the browsing frequency. For example, the history analysis unit can set a high priority for emails from senders that the user frequently browses. The history analysis unit can also set a high priority for emails that contain keywords that the user has considered important in the past. Furthermore, the history analysis unit can set a low priority for emails from senders that the user does not browse often. This enables email management based on the user's browsing history, allowing for efficient email processing.

[0086] The email management system may include a filtering unit that estimates a user's emotions and adjusts email filtering criteria based on the estimated emotions. For example, if the user is feeling stressed, the filtering unit may set filtering criteria to display only important emails. If the user is feeling relaxed, the filtering unit may set filtering criteria to display all emails. If the user is in a hurry, the filtering unit may set filtering criteria to display only urgent emails. This provides flexible filtering according to the user's emotions and enables efficient email processing.

[0087] The email management system can include a transmission analysis unit that analyzes a user's email sending history and adjusts the priority of emails based on sending frequency. For example, the transmission analysis unit can set a high priority for emails to senders to which the user frequently sends. The transmission analysis unit can also set a high priority for emails that contain content that the user has previously considered important. Furthermore, the transmission analysis unit can set a low priority for emails to senders to which the user does not often send. This enables email management based on the user's sending history and allows for efficient email processing.

[0088] The processing flow of the second embodiment will be briefly explained below.

[0089] Step 1: The analysis unit analyzes the content of the email. The analysis unit analyzes, for example, the subject, body, and sender information of the email to understand the content of the email. For example, if the subject contains keywords such as "important" or "urgent," the analysis unit classifies the email as an urgent email. The analysis unit can also analyze the body of the email to determine the type and importance of the email. For example, the analysis unit can use natural language processing technology to analyze the content of the email and extract important information. Step 2: The selection unit selects an icon based on the content analyzed by the analysis unit. For example, the selection unit can select a "Work" icon for work-related emails, a "Private" icon for private emails, and an "Urgent" icon for urgent emails. The selection unit can select an appropriate icon depending on the type and importance of the email. Step 3: The display unit displays the icon selected by the selection unit. The display unit can display the icon on an email list screen, for example. The display unit can display the icon so that the user can understand the type and importance of the email at a glance.

[0090] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0091] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0092] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0093] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0097] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0098] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0099] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0100] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0101] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0103] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0105] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0106] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0108] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0109] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0110] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0111] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0112] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0113] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0114] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0116] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0117] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0118] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0119] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0120] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0121] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0122] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0124] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0125] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 7, a 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.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0161] [Explanation of symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an analysis unit that analyzes the content of the email; a selection unit that selects an icon based on the content analyzed by the analysis unit; a display unit that displays the icon selected by the selection unit; Equipped with A system characterized by:

2. Equipped with a determination unit that determines the priority of emails The system of claim 1 .

3. Equipped with a classification unit that classifies emails based on the judgment results 3. The system of claim 2.

4. It has a notification section that notifies the user of classified emails.

4. The system of claim 3.

5. The analysis unit Estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. The system of claim 1 .

6. The analysis unit Refer to past analysis results of emails and adjust the analysis algorithm The system of claim 1 .

7. The analysis unit Analyze based on email sender attribute information The system of claim 1 .

8. The analysis unit Estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. The system of claim 1 .

9. The analysis unit Perform geographic analysis of emails The system of claim 1 .

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