System

Abstract

To provide a system for efficiently managing unread mails.SOLUTION: A system includes an unread mail analysis unit, an icon addition unit, and a category classification unit. The unread mail analysis unit analyzes an unread mail. The icon addition unit adds an icon based on the content of the mail analyzed by the unread mail analysis unit. A category classification part classifies the mail to which the icon is imparted by the icon imparting part for each category.SELECTED DRAWING: Figure 1

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 of making it difficult to efficiently manage unread emails in an inbox.

[0005] The system according to the embodiment aims to efficiently manage unread emails. [Means for solving the problem]

[0006] The system according to the embodiment includes an unread email analysis unit, an icon assignment unit, and a category classification unit. The unread email analysis unit analyzes unread emails. The icon assignment unit assigns icons based on the content of the email analyzed by the unread email analysis unit. The category classification unit classifies the emails to which icons have been assigned by the icon assignment unit into categories. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently manage unread emails. [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) The email software according to the embodiment of the present invention is a system that automatically analyzes unread emails in the inbox, and uses a generation AI to assign icons that indicate the contents of the emails and categorize them. This allows the email software to help users efficiently manage their emails and ensure that they do not miss important emails.

[0029] The email software according to the embodiment includes an unread email analysis unit, an icon assignment unit, and a category classification unit. The unread email analysis unit analyzes unread emails. For example, the unread email analysis unit analyzes the body and subject of an email using natural language processing technology to extract important information. The unread email analysis unit can also understand the content of the email using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the unread email analysis unit detects keywords such as a meeting schedule or an invoice to understand the content of the email. The icon assignment unit assigns an icon based on the content of the email analyzed by the unread email analysis unit. For example, the icon assignment unit assigns a calendar icon to emails related to meeting schedules and an invoice icon to emails related to invoices. The icon assignment unit can also select an appropriate icon to allow a user to understand the content of the email at a glance. For example, the icon assignment unit can assign a star icon to important emails. The category classification unit classifies the emails to which icons have been assigned by the icon assignment unit into categories. For example, the categorization unit classifies emails into work-related emails, personal emails, advertising emails, etc. The categorization unit can also classify emails based on categories set by the user. For example, the categorization unit can group emails related to a specific project into one category. This allows the email software according to the embodiment to enable the user to efficiently manage emails and prevent important emails from being overlooked. For example, the email software can keep track of important meeting schedules and prevent users from forgetting to pay bills. Furthermore, automatically classifying advertising emails can prevent the inbox from becoming cluttered.

[0030] The unread email analysis unit can evaluate the sender's trustworthiness and importance by referring to the sender's past email history. For example, the unread email analysis unit uses AI to analyze the sender's past email history and evaluate their trustworthiness. For example, it prioritizes analyzing emails from senders who have sent important emails in the past. The unread email analysis unit also evaluates the sender's importance based on the sender's past email history. For example, it prioritizes analyzing emails from senders with whom the sender frequently communicates. The unread email analysis unit also uses AI to evaluate the sender's past email history and evaluate their trustworthiness and importance. For example, it filters out emails from senders who have sent spam emails in the past. This allows the sender's trustworthiness and importance to be evaluated and important emails to be analyzed preferentially.

[0031] In addition to analyzing unread emails, the unread email analysis unit also analyzes the contents of attachments, allowing for a comprehensive evaluation of the importance of emails. For example, the unread email analysis unit uses AI to analyze the contents of attachments in addition to the body and subject of unread emails. For example, it prioritizes the analysis of emails with important documents attached. The unread email analysis unit also analyzes the contents of attachments to comprehensively evaluate the importance of emails. For example, it prioritizes the analysis of emails with contracts or invoices attached. The unread email analysis unit also comprehensively analyzes the body, subject, and contents of attachments of unread emails to evaluate the importance of emails. For example, it prioritizes the analysis of emails that contain important information. This allows for a comprehensive evaluation of the importance of emails by also analyzing the contents of attachments.

[0032] The unread email analysis unit can automatically translate emails written in different languages ​​and analyze their contents. For example, the unread email analysis unit uses AI to automatically translate emails written in different languages ​​and analyze their contents. For example, emails written in English or French are translated into Japanese and analyzed. The unread email analysis unit also uses an automatic translation function to analyze the contents of emails written in different languages. For example, emails written in Spanish or Chinese are translated and analyzed. The unread email analysis unit also adds a function to automatically translate emails written in different languages ​​and analyze their contents. For example, a multilingual email analysis system can be built, which makes it possible to analyze emails written in different languages.

[0033] The icon assignment unit can analyze not only the content of the email but also the sender's profile information and assign an appropriate icon. For example, AI can analyze the email content and the sender's profile information and assign an appropriate icon. For example, if the sender is a boss, an important icon can be assigned. The icon assignment unit can also assign an appropriate icon based on the email content and the sender's profile information. For example, if the sender is a customer, a customer icon can be assigned. The icon assignment unit can also analyze the email content and the sender's profile information and assign an appropriate icon. For example, if the sender is a friend, a private icon can be assigned. This makes it possible to assign a more appropriate icon by analyzing the sender's profile information.

[0034] The icon assigning unit allows users to customize the icon design and can generate icons that suit the preferences of each user. The icon assigning unit, for example, provides a function that allows users to customize the icon design. For example, it is equipped with an icon editor that allows users to freely change the color and shape. The icon assigning unit also builds a system that generates icons that suit the preferences of each user. For example, it automatically generates icons based on a theme selected by the user. The icon assigning unit also allows users to customize the icon design and generates icons that suit the preferences of each user. For example, it uses images uploaded by the user as icons. This allows users to customize the icon design, making it possible to generate icons that suit the preferences of each user.

[0035] The icon assignment unit can add a function that displays a summary of an email or important points in a pop-up when an icon is clicked. The icon assignment unit adds a function that displays a summary of an email or important points in a pop-up when an icon is clicked. For example, it displays a summary of a meeting schedule or an invoice. The icon assignment unit also adds a function that displays a summary of an email or important points in a pop-up so that the user can understand the content at a glance. For example, it highlights important information. The icon assignment unit also adds a function that displays a summary of an email or important points in a pop-up when an icon is clicked. For example, it extracts and displays the main points of the email body. This makes it possible to display a summary of an email or important points in a pop-up by clicking an icon.

[0036] The categorization unit can perform highly accurate categorization by referring not only to the content of the email but also to the sender's past email history. For example, AI can analyze the content of the email and the sender's past email history to perform highly accurate categorization. For example, it can refer to emails that have been classified in the same category in the past. The categorization unit can also build a system that performs highly accurate categorization based on the content of the email and the sender's past email history. For example, it can classify emails from senders with whom the sender frequently communicates into a specific category. The categorization unit can also perform highly accurate categorization by using AI to refer to the content of the email and the sender's past email history. For example, it can prioritize classifying emails from senders who have sent important emails in the past. This makes it possible to perform more accurate categorization by referring to the sender's past email history.

[0037] The category classification unit adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, the category classification unit adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, it learns categories that the user has manually corrected. The category classification unit also builds a system in which the AI ​​improves the accuracy of category classification based on user feedback. For example, it learns the user's correction history and reflects it in subsequent classifications. The category classification unit also adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, if a user manually classifies a specific email, it learns that information and reflects it in subsequent classifications. In this way, the accuracy of category classification can be improved by learning user feedback.

[0038] In addition to categorizing emails, the categorization unit can automatically set the priority of emails and display important emails at the top. For example, the categorization unit uses AI to analyze the content of emails and automatically set the priority in addition to categorizing them. For example, important emails are displayed at the top. The categorization unit also builds a system that automatically sets the priority of emails and displays important emails at the top. For example, urgent emails are displayed first. The categorization unit also builds a system that automatically sets the priority of emails and displays important emails at the top. For example, emails containing important meeting schedules are displayed at the top. In this way, by automatically setting the priority of emails and displaying important emails at the top, it is possible to prevent users from missing important emails.

[0039] The categorization unit can provide an interactive dashboard to visually display the results of the categorization. The categorization unit provides, for example, an interactive dashboard to visually display the results of the categorization. For example, the number of emails in each category and their importance are displayed in a graph. The categorization unit also uses the interactive dashboard to build a system that visually displays the results of the categorization. For example, the system allows the user to click to access detailed information. The categorization unit also provides an interactive dashboard to visually display the results of the categorization. For example, the priority and urgency of emails are displayed in different colors. In this way, by visually displaying the results of the categorization, the user can grasp the status of their emails at a glance.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The unread email analysis unit can automatically add events to a user's schedule based on the content of emails. For example, it can analyze emails containing meeting schedules and automatically add the events to the user's calendar. The unread email analysis unit can also analyze emails containing travel reservation information and add flight and hotel reservations to the calendar. The unread email analysis unit can also set reminders related to the user's schedule. For example, it can set a reminder when a bill payment deadline is approaching. This allows users to efficiently manage their schedules without missing important events or tasks.

[0042] The unread email analysis unit can refer to the sender's past email history and evaluate the sender's trustworthiness and importance. For example, it prioritizes analysis of emails from senders who have sent important emails in the past. The unread email analysis unit also evaluates the sender's importance based on the sender's past email history. For example, it prioritizes analysis of emails from senders with whom you frequently communicate. The unread email analysis unit also uses AI to refer to the sender's past email history and evaluate trustworthiness and importance. For example, it filters out emails from senders who have sent spam in the past. This allows the sender's trustworthiness and importance to be evaluated first, so important emails can be analyzed first.

[0043] In addition to analyzing unread emails, the unread email analysis unit also analyzes the contents of attachments, allowing for a comprehensive evaluation of the importance of emails. For example, AI analyzes the contents of attachments in addition to the body and subject of unread emails. For example, it prioritizes the analysis of emails with important documents attached. The unread email analysis unit also analyzes the contents of attachments to comprehensively evaluate the importance of emails. For example, it prioritizes the analysis of emails with contracts or invoices attached. The unread email analysis unit also comprehensively analyzes the body, subject, and contents of attachments of unread emails to evaluate the importance of emails. For example, it prioritizes the analysis of emails that contain important information. This allows for a comprehensive evaluation of the importance of emails by also analyzing the contents of attachments.

[0044] The unread email analysis unit can automatically translate emails written in different languages ​​and analyze their contents. For example, AI can automatically translate emails written in different languages ​​and analyze their contents. For example, emails written in English or French can be translated into Japanese and analyzed. The unread email analysis unit can also use an automatic translation function to analyze the contents of emails written in different languages. For example, emails written in Spanish or Chinese can be translated and analyzed. The unread email analysis unit can also add a function to automatically translate emails written in different languages ​​and analyze their contents. For example, a multilingual email analysis system can be built. This allows emails written in different languages ​​to be analyzed.

[0045] The icon assignment unit can analyze not only the content of the email but also the sender's profile information and assign an appropriate icon. For example, AI can analyze the content of the email and the sender's profile information and assign an appropriate icon. For example, if the sender is a boss, an important icon can be assigned. The icon assignment unit can also assign an appropriate icon based on the content of the email and the sender's profile information. For example, if the sender is a customer, a customer icon can be assigned. The icon assignment unit can also analyze the content of the email and the sender's profile information and assign an appropriate icon. For example, if the sender is a friend, a private icon can be assigned. This makes it possible to assign a more appropriate icon by analyzing the sender's profile information.

[0046] The icon assigning unit allows users to customize the icon design and can generate icons that suit the preferences of each user. For example, it provides a function that allows users to customize the icon design. For example, it is equipped with an icon editor that allows users to freely change the color and shape. The icon assigning unit also builds a system that generates icons that suit the preferences of each user. For example, it automatically generates icons based on a theme selected by the user. The icon assigning unit also allows users to customize the icon design and generates icons that suit the preferences of each user. For example, it uses images uploaded by the user as icons. This allows users to customize the icon design, making it possible to generate icons that suit the preferences of each user.

[0047] The categorization unit can perform highly accurate categorization by referring not only to the content of the email but also to the sender's past email history. For example, AI can analyze the content of the email and the sender's past email history to perform highly accurate categorization. For example, it can refer to emails that have been classified in the same category in the past. The categorization unit can also build a system that performs highly accurate categorization based on the email content and the sender's past email history. For example, it can classify emails from senders with whom the sender frequently communicates into a specific category. The categorization unit can also perform highly accurate categorization by using AI to refer to the email content and the sender's past email history. For example, it can prioritize classifying emails from senders who have sent important emails in the past. This makes it possible to perform more accurate categorization by referring to the sender's past email history.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The unread email analysis unit analyzes unread emails. For example, the unread email analysis unit uses natural language processing technology to analyze the body and subject of the email and extract important information. The unread email analysis unit can also use generative AI (e.g., text generation AI or multimodal generation AI) to understand the content of the email. For example, the unread email analysis unit detects keywords such as meeting schedules or invoices and understands the content of the email. Step 2: The icon assigning unit assigns an icon based on the content of the email analyzed by the unread email analysis unit. For example, the icon assigning unit assigns a calendar icon to an email about a meeting schedule and an invoice icon to an email about an invoice. The icon assigning unit can also select an appropriate icon to enable the user to understand the content of the email at a glance. For example, the icon assigning unit can assign a star icon to important emails. Step 3: The categorization unit categorizes the emails to which icons have been assigned by the icon assignment unit into categories. For example, the categorization unit categorizes emails into work-related emails, personal emails, advertising emails, etc. The categorization unit can also categorize emails based on categories set by the user. For example, the categorization unit can group emails related to a specific project into one category.

[0050] (Example 2) The email software according to the embodiment of the present invention is a system that automatically analyzes unread emails in the inbox, and uses a generation AI to assign icons that indicate the contents of the emails and categorize them. This allows the email software to help users efficiently manage their emails and ensure that they do not miss important emails.

[0051] The email software according to the embodiment includes an unread email analysis unit, an icon assignment unit, and a category classification unit. The unread email analysis unit analyzes unread emails. For example, the unread email analysis unit analyzes the body and subject of an email using natural language processing technology to extract important information. The unread email analysis unit can also understand the content of the email using a generation AI (e.g., a text generation AI or a multimodal generation AI). For example, the unread email analysis unit detects keywords such as a meeting schedule or an invoice to understand the content of the email. The icon assignment unit assigns an icon based on the content of the email analyzed by the unread email analysis unit. For example, the icon assignment unit assigns a calendar icon to emails related to meeting schedules and an invoice icon to emails related to invoices. The icon assignment unit can also select an appropriate icon to allow a user to understand the content of the email at a glance. For example, the icon assignment unit can assign a star icon to important emails. The category classification unit classifies the emails to which icons have been assigned by the icon assignment unit into categories. For example, the categorization unit classifies emails into work-related emails, personal emails, advertising emails, etc. The categorization unit can also classify emails based on categories set by the user. For example, the categorization unit can group emails related to a specific project into one category. This allows the email software according to the embodiment to enable the user to efficiently manage emails and prevent important emails from being overlooked. For example, the email software can keep track of important meeting schedules and prevent users from forgetting to pay bills. Furthermore, automatically classifying advertising emails can prevent the inbox from becoming cluttered.

[0052] The unread email analysis unit performs sentiment analysis on unread emails and can prioritize analyzing emotionally important emails. For example, the unread email analysis unit uses AI to analyze the body and subject of unread emails and perform sentiment analysis. For example, emails that show positive emotions are prioritized in the analysis and a notification is sent to the user. The unread email analysis unit also uses sentiment analysis to prioritize analyzing emails that show negative emotions and display a warning to the user. For example, emails containing emotions such as anger or sadness are detected. The unread email analysis unit also prioritizes analyzing emotionally important emails based on the results of the sentiment analysis and a notification is sent to the user. For example, emails with high sentiment scores are prioritized in the analysis. This prioritizes the analysis of emotionally important emails, preventing the user from missing important emails.

[0053] The unread email analysis unit can evaluate the sender's trustworthiness and importance by referring to the sender's past email history. For example, the unread email analysis unit uses AI to analyze the sender's past email history and evaluate their trustworthiness. For example, it prioritizes analyzing emails from senders who have sent important emails in the past. The unread email analysis unit also evaluates the sender's importance based on the sender's past email history. For example, it prioritizes analyzing emails from senders with whom the sender frequently communicates. The unread email analysis unit also uses AI to evaluate the sender's past email history and evaluate their trustworthiness and importance. For example, it filters out emails from senders who have sent spam emails in the past. This allows the sender's trustworthiness and importance to be evaluated and important emails to be analyzed preferentially.

[0054] The unread email analysis unit uses the emotion estimation function to estimate the emotion the user had when receiving the email, and can prioritize analyzing emotionally important emails. The unread email analysis unit, for example, uses the emotion estimation function to estimate the emotion the user had when receiving the email. For example, emails that evoke positive emotions are prioritized for analysis. The unread email analysis unit also estimates the emotion the user had when receiving the email, and prioritizes analyzing emotionally important emails. For example, emails with high emotion scores are prioritized for analysis. The unread email analysis unit also uses the emotion estimation function to estimate the emotion the user had when receiving the email, and prioritizes analyzing emotionally important emails. For example, emails that evoke negative emotions are warned. This makes it possible to prioritize analyzing important emails based on the user's emotions.

[0055] In addition to analyzing unread emails, the unread email analysis unit also analyzes the contents of attachments, allowing for a comprehensive evaluation of the importance of emails. For example, the unread email analysis unit uses AI to analyze the contents of attachments in addition to the body and subject of unread emails. For example, it prioritizes the analysis of emails with important documents attached. The unread email analysis unit also analyzes the contents of attachments to comprehensively evaluate the importance of emails. For example, it prioritizes the analysis of emails with contracts or invoices attached. The unread email analysis unit also comprehensively analyzes the body, subject, and contents of attachments of unread emails to evaluate the importance of emails. For example, it prioritizes the analysis of emails that contain important information. This allows for a comprehensive evaluation of the importance of emails by also analyzing the contents of attachments.

[0056] The unread email analysis unit can automatically translate emails written in different languages ​​and analyze their contents. For example, the unread email analysis unit uses AI to automatically translate emails written in different languages ​​and analyze their contents. For example, emails written in English or French are translated into Japanese and analyzed. The unread email analysis unit also uses an automatic translation function to analyze the contents of emails written in different languages. For example, emails written in Spanish or Chinese are translated and analyzed. The unread email analysis unit also adds a function to automatically translate emails written in different languages ​​and analyze their contents. For example, a multilingual email analysis system can be built, which makes it possible to analyze emails written in different languages.

[0057] The unread email analysis unit can use the emotion estimation function to predict the emotional impact of an email before the user opens it and notify the user at an appropriate time. The unread email analysis unit, for example, uses the emotion estimation function to predict the emotional impact of an email before the user opens it. For example, it warns the user about emails that evoke negative emotions. The unread email analysis unit can also predict the emotional impact of an email before the user opens it and notify the user at an appropriate time. For example, it can prioritize notifications of emails that evoke positive emotions. The unread email analysis unit can also use the emotion estimation function to predict the emotional impact of an email before the user opens it and notify the user at an appropriate time. For example, it can prioritize notifications of emails with high emotion scores. This allows the unread email analysis unit to predict the emotional impact of an email before the user opens it and notify the user at an appropriate time.

[0058] The icon assignment unit can analyze not only the content of the email but also the sender's profile information and assign an appropriate icon. For example, AI can analyze the email content and the sender's profile information and assign an appropriate icon. For example, if the sender is a boss, an important icon can be assigned. The icon assignment unit can also assign an appropriate icon based on the email content and the sender's profile information. For example, if the sender is a customer, a customer icon can be assigned. The icon assignment unit can also analyze the email content and the sender's profile information and assign an appropriate icon. For example, if the sender is a friend, a private icon can be assigned. This makes it possible to assign a more appropriate icon by analyzing the sender's profile information.

[0059] The icon assigning unit allows users to customize the icon design and can generate icons that suit the preferences of each user. The icon assigning unit, for example, provides a function that allows users to customize the icon design. For example, it is equipped with an icon editor that allows users to freely change the color and shape. The icon assigning unit also builds a system that generates icons that suit the preferences of each user. For example, it automatically generates icons based on a theme selected by the user. The icon assigning unit also allows users to customize the icon design and generates icons that suit the preferences of each user. For example, it uses images uploaded by the user as icons. This allows users to customize the icon design, making it possible to generate icons that suit the preferences of each user.

[0060] The icon assigning unit can use the emotion estimation function to change the color and shape of an icon based on the emotion the user felt when opening the email. The icon assigning unit, for example, uses the emotion estimation function to change the color and shape of an icon based on the emotion the user felt when opening the email. For example, a bright-colored icon is used to indicate a positive emotion. The icon assigning unit also builds a system that changes the color and shape of an icon based on the emotion the user felt when opening the email. For example, a dark-colored icon is used to indicate a negative emotion. The icon assigning unit also uses the emotion estimation function to change the color and shape of an icon based on the emotion the user felt when opening the email. For example, the shape of the icon is changed according to the emotion score. In this way, by changing the color and shape of the icon based on the user's emotion, the emotional impact can be visually indicated.

[0061] The icon assignment unit can add a function that displays a summary of an email or important points in a pop-up when an icon is clicked. The icon assignment unit adds a function that displays a summary of an email or important points in a pop-up when an icon is clicked. For example, it displays a summary of a meeting schedule or an invoice. The icon assignment unit also adds a function that displays a summary of an email or important points in a pop-up so that the user can understand the content at a glance. For example, it highlights important information. The icon assignment unit also adds a function that displays a summary of an email or important points in a pop-up when an icon is clicked. For example, it extracts and displays the main points of the email body. This makes it possible to display a summary of an email or important points in a pop-up by clicking an icon.

[0062] The icon assignment unit can use the emotion estimation function to predict the emotional impact of an email before the user opens it and adjust the icon design. The icon assignment unit, for example, uses the emotion estimation function to predict the emotional impact of an email before the user opens it and adjust the icon design. For example, a warning icon is attached to an email that evokes negative emotions. The icon assignment unit also builds a system that predicts the emotional impact of an email before the user opens it and adjusts the icon design. For example, a brightly colored icon is attached to an email that evokes positive emotions. The icon assignment unit also uses the emotion estimation function to predict the emotional impact of an email before the user opens it and adjust the icon design. For example, the shape or color of the icon is changed depending on the emotion score. This makes it possible to predict the emotional impact of an email before the user opens it and adjust the icon design.

[0063] The categorization unit can perform highly accurate categorization by referring not only to the content of the email but also to the sender's past email history. For example, AI can analyze the content of the email and the sender's past email history to perform highly accurate categorization. For example, it can refer to emails that have been classified in the same category in the past. The categorization unit can also build a system that performs highly accurate categorization based on the content of the email and the sender's past email history. For example, it can classify emails from senders with whom the sender frequently communicates into a specific category. The categorization unit can also perform highly accurate categorization by using AI to refer to the content of the email and the sender's past email history. For example, it can prioritize classifying emails from senders who have sent important emails in the past. This makes it possible to perform more accurate categorization by referring to the sender's past email history.

[0064] The category classification unit adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, the category classification unit adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, it learns categories that the user has manually corrected. The category classification unit also builds a system in which the AI ​​improves the accuracy of category classification based on user feedback. For example, it learns the user's correction history and reflects it in subsequent classifications. The category classification unit also adds a function that allows users to provide feedback on the results of category classification, and the AI ​​can learn from that feedback. For example, if a user manually classifies a specific email, it learns that information and reflects it in subsequent classifications. In this way, the accuracy of category classification can be improved by learning user feedback.

[0065] The categorization unit can use the emotion estimation function to recategorize categories based on the emotion a user had when opening an email. The categorization unit, for example, uses the emotion estimation function to recategorize categories based on the emotion a user had when opening an email. For example, it recategorizes emails that indicate positive emotions into a specific category. The categorization unit also builds a system for recategorizing categories based on the emotion a user had when opening an email. For example, it recategorizes emails that indicate negative emotions into another category. The categorization unit also uses the emotion estimation function to recategorize categories based on the emotion a user had when opening an email. For example, it recategorizes emails into an appropriate category depending on the emotion score. This allows for more appropriate categorization by recategorizing categories based on the user's emotions.

[0066] In addition to categorizing emails, the categorization unit can automatically set the priority of emails and display important emails at the top. For example, the categorization unit uses AI to analyze the content of emails and automatically set the priority in addition to categorizing them. For example, important emails are displayed at the top. The categorization unit also builds a system that automatically sets the priority of emails and displays important emails at the top. For example, urgent emails are displayed first. The categorization unit also builds a system that automatically sets the priority of emails and displays important emails at the top. For example, emails containing important meeting schedules are displayed at the top. In this way, by automatically setting the priority of emails and displaying important emails at the top, it is possible to prevent users from missing important emails.

[0067] The categorization unit can provide an interactive dashboard to visually display the results of the categorization. The categorization unit provides, for example, an interactive dashboard to visually display the results of the categorization. For example, the number of emails in each category and their importance are displayed in a graph. The categorization unit also uses the interactive dashboard to build a system that visually displays the results of the categorization. For example, the system allows the user to click to access detailed information. The categorization unit also provides an interactive dashboard to visually display the results of the categorization. For example, the priority and urgency of emails are displayed in different colors. In this way, by visually displaying the results of the categorization, the user can grasp the status of their emails at a glance.

[0068] The category classification unit can use the emotion estimation function to identify the category in which the user is most interested and prioritize displaying emails in that category. The category classification unit, for example, uses the emotion estimation function to identify the category in which the user is most interested and prioritize displaying emails in that category. For example, emails in a category that indicates positive emotions are displayed at the top. The category classification unit also builds a system that identifies the category in which the user is most interested and prioritize displaying emails in that category. For example, emails in a category with a high emotion score are displayed preferentially. The category classification unit also uses the emotion estimation function to identify the category in which the user is most interested and prioritize displaying emails in that category. For example, a category is identified based on the user's emotional response and emails are displayed. This allows emails in the category in which the user is most interested to be displayed preferentially, thereby improving user convenience.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The unread email analysis unit can automatically add events to a user's schedule based on the content of emails. For example, it can analyze emails containing meeting schedules and automatically add the events to the user's calendar. The unread email analysis unit can also analyze emails containing travel reservation information and add flight and hotel reservations to the calendar. The unread email analysis unit can also set reminders related to the user's schedule. For example, it can set a reminder when a bill payment deadline is approaching. This allows users to efficiently manage their schedules without missing important events or tasks.

[0071] The unread email analysis unit can use the emotion estimation function to predict the emotional impact of an email before the user opens it and notify the user at the appropriate time. For example, it can warn the user of emails that will evoke negative emotions. The unread email analysis unit can also predict the emotional impact of an email before the user opens it and notify the user at the appropriate time. For example, it can prioritize notifications of emails that evoke positive emotions. The unread email analysis unit can also use the emotion estimation function to predict the emotional impact of an email before the user opens it and notify the user at the appropriate time. For example, it can prioritize notifications of emails with high emotion scores. This allows the unread email analysis unit to predict the emotional impact of an email before the user opens it and notify the user at the appropriate time.

[0072] The unread email analysis unit can refer to the sender's past email history and evaluate the sender's trustworthiness and importance. For example, it prioritizes analysis of emails from senders who have sent important emails in the past. The unread email analysis unit also evaluates the sender's importance based on the sender's past email history. For example, it prioritizes analysis of emails from senders with whom you frequently communicate. The unread email analysis unit also uses AI to refer to the sender's past email history and evaluate trustworthiness and importance. For example, it filters out emails from senders who have sent spam in the past. This allows the sender's trustworthiness and importance to be evaluated first, so important emails can be analyzed first.

[0073] The unread email analysis unit uses the emotion estimation function to estimate the emotion the user had when receiving the email, and can prioritize analyzing emotionally important emails. For example, emails that evoke positive emotions are prioritized for analysis. The unread email analysis unit also estimates the emotion the user had when receiving the email, and prioritize analyzing emotionally important emails. For example, emails with high emotion scores are prioritized for analysis. The unread email analysis unit also uses the emotion estimation function to estimate the emotion the user had when receiving the email, and prioritize analyzing emotionally important emails. For example, emails that evoke negative emotions are warned. This allows important emails to be prioritized for analysis based on the user's emotions.

[0074] In addition to analyzing unread emails, the unread email analysis unit also analyzes the contents of attachments, allowing for a comprehensive evaluation of the importance of emails. For example, AI analyzes the contents of attachments in addition to the body and subject of unread emails. For example, it prioritizes the analysis of emails with important documents attached. The unread email analysis unit also analyzes the contents of attachments to comprehensively evaluate the importance of emails. For example, it prioritizes the analysis of emails with contracts or invoices attached. The unread email analysis unit also comprehensively analyzes the body, subject, and contents of attachments of unread emails to evaluate the importance of emails. For example, it prioritizes the analysis of emails that contain important information. This allows for a comprehensive evaluation of the importance of emails by also analyzing the contents of attachments.

[0075] The unread email analysis unit can automatically translate emails written in different languages ​​and analyze their contents. For example, AI can automatically translate emails written in different languages ​​and analyze their contents. For example, emails written in English or French can be translated into Japanese and analyzed. The unread email analysis unit can also use an automatic translation function to analyze the contents of emails written in different languages. For example, emails written in Spanish or Chinese can be translated and analyzed. The unread email analysis unit can also add a function to automatically translate emails written in different languages ​​and analyze their contents. For example, a multilingual email analysis system can be built. This allows emails written in different languages ​​to be analyzed.

[0076] The icon assignment unit can analyze not only the content of the email but also the sender's profile information and assign an appropriate icon. For example, AI can analyze the content of the email and the sender's profile information and assign an appropriate icon. For example, if the sender is a boss, an important icon can be assigned. The icon assignment unit can also assign an appropriate icon based on the content of the email and the sender's profile information. For example, if the sender is a customer, a customer icon can be assigned. The icon assignment unit can also analyze the content of the email and the sender's profile information and assign an appropriate icon. For example, if the sender is a friend, a private icon can be assigned. This makes it possible to assign a more appropriate icon by analyzing the sender's profile information.

[0077] The icon assigning unit allows users to customize the icon design and can generate icons that suit the preferences of each user. For example, it provides a function that allows users to customize the icon design. For example, it is equipped with an icon editor that allows users to freely change the color and shape. The icon assigning unit also builds a system that generates icons that suit the preferences of each user. For example, it automatically generates icons based on a theme selected by the user. The icon assigning unit also allows users to customize the icon design and generates icons that suit the preferences of each user. For example, it uses images uploaded by the user as icons. This allows users to customize the icon design, making it possible to generate icons that suit the preferences of each user.

[0078] The icon assigning unit can use the emotion estimation function to change the color and shape of an icon based on the emotion the user felt when opening the email. For example, the emotion estimation function is used to change the color and shape of an icon based on the emotion the user felt when opening the email. For example, a bright-colored icon is used to indicate a positive emotion. The icon assigning unit also builds a system that changes the color and shape of an icon based on the emotion the user felt when opening the email. For example, a dark-colored icon is used to indicate a negative emotion. The icon assigning unit also uses the emotion estimation function to change the color and shape of an icon based on the emotion the user felt when opening the email. For example, the shape of the icon is changed according to the emotion score. In this way, by changing the color and shape of an icon based on the user's emotion, the emotional impact can be visually indicated.

[0079] The categorization unit can perform highly accurate categorization by referring not only to the content of the email but also to the sender's past email history. For example, AI can analyze the content of the email and the sender's past email history to perform highly accurate categorization. For example, it can refer to emails that have been classified in the same category in the past. The categorization unit can also build a system that performs highly accurate categorization based on the email content and the sender's past email history. For example, it can classify emails from senders with whom the sender frequently communicates into a specific category. The categorization unit can also perform highly accurate categorization by using AI to refer to the email content and the sender's past email history. For example, it can prioritize classifying emails from senders who have sent important emails in the past. This makes it possible to perform more accurate categorization by referring to the sender's past email history.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The unread email analysis unit analyzes unread emails. For example, the unread email analysis unit uses natural language processing technology to analyze the body and subject of the email and extract important information. The unread email analysis unit can also use generative AI (e.g., text generation AI or multimodal generation AI) to understand the content of the email. For example, the unread email analysis unit detects keywords such as meeting schedules or invoices and understands the content of the email. Step 2: The icon assigning unit assigns an icon based on the content of the email analyzed by the unread email analysis unit. For example, the icon assigning unit assigns a calendar icon to an email about a meeting schedule and an invoice icon to an email about an invoice. The icon assigning unit can also select an appropriate icon to enable the user to understand the content of the email at a glance. For example, the icon assigning unit can assign a star icon to important emails. Step 3: The categorization unit categorizes the emails to which icons have been assigned by the icon assignment unit into categories. For example, the categorization unit categorizes emails into work-related emails, personal emails, advertising emails, etc. The categorization unit can also categorize emails based on categories set by the user. For example, the categorization unit can group emails related to a specific project into one category.

[0082] 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.

[0083] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0111] 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.

[0112] 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.

[0113] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 model 58 includes AI 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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. [Explanation of symbols]

[0149] 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 unread email analysis unit that analyzes unread emails; an icon assigning unit that assigns an icon based on the content of the email analyzed by the unread email analyzing unit; a category classification unit that classifies the emails to which the icons have been assigned by the icon assignment unit into categories. A system characterized by:

2. The unread email analysis unit Sentiment analysis is performed on the unread emails, and emails that are emotionally important are prioritized for analysis.

2. The system of claim 1.

3. The unread email analysis unit Refer to the sender's past email history and evaluate the sender's reliability and importance.

2. The system of claim 1.

4. The unread email analysis unit Estimate the user's emotions when receiving emails and prioritize analysis of emotionally important emails 2. The system of claim 1.

5. The unread email analysis unit In addition to analyzing the unread emails, the contents of the attachments are also analyzed to comprehensively evaluate the importance of the emails.

2. The system of claim 1.

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