System

Abstract

An object of a system according to an embodiment is to collectively manage a plurality of mail accounts and automatically determine a spam mail or a mail having a virus risk.SOLUTION: A system according to an embodiment includes a registration unit, an acquisition unit, a classification unit, a determination unit, and a mounting unit. The registration unit registers a plurality of mail accounts. The acquisition unit automatically acquires an e-mail that reaches each account registered by the registration unit. The classification unit classifies the mail acquired by the acquisition unit into categories based on the from or the content. The determination unit determines a spam mail or a mail having a virus risk from among the mails classified by the classification unit. The mounting unit mounts and manages the mail determined by the determination unit on the smartphone.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 technology makes it difficult to manage multiple email accounts at once, and it is time-consuming to distinguish between spam and virus-related emails.

[0005] The system according to the embodiment aims to collectively manage multiple email accounts and automatically identify spam emails and emails that pose a virus risk. [Means for solving the problem]

[0006] The system according to the embodiment includes a registration unit, an acquisition unit, a classification unit, a discrimination unit, and a mounting unit. The registration unit registers multiple email accounts. The acquisition unit automatically acquires emails received by each account registered by the registration unit. The classification unit categorizes the emails acquired by the acquisition unit based on the "from" or content. The discrimination unit discriminates spam emails and emails with virus risks from the emails classified by the classification unit. The mounting unit loads the emails classified by the discrimination unit into a smartphone and manages them. [Effects of the Invention]

[0007] The system according to the embodiment can manage multiple email accounts in one place and automatically identify spam emails and emails with virus risks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An email management system according to an embodiment of the present invention manages multiple email accounts centrally and automates categorization and spam detection. The email management system allows users to register multiple email accounts, automatically retrieves emails received in each account, and categorizes them based on the "from" address and content. Furthermore, it automatically identifies spam and virus-risk emails. This system is also installed on smartphones, allowing users to manage multiple email accounts no matter where they are. For example, a user can register multiple email accounts in the email management system by simply entering login information for each account. For example, different email services such as Gmail (registered trademark), Yahoo! (registered trademark), and Outlook (registered trademark) can be managed with a single tool. The email management system then automatically retrieves emails received in each account and categorizes them based on the "from" address and content. For example, emails can be classified as work-related, personal, or promotional. This allows users to efficiently manage their emails without missing important emails. Furthermore, the email management system automatically identifies spam and virus-risk emails. For example, it uses spam filters and virus scanning functions to detect dangerous emails and display warnings to the user. This allows users to use email safely. In addition, email management systems are installed on smartphones, allowing users to manage multiple email accounts no matter where they are. For example, users can easily check and reply to emails even when they are out and about. This improves user convenience. As a result, email management systems improve user convenience by managing multiple email accounts in one place and automating categorization and spam detection. As a result, email management systems make users' email management more efficient, preventing important emails from being overlooked and automatically identifying spam and emails with virus risks. For example, since users can manage multiple email accounts no matter where they are, they can easily check and reply to emails even when they are out and about.This is expected to improve user convenience and the effectiveness of companies' direct mail (DM).

[0029] An email management system according to an embodiment includes a registration unit, an acquisition unit, a classification unit, a discrimination unit, and a built-in unit. The registration unit allows a user to register multiple email accounts. The user can manage different email services, such as Gmail, Yahoo! Mail, and Outlook, using a single tool. The acquisition unit automatically acquires emails received by each account registered by the registration unit. The acquisition unit can acquire emails, for example, using periodic polling or push notifications. The classification unit categorizes the emails acquired by the acquisition unit based on the sender or content. The classification unit can classify emails into categories such as work-related emails, personal emails, and promotional emails. The discrimination unit discriminates spam emails and emails with virus risks from the emails classified by the classification unit. The discrimination unit can detect dangerous emails using, for example, a spam filter or a virus scan function and display a warning to the user. The built-in unit installs and manages emails classified by the discrimination unit on a smartphone. The built-in unit can manage emails and set notifications using, for example, a dedicated app. As a result, the email management system according to the embodiment manages multiple email accounts in a centralized manner and automates categorization and spam email identification, thereby improving user convenience.

[0030] The discrimination unit detects dangerous emails using a spam filter and a virus scan function and includes a warning unit that displays a warning to the user. The spam filter detects spam emails using techniques such as blacklists, whitelists, and Bayesian filtering. The virus scan function detects emails with a virus risk using techniques such as signature-based scanning and heuristic scanning. The warning unit displays a warning message to the user when a dangerous email is detected. This improves email safety by detecting dangerous emails and displaying a warning to the user.

[0031] The classification unit can classify emails into work-related emails, private emails, and promotional emails. The classification unit classifies emails into work-related emails, private emails, promotional emails, etc. based on, for example, the sender's domain or email keywords. For example, work-related emails are classified as emails sent by senders with a company domain or emails containing work-related keywords. Private emails are classified as emails sent by senders with a personal domain or emails containing private content. Promotional emails are classified as emails containing keywords related to advertisements or campaigns. By categorizing emails, users can manage their emails without missing important emails.

[0032] The installed unit can enable a user to manage multiple email accounts wherever they are. For example, the installed unit can install a dedicated app on a smartphone, allowing the user to manage multiple email accounts wherever they are. For example, the user can easily check and reply to emails even when they are out and about. The installed unit can manage emails anywhere as long as there is an internet connection. This allows the user to easily check and reply to emails even when they are out and about.

[0033] The on-board unit may include an improvement unit for improving the effectiveness of a company's direct mail. The improvement unit provides, for example, a function for improving the open rate and click rate of direct mail. For example, the improvement unit enhances the notification function so that users do not miss direct mail. It is also possible to analyze the content of direct mail and send personalized mail based on the user's interests and concerns. This improves the effectiveness of a company's direct mail.

[0034] At the time of registration, the registration unit can suggest the optimal registration method by referring to the user's past email account registration history. For example, the registration unit preferentially displays email services that the user has frequently used in the past. It suggests registration methods (manual, import, etc.) that the user has used in the past. It predicts and suggests email services that will be used during specific time periods from the user's past registration history. In this way, it is possible to suggest the optimal registration method by referring to the user's past registration history.

[0035] The registration unit can select an appropriate registration means depending on the user's input method during registration. For example, if the user selects voice input, the registration unit registers the account information using voice recognition technology. If the user selects text input, the registration unit provides a simple form for the user to enter the account information. If the user selects image input, the registration unit registers the account information using a two-dimensional code (e.g., QR Code (registered trademark)) or a screenshot. This allows the registration process to be made more efficient by selecting the optimal registration means depending on the user's input method.

[0036] The registration unit can optimize the registration process based on the user's current device information during registration. For example, if the user is using a smartphone, the registration unit provides a mobile-friendly interface. If the user is using a tablet, the registration unit provides an interface optimized for a large screen. If the user is using a desktop, the registration unit provides detailed input options. This improves user convenience by optimizing the registration process based on the user's device information.

[0037] During registration, the registration unit can prioritize registering highly relevant email accounts taking into account the user's geographical location information. For example, if the user is in a specific area, the registration unit prioritizes displaying email services that are commonly used in that area. If the user is traveling, the registration unit prioritizes registering email accounts to be used at the travel destination. If the user is at home, the registration unit prioritizes registering home email accounts. This improves user convenience by prioritizing registration of highly relevant email accounts based on the user's geographical location information.

[0038] At the time of registration, the registration unit can analyze the user's social media activity and suggest related email accounts. For example, the registration unit automatically suggests email accounts used by the user on social media. It suggests related email accounts by referring to the activity of the user's friends on social media. It analyzes the content of the user's posts on social media and suggests related email accounts. In this way, it is possible to suggest related email accounts by analyzing the user's social media activity.

[0039] The registration unit can adjust the registration method by reflecting the user's past feedback at the time of registration. For example, the registration unit suggests an optimal registration method based on the user's past feedback. The registration unit omits certain registration steps based on the user's past feedback. The registration process is improved based on the user's past feedback. In this way, the optimal registration method can be provided by reflecting the user's past feedback.

[0040] During acquisition, the acquisition unit can analyze the user's past email acquisition history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of emails that the user has frequently acquired in the past. From the user's past acquisition history, it predicts and suggests emails to be acquired during a specific time period. It analyzes the user's past acquisition history and suggests the most efficient acquisition method. In this way, the optimal acquisition method can be suggested by analyzing the user's past acquisition history.

[0041] The acquisition unit can perform filtering based on the user's current project or area of ​​interest when acquiring emails. For example, the acquisition unit prioritizes acquiring emails related to a project the user is currently working on. The acquisition unit filters related emails based on the user's area of ​​interest. The acquisition unit analyzes the user's current project or area of ​​interest and acquires the most appropriate emails. In this way, important emails can be prioritized by filtering emails based on the user's current project or area of ​​interest.

[0042] The acquisition unit can select the optimal acquisition means depending on the user's input method at the time of acquisition. For example, if the user selects voice input, the acquisition unit acquires the email using voice recognition technology. If the user selects text input, the acquisition unit acquires the email by providing a simple form. If the user selects image input, the acquisition unit acquires the email using a two-dimensional code or screenshot. In this way, the optimal acquisition means is selected depending on the user's input method, thereby streamlining the email acquisition process.

[0043] The acquisition unit can prioritize acquiring highly relevant emails by taking into consideration the user's geographical location information when acquiring emails. For example, if the user is in a specific area, the acquisition unit prioritizes acquiring emails related to that area. If the user is traveling, the acquisition unit prioritizes acquiring emails related to the travel destination. If the user is at home, the acquisition unit prioritizes acquiring emails for home use. This improves user convenience by prioritizing the acquisition of highly relevant emails based on the user's geographical location information.

[0044] At the time of acquisition, the acquisition unit can analyze the user's social media activity and acquire related emails. For example, the acquisition unit automatically acquires the email account the user uses for social media. The acquisition unit acquires related emails by referring to the activity of the user's friends on social media. The acquisition unit analyzes the content of the user's posts on social media and acquires related emails. In this way, related emails can be acquired by analyzing the user's social media activity.

[0045] The acquisition unit can customize the acquisition method by reflecting the user's past feedback during acquisition. For example, the acquisition unit suggests an optimal acquisition method based on feedback provided by the user in the past. The acquisition unit omits specific acquisition steps based on the user's past feedback. The acquisition unit improves the acquisition process by referring to the user's past feedback. In this way, the optimal acquisition method can be provided by reflecting the user's past feedback.

[0046] The classification unit can adjust the level of detail of the classification based on the importance of the email when classifying. For example, the classification unit classifies important emails in detail and other emails in a simplified manner. Emails with high importance are classified as a priority, and other emails are put off until later. The level of detail of the classification is dynamically adjusted according to the importance. In this way, by adjusting the level of detail of the classification based on the importance of the email, important emails can be managed with priority.

[0047] The classification unit can apply different classification algorithms depending on the category of email during classification. For example, the classification unit applies a specific algorithm to work-related emails and another algorithm to personal emails. It applies a dedicated algorithm to promotional emails and a general algorithm to other emails. The most appropriate classification algorithm is selected depending on the category of email. This improves classification accuracy by applying the most appropriate classification algorithm depending on the category of email.

[0048] The classification unit can improve the accuracy of classification based on the user's past classification results during classification. For example, the classification unit automatically classifies similar emails by referring to emails that the user has previously classified. The classification unit analyzes the user's past classification results and proposes an optimal classification method. The classification algorithm is improved based on the user's past classification history. In this way, the accuracy of classification is improved by referring to the user's past classification results.

[0049] The classification unit can determine the priority of classification based on the time of submission of emails during classification. For example, the classification unit prioritizes classification of recently received emails. It prioritizes classification of new emails, leaving older emails for later. The classification priority is dynamically adjusted according to the time of submission. In this way, by determining the priority of classification based on the time of submission of emails, it is possible to prioritize management of the most recent emails.

[0050] The classification unit can set the order of classification based on the relevance of emails when classifying them. For example, the classification unit prioritizes classifying highly relevant emails. It prioritizes classifying important emails, leaving less relevant emails for later. It dynamically adjusts the order of classification according to the relevance of emails. This allows important emails to be managed with priority by adjusting the order of classification based on the relevance of emails.

[0051] The classification unit can adjust the use of technical terms for classification according to the user's level of expertise during classification. For example, if the user has technical expertise, the classification unit uses technical terms for classification. If the user does not have technical expertise, the classification unit uses simple terms for classification. The classification terminology is dynamically adjusted according to the user's level of expertise. This helps the user understand by adjusting the technical terms for classification according to the user's level of expertise.

[0052] The discrimination unit can improve the accuracy of discrimination based on the interrelationships between emails when discriminating. For example, the discrimination unit groups related emails and discriminates them all at once. The discrimination unit analyzes the interrelationships between emails and discriminates the risk of spam or viruses. The discrimination algorithm is improved based on the interrelationships between emails. In this way, the accuracy of discrimination is improved by taking the interrelationships between emails into consideration.

[0053] The discrimination unit can make discrimination taking into account the attribute information of the email sender. For example, the discrimination unit prioritizes discrimination of emails from trusted senders. Discrimination of emails from unknown senders is performed in detail. The discrimination algorithm is adjusted based on the attribute information of the sender. In this way, the accuracy of discrimination is improved by taking into account the attribute information of the email sender.

[0054] The discrimination unit can weight the discrimination based on the frequency of email submission when discriminating. For example, the discrimination unit performs detailed discrimination on frequently sent emails and simple discrimination on emails that are submitted infrequently. The discrimination weight is dynamically adjusted according to the submission frequency. In this way, by weighting the discrimination based on the frequency of email submission, important emails can be prioritized.

[0055] The discrimination unit can make discrimination taking into account the geographical distribution of emails. For example, the discrimination unit prioritizes discrimination of emails from a specific region. It prioritizes discrimination of emails from geographically close senders. It adjusts the discrimination algorithm based on the geographical distribution of emails. In this way, the accuracy of discrimination is improved by taking into account the geographical distribution of emails.

[0056] The discrimination unit can improve the accuracy of discrimination by referring to literature related to the email when discriminating. The discrimination unit, for example, refers to related literature to discriminate the risk of spam or viruses. The discrimination unit improves the accuracy of discrimination by referring to literature related to the content of the email. The discrimination algorithm is improved based on the related literature. In this way, the accuracy of discrimination is improved by referring to literature related to the email.

[0057] The discrimination unit can discriminate based on the market value of the email when discriminating. For example, the discrimination unit discriminates emails with high market value with priority. Discrimination of emails with low market value is simplified. The discrimination algorithm is adjusted based on the market value of the email. In this way, important emails can be discriminated with priority by taking the market value of the email into consideration.

[0058] When the device is installed, the onboard unit can select the optimal function by referring to the user's past usage history. For example, the onboard unit prioritizes the installation of functions that the user has frequently used in the past. Based on the user's past usage history, the onboard unit predicts and suggests functions that will be used during specific time periods. The user's past usage history is analyzed and the most efficient function is suggested. This makes it possible to provide the optimal function by referring to the user's past usage history.

[0059] During onboarding, the onboarding unit can optimize the onboarding process based on the user's current device information. For example, if the user is using a smartphone, the onboarding unit provides mobile-friendly functions. If the user is using a tablet, the onboarding unit provides functions optimized for large screens. If the user is using a desktop, the onboarding unit provides detailed functions. This improves user convenience by optimizing the onboarding process based on the user's device information.

[0060] The onboarding unit can improve the onboard functions by reflecting user feedback during onboarding. For example, the onboarding unit suggests optimal functions based on feedback previously provided by the user. Improves specific functions based on the user's past feedback. Improves the onboarding process by referring to the user's past feedback. In this way, optimal functions can be provided by reflecting user feedback.

[0061] When the onboard unit is installed, it can select appropriate functions based on the user's geographical location information. For example, if the user is in a specific area, the onboard unit will prioritize displaying functions that are commonly used in that area. If the user is traveling, the onboard unit will prioritize installing functions to be used at the travel destination. If the user is at home, the onboard unit will prioritize installing functions for home use. This improves user convenience by providing optimal functions based on the user's geographical location information.

[0062] When installed, the on-board unit can analyze the user's social media activity and suggest related functions. For example, the on-board unit automatically suggests functions that the user uses on social media. It suggests related functions based on the activity of the user's friends on social media. It analyzes the content of the user's social media posts and suggests related functions. In this way, it is possible to provide related functions by analyzing the user's social media activity.

[0063] The mounting unit can customize the mounting method by reflecting the user's past feedback during mounting. For example, the mounting unit suggests the optimal mounting method based on the user's past feedback. The mounting unit omits specific mounting steps based on the user's past feedback. The mounting process is improved based on the user's past feedback. In this way, the optimal mounting method can be provided by reflecting the user's past feedback.

[0064] The warning unit can adjust the level of detail of the warning based on the risk level of the email when issuing the warning. For example, the warning unit displays a detailed warning for high-risk emails and a simple warning for low-risk emails. The level of detail of the warning is dynamically adjusted according to the risk level of the email. In this way, user convenience is improved by adjusting the level of detail of the warning based on the risk level of the email.

[0065] The warning unit can apply different warning algorithms depending on the category of email when issuing a warning. For example, the warning unit applies a specific warning algorithm to spam emails and another warning algorithm to virus emails. It applies a dedicated warning algorithm to promotional emails and a general warning algorithm to other emails. The most appropriate warning algorithm is selected depending on the category of email. This improves the accuracy of warnings by applying the most appropriate warning algorithm depending on the category of email.

[0066] The warning unit can improve the accuracy of warnings by referring to the user's past warning history when issuing a warning. For example, the warning unit refers to warnings the user has received in the past and displays a warning for similar emails. The warning unit analyzes the user's past warning history and suggests the optimal warning method. The warning algorithm is improved based on the user's past warning history. In this way, the accuracy of warnings is improved by referring to the user's past warning history.

[0067] When issuing a warning, the warning unit can determine the priority of the warning based on the time of submission of the email. For example, the warning unit prioritizes displaying warnings for recently received emails. Warnings for older emails are left to be displayed later, and warnings for newer emails are prioritized. The priority of the warning is dynamically adjusted according to the time of submission. In this way, by determining the priority of the warning based on the time of submission of the email, the most recent warning can be displayed preferentially.

[0068] The warning unit can set the order of warnings based on the relevance of emails when issuing a warning. For example, the warning unit prioritizes displaying warnings for highly relevant emails. It prioritizes displaying warnings for important emails, leaving warnings for less relevant emails for later. It dynamically adjusts the order of warnings according to the relevance of emails. By adjusting the order of warnings based on the relevance of emails, important warnings can be displayed with priority.

[0069] The warning unit can adjust the use of technical terms in the warning depending on the user's level of expertise when issuing a warning. For example, if the user has technical expertise, the warning unit issues a warning using technical terms. If the user does not have technical expertise, the warning unit issues a warning using simple terms. The warning terminology is dynamically adjusted depending on the user's level of expertise. This helps the user understand the warning by adjusting the technical terms in the warning depending on the user's level of expertise.

[0070] When making improvements, the improvement department can analyze the user's past usage history and select the optimal improvement method. For example, the improvement department proposes the optimal improvement method based on functions that the user has frequently used in the past. From the user's past usage history, it predicts which functions will be used at specific times and proposes an improvement method. It analyzes the user's past usage history and proposes the most efficient improvement method. In this way, it is possible to provide the optimal improvement method by analyzing the user's past usage history.

[0071] During improvement, the improvement unit can optimize the improvement process based on the user's current device information. For example, if the user is using a smartphone, the improvement unit provides a mobile-friendly improvement method. If the user is using a tablet, the improvement unit provides an improvement method optimized for a large screen. If the user is using a desktop, the improvement unit provides a detailed improvement method. This improves user convenience by optimizing the improvement process based on the user's device information.

[0072] The improvement department can improve the improvement method by reflecting the user's feedback when making an improvement. For example, the improvement department proposes an optimal improvement method based on feedback provided by the user in the past. It omits specific improvement steps from the user's past feedback. It improves the improvement process by referring to the user's past feedback. In this way, it is possible to provide an optimal improvement method by reflecting the user's feedback.

[0073] When making an improvement, the improvement unit can select the optimal improvement method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the improvement unit will provide preferentially improvement methods that are commonly used in that area. If the user is traveling, the improvement unit will provide preferentially improvement methods that are used at the user's destination. If the user is at home, the improvement unit will provide preferentially home improvement methods. In this way, convenience for the user is improved by providing the optimal improvement method based on the user's geographical location information.

[0074] When making improvements, the improvement unit can analyze the user's social media activity and suggest means for improvement. For example, the improvement unit automatically suggests improvement methods used by the user on social media. It suggests related improvement methods by referring to the activities of the user's friends on social media. It analyzes the content of the user's posts on social media and suggests related improvement methods. In this way, it is possible to provide related improvement methods by analyzing the user's social media activity.

[0075] When making improvements, the improvement department can customize the improvement method by reflecting the user's past feedback. For example, the improvement department proposes the optimal improvement method based on the user's past feedback. It omits specific improvement steps from the user's past feedback. It improves the improvement process by referring to the user's past feedback. In this way, it is possible to provide the optimal improvement method by reflecting the user's past feedback.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The email management system may further include a voice assistant unit. The voice assistant unit enables the user to manage emails using voice commands. For example, when the user issues a voice command such as "Check new emails," the voice assistant unit instructs the acquisition unit to acquire new emails. When the user issues a command such as "Delete spam emails," the voice assistant unit instructs the classification unit to delete the spam emails. When the user issues a command such as "Show work-related emails," the voice assistant unit instructs the classification unit to display work-related emails. This allows the user to efficiently manage emails using voice commands.

[0078] The email management system may further include a translation unit. The translation unit can automatically translate emails received by a user. For example, if a user receives an email in a foreign language, the translation unit translates the email into the user's native language. When a user replies, the translation unit can also translate the reply into the recipient's language. Furthermore, the translation unit can perform context analysis to provide an appropriate translation based on the content of the email. This allows users to communicate across language barriers.

[0079] The email management system may further include a schedule linking unit. The schedule linking unit may link with the user's calendar app and automatically update the schedule based on the contents of emails. For example, when a user receives an invitation email for a meeting, the schedule linking unit automatically adds the date and time of the meeting to the calendar. In addition, if the user changes their schedule, the schedule linking unit may notify the user of the change by email. Furthermore, the schedule linking unit may set email priorities based on the user's schedule. This allows the user to manage their schedule more efficiently.

[0080] The email management system may further include an archive unit. The archive unit provides a function for the user to efficiently manage past emails. For example, the user can archive emails from a specific period. The archive unit may also automatically archive emails based on the content of the emails. Furthermore, the archive unit provides a search function for the user to easily search archived emails. This allows the user to efficiently manage past emails and quickly find the information they need.

[0081] The email management system may further include a notification customization unit. The notification customization unit can customize the notification method for emails received by the user. For example, if the user receives an important email, the notification customization unit can provide a push notification or a voice notification. Also, if the user does not want to receive notifications during a specific time period, the notification customization unit can turn off notifications during that time period. Furthermore, the notification customization unit can dynamically change the notification method based on the user's schedule or current situation. This allows the user to customize the notification method to suit their needs.

[0082] The processing flow of the first embodiment will be briefly explained below.

[0083] Step 1: In the registration section, users register multiple email accounts. Users can manage different email services, such as Gmail, Yahoo! Mail, and Outlook, with a single tool. Step 2: The acquisition unit automatically acquires emails that arrive at each account registered by the registration unit. The acquisition unit can acquire emails by using, for example, periodic polling or push notifications. Step 3: The classifier categorizes the emails acquired by the acquirer based on the emails' origin or content. For example, the classifier can categorize emails into work-related emails, personal emails, promotional emails, etc. Step 4: The discrimination unit discriminates between spam and virus-risk emails from among the emails classified by the classification unit. The discrimination unit can detect dangerous emails using, for example, a spam filter or virus scanning function and display a warning to the user. Step 5: The loading unit loads the email identified by the identification unit into the smartphone and manages it. The loading unit can manage the email using a dedicated app, for example, and set notifications.

[0084] (Example 2) An email management system according to an embodiment of the present invention manages multiple email accounts centrally and automates categorization and spam detection. The email management system allows users to register multiple email accounts, automatically retrieves emails received in each account, and categorizes them based on the "from" field and content. Furthermore, it automatically detects spam and virus-risk emails. This system is also installed on smartphones, allowing users to manage multiple email accounts no matter where they are. For example, a user can register multiple email accounts in the email management system by simply entering login information for each account. For example, different email services such as Gmail, Yahoo! Mail, and Outlook can be managed with a single tool. The email management system then automatically retrieves emails received in each account and categorizes them based on the "from" field and content. For example, emails can be classified as work-related, personal, or promotional. This allows users to efficiently manage their email without missing important emails. Furthermore, the email management system automatically identifies spam and virus-risk emails. For example, it uses spam filters and virus scanning functions to detect dangerous emails and display warnings to users. This allows users to use email safely. In addition, email management systems are installed on smartphones, allowing users to manage multiple email accounts no matter where they are. For example, they can easily check and reply to emails even when they are out and about. This improves user convenience. As a result, email management systems improve user convenience by managing multiple email accounts in one place and automating categorization and spam detection. This makes email management more efficient for users, preventing important emails from being overlooked and automatically identifying spam and emails with virus risks. For example, since users can manage multiple email accounts no matter where they are, they can easily check and reply to emails even when they are out and about. This improves user convenience and is expected to improve the effectiveness of corporate direct mail (DM).

[0085] An email management system according to an embodiment includes a registration unit, an acquisition unit, a classification unit, a discrimination unit, and a built-in unit. The registration unit allows a user to register multiple email accounts. The user can manage different email services, such as Gmail, Yahoo! Mail, and Outlook, using a single tool. The acquisition unit automatically acquires emails received by each account registered by the registration unit. The acquisition unit can acquire emails, for example, using periodic polling or push notifications. The classification unit categorizes the emails acquired by the acquisition unit based on the sender or content. The classification unit can classify emails into categories such as work-related emails, personal emails, and promotional emails. The discrimination unit discriminates spam emails and emails with virus risks from the emails classified by the classification unit. The discrimination unit can detect dangerous emails using, for example, a spam filter or a virus scan function and display a warning to the user. The built-in unit installs and manages emails classified by the discrimination unit on a smartphone. The built-in unit can manage emails and set notifications using, for example, a dedicated app. As a result, the email management system according to the embodiment manages multiple email accounts in a centralized manner and automates categorization and spam email identification, thereby improving user convenience.

[0086] The discrimination unit detects dangerous emails using a spam filter and a virus scan function and includes a warning unit that displays a warning to the user. The spam filter detects spam emails using techniques such as blacklists, whitelists, and Bayesian filtering. The virus scan function detects emails with a virus risk using techniques such as signature-based scanning and heuristic scanning. The warning unit displays a warning message to the user when a dangerous email is detected. This improves email safety by detecting dangerous emails and displaying a warning to the user.

[0087] The classification unit can classify emails into work-related emails, private emails, and promotional emails. The classification unit classifies emails into work-related emails, private emails, promotional emails, etc. based on, for example, the sender's domain or email keywords. For example, work-related emails are classified as emails sent by senders with a company domain or emails containing work-related keywords. Private emails are classified as emails sent by senders with a personal domain or emails containing private content. Promotional emails are classified as emails containing keywords related to advertisements or campaigns. By categorizing emails, users can manage their emails without missing important emails.

[0088] The installed unit can enable a user to manage multiple email accounts wherever they are. For example, the installed unit can install a dedicated app on a smartphone, allowing the user to manage multiple email accounts wherever they are. For example, the user can easily check and reply to emails even when they are out and about. The installed unit can manage emails anywhere as long as there is an internet connection. This allows the user to easily check and reply to emails even when they are out and about.

[0089] The on-board unit may include an improvement unit for improving the effectiveness of a company's direct mail. The improvement unit provides, for example, a function for improving the open rate and click rate of direct mail. For example, the improvement unit enhances the notification function so that users do not miss direct mail. It is also possible to analyze the content of direct mail and send personalized mail based on the user's interests and concerns. This improves the effectiveness of a company's direct mail.

[0090] The registration unit can estimate the user's emotions and customize the registration process based on the estimated user emotions. The registration unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the registration unit provides a simple interface and minimizes the registration steps. If the user is relaxed, the registration unit provides detailed input options and suggests a customizable registration method. If the user is in a hurry, the registration unit prioritizes voice input to enable quick account registration. This improves user convenience by optimizing the registration process according to the user's emotions.

[0091] At the time of registration, the registration unit can suggest the optimal registration method by referring to the user's past email account registration history. For example, the registration unit preferentially displays email services that the user has frequently used in the past. It suggests registration methods (manual, import, etc.) that the user has used in the past. It predicts and suggests email services that will be used during specific time periods from the user's past registration history. In this way, it is possible to suggest the optimal registration method by referring to the user's past registration history.

[0092] The registration unit can select an appropriate registration means depending on the user's input method during registration. For example, if the user selects voice input, the registration unit registers the account information using voice recognition technology. If the user selects text input, the registration unit provides a simple form for the user to enter the account information. If the user selects image input, the registration unit registers the account information using a two-dimensional code (e.g., a QR code) or a screenshot. This allows the registration process to be made more efficient by selecting the optimal registration means depending on the user's input method.

[0093] The registration unit can optimize the registration process based on the user's current device information during registration. For example, if the user is using a smartphone, the registration unit provides a mobile-friendly interface. If the user is using a tablet, the registration unit provides an interface optimized for a large screen. If the user is using a desktop, the registration unit provides detailed input options. This improves user convenience by optimizing the registration process based on the user's device information.

[0094] The registration unit can estimate the user's emotions and determine the priority of email accounts to be registered based on the estimated user's emotions. The registration unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the registration unit prioritizes the most important email accounts. If the user is relaxed, the registration unit provides an option to register all email accounts at once. If the user is in a hurry, the registration unit prioritizes the most frequently used email accounts. In this way, by prioritizing email accounts according to the user's emotions, important accounts can be registered preferentially.

[0095] During registration, the registration unit can prioritize registering highly relevant email accounts taking into account the user's geographical location information. For example, if the user is in a specific area, the registration unit prioritizes displaying email services that are commonly used in that area. If the user is traveling, the registration unit prioritizes registering email accounts to be used at the travel destination. If the user is at home, the registration unit prioritizes registering home email accounts. This improves user convenience by prioritizing registration of highly relevant email accounts based on the user's geographical location information.

[0096] At the time of registration, the registration unit can analyze the user's social media activity and suggest related email accounts. For example, the registration unit automatically suggests email accounts used by the user on social media. It suggests related email accounts by referring to the activity of the user's friends on social media. It analyzes the content of the user's posts on social media and suggests related email accounts. In this way, it is possible to suggest related email accounts by analyzing the user's social media activity.

[0097] The registration unit can adjust the registration method by reflecting the user's past feedback at the time of registration. For example, the registration unit suggests an optimal registration method based on the user's past feedback. The registration unit omits certain registration steps based on the user's past feedback. The registration process is improved based on the user's past feedback. In this way, the optimal registration method can be provided by reflecting the user's past feedback.

[0098] The acquisition unit can estimate the user's emotions and adjust the timing of email acquisition based on the estimated user emotions. The acquisition unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the acquisition frequency of emails is reduced. If the user is relaxed, the acquisition frequency of emails is increased. If the user is in a hurry, only important emails are acquired as a priority. In this way, the timing of email acquisition is adjusted according to the user's emotions, thereby improving user convenience.

[0099] During acquisition, the acquisition unit can analyze the user's past email acquisition history and select the optimal acquisition method. For example, the acquisition unit prioritizes acquisition of emails that the user has frequently acquired in the past. From the user's past acquisition history, it predicts and suggests emails to be acquired during a specific time period. It analyzes the user's past acquisition history and suggests the most efficient acquisition method. In this way, the optimal acquisition method can be suggested by analyzing the user's past acquisition history.

[0100] The acquisition unit can perform filtering based on the user's current project or area of ​​interest when acquiring emails. For example, the acquisition unit prioritizes acquiring emails related to a project the user is currently working on. The acquisition unit filters related emails based on the user's area of ​​interest. The acquisition unit analyzes the user's current project or area of ​​interest and acquires the most appropriate emails. In this way, important emails can be prioritized by filtering emails based on the user's current project or area of ​​interest.

[0101] The acquisition unit can select the optimal acquisition means depending on the user's input method at the time of acquisition. For example, if the user selects voice input, the acquisition unit acquires the email using voice recognition technology. If the user selects text input, the acquisition unit acquires the email by providing a simple form. If the user selects image input, the acquisition unit acquires the email using a two-dimensional code or screenshot. In this way, the optimal acquisition means is selected depending on the user's input method, thereby streamlining the email acquisition process.

[0102] The acquisition unit can estimate the user's emotions and determine the priority of emails to be acquired based on the estimated user's emotions. The acquisition unit estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, important emails are acquired first. If the user is relaxed, all emails are acquired at once. If the user is in a hurry, the most important emails are acquired first. In this way, important emails can be acquired first by determining the priority of emails according to the user's emotions.

[0103] The acquisition unit can prioritize acquiring highly relevant emails by taking into consideration the user's geographical location information when acquiring emails. For example, if the user is in a specific area, the acquisition unit prioritizes acquiring emails related to that area. If the user is traveling, the acquisition unit prioritizes acquiring emails related to the travel destination. If the user is at home, the acquisition unit prioritizes acquiring emails for home use. This improves user convenience by prioritizing the acquisition of highly relevant emails based on the user's geographical location information.

[0104] At the time of acquisition, the acquisition unit can analyze the user's social media activity and acquire related emails. For example, the acquisition unit automatically acquires the email account the user uses for social media. The acquisition unit acquires related emails by referring to the activity of the user's friends on social media. The acquisition unit analyzes the content of the user's posts on social media and acquires related emails. In this way, related emails can be acquired by analyzing the user's social media activity.

[0105] The acquisition unit can customize the acquisition method by reflecting the user's past feedback during acquisition. For example, the acquisition unit suggests an optimal acquisition method based on feedback provided by the user in the past. The acquisition unit omits specific acquisition steps based on the user's past feedback. The acquisition unit improves the acquisition process by referring to the user's past feedback. In this way, the optimal acquisition method can be provided by reflecting the user's past feedback.

[0106] The classification unit can estimate the user's emotions and set classification criteria based on the estimated user emotions. The classification unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, simple classification criteria are applied. If the user is relaxed, detailed classification criteria are applied. If the user is in a hurry, important emails are prioritized. In this way, user convenience is improved by adjusting the classification criteria according to the user's emotions.

[0107] The classification unit can adjust the level of detail of the classification based on the importance of the email when classifying. For example, the classification unit classifies important emails in detail and other emails in a simplified manner. Emails with high importance are classified as a priority, and other emails are put off until later. The level of detail of the classification is dynamically adjusted according to the importance. In this way, by adjusting the level of detail of the classification based on the importance of the email, important emails can be managed with priority.

[0108] The classification unit can apply different classification algorithms depending on the category of email during classification. For example, the classification unit applies a specific algorithm to work-related emails and another algorithm to personal emails. It applies a dedicated algorithm to promotional emails and a general algorithm to other emails. The most appropriate classification algorithm is selected depending on the category of email. This improves classification accuracy by applying the most appropriate classification algorithm depending on the category of email.

[0109] The classification unit can improve the accuracy of classification based on the user's past classification results during classification. For example, the classification unit automatically classifies similar emails by referring to emails that the user has previously classified. The classification unit analyzes the user's past classification results and proposes an optimal classification method. The classification algorithm is improved based on the user's past classification history. In this way, the accuracy of classification is improved by referring to the user's past classification results.

[0110] The classification unit can estimate the user's emotions and determine the priority of classification based on the estimated user's emotions. The classification unit estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, important emails are prioritized for classification. If the user is relaxed, all emails are classified at once. If the user is in a hurry, the most important emails are prioritized for classification. In this way, by determining the priority of classification according to the user's emotions, important emails can be prioritized for classification.

[0111] The classification unit can determine the priority of classification based on the time of submission of emails during classification. For example, the classification unit prioritizes classification of recently received emails. It prioritizes classification of new emails, leaving older emails for later. The classification priority is dynamically adjusted according to the time of submission. In this way, by determining the priority of classification based on the time of submission of emails, it is possible to prioritize management of the most recent emails.

[0112] The classification unit can set the order of classification based on the relevance of emails when classifying them. For example, the classification unit prioritizes classifying highly relevant emails. It prioritizes classifying important emails, leaving less relevant emails for later. It dynamically adjusts the order of classification according to the relevance of emails. This allows important emails to be managed with priority by adjusting the order of classification based on the relevance of emails.

[0113] The classification unit can adjust the use of technical terms for classification according to the user's level of expertise during classification. For example, if the user has technical expertise, the classification unit uses technical terms for classification. If the user does not have technical expertise, the classification unit uses simple terms for classification. The classification terminology is dynamically adjusted according to the user's level of expertise. This helps the user understand by adjusting the technical terms for classification according to the user's level of expertise.

[0114] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated user emotions. The discrimination unit estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple discrimination criterion is applied. If the user is relaxed, a detailed discrimination criterion is applied. If the user is in a hurry, important emails are prioritized. In this way, the discrimination criteria are adjusted according to the user's emotions, thereby improving user convenience.

[0115] The discrimination unit can improve the accuracy of discrimination based on the interrelationships between emails when discriminating. For example, the discrimination unit groups related emails and discriminates them all at once. The discrimination unit analyzes the interrelationships between emails and discriminates the risk of spam or viruses. The discrimination algorithm is improved based on the interrelationships between emails. In this way, the accuracy of discrimination is improved by taking the interrelationships between emails into consideration.

[0116] The discrimination unit can make discrimination taking into account the attribute information of the email sender. For example, the discrimination unit prioritizes discrimination of emails from trusted senders. Discrimination of emails from unknown senders is performed in detail. The discrimination algorithm is adjusted based on the attribute information of the sender. In this way, the accuracy of discrimination is improved by taking into account the attribute information of the email sender.

[0117] The discrimination unit can weight the discrimination based on the frequency of email submission when discriminating. For example, the discrimination unit performs detailed discrimination on frequently sent emails and simple discrimination on emails that are submitted infrequently. The discrimination weight is dynamically adjusted according to the submission frequency. In this way, by weighting the discrimination based on the frequency of email submission, important emails can be prioritized.

[0118] The discrimination unit can estimate the user's emotion and set the order in which the discrimination results are displayed based on the estimated user's emotion. The discrimination unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, important emails are displayed with priority. If the user is relaxed, all emails are displayed at once. If the user is in a hurry, the most important emails are displayed with priority. In this way, the order in which the discrimination results are displayed can be adjusted according to the user's emotion, allowing important emails to be displayed with priority.

[0119] The discrimination unit can make discrimination taking into account the geographical distribution of emails. For example, the discrimination unit prioritizes discrimination of emails from a specific region. It prioritizes discrimination of emails from geographically close senders. It adjusts the discrimination algorithm based on the geographical distribution of emails. In this way, the accuracy of discrimination is improved by taking into account the geographical distribution of emails.

[0120] The discrimination unit can improve the accuracy of discrimination by referring to literature related to the email when discriminating. The discrimination unit, for example, refers to related literature to discriminate the risk of spam or viruses. The discrimination unit improves the accuracy of discrimination by referring to literature related to the content of the email. The discrimination algorithm is improved based on the related literature. In this way, the accuracy of discrimination is improved by referring to literature related to the email.

[0121] The discrimination unit can discriminate based on the market value of the email when discriminating. For example, the discrimination unit discriminates emails with high market value with priority. Discrimination of emails with low market value is simplified. The discrimination algorithm is adjusted based on the market value of the email. In this way, important emails can be discriminated with priority by taking the market value of the email into consideration.

[0122] The onboard unit can estimate the user's emotions and determine the priority of functions to be installed based on the estimated user's emotions. The onboard unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the most important functions are installed first. If the user is relaxed, all functions are installed at once. If the user is in a hurry, the most frequently used functions are installed first. In this way, the priority of functions to be installed according to the user's emotions is determined, thereby improving user convenience.

[0123] When the device is installed, the onboard unit can select the optimal function by referring to the user's past usage history. For example, the onboard unit prioritizes the installation of functions that the user has frequently used in the past. Based on the user's past usage history, the onboard unit predicts and suggests functions that will be used during specific time periods. The user's past usage history is analyzed and the most efficient function is suggested. This makes it possible to provide the optimal function by referring to the user's past usage history.

[0124] During onboarding, the onboarding unit can optimize the onboarding process based on the user's current device information. For example, if the user is using a smartphone, the onboarding unit provides mobile-friendly functions. If the user is using a tablet, the onboarding unit provides functions optimized for large screens. If the user is using a desktop, the onboarding unit provides detailed functions. This improves user convenience by optimizing the onboarding process based on the user's device information.

[0125] The onboarding unit can improve the onboard functions by reflecting user feedback during onboarding. For example, the onboarding unit suggests optimal functions based on feedback previously provided by the user. Improves specific functions based on the user's past feedback. Improves the onboarding process by referring to the user's past feedback. In this way, optimal functions can be provided by reflecting user feedback.

[0126] The on-board unit can estimate the user's emotions and adjust the display method of the on-board functions based on the estimated user emotions. The on-board unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple display method is provided. If the user is relaxed, a detailed display method is provided. If the user is in a hurry, a display method that focuses on the main points is provided. In this way, the display method is adjusted according to the user's emotions, improving user convenience.

[0127] When the onboard unit is installed, it can select appropriate functions based on the user's geographical location information. For example, if the user is in a specific area, the onboard unit will prioritize displaying functions that are commonly used in that area. If the user is traveling, the onboard unit will prioritize installing functions to be used at the travel destination. If the user is at home, the onboard unit will prioritize installing functions for home use. This improves user convenience by providing optimal functions based on the user's geographical location information.

[0128] When installed, the on-board unit can analyze the user's social media activity and suggest related functions. For example, the on-board unit automatically suggests functions that the user uses on social media. It suggests related functions based on the activity of the user's friends on social media. It analyzes the content of the user's social media posts and suggests related functions. In this way, it is possible to provide related functions by analyzing the user's social media activity.

[0129] The mounting unit can customize the mounting method by reflecting the user's past feedback during mounting. For example, the mounting unit suggests the optimal mounting method based on the user's past feedback. The mounting unit omits specific mounting steps based on the user's past feedback. The mounting process is improved based on the user's past feedback. In this way, the optimal mounting method can be provided by reflecting the user's past feedback.

[0130] The warning unit can estimate the user's emotions and set a warning display method based on the estimated user's emotions. The warning unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple, highly visible warning is displayed. If the user is relaxed, a detailed warning is displayed. If the user is in a hurry, a warning that focuses on the main points is displayed. In this way, the warning display method is adjusted according to the user's emotions, thereby improving user convenience.

[0131] The warning unit can adjust the level of detail of the warning based on the risk level of the email when issuing the warning. For example, the warning unit displays a detailed warning for high-risk emails and a simple warning for low-risk emails. The level of detail of the warning is dynamically adjusted according to the risk level of the email. In this way, user convenience is improved by adjusting the level of detail of the warning based on the risk level of the email.

[0132] The warning unit can apply different warning algorithms depending on the category of email when issuing a warning. For example, the warning unit applies a specific warning algorithm to spam emails and another warning algorithm to virus emails. It applies a dedicated warning algorithm to promotional emails and a general warning algorithm to other emails. The most appropriate warning algorithm is selected depending on the category of email. This improves the accuracy of warnings by applying the most appropriate warning algorithm depending on the category of email.

[0133] The warning unit can improve the accuracy of warnings by referring to the user's past warning history when issuing a warning. For example, the warning unit refers to warnings the user has received in the past and displays a warning for similar emails. The warning unit analyzes the user's past warning history and suggests the optimal warning method. The warning algorithm is improved based on the user's past warning history. In this way, the accuracy of warnings is improved by referring to the user's past warning history.

[0134] The warning unit can estimate the user's emotion and determine the priority of warnings based on the estimated user's emotion. The warning unit estimates the user's emotion using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the most important warning is displayed with priority. If the user is relaxed, all warnings are displayed at once. If the user is in a hurry, the most important warning is displayed with priority. In this way, by determining the priority of warnings according to the user's emotion, important warnings can be displayed with priority.

[0135] When issuing a warning, the warning unit can determine the priority of the warning based on the time of submission of the email. For example, the warning unit prioritizes displaying warnings for recently received emails. Warnings for older emails are left to be displayed later, and warnings for newer emails are prioritized. The priority of the warning is dynamically adjusted according to the time of submission. In this way, by determining the priority of the warning based on the time of submission of the email, the most recent warning can be displayed preferentially.

[0136] The warning unit can set the order of warnings based on the relevance of emails when issuing a warning. For example, the warning unit prioritizes displaying warnings for highly relevant emails. It prioritizes displaying warnings for important emails, leaving warnings for less relevant emails for later. It dynamically adjusts the order of warnings according to the relevance of emails. By adjusting the order of warnings based on the relevance of emails, important warnings can be displayed with priority.

[0137] The warning unit can adjust the use of technical terms in the warning depending on the user's level of expertise when issuing a warning. For example, if the user has technical expertise, the warning unit issues a warning using technical terms. If the user does not have technical expertise, the warning unit issues a warning using simple terms. The warning terminology is dynamically adjusted depending on the user's level of expertise. This helps the user understand the warning by adjusting the technical terms in the warning depending on the user's level of expertise.

[0138] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user's emotions. The improvement unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple improvement method is suggested. If the user is relaxed, a detailed improvement method is suggested. If the user is in a hurry, a quick improvement method is suggested. In this way, the improvement method is adjusted according to the user's emotions, thereby improving user convenience.

[0139] When making improvements, the improvement department can analyze the user's past usage history and select the optimal improvement method. For example, the improvement department proposes the optimal improvement method based on functions that the user has frequently used in the past. From the user's past usage history, it predicts which functions will be used at specific times and proposes an improvement method. It analyzes the user's past usage history and proposes the most efficient improvement method. In this way, it is possible to provide the optimal improvement method by analyzing the user's past usage history.

[0140] During improvement, the improvement unit can optimize the improvement process based on the user's current device information. For example, if the user is using a smartphone, the improvement unit provides a mobile-friendly improvement method. If the user is using a tablet, the improvement unit provides an improvement method optimized for a large screen. If the user is using a desktop, the improvement unit provides a detailed improvement method. This improves user convenience by optimizing the improvement process based on the user's device information.

[0141] The improvement department can improve the improvement method by reflecting the user's feedback when making an improvement. For example, the improvement department proposes an optimal improvement method based on feedback provided by the user in the past. It omits specific improvement steps from the user's past feedback. It improves the improvement process by referring to the user's past feedback. In this way, it is possible to provide an optimal improvement method by reflecting the user's feedback.

[0142] The improvement unit can estimate the user's emotions and set priorities for improvements based on the estimated user's emotions. The improvement unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the most important improvements are executed first. If the user is relaxed, all improvements are executed at once. If the user is in a hurry, the most important improvements are executed first. In this way, by determining the priorities of improvements according to the user's emotions, important improvements can be executed first.

[0143] When making an improvement, the improvement unit can select the optimal improvement method by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the improvement unit will provide preferentially improvement methods that are commonly used in that area. If the user is traveling, the improvement unit will provide preferentially improvement methods that are used at the user's destination. If the user is at home, the improvement unit will provide preferentially home improvement methods. In this way, convenience for the user is improved by providing the optimal improvement method based on the user's geographical location information.

[0144] When making improvements, the improvement unit can analyze the user's social media activity and suggest means for improvement. For example, the improvement unit automatically suggests improvement methods used by the user on social media. It suggests related improvement methods by referring to the activities of the user's friends on social media. It analyzes the content of the user's posts on social media and suggests related improvement methods. In this way, it is possible to provide related improvement methods by analyzing the user's social media activity.

[0145] When making improvements, the improvement department can customize the improvement method by reflecting the user's past feedback. For example, the improvement department proposes the optimal improvement method based on the user's past feedback. It omits specific improvement steps from the user's past feedback. It improves the improvement process by referring to the user's past feedback. In this way, it is possible to provide the optimal improvement method by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, classification unit, discrimination unit, and on-board unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart device 14, and a user registers multiple email accounts. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and automatically acquires emails arriving at each account. The classification unit is realized by the control unit 46A of the smart device 14, and categorizes the acquired emails. The discrimination unit is realized by the specific processing unit 290 of the data processing device 12, and distinguishes between spam emails and emails with virus risks. The on-board unit is realized by the control unit 46A of the smart device 14, and manages emails and sets notifications. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, classification unit, discrimination unit, and on-board unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the smart glasses 214, and a user registers multiple email accounts. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and automatically acquires emails received in each account. The classification unit is realized by the control unit 46A of the smart glasses 214, and categorizes the acquired emails. The discrimination unit is realized by the specific processing unit 290 of the data processing device 12, and distinguishes between spam emails and emails with virus risks. The on-board unit is realized by the control unit 46A of the smart glasses 214, and manages emails and sets notifications. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, classification unit, discrimination unit, and on-board unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the headset type terminal 314, and a user registers multiple email accounts. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and automatically acquires emails arriving at each account. The classification unit is realized by the control unit 46A of the headset type terminal 314, and categorizes the acquired emails. The discrimination unit is realized by the specific processing unit 290 of the data processing device 12, and distinguishes between spam emails and emails with virus risks. The on-board unit is realized by the control unit 46A of the headset type terminal 314, and manages emails and sets notifications. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned registration unit, acquisition unit, classification unit, discrimination unit, and on-board unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the registration unit is realized by the control unit 46A of the robot 414, and a user registers multiple email accounts. The acquisition unit is realized by the specific processing unit 290 of the data processing device 12, and automatically acquires emails arriving at each account. The classification unit is realized by the control unit 46A of the robot 414, and categorizes the acquired emails. The discrimination unit is realized by the specific processing unit 290 of the data processing device 12, and distinguishes between spam emails and emails with virus risks. The on-board unit is realized by the control unit 46A of the robot 414, and manages emails and sets notifications.

[0146] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0147] The email management system may further include a voice assistant unit. The voice assistant unit enables the user to manage emails using voice commands. For example, when the user issues a voice command such as "Check new emails," the voice assistant unit instructs the acquisition unit to acquire new emails. When the user issues a command such as "Delete spam emails," the voice assistant unit instructs the classification unit to delete the spam emails. When the user issues a command such as "Show work-related emails," the voice assistant unit instructs the classification unit to display work-related emails. This allows the user to efficiently manage emails using voice commands.

[0148] The email management system may further include a translation unit. The translation unit can automatically translate emails received by a user. For example, if a user receives an email in a foreign language, the translation unit translates the email into the user's native language. When a user replies, the translation unit can also translate the reply into the recipient's language. Furthermore, the translation unit can perform context analysis to provide an appropriate translation based on the content of the email. This allows users to communicate across language barriers.

[0149] The email management system may further include a schedule linking unit. The schedule linking unit may link with the user's calendar app and automatically update the schedule based on the contents of emails. For example, when a user receives an invitation email for a meeting, the schedule linking unit automatically adds the date and time of the meeting to the calendar. In addition, if the user changes their schedule, the schedule linking unit may notify the user of the change by email. Furthermore, the schedule linking unit may set email priorities based on the user's schedule. This allows the user to manage their schedule more efficiently.

[0150] The email management system may further include an archive unit. The archive unit provides a function for the user to efficiently manage past emails. For example, the user can archive emails from a specific period. The archive unit may also automatically archive emails based on the content of the emails. Furthermore, the archive unit provides a search function for the user to easily search archived emails. This allows the user to efficiently manage past emails and quickly find the information they need.

[0151] The email management system may further include a notification customization unit. The notification customization unit can customize the notification method for emails received by the user. For example, if the user receives an important email, the notification customization unit can provide a push notification or a voice notification. Also, if the user does not want to receive notifications during a specific time period, the notification customization unit can turn off notifications during that time period. Furthermore, the notification customization unit can dynamically change the notification method based on the user's schedule or current situation. This allows the user to customize the notification method to suit their needs.

[0152] The acquisition unit can estimate the user's emotions and adjust the timing of email acquisition based on the estimated user emotions. The acquisition unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the acquisition frequency of emails is reduced. If the user is relaxed, the acquisition frequency of emails is increased. If the user is in a hurry, only important emails are acquired as a priority. In this way, the timing of email acquisition is adjusted according to the user's emotions, thereby improving user convenience.

[0153] The classification unit can estimate the user's emotions and set classification criteria based on the estimated user emotions. The classification unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, simple classification criteria are applied. If the user is relaxed, detailed classification criteria are applied. If the user is in a hurry, important emails are prioritized. In this way, user convenience is improved by adjusting the classification criteria according to the user's emotions.

[0154] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated user emotions. The discrimination unit estimates the user's emotions using techniques such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple discrimination criterion is applied. If the user is relaxed, a detailed discrimination criterion is applied. If the user is in a hurry, important emails are prioritized. In this way, the discrimination criteria are adjusted according to the user's emotions, thereby improving user convenience.

[0155] The onboard unit can estimate the user's emotions and determine the priority of functions to be installed based on the estimated user's emotions. The onboard unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, the most important functions are installed first. If the user is relaxed, all functions are installed at once. If the user is in a hurry, the most frequently used functions are installed first. In this way, the priority of functions to be installed according to the user's emotions is determined, thereby improving user convenience.

[0156] The warning unit can estimate the user's emotions and set a warning display method based on the estimated user's emotions. The warning unit estimates the user's emotions using technologies such as facial expression recognition, voice analysis, and text analysis. For example, if the user is feeling stressed, a simple, highly visible warning is displayed. If the user is relaxed, a detailed warning is displayed. If the user is in a hurry, a warning that focuses on the main points is displayed. In this way, the warning display method is adjusted according to the user's emotions, thereby improving user convenience.

[0157] The processing flow of the second embodiment will be briefly explained below.

[0158] Step 1: In the registration section, users register multiple email accounts. Users can manage different email services, such as Gmail, Yahoo! Mail, and Outlook, with a single tool. Step 2: The acquisition unit automatically acquires emails that arrive at each account registered by the registration unit. The acquisition unit can acquire emails by using, for example, periodic polling or push notifications. Step 3: The classifier categorizes the emails acquired by the acquirer based on the emails' origin or content. For example, the classifier can categorize emails into work-related emails, personal emails, promotional emails, etc. Step 4: The discrimination unit discriminates between spam and virus-risk emails from among the emails classified by the classification unit. The discrimination unit can detect dangerous emails using, for example, a spam filter or virus scanning function and display a warning to the user. Step 5: The loading unit loads the email identified by the identification unit into the smartphone and manages it. The loading unit can manage the email using a dedicated app, for example, and set notifications.

[0159] 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.

[0160] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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 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.

[0177] 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.

[0178] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0179] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0190] 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.

[0191] 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.

[0192] 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 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.

[0193] 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.

[0194] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0195] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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).

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0207] 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.

[0208] 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.

[0209] 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 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.

[0210] 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.

[0211] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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."

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] [Explanation of symbols]

[0231] 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. a registration section for registering multiple email accounts; an acquisition unit that automatically acquires emails that arrive at each account registered by the registration unit; a classification unit that categorizes the emails acquired by the acquisition unit based on the email address or content; a discrimination unit that discriminates spam mail or mail with a virus risk from the mail classified by the classification unit; a loading unit that loads the email identified by the determination unit into a smartphone and manages the email; Equipped with A system characterized by:

2. The determination unit It has a warning section that uses spam filters and virus scanning functions to detect dangerous emails and display a warning to the user.

2. The system of claim 1.

3. The classification unit Categorize emails into work-related, personal, and promotional 2. The system of claim 1.

4. The mounting section is Allows users to manage multiple email accounts wherever they are 2. The system of claim 1.

5. The mounting section is Establish an improvement department to improve the effectiveness of direct mail for companies 2. The system of claim 1.

6. The registration unit Infer user sentiment and customize the registration process based on the inferred user sentiment 2. The system of claim 1.

7. The registration unit When registering, the system will suggest the best way to register by referring to the user's past email account registration history.

2. The system of claim 1.

8. The registration unit During registration, select the appropriate registration method depending on the user's input method 2. The system of claim 1.

9. The registration unit During enrollment, optimize the enrollment process based on the user's current device information 2. The system of claim 1.

10. The registration unit Estimate user sentiment and prioritize email accounts to register based on the estimated sentiment 2. The system of claim 1.

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