system

Abstract

The system according to this embodiment aims to automatically analyze emails received by the user and efficiently sort out unnecessary emails. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a determination unit, a sorting unit, and a provision unit. The reception unit receives emails received by the user. The analysis unit analyzes the emails received by the reception unit. The determination unit determines which emails are unnecessary based on the information analyzed by the analysis unit. The sorting unit sorts the unnecessary emails determined by the determination unit. The provision unit provides the emails sorted by the sorting unit so that the user can confirm them.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is cumbersome to manually sort unnecessary emails from a large number of emails received by a user, and it is difficult to manage them efficiently.

[0005] The system according to the embodiment aims to automatically analyze emails received by a user and efficiently sort unnecessary emails.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, a sorting unit, and a provision unit. The reception unit receives emails from the user. The analysis unit analyzes the emails received by the reception unit. The determination unit determines which emails are unnecessary based on the information analyzed by the analysis unit. The sorting unit sorts the unnecessary emails determined by the determination unit. The provision unit provides the emails sorted by the sorting unit so that the user can confirm them. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze emails received by the user and efficiently sort out unnecessary emails. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The email sorting system according to an embodiment of the present invention is a system that automatically sorts unnecessary emails received by a user using a generative AI. This email sorting system uses a generative AI to analyze emails received by the user and determine unnecessary emails based on information such as the email content, sender, and subject. Next, the generative AI automatically sorts the emails it determines are unnecessary into specific folders. At this stage, the emails are not deleted, and the user can check the sorted emails as needed. This mechanism makes it easier for users to organize their inboxes and prevents them from missing important emails. Furthermore, it targets all age groups and is a service that anyone can easily use. For example, if a business person receives a large volume of emails, the generative AI automatically sorts out unnecessary emails, allowing them to focus on important emails. General users can also check only the emails they need without being bothered by advertising emails or spam. By utilizing generative AI, this service enables more accurate sorting than conventional spam filtering functions. For example, by analyzing the entire content of the email, not just specific keywords or senders, it can more accurately determine unnecessary emails. Moreover, since the sorted emails are not deleted, the user can check them as needed. For example, it can help you find important emails that have been mistakenly sorted, allowing you to use it with peace of mind. Thus, an email sorting system utilizing generation AI is a groundbreaking system that organizes the user's inbox, ensuring important emails are not missed and enabling efficient email management. In this way, the email sorting system organizes the user's inbox, ensuring important emails are not missed and enabling efficient email management.

[0029] The email sorting system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, a sorting unit, and a provision unit. The reception unit receives emails received by the user. The reception unit can, for example, receive all emails received by the user. The analysis unit analyzes the emails received by the reception unit. The analysis unit analyzes information such as the content of the email, the sender, and the subject, for example, using a generation AI. The determination unit determines unnecessary emails based on the information analyzed by the analysis unit. The determination unit determines unnecessary emails, for example, using a generation AI. The sorting unit sorts the unnecessary emails determined by the determination unit. The sorting unit automatically sorts the emails determined to be unnecessary into a specific folder, for example. The provision unit provides the emails sorted by the sorting unit so that the user can check them. The provision unit allows the user to check the sorted emails as needed. As a result, the email sorting system can efficiently analyze, determine, sort, and provide emails received by the user.

[0030] The reception unit receives emails from users. For example, the reception unit can receive all emails received by a user. Specifically, the reception unit interacts with the user's mail server and retrieves emails using protocols such as POP3 and IMAP. This allows for centralized management of all received emails, regardless of the email client or service used by the user. Furthermore, the reception unit retrieves emails in real time upon receipt and passes them on to the next processing step without delay. This enables the system to start operating the moment a user receives an email, allowing for rapid email processing. The reception unit also extracts email metadata (sender, received date and time, subject, etc.) and prepares it for the analysis unit. This allows the analysis unit to efficiently analyze the email content. Additionally, the reception unit can perform filtering based on specific conditions according to user settings. For example, it can be configured to prioritize emails from specific domains or emails containing specific keywords. This enables flexible email reception tailored to user needs.

[0031] The analysis unit analyzes emails received by the reception unit. For example, the analysis unit uses generative AI to analyze information such as the email content, sender, and subject. Specifically, the generative AI uses natural language processing technology to analyze the email body and extract important keywords and phrases. Furthermore, the generative AI analyzes the email sender address and domain to evaluate its reliability and potential as spam. Regarding the subject, the generative AI uses pattern recognition technology to detect characteristics of spam and phishing emails. This allows the analysis unit to analyze the email content in detail and evaluate its importance and urgency. Furthermore, the analysis unit can learn from past email data and the user's email usage history to perform more accurate analysis. For example, it can learn the characteristics of emails that the user previously deemed important and then determine new emails with similar characteristics as important. The analysis unit also analyzes email attachments, evaluating their type and content. This allows the analysis unit to comprehensively analyze the entire email content and provide the necessary information to the subsequent decision unit.

[0032] The judgment unit determines unnecessary emails based on the information analyzed by the analysis unit. The judgment unit uses, for example, a generation AI to determine unnecessary emails. Specifically, the generation AI uses a spam filtering algorithm to evaluate the content of emails and the reliability of the sender, and identifies unnecessary emails. For example, characteristics of spam emails include the overuse of specific keywords or phrases, links, and unclear senders. The generation AI scores emails based on these characteristics and determines that emails with a score above a certain level are unnecessary. The generation AI also learns the user's past email processing history and improves its judgment accuracy based on the characteristics of emails that the user has deemed unnecessary. Furthermore, the judgment unit can also detect phishing emails and emails containing malware. The generation AI analyzes the content of links and attachments in emails and evaluates whether they contain dangerous elements. As a result, the judgment unit can accurately determine unnecessary or dangerous emails for the user and pass them on to the next sorting unit.

[0033] The sorting unit sorts unnecessary emails determined by the judgment unit. For example, the sorting unit automatically sorts emails determined to be unnecessary into specific folders. Specifically, the sorting unit analyzes the user's email folder structure and moves unnecessary emails to the spam folder or trash folder. Furthermore, if the user has set up custom folders, it can also sort emails based on specific conditions. For example, it is possible to set up sorting of emails from a specific sender or emails containing specific keywords into designated folders. In addition, the sorting unit continuously learns sorting rules based on user feedback and improves accuracy. For example, if a user manually corrects an email that was incorrectly sorted, the sorting unit learns that information and reflects it in subsequent sorting. In this way, the sorting unit can achieve flexible email sorting that meets the user's needs and improve the efficiency of email management.

[0034] The service provider makes it possible for users to check emails that have been sorted by the sorting unit. For example, the service provider allows users to check sorted emails as needed. Specifically, the service provider works in conjunction with the user's email client or webmail interface to display sorted emails in the appropriate folders. This allows users to easily check emails moved to spam or trash folders and restore them if necessary. Furthermore, the service provider provides an email preview function, allowing users to easily check the content of emails. For example, it displays the subject, sender, and a portion of the body of the email, allowing users to understand the content without opening the email. The service provider also collects user feedback and provides data to improve the accuracy and usability of the entire system. For example, if a user reports an email that has been sorted incorrectly, that information is fed back to the analysis and judgment units and reflected in future processing. In this way, the service provider can provide a user-friendly interface and improve the efficiency of email management.

[0035] The analysis unit can determine unnecessary emails based on information such as the email content, sender, and subject. For example, the analysis unit can determine unnecessary emails if they contain specific keywords. The analysis unit can also determine unnecessary emails based on the sender's domain. Furthermore, the analysis unit can determine unnecessary emails based on subject line patterns. This improves the accuracy of the determination by determining unnecessary emails based on information such as the email content, sender, and subject. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the email content into a generation AI, which can then determine unnecessary emails.

[0036] The sorting unit can automatically sort emails deemed unnecessary into specific folders. For example, the sorting unit can automatically sort emails deemed unnecessary into a spam folder. It can also automatically sort them into an advertising folder. Furthermore, it can automatically sort them into an archive folder. This organizes the user's inbox by automatically sorting emails deemed unnecessary into specific folders. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input emails deemed unnecessary into a generation AI, which can then sort them into specific folders.

[0037] The service provider can enable users to check sorted emails as needed. For example, the service provider can notify users of sorted emails using a notification function. The service provider can also enable users to search for sorted emails using a search function. Furthermore, the service provider can enable users to filter sorted emails using a filtering function. This allows users to check sorted emails as needed, including important emails that have been mistakenly sorted. Some or all of the above processing in the service provider may be performed using a generation AI, or not using a generation AI. For example, the service provider can input sorted emails into a generation AI, which can then notify the user.

[0038] The reception unit can receive all emails received by a user. For example, the reception unit can receive all emails that arrive in a specific account. It can also receive all emails received during a specific period. Furthermore, it can receive all emails that arrive in a specific folder. This ensures that all emails received by the user are processed within the system. Some or all of the processing described above in the reception unit may be performed using a generation AI, or not. For example, the reception unit can input all emails received by the user into a generation AI, which can then accept them.

[0039] The reception unit can analyze the user's past email reception history and select the optimal reception method. For example, the reception unit can accept emails according to the time slots the user frequently received emails in the past. The reception unit can also learn patterns of emails that the user has previously deemed important and prioritize accepting similar emails. Furthermore, the reception unit can automatically filter and reject emails that the user has previously identified as spam. In this way, the optimal reception method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past email reception history into a generative AI, which can then select the optimal reception method.

[0040] The reception system can filter incoming emails based on the user's current projects and areas of interest. For example, it can prioritize emails related to projects the user is currently working on. It can also prioritize newsletters and information related to the user's areas of interest. Furthermore, it can automatically filter out emails from areas the user has not expressed interest in. This allows the system to prioritize receiving highly relevant emails by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using or without a generative AI. For example, the reception system can input information about the user's current projects and areas of interest into a generative AI, which can then perform the filtering.

[0041] The reception desk can prioritize receiving emails that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving emails related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving emails containing information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving emails related to their home. This allows the reception desk to provide users with useful information by prioritizing emails based on their geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant emails.

[0042] The reception desk can analyze a user's social media activity when receiving emails and receive relevant emails. For example, the reception desk can prioritize receiving emails related to topics the user has shown interest in on social media. It can also prioritize receiving emails from accounts the user follows. Furthermore, it can prioritize receiving emails related to groups or events the user participates in. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving relevant emails. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's social media activity data into a generative AI, which can then receive relevant emails.

[0043] The analysis unit can adjust the level of detail in its analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis for important emails. It can also perform a simplified analysis for general emails. Furthermore, it can perform a minimal analysis for spam emails. This allows for detailed analysis of important emails by adjusting the level of detail based on the importance of the email. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input email importance data into a generation AI, which can then adjust the level of detail in its analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, in the case of business emails, the analysis unit applies a business-related analysis algorithm. It can also apply an advertising-related analysis algorithm to advertising emails. Furthermore, it can apply a private-related analysis algorithm to private emails. This allows for optimal analysis for each category by applying different analysis algorithms depending on the email category. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input email category data into a generation AI, which can then apply different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on when the emails were received. For example, the analysis unit may prioritize the analysis of recently received emails. It can also prioritize the analysis of emails received immediately before an important event. Furthermore, the analysis unit may prioritize the analysis of emails received by the user during a specific time period. This allows for the provision of timely analysis results by determining the priority of analysis based on when the emails were received. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input email reception time data into a generating AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit may prioritize analyzing emails related to topics that the user has shown interest in. It can also prioritize analyzing emails from accounts that the user follows. Furthermore, it can prioritize analyzing emails related to groups or events that the user participates in. By adjusting the order of analysis based on the relevance of the emails, it is possible to prioritize the analysis of highly relevant emails. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input email relevance data into a generative AI, which can then adjust the order of analysis.

[0047] The judgment unit can improve the accuracy of its judgment by considering the relationships between emails during the judgment process. For example, the judgment unit can judge multiple emails from the same sender at once. It can also group emails related to the same topic and judge them together. Furthermore, the judgment unit can improve the accuracy of its judgment by referring to the judgment results of past emails. In this way, the accuracy of the judgment is improved by considering the relationships between emails. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input email relationship data into a generating AI, which can then improve the accuracy of the judgment.

[0048] The judgment unit can make a judgment by considering the attribute information of the email sender. For example, the judgment unit may determine an email to be important if the sender is trustworthy. The judgment unit may also determine an email to be unnecessary if the sender is unknown. Furthermore, the judgment unit may also determine an email to be unnecessary if the sender is registered on a spam list. This makes it possible to make a highly reliable judgment by considering the attribute information of the email sender. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input the sender's attribute information into a generating AI, and the generating AI can perform the judgment.

[0049] The determination unit can make a determination by considering the geographical distribution of emails. For example, if the user is in a specific region, the determination unit will determine that emails related to that region are important. The determination unit can also determine that emails containing information related to the user's travel destination are important if the user is traveling. Furthermore, if the determination unit is at home, the determination unit can determine that emails related to home are important. In this way, by considering the geographical distribution of emails, important information related to a region can be prioritized. Some or all of the above processing in the determination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the determination unit can input geographical distribution data of emails into a generation AI, and the generation AI can perform the determination.

[0050] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the email during the judgment process. For example, the judgment unit makes a judgment by referring to literature related to the content of the email. The judgment unit can also make a judgment by referring to literature related to the sender of the email. Furthermore, the judgment unit can make a judgment by referring to literature related to the topic of the email. In this way, the accuracy of the judgment is improved by referring to relevant literature related to the email. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input email-related literature data into a generating AI, and the generating AI can improve the accuracy of the judgment.

[0051] The sorting unit can improve the accuracy of sorting by considering the relationships between emails during the sorting process. For example, the sorting unit can sort multiple emails from the same sender in a single batch. It can also group emails related to the same topic and sort them accordingly. Furthermore, the sorting unit can improve the accuracy of sorting by referring to the sorting results of past emails. This improves the accuracy of sorting by considering the relationships between emails. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input email relationship data into a generation AI, which can then improve the accuracy of sorting.

[0052] The sorting unit can sort emails while considering the sender's attribute information. For example, if the sender is trustworthy, the sorting unit can sort the email into an important folder. It can also sort emails into a spam folder if the sender is unknown. Furthermore, if the sender is on a spam list, the sorting unit can sort emails into the spam folder. This allows for more reliable sorting by considering the sender's attribute information. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input the sender's attribute information into a generation AI, which can then perform the sorting.

[0053] The sorting unit can sort emails considering their geographical distribution. For example, if a user is in a specific region, the sorting unit can prioritize sorting emails related to that region. Furthermore, if a user is traveling, the sorting unit can prioritize sorting emails containing information related to their travel destination. Additionally, if a user is at home, the sorting unit can prioritize sorting emails related to their home. This allows for the prioritization of important region-related information by considering the geographical distribution of emails. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input geographical distribution data of emails into a generation AI, which can then perform the sorting.

[0054] The sorting unit can improve the accuracy of sorting by referring to relevant literature for each email during the sorting process. For example, the sorting unit may sort emails by referring to literature related to the content of the email. It can also sort emails by referring to literature related to the sender of the email. Furthermore, the sorting unit may sort emails by referring to literature related to the topic of the email. This improves the accuracy of sorting by referring to relevant literature for each email. Some or all of the above processing in the sorting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the sorting unit can input email-related literature data into a generative AI, which can then improve the accuracy of sorting.

[0055] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also exclude display methods that the user has avoided in the past. Furthermore, the service provider can learn and provide the optimal display method from the user's past operation history. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method.

[0056] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The analysis unit can analyze a user's past email viewing history and estimate the importance of an email. For example, it can determine that emails from senders the user has frequently opened in the past are important. It can also determine that emails from senders the user has previously marked as spam are unnecessary. Furthermore, if a user has previously determined that emails containing specific keywords are important, it can prioritize the analysis of emails containing similar keywords. This allows for a more accurate estimation of email importance based on the user's past email viewing history.

[0059] The reception desk can prioritize receiving emails that are highly relevant to the user based on their geographical location. For example, if a user is in a specific region, it will prioritize receiving emails related to that region. Similarly, if a user is traveling, it can prioritize receiving emails containing information related to their travel destination. Furthermore, if a user is at home, it can prioritize receiving emails related to their home. This allows the system to provide users with useful information by prioritizing emails that are highly relevant based on their geographical location.

[0060] The analysis unit can automatically generate reply templates based on the content of emails. For example, in the case of business emails, it can generate business-related reply templates. It can also generate personal-related reply templates for private emails. Furthermore, it can generate reply templates for advertising emails. This streamlines the user's reply process by automatically generating reply templates based on email content.

[0061] The sorting function can sort emails by considering the sender's attribute information. For example, if the sender is trustworthy, the email can be sorted into an important folder. If the sender is unknown, the email can be sorted into the spam folder. Furthermore, if the sender is on a spam list, the email can also be sorted into the spam folder. This allows for highly reliable sorting by considering the sender's attribute information.

[0062] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing display methods that the user has preferred in the past. It can also exclude display methods that the user has avoided in the past. Furthermore, it can learn and provide the optimal display method from the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The reception desk receives emails from the user. For example, it can receive all emails the user has received. Step 2: The analysis unit analyzes the emails received by the reception unit. For example, it uses a generation AI to analyze information such as the content of the email, the sender, and the subject. Step 3: The determination unit determines unnecessary emails based on the information analyzed by the analysis unit. For example, it may use a generation AI to determine unnecessary emails. Step 4: The sorting unit sorts the unnecessary emails determined by the judgment unit. For example, it automatically sorts emails determined to be unnecessary into a specific folder. Step 5: The delivery unit provides the emails sorted by the sorting unit so that users can check them. For example, it allows users to check sorted emails as needed.

[0065] (Example of form 2) The email sorting system according to an embodiment of the present invention is a system that automatically sorts unnecessary emails received by a user using a generative AI. This email sorting system uses a generative AI to analyze emails received by the user and determine unnecessary emails based on information such as the email content, sender, and subject. Next, the generative AI automatically sorts the emails it determines are unnecessary into specific folders. At this stage, the emails are not deleted, and the user can check the sorted emails as needed. This mechanism makes it easier for users to organize their inboxes and prevents them from missing important emails. Furthermore, it targets all age groups and is a service that anyone can easily use. For example, if a business person receives a large volume of emails, the generative AI automatically sorts out unnecessary emails, allowing them to focus on important emails. General users can also check only the emails they need without being bothered by advertising emails or spam. By utilizing generative AI, this service enables more accurate sorting than conventional spam filtering functions. For example, by analyzing the entire content of the email, not just specific keywords or senders, it can more accurately determine unnecessary emails. Moreover, since the sorted emails are not deleted, the user can check them as needed. For example, it can help you find important emails that have been mistakenly sorted, allowing you to use it with peace of mind. Thus, an email sorting system utilizing generation AI is a groundbreaking system that organizes the user's inbox, ensuring important emails are not missed and enabling efficient email management. In this way, the email sorting system organizes the user's inbox, ensuring important emails are not missed and enabling efficient email management.

[0066] The email sorting system according to this embodiment comprises a reception unit, an analysis unit, a determination unit, a sorting unit, and a provision unit. The reception unit receives emails received by the user. The reception unit can, for example, receive all emails received by the user. The analysis unit analyzes the emails received by the reception unit. The analysis unit analyzes information such as the content of the email, the sender, and the subject, for example, using a generation AI. The determination unit determines unnecessary emails based on the information analyzed by the analysis unit. The determination unit determines unnecessary emails, for example, using a generation AI. The sorting unit sorts the unnecessary emails determined by the determination unit. The sorting unit automatically sorts the emails determined to be unnecessary into a specific folder, for example. The provision unit provides the emails sorted by the sorting unit so that the user can check them. The provision unit allows the user to check the sorted emails as needed. As a result, the email sorting system can efficiently analyze, determine, sort, and provide emails received by the user.

[0067] The reception unit receives emails from users. For example, the reception unit can receive all emails received by a user. Specifically, the reception unit interacts with the user's mail server and retrieves emails using protocols such as POP3 and IMAP. This allows for centralized management of all received emails, regardless of the email client or service used by the user. Furthermore, the reception unit retrieves emails in real time upon receipt and passes them on to the next processing step without delay. This enables the system to start operating the moment a user receives an email, allowing for rapid email processing. The reception unit also extracts email metadata (sender, received date and time, subject, etc.) and prepares it for the analysis unit. This allows the analysis unit to efficiently analyze the email content. Additionally, the reception unit can perform filtering based on specific conditions according to user settings. For example, it can be configured to prioritize emails from specific domains or emails containing specific keywords. This enables flexible email reception tailored to user needs.

[0068] The analysis unit analyzes emails received by the reception unit. For example, the analysis unit uses generative AI to analyze information such as the email content, sender, and subject. Specifically, the generative AI uses natural language processing technology to analyze the email body and extract important keywords and phrases. Furthermore, the generative AI analyzes the email sender address and domain to evaluate its reliability and potential as spam. Regarding the subject, the generative AI uses pattern recognition technology to detect characteristics of spam and phishing emails. This allows the analysis unit to analyze the email content in detail and evaluate its importance and urgency. Furthermore, the analysis unit can learn from past email data and the user's email usage history to perform more accurate analysis. For example, it can learn the characteristics of emails that the user previously deemed important and then determine new emails with similar characteristics as important. The analysis unit also analyzes email attachments, evaluating their type and content. This allows the analysis unit to comprehensively analyze the entire email content and provide the necessary information to the subsequent decision unit.

[0069] The judgment unit determines unnecessary emails based on the information analyzed by the analysis unit. The judgment unit uses, for example, a generation AI to determine unnecessary emails. Specifically, the generation AI uses a spam filtering algorithm to evaluate the content of emails and the reliability of the sender, and identifies unnecessary emails. For example, characteristics of spam emails include the overuse of specific keywords or phrases, links, and unclear senders. The generation AI scores emails based on these characteristics and determines that emails with a score above a certain level are unnecessary. The generation AI also learns the user's past email processing history and improves its judgment accuracy based on the characteristics of emails that the user has deemed unnecessary. Furthermore, the judgment unit can also detect phishing emails and emails containing malware. The generation AI analyzes the content of links and attachments in emails and evaluates whether they contain dangerous elements. As a result, the judgment unit can accurately determine unnecessary or dangerous emails for the user and pass them on to the next sorting unit.

[0070] The sorting unit sorts unnecessary emails determined by the judgment unit. For example, the sorting unit automatically sorts emails determined to be unnecessary into specific folders. Specifically, the sorting unit analyzes the user's email folder structure and moves unnecessary emails to the spam folder or trash folder. Furthermore, if the user has set up custom folders, it can also sort emails based on specific conditions. For example, it is possible to set up sorting of emails from a specific sender or emails containing specific keywords into designated folders. In addition, the sorting unit continuously learns sorting rules based on user feedback and improves accuracy. For example, if a user manually corrects an email that was incorrectly sorted, the sorting unit learns that information and reflects it in subsequent sorting. In this way, the sorting unit can achieve flexible email sorting that meets the user's needs and improve the efficiency of email management.

[0071] The service provider makes it possible for users to check emails that have been sorted by the sorting unit. For example, the service provider allows users to check sorted emails as needed. Specifically, the service provider works in conjunction with the user's email client or webmail interface to display sorted emails in the appropriate folders. This allows users to easily check emails moved to spam or trash folders and restore them if necessary. Furthermore, the service provider provides an email preview function, allowing users to easily check the content of emails. For example, it displays the subject, sender, and a portion of the body of the email, allowing users to understand the content without opening the email. The service provider also collects user feedback and provides data to improve the accuracy and usability of the entire system. For example, if a user reports an email that has been sorted incorrectly, that information is fed back to the analysis and judgment units and reflected in future processing. In this way, the service provider can provide a user-friendly interface and improve the efficiency of email management.

[0072] The analysis unit can determine unnecessary emails based on information such as the email content, sender, and subject. For example, the analysis unit can determine unnecessary emails if they contain specific keywords. The analysis unit can also determine unnecessary emails based on the sender's domain. Furthermore, the analysis unit can determine unnecessary emails based on subject line patterns. This improves the accuracy of the determination by determining unnecessary emails based on information such as the email content, sender, and subject. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input the email content into a generation AI, which can then determine unnecessary emails.

[0073] The sorting unit can automatically sort emails deemed unnecessary into specific folders. For example, the sorting unit can automatically sort emails deemed unnecessary into a spam folder. It can also automatically sort them into an advertising folder. Furthermore, it can automatically sort them into an archive folder. This organizes the user's inbox by automatically sorting emails deemed unnecessary into specific folders. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input emails deemed unnecessary into a generation AI, which can then sort them into specific folders.

[0074] The service provider can enable users to check sorted emails as needed. For example, the service provider can notify users of sorted emails using a notification function. The service provider can also enable users to search for sorted emails using a search function. Furthermore, the service provider can enable users to filter sorted emails using a filtering function. This allows users to check sorted emails as needed, including important emails that have been mistakenly sorted. Some or all of the above processing in the service provider may be performed using a generation AI, or not using a generation AI. For example, the service provider can input sorted emails into a generation AI, which can then notify the user.

[0075] The reception unit can receive all emails received by a user. For example, the reception unit can receive all emails that arrive in a specific account. It can also receive all emails received during a specific period. Furthermore, it can receive all emails that arrive in a specific folder. This ensures that all emails received by the user are processed within the system. Some or all of the processing described above in the reception unit may be performed using a generation AI, or not. For example, the reception unit can input all emails received by the user into a generation AI, which can then accept them.

[0076] The reception system can estimate the user's emotions and adjust the timing of email reception based on the estimated emotions. For example, if the user is stressed, the reception system can delay email reception and notify them when they are relaxed. Alternatively, if the user is focused, the reception system can immediately notify them of only important emails, notifying them of others later. Furthermore, if the user is relaxed, the reception system can immediately notify them of all emails. This reduces user stress by adjusting the timing of email reception based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception system may be performed using or without generative AI. For example, the reception system can input user emotion data into a generative AI, which can then adjust the timing of email reception.

[0077] The reception unit can analyze the user's past email reception history and select the optimal reception method. For example, the reception unit can accept emails according to the time slots the user frequently received emails in the past. The reception unit can also learn patterns of emails that the user has previously deemed important and prioritize accepting similar emails. Furthermore, the reception unit can automatically filter and reject emails that the user has previously identified as spam. In this way, the optimal reception method can be selected by analyzing the user's past email reception history. Some or all of the above processing in the reception unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception unit can input the user's past email reception history into a generative AI, which can then select the optimal reception method.

[0078] The reception system can filter incoming emails based on the user's current projects and areas of interest. For example, it can prioritize emails related to projects the user is currently working on. It can also prioritize newsletters and information related to the user's areas of interest. Furthermore, it can automatically filter out emails from areas the user has not expressed interest in. This allows the system to prioritize receiving highly relevant emails by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using or without a generative AI. For example, the reception system can input information about the user's current projects and areas of interest into a generative AI, which can then perform the filtering.

[0079] The reception desk can estimate the user's emotions and determine the priority of emails to receive based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize receiving only important emails. If the user is relaxed, the reception desk can also prioritize receiving all emails equally. Furthermore, if the user is in a hurry, the reception desk can prioritize receiving urgent emails. In this way, important emails can be received preferentially by determining the priority of emails to receive based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using generative AI or not. For example, the reception desk can input user emotion data into a generative AI, and the generative AI can determine the priority of emails.

[0080] The reception desk can prioritize receiving emails that are highly relevant based on the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize receiving emails related to that region. Furthermore, if the user is traveling, the reception desk can prioritize receiving emails containing information related to their travel destination. Additionally, if the user is at home, the reception desk can prioritize receiving emails related to their home. This allows the reception desk to provide users with useful information by prioritizing emails based on their geographical location. Some or all of the above processing in the reception desk may be performed using a generative AI, or it may be performed without a generative AI. For example, the reception desk can input the user's geographical location information into a generative AI, which can then prioritize receiving highly relevant emails.

[0081] The reception desk can analyze a user's social media activity when receiving emails and receive relevant emails. For example, the reception desk can prioritize receiving emails related to topics the user has shown interest in on social media. It can also prioritize receiving emails from accounts the user follows. Furthermore, it can prioritize receiving emails related to groups or events the user participates in. In this way, by analyzing the user's social media activity, it is possible to prioritize receiving relevant emails. Some or all of the above processing in the reception desk may be performed using a generative AI, or not. For example, the reception desk can input the user's social media activity data into a generative AI, which can then receive relevant emails.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, by adjusting the presentation of the analysis based on the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, and the generative AI can adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail in its analysis based on the importance of the email. For example, the analysis unit performs a detailed analysis for important emails. It can also perform a simplified analysis for general emails. Furthermore, it can perform a minimal analysis for spam emails. This allows for detailed analysis of important emails by adjusting the level of detail based on the importance of the email. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input email importance data into a generation AI, which can then adjust the level of detail in its analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the email category during analysis. For example, in the case of business emails, the analysis unit applies a business-related analysis algorithm. It can also apply an advertising-related analysis algorithm to advertising emails. Furthermore, it can apply a private-related analysis algorithm to private emails. This allows for optimal analysis for each category by applying different analysis algorithms depending on the email category. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input email category data into a generation AI, which can then apply different analysis algorithms.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is in a hurry, the analysis unit can provide a brief analysis. By adjusting the length of the analysis based on the user's emotions, the analysis unit can provide the user with an analysis result of an appropriate length. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using the generative AI or not. For example, the analysis unit can input the user's emotion data into the generative AI, which can then adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on when the emails were received. For example, the analysis unit may prioritize the analysis of recently received emails. It can also prioritize the analysis of emails received immediately before an important event. Furthermore, the analysis unit may prioritize the analysis of emails received by the user during a specific time period. This allows for the provision of timely analysis results by determining the priority of analysis based on when the emails were received. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input email reception time data into a generating AI, which can then determine the priority of analysis.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the emails during the analysis process. For example, the analysis unit may prioritize analyzing emails related to topics that the user has shown interest in. It can also prioritize analyzing emails from accounts that the user follows. Furthermore, it can prioritize analyzing emails related to groups or events that the user participates in. By adjusting the order of analysis based on the relevance of the emails, it is possible to prioritize the analysis of highly relevant emails. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit can input email relevance data into a generative AI, which can then adjust the order of analysis.

[0088] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is stressed, the judgment unit can use strict criteria to determine unnecessary emails. Conversely, if the user is relaxed, the judgment unit can use lenient criteria to determine unnecessary emails. Furthermore, if the user is in a hurry, the judgment unit can make a quick judgment. This allows for judgments tailored to the user's situation by adjusting the judgment criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the judgment unit may be performed using or without a generative AI. For example, the judgment unit can input user emotion data into a generative AI, which can then adjust the judgment criteria.

[0089] The judgment unit can improve the accuracy of its judgment by considering the relationships between emails during the judgment process. For example, the judgment unit can judge multiple emails from the same sender at once. It can also group emails related to the same topic and judge them together. Furthermore, the judgment unit can improve the accuracy of its judgment by referring to the judgment results of past emails. In this way, the accuracy of the judgment is improved by considering the relationships between emails. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input email relationship data into a generating AI, which can then improve the accuracy of the judgment.

[0090] The judgment unit can make a judgment by considering the attribute information of the email sender. For example, the judgment unit may determine an email to be important if the sender is trustworthy. The judgment unit may also determine an email to be unnecessary if the sender is unknown. Furthermore, the judgment unit may also determine an email to be unnecessary if the sender is registered on a spam list. This makes it possible to make a highly reliable judgment by considering the attribute information of the email sender. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input the sender's attribute information into a generating AI, and the generating AI can perform the judgment.

[0091] The judgment unit can estimate the user's emotions and adjust the order in which the judgment results are displayed based on the estimated emotions. For example, if the user is feeling stressed, the judgment unit can display important emails first. Alternatively, if the user is relaxed, the judgment unit can display all emails equally. Furthermore, if the user is in a hurry, the judgment unit can display urgent emails first. This allows for prioritizing the display of information important to the user by adjusting the order in which the judgment results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the judgment unit may be performed using or without a generative AI. For example, the judgment unit can input user emotion data into a generative AI, which can then adjust the order in which the judgment results are displayed.

[0092] The determination unit can make a determination by considering the geographical distribution of emails. For example, if the user is in a specific region, the determination unit will determine that emails related to that region are important. The determination unit can also determine that emails containing information related to the user's travel destination are important if the user is traveling. Furthermore, if the determination unit is at home, the determination unit can determine that emails related to home are important. In this way, by considering the geographical distribution of emails, important information related to a region can be prioritized. Some or all of the above processing in the determination unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the determination unit can input geographical distribution data of emails into a generation AI, and the generation AI can perform the determination.

[0093] The judgment unit can improve the accuracy of its judgment by referring to relevant literature related to the email during the judgment process. For example, the judgment unit makes a judgment by referring to literature related to the content of the email. The judgment unit can also make a judgment by referring to literature related to the sender of the email. Furthermore, the judgment unit can make a judgment by referring to literature related to the topic of the email. In this way, the accuracy of the judgment is improved by referring to relevant literature related to the email. Some or all of the above processing in the judgment unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the judgment unit can input email-related literature data into a generating AI, and the generating AI can improve the accuracy of the judgment.

[0094] The sorting unit can estimate the user's emotions and adjust the sorting method based on the estimated emotions. For example, if the user is stressed, the sorting unit can prioritize sorting only important emails. If the user is relaxed, the sorting unit can sort all emails equally. Furthermore, if the user is in a hurry, the sorting unit can prioritize sorting urgent emails. By adjusting the sorting method based on the user's emotions, appropriate sorting according to the user's situation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using or without a generative AI. For example, the sorting unit can input user emotion data into a generative AI, which can then adjust the sorting method.

[0095] The sorting unit can improve the accuracy of sorting by considering the relationships between emails during the sorting process. For example, the sorting unit can sort multiple emails from the same sender in a single batch. It can also group emails related to the same topic and sort them accordingly. Furthermore, the sorting unit can improve the accuracy of sorting by referring to the sorting results of past emails. This improves the accuracy of sorting by considering the relationships between emails. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input email relationship data into a generation AI, which can then improve the accuracy of sorting.

[0096] The sorting unit can sort emails while considering the sender's attribute information. For example, if the sender is trustworthy, the sorting unit can sort the email into an important folder. It can also sort emails into a spam folder if the sender is unknown. Furthermore, if the sender is on a spam list, the sorting unit can sort emails into the spam folder. This allows for more reliable sorting by considering the sender's attribute information. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input the sender's attribute information into a generation AI, which can then perform the sorting.

[0097] The sorting unit can estimate the user's emotions and determine sorting priorities based on the estimated emotions. For example, if the user is stressed, the sorting unit can prioritize sorting only important emails. If the user is relaxed, the sorting unit can sort all emails equally. Furthermore, if the user is in a hurry, the sorting unit can prioritize sorting urgent emails. In this way, important emails can be prioritized by determining sorting priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using or without a generative AI. For example, the sorting unit can input user emotion data into a generative AI, and the generative AI can determine the sorting priorities.

[0098] The sorting unit can sort emails considering their geographical distribution. For example, if a user is in a specific region, the sorting unit can prioritize sorting emails related to that region. Furthermore, if a user is traveling, the sorting unit can prioritize sorting emails containing information related to their travel destination. Additionally, if a user is at home, the sorting unit can prioritize sorting emails related to their home. This allows for the prioritization of important region-related information by considering the geographical distribution of emails. Some or all of the above processing in the sorting unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the sorting unit can input geographical distribution data of emails into a generation AI, which can then perform the sorting.

[0099] The sorting unit can improve the accuracy of sorting by referring to relevant literature for each email during the sorting process. For example, the sorting unit may sort emails by referring to literature related to the content of the email. It can also sort emails by referring to literature related to the sender of the email. Furthermore, the sorting unit may sort emails by referring to literature related to the topic of the email. This improves the accuracy of sorting by referring to relevant literature for each email. Some or all of the above processing in the sorting unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the sorting unit can input email-related literature data into a generative AI, which can then improve the accuracy of sorting.

[0100] The service provider can estimate the user's emotions and adjust how emails are displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a concise and easily readable display. If the user is relaxed, the service provider can also provide a display that includes detailed information. Furthermore, if the user is in a hurry, the service provider can provide a display that gets straight to the point. By adjusting how emails are displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to read. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can adjust the display method.

[0101] The service provider can select the optimal display method by referring to the user's past operation history when providing the service. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. The service provider can also exclude display methods that the user has avoided in the past. Furthermore, the service provider can learn and provide the optimal display method from the user's past operation history. This allows the service provider to provide the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past operation history data into a generation AI, and the generation AI can select the optimal display method.

[0102] The service provider can estimate the user's emotions and adjust the email instructions based on those emotions. For example, if the user is stressed, the service provider may simplify the instructions. If the user is relaxed, the service provider may also provide detailed instructions. Furthermore, if the user is in a hurry, the service provider may provide instructions that allow for quick operation. By adjusting the email instructions based on the user's emotions, the service becomes user-friendly. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI, which can then adjust the instructions.

[0103] The service provider can select the optimal display method at the time of delivery, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Additionally, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. This allows the service provider to provide the optimal display method by considering the user's device information. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's device information into a generation AI, which can then select the optimal display method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception system can estimate the user's emotions and adjust how emails are received based on those estimates. For example, if a user is stressed, the reception system can immediately receive only important emails and notify them of others later. If the user is relaxed, it can also immediately receive all emails. Furthermore, if the user is in a hurry, it can prioritize receiving urgent emails. By adjusting how emails are received based on the user's emotions, this system can reduce user stress and prevent them from missing important emails.

[0106] The analysis unit can analyze a user's past email viewing history and estimate the importance of an email. For example, it can determine that emails from senders the user has frequently opened in the past are important. It can also determine that emails from senders the user has previously marked as spam are unnecessary. Furthermore, if a user has previously determined that emails containing specific keywords are important, it can prioritize the analysis of emails containing similar keywords. This allows for a more accurate estimation of email importance based on the user's past email viewing history.

[0107] The sorting unit can estimate the user's emotions and determine sorting priorities based on those emotions. For example, if the user is stressed, it can prioritize sorting only important emails. If the user is relaxed, it can sort all emails equally. Furthermore, if the user is in a hurry, it can prioritize sorting urgent emails. In this way, by determining sorting priorities based on the user's emotions, important emails can be prioritized.

[0108] The delivery unit can estimate the user's emotions and adjust how emails are displayed based on those estimates. For example, if a user is stressed, it can provide a concise and highly visible display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how emails are displayed based on the user's emotions, it becomes possible to provide a display that is easy for the user to read.

[0109] The reception desk can prioritize receiving emails that are highly relevant to the user based on their geographical location. For example, if a user is in a specific region, it will prioritize receiving emails related to that region. Similarly, if a user is traveling, it can prioritize receiving emails containing information related to their travel destination. Furthermore, if a user is at home, it can prioritize receiving emails related to their home. This allows the system to provide users with useful information by prioritizing emails that are highly relevant based on their geographical location.

[0110] The analysis unit can automatically generate reply templates based on the content of emails. For example, in the case of business emails, it can generate business-related reply templates. It can also generate personal-related reply templates for private emails. Furthermore, it can generate reply templates for advertising emails. This streamlines the user's reply process by automatically generating reply templates based on email content.

[0111] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on those emotions. For example, if the user is stressed, it can use stricter criteria to identify unnecessary emails. Conversely, if the user is relaxed, it can use more lenient criteria to identify unnecessary emails. Furthermore, if the user is in a hurry, it can make a quick judgment. In this way, by adjusting the judgment criteria based on the user's emotions, it becomes possible to make judgments that are appropriate to the user's situation.

[0112] The sorting function can sort emails by considering the sender's attribute information. For example, if the sender is trustworthy, the email can be sorted into an important folder. If the sender is unknown, the email can be sorted into the spam folder. Furthermore, if the sender is on a spam list, the email can also be sorted into the spam folder. This allows for highly reliable sorting by considering the sender's attribute information.

[0113] The service provider can select the optimal display method by referring to the user's past operation history. For example, it can prioritize providing display methods that the user has preferred in the past. It can also exclude display methods that the user has avoided in the past. Furthermore, it can learn and provide the optimal display method from the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history.

[0114] The delivery unit can estimate the user's emotions and adjust the instructions for using the email based on those estimates. For example, if the user is stressed, the instructions can be simplified. If the user is relaxed, detailed instructions can be provided. Furthermore, if the user is in a hurry, instructions that allow for quick operation can be provided. By adjusting the instructions for using the email based on the user's emotions, a user-friendly experience can be achieved.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives emails from the user. For example, it can receive all emails the user has received. Step 2: The analysis unit analyzes the emails received by the reception unit. For example, it uses a generation AI to analyze information such as the content of the email, the sender, and the subject. Step 3: The determination unit determines unnecessary emails based on the information analyzed by the analysis unit. For example, it may use a generation AI to determine unnecessary emails. Step 4: The sorting unit sorts the unnecessary emails determined by the judgment unit. For example, it automatically sorts emails determined to be unnecessary into a specific folder. Step 5: The delivery unit provides the emails sorted by the sorting unit so that users can check them. For example, it allows users to check sorted emails as needed.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, sorting unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives emails received by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes information such as the content of the email, the sender, and the subject using a generation AI. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines unnecessary emails based on the analyzed information. The sorting unit is implemented by the control unit 46A of the smart device 14 and automatically sorts emails determined to be unnecessary into a specific folder. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the sorted emails so that the user can confirm them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, sorting unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives emails received by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes information such as the content of the email, the sender, and the subject using a generation AI. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines unnecessary emails based on the analyzed information. The sorting unit is implemented by the control unit 46A of the smart glasses 214 and automatically sorts emails determined to be unnecessary into a specific folder. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the sorted emails so that the user can confirm them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, sorting unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives emails received by the user. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes information such as the content of the email, the sender, and the subject using a generation AI. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines unnecessary emails based on the analyzed information. The sorting unit is implemented by the control unit 46A of the headset terminal 314 and automatically sorts emails determined to be unnecessary into a specific folder. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the sorted emails so that the user can confirm them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the reception unit, analysis unit, determination unit, sorting unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives emails received by the user. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and analyzes information such as the content of the email, the sender, and the subject using a generation AI. The determination unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines unnecessary emails based on the analyzed information. The sorting unit is implemented by, for example, the control unit 46A of the robot 414 and automatically sorts emails determined to be unnecessary into a specific folder. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the sorted emails so that the user can confirm them. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) The reception desk receives emails from users, An analysis unit analyzes emails received by the aforementioned reception unit, A determination unit that determines unnecessary emails based on the information analyzed by the aforementioned analysis unit, A sorting unit sorts out unnecessary emails determined by the aforementioned determination unit, The system includes a provisioning unit that provides emails sorted by the aforementioned sorting unit so that users can check them. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identify unnecessary emails based on information such as the email's content, sender, and subject line. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned distribution unit is Emails deemed unnecessary are automatically moved to a specific folder. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Allow users to check sorted emails as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The user accepts all emails they receive. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of email reception based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the user's past email receiving history and select the optimal receiving method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving emails, filters them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and determines the priority of emails to be received based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When receiving emails, the system prioritizes sending emails that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving an email, the system analyzes the user's social media activity and sends relevant emails. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the email. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the email category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the email was received. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the emails. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a judgment, we improve the accuracy of the judgment by considering the relationships between emails. The system described in Appendix 1, characterized by the features described herein. (Note 20) The determination unit, When making a determination, the sender's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The determination unit, It estimates the user's emotions and adjusts the order in which the judgment results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The determination unit, When making a determination, the geographical distribution of the emails is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The determination unit, During the assessment process, we refer to the relevant literature in the email to improve the accuracy of the assessment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned distribution unit is It estimates the user's emotions and adjusts the sorting method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit is When sorting emails, we improve the accuracy of the sorting process by considering the relationships between emails. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned distribution unit is When sorting emails, the sender's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned distribution unit is It estimates the user's emotions and determines the sorting priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit is When sorting emails, the geographical distribution of the emails is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit is When sorting emails, we refer to related literature to improve sorting accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts how emails are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and adjusts the email instructions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk receives emails from users, An analysis unit analyzes emails received by the aforementioned reception unit, A determination unit that determines unnecessary emails based on the information analyzed by the aforementioned analysis unit, A sorting unit sorts out unnecessary emails determined by the aforementioned determination unit, The system includes a provisioning unit that provides emails sorted by the aforementioned sorting unit so that users can check them. A system characterized by the following features.

2. The aforementioned analysis unit, Identify unnecessary emails based on information such as the email's content, sender, and subject line. The system according to feature 1.

3. The aforementioned distribution unit is Emails deemed unnecessary are automatically moved to a specific folder. The system according to feature 1.

4. The aforementioned supply unit is, Allow users to check sorted emails as needed. The system according to feature 1.

5. The aforementioned reception unit is The user accepts all emails they receive. The system according to feature 1.

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

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

8. The aforementioned reception unit is When receiving emails, filters them based on the user's current projects and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is It estimates the user's emotions and determines the priority of emails to be received based on the estimated user emotions. The system according to feature 1.

10. The aforementioned reception unit is When receiving emails, the system prioritizes sending emails that are highly relevant based on the user's geographical location. The system according to feature 1.

Images