System

Abstract

An object of a system according to an embodiment is to automatically reflect request contents and a deadline of a mail on a calendar.SOLUTION: A system includes a reception part, an analysis part, a calendar addition part, and a reminder part. The receiving unit receives a mail. The analysis unit analyzes the mail text received by the reception unit and extracts the request content or the deadline. The calendar addition part adds the schedule to the calendar on the basis of the information extracted by the analysis part. The reminder unit sets a reminder based on the plan added by the calendar addition unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Previous technology required users to manually add email requests and deadlines to their calendars, which was inefficient.

[0005] The system according to the embodiment aims to automatically reflect the request contents and deadlines of emails on a calendar. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, an analyzing unit, a calendar adding unit, and a reminder unit. The receiving unit receives email. The analyzing unit analyzes the body of the email received by the receiving unit and extracts the request content or deadline. The calendar adding unit adds an event to the calendar based on the information extracted by the analyzing unit. The reminder unit sets a reminder based on the event added by the calendar adding unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically reflect the request content and deadline of an email on a calendar. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically updates a calendar with requests and deadlines from the body of received emails. When an email is received, AI analyzes the email body and extracts the request and deadline. Next, the system automatically adds an event to the calendar based on the extracted information. By including the email sender and subject in the calendar event, it is possible to clearly identify who made the request and what the subject is. This allows the system to efficiently manage a large number of emails without overlooking the request or deadline. Furthermore, reminders allow the user to confirm who made the request and what the subject is, ensuring that requests are not forgotten.

[0029] The calendar update system according to the embodiment includes a receiving unit, an analyzing unit, a calendar adding unit, and a reminder unit. The receiving unit receives emails. The receiving unit can retrieve emails from a mail server using protocols such as POP3 and IMAP. The receiving unit can also be triggered by receiving emails. The analyzing unit analyzes the email body received by the receiving unit and extracts the request content or deadline. The analyzing unit can analyze the email body using natural language processing technology, for example, and extract the request content and deadline. The analyzing unit can also extract important information from the email body using a keyword extraction algorithm. For example, the analyzing unit can extract the request content "submit the report" and the deadline "next Friday" from the email body saying "Please submit the report by next Friday." The calendar adding unit adds an event to the calendar based on the information extracted by the analyzing unit. The calendar adding unit can add the event to a calendar app, such as Google® Calendar or Outlook® Calendar. The calendar adding unit can also include the sender and subject of the email in the calendar event. For example, the calendar adding unit can add an event titled "Submit report" to "Next Friday" and include the sender and subject of the email in the event. The reminder unit sets a reminder based on the event added by the calendar adding unit. The reminder unit can set a reminder, for example, the day before the deadline and notify the user. The reminder unit can also include the sender and subject of the email in the reminder. For example, the reminder unit can set a reminder for the event titled "Submit report" the day before the deadline and include the sender and subject of the email in the reminder. As a result, the calendar reflection system according to the embodiment enables efficient task management by automatically reflecting the request content and deadline from the email body in the calendar and setting reminders.

[0030] The analysis unit can extract the sender or subject of an email. For example, the analysis unit can analyze email header information and extract the sender's email address or name. The analysis unit can also extract the subject from the email body. For example, the analysis unit can extract the sender's email address from the "From" field of the email header and extract the subject from the "Subject" field. By extracting the email sender and subject, information about the appointment added to the calendar becomes clear. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input email header information into a generation AI and have the generation AI extract the sender and subject.

[0031] The calendar adding unit can include the email sender or subject when adding an event to a calendar based on the extracted information. The calendar adding unit can include the email sender and subject when adding an event to a calendar app such as Google Calendar or Outlook Calendar. For example, the calendar adding unit can add an event titled "Submit Report" to "Next Friday" and include the email sender and subject in the event. By including the email sender and subject in the event added to the calendar, the origin of the event becomes clear. Some or all of the above-described processing in the calendar adding unit can be performed using, or without, AI. For example, the calendar adding unit can input the sender and subject information extracted by the analysis unit into a generation AI and cause the generation AI to perform processing to include the sender and subject when adding an event to a calendar.

[0032] The reminder unit can set a reminder based on an event added to the calendar and notify the user. For example, the reminder unit can set a reminder the day before a deadline and notify the user. The reminder unit can notify the user by methods such as push notification, email notification, or alarm. The reminder unit can also include the sender and subject of an email in the reminder. For example, the reminder unit can set a reminder for an event such as "Submit a report" the day before the deadline and include the sender and subject of the email in the reminder. This allows the user to remember to attend to an important event by setting a reminder and notifying the user. Some or all of the above-described processing in the reminder unit may be performed using, or without, AI. For example, the reminder unit can input information about an event added to the calendar into a generation AI and have the generation AI set the reminder and notify the user.

[0033] The reminder unit can set a reminder notification method. The reminder unit can set a reminder notification method, such as push notification, email notification, or alarm. For example, the reminder unit can set a reminder notification method to be sent by push notification according to the user's preference. The reminder unit can also set a reminder notification method to be sent by email notification. Furthermore, the reminder unit can also set a reminder notification method to be sent by alarm. This allows notifications to be sent according to the user's preference by setting the reminder notification method. Some or all of the above-mentioned processing in the reminder unit can be performed using, for example, AI, or can be performed without using AI. For example, the reminder unit can input the user's preferred notification method into the generation AI and have the generation AI set the reminder notification method.

[0034] The receiving unit can analyze the user's past email reception history and select an appropriate reception method. For example, the receiving unit can analyze the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. If the user has frequently checked email during a specific time period in the past, the receiving unit can concentrate on receiving emails during that time period. Furthermore, the receiving unit can learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. For example, the receiving unit can analyze the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. If the user has frequently checked email during a specific time period in the past, the receiving unit can concentrate on receiving emails during that time period. Furthermore, the receiving unit can learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. By analyzing the past email reception history, the optimal email reception method can be provided to the user. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without AI. For example, the receiving unit can input the user's past email reception history into a generation AI and have the generation AI select the optimal reception method.

[0035] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, the receiving unit can prioritize receiving emails related to the user's current projects. The receiving unit can also prioritize receiving emails related to areas of interest to the user. Furthermore, the receiving unit can filter and receive emails related to topics in which the user has previously shown interest. For example, the receiving unit can prioritize receiving emails related to the user's current projects. The receiving unit can also prioritize receiving emails related to areas of interest to the user. Furthermore, the receiving unit can filter and receive emails related to topics in which the user has previously shown interest. In this way, by filtering emails based on the user's current projects and areas of interest, important emails can be preferentially received. Some or all of the above-described processing in the receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the receiving unit can input information about the user's current projects and areas of interest to the generation AI and have the generation AI perform the filtering.

[0036] When receiving an email, the receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user prefers voice input, the receiving unit can read out the contents of the email aloud. Furthermore, if the user prefers text input, the receiving unit can display the email in text format. Furthermore, if the user prefers images, the receiving unit can display the contents of the email as an image. For example, if the user prefers voice input, the receiving unit can read out the contents of the email aloud. Furthermore, if the user prefers text input, the receiving unit can display the contents of the email as a text format. Furthermore, if the user prefers images, the receiving unit can display the contents of the email as an image. By selecting the optimal receiving means depending on the user's input method, user convenience is improved. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input information about the user's input method into a generation AI and cause the generation AI to select the optimal receiving means.

[0037] When receiving emails, the receiving unit can prioritize receiving relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific location, the receiving unit can prioritize receiving emails related to that location. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the home. For example, when the user is in a specific location, the receiving unit can prioritize receiving emails related to the location. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the home. In this way, by taking the user's geographical location information into account, highly relevant emails can be received preferentially. Some or all of the above-described processing in the receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant emails.

[0038] The receiving unit can analyze the user's social media activity when receiving emails and receive relevant emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also prioritize receiving emails related to topics the user has shown interest in on social media. Furthermore, the receiving unit can analyze the user's social media activity and filter and receive relevant emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also prioritize receiving emails related to topics the user has shown interest in on social media. Furthermore, the receiving unit can analyze the user's social media activity and filter and receive relevant emails. In this way, by analyzing the user's social media activity, relevant emails can be preferentially received. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input data on the user's social media activity into the generation AI and cause the generation AI to receive relevant emails.

[0039] When receiving email, the receiving unit can adjust the receiving method by reflecting the user's past feedback. For example, the receiving unit can learn the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. The receiving unit can also learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. The receiving unit can also learn the format of emails that the user has previously preferred and prioritize receiving emails with similar formats. For example, the receiving unit can learn the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. The receiving unit can also learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. The receiving unit can also learn the format of emails that the user has previously preferred and prioritize receiving emails with similar formats. In this way, by reflecting the user's past feedback, an optimal receiving method can be provided. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI or without AI. For example, the receiving unit can input data of the user's past feedback into the generating AI and cause the generating AI to adjust the receiving method.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit can display detailed analysis results for important emails. The analysis unit can also display brief analysis results for non-important emails. Furthermore, the analysis unit can quickly display analysis results for urgent emails. For example, the analysis unit can display detailed analysis results for important emails. The analysis unit can also display brief analysis results for non-important emails. Furthermore, the analysis unit can quickly display analysis results for urgent emails. In this way, by adjusting the level of detail of the analysis based on the importance of the email, important information can be provided preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the importance of the email to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply a different analysis algorithm depending on the category of the email. For example, in the case of business email, the analysis unit can apply a business analysis algorithm. Furthermore, in the case of private email, the analysis unit can apply a private analysis algorithm. Furthermore, in the case of spam email, the analysis unit can apply an analysis algorithm for spam filtering. For example, in the case of business email, the analysis unit can apply a business analysis algorithm. Furthermore, in the case of private email, the analysis unit can apply a private analysis algorithm. Furthermore, in the case of spam email, the analysis unit can apply an analysis algorithm for spam filtering. In this way, by applying an appropriate analysis algorithm depending on the category of the email, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the category of the email to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can learn the characteristics of emails that the user previously determined to be important and prioritize analyzing similar emails. The analysis unit can also learn the characteristics of emails that the user previously determined to be spam and filter similar emails. Furthermore, the analysis unit can learn the format of analysis results that the user previously preferred and display the analysis results in a similar format. For example, the analysis unit can learn the characteristics of emails that the user previously determined to be important and prioritize analyzing similar emails. The analysis unit can also learn the characteristics of emails that the user previously determined to be spam and filter similar emails. Furthermore, the analysis unit can learn the format of analysis results that the user previously preferred and display the analysis results in a similar format. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can set analysis priorities based on the time of receipt of the email. For example, the analysis unit can give top priority to analysis of urgent emails. The analysis unit can also give priority to analysis of important emails. Furthermore, the analysis unit can postpone analysis of non-important emails. For example, the analysis unit gives top priority to analysis of urgent emails. The analysis unit can also give priority to analysis of important emails. Furthermore, the analysis unit can postpone analysis of non-important emails. In this way, by determining the analysis priority based on the time of receipt of the emails, urgent emails can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the time of receipt of the emails to the generation AI and have the generation AI set the analysis priorities.

[0044] During analysis, the analysis unit can set the analysis order based on the relevance of emails. For example, the analysis unit can prioritize analyzing emails related to a project currently underway by the user. The analysis unit can also prioritize analyzing emails related to fields in which the user is interested. Furthermore, the analysis unit can prioritize analyzing emails related to topics in which the user has previously shown interest. For example, the analysis unit prioritizes analyzing emails related to a project currently underway by the user. The analysis unit can also prioritize analyzing emails related to fields in which the user is interested. Furthermore, the analysis unit can prioritize analyzing emails related to topics in which the user has previously shown interest. In this way, by adjusting the analysis order based on the relevance of emails, important emails can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of emails to the generation AI and cause the generation AI to set the analysis order.

[0045] During analysis, the analysis unit can set the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can display analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can display concise and easy-to-understand analysis results. Furthermore, the analysis unit can display analysis results that use appropriate technical terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can display analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can display analysis results that are concise and easy-to-understand. Furthermore, the analysis unit can display analysis results that use appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input information about the user's level of expertise to the generation AI and cause the generation AI to use technical terms.

[0046] The calendar addition unit can adjust the level of detail added based on the importance of the email when adding the email to the calendar. For example, the calendar addition unit can add detailed information to the calendar for important emails. Furthermore, the calendar addition unit can add brief information to the calendar for non-important emails. Furthermore, the calendar addition unit can quickly add urgent emails to the calendar. For example, the calendar addition unit can add detailed information to the calendar for important emails. Furthermore, the calendar addition unit can add brief information to the calendar for non-important emails. Furthermore, the calendar addition unit can quickly add urgent emails to the calendar. In this way, by adjusting the level of detail added to the calendar based on the importance of the email, important information can be preferentially added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information on the importance of the email to the generation AI and cause the generation AI to adjust the level of detail added.

[0047] When adding an email to the calendar, the calendar adding unit can apply a different adding algorithm depending on the email category. For example, in the case of business email, the calendar adding unit can apply a business adding algorithm. Furthermore, in the case of private email, the calendar adding unit can also apply a private adding algorithm. Furthermore, in the case of spam email, the calendar adding unit can apply a spam filtering adding algorithm. For example, in the case of business email, the calendar adding unit can apply a business adding algorithm. Furthermore, in the case of private email, the calendar adding unit can also apply a private adding algorithm. Furthermore, in the case of spam email, the calendar adding unit can apply a spam filtering adding algorithm. In this way, by applying an appropriate adding algorithm depending on the email category, the accuracy of calendar addition is improved. Some or all of the above-mentioned processing in the calendar adding unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar adding unit can input email category information to the generation AI and cause the generation AI to apply an appropriate adding algorithm.

[0048] When adding an item to the calendar, the calendar adding unit can improve the accuracy of the addition by referring to the user's past calendar addition results. The calendar adding unit, for example, can learn the characteristics of events that the user previously determined to be important and prioritize adding similar events to the calendar. The calendar adding unit can also learn the characteristics of events that the user previously determined to be spam and filter similar events. The calendar adding unit can also learn the calendar addition format that the user previously preferred and add similar events to the calendar in a similar format. For example, the calendar adding unit can learn the characteristics of events that the user previously determined to be important and prioritize adding similar events to the calendar. The calendar adding unit can also learn the characteristics of events that the user previously determined to be spam and filter similar events. The calendar adding unit can also learn the calendar addition format that the user previously preferred and add similar events to the calendar in a similar format. In this way, by referring to the user's past calendar addition results, the accuracy of the calendar addition is improved. Some or all of the above-mentioned processing in the calendar adding unit may be performed, for example, using AI or without AI. For example, the calendar addition unit can input data of the user's past calendar addition results into the generation AI and cause the generation AI to improve the accuracy of the additions.

[0049] The calendar addition unit can set a priority for adding emails to the calendar based on the time of receipt of the email when adding the email. For example, the calendar addition unit can add urgent emails to the calendar with the highest priority. The calendar addition unit can also add important emails to the calendar with a higher priority. The calendar addition unit can also add non-important emails to the calendar at a later date. For example, the calendar addition unit can add urgent emails to the calendar with the highest priority. The calendar addition unit can also add important emails to the calendar with a higher priority. The calendar addition unit can also add non-important emails to the calendar at a later date. In this way, by determining the priority for adding emails to the calendar based on the time of receipt of the emails, urgent emails can be quickly added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information about the time of receipt of the email to the generation AI and cause the generation AI to set the priority for adding the email.

[0050] The calendar addition unit can set the order of emails to be added to the calendar based on the relevance of the emails when adding them to the calendar. For example, the calendar addition unit can prioritize adding emails related to a project currently underway by the user to the calendar. The calendar addition unit can also prioritize adding emails related to areas of interest to the calendar. Furthermore, the calendar addition unit can prioritize adding emails related to topics in which the user has previously shown interest to the calendar. For example, the calendar addition unit can prioritize adding emails related to a project currently underway by the user to the calendar. The calendar addition unit can also prioritize adding emails related to areas of interest to the calendar. Furthermore, the calendar addition unit can prioritize adding emails related to topics in which the user has previously shown interest to the calendar. In this way, by adjusting the order of emails to be added to the calendar based on the relevance of the emails, important emails can be prioritized to be added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information about the relevance of the emails to a generation AI and cause the generation AI to set the order of addition.

[0051] The calendar adding unit can set the use of additional technical terms according to the user's level of expertise when adding a calendar. For example, if the user has technical expertise, the calendar adding unit can provide a calendar adding method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the calendar adding unit can provide a concise and easy-to-understand calendar adding method. Furthermore, the calendar adding unit can provide a calendar adding method that uses appropriate technical terms according to the user's level of expertise. For example, if the user has technical expertise, the calendar adding unit can provide a calendar adding method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the calendar adding unit can provide a concise and easy-to-understand calendar adding method. Furthermore, the calendar adding unit can provide a calendar adding method that uses appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of additional technical terms according to the user's level of expertise, a calendar adding method that is easy for the user to understand can be provided. Some or all of the above-described processing in the calendar adding unit may be performed using, for example, AI, or may be performed without AI. For example, the calendar adding unit can input information about the user's level of expertise to a generation AI and cause the generation AI to use technical terms.

[0052] When setting a reminder, the reminder unit can adjust the level of detail in the notification based on the importance of the email. For example, the reminder unit can set a detailed reminder for an important email. The reminder unit can also set a brief reminder for a non-important email. Furthermore, the reminder unit can also set a quick reminder for an urgent email. For example, the reminder unit can set a detailed reminder for an important email. The reminder unit can also set a brief reminder for a non-important email. Furthermore, the reminder unit can also set a quick reminder for an urgent email. In this way, by adjusting the level of detail in the notification based on the importance of the email, important information can be notified preferentially. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input information about the importance of the email to the generation AI and cause the generation AI to adjust the level of detail in the notification.

[0053] When setting a reminder, the reminder unit can apply a different notification algorithm depending on the category of the email. For example, in the case of a business email, the reminder unit can apply a business notification algorithm. Furthermore, in the case of a private email, the reminder unit can apply a private notification algorithm. Furthermore, in the case of spam email, the reminder unit can apply a spam filtering notification algorithm. For example, in the case of a business email, the reminder unit can apply a business notification algorithm. Furthermore, in the case of a private email, the reminder unit can apply a private notification algorithm. Furthermore, in the case of spam email, the reminder unit can apply a spam filtering notification algorithm. In this way, by applying an appropriate notification algorithm depending on the category of the email, the accuracy of notifications is improved. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input email category information to the generation AI and cause the generation AI to apply an appropriate notification algorithm.

[0054] When setting a reminder, the reminder unit can improve the accuracy of notifications by referring to the user's past reminder results. For example, the reminder unit can learn the characteristics of reminders that the user previously determined to be important and prioritize setting similar reminders. The reminder unit can also learn the characteristics of reminders that the user previously determined to be spam and filter similar reminders. Furthermore, the reminder unit can learn the reminder formats that the user previously preferred and set reminders in a similar format. For example, the reminder unit can learn the characteristics of reminders that the user previously determined to be important and prioritize setting similar reminders. The reminder unit can also learn the characteristics of reminders that the user previously determined to be spam and filter similar reminders. Furthermore, the reminder unit can learn the reminder formats that the user previously preferred and set reminders in a similar format. This improves the accuracy of notifications by referring to the user's past reminder results. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit may input data on the user's past reminder results into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0055] When setting a reminder, the reminder unit can set the priority of notifications based on the time of receipt of the email. For example, in the case of an urgent email, the reminder unit can set a reminder as the highest priority. The reminder unit can also set a reminder as a priority for an important email. Furthermore, the reminder unit can set a reminder for a later date for a non-important email. For example, the reminder unit can set a reminder as the highest priority for an urgent email. The reminder unit can also set a reminder as a priority for an important email. Furthermore, the reminder unit can set a reminder for a later date for a non-important email. In this way, by determining the priority of notifications based on the time of receipt of the email, urgent emails can be notified promptly. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input information about the time of receipt of the email to the generation AI and have the generation AI set the priority of notifications.

[0056] When setting a reminder, the reminder unit can set the order of notifications based on the relevance of the emails. For example, the reminder unit can prioritize reminders for emails related to a project the user is currently working on. The reminder unit can also prioritize reminders for emails related to fields in which the user is interested. Furthermore, the reminder unit can also prioritize reminders for emails related to topics in which the user has previously shown interest. For example, the reminder unit prioritizes reminders for emails related to a project the user is currently working on. The reminder unit can also prioritize reminders for emails related to fields in which the user is interested. Furthermore, the reminder unit can also prioritize reminders for emails related to topics in which the user has previously shown interest. In this way, by adjusting the order of notifications based on the relevance of the emails, important emails can be prioritized. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or without AI. For example, the reminder unit can input email relevance information into the generation AI and have the generation AI set the notification order.

[0057] When setting a reminder, the reminder unit can set the use of technical terminology in the notification according to the user's level of expertise. For example, if the user has technical knowledge, the reminder unit can set a reminder that uses a lot of technical terminology. Furthermore, if the user does not have technical knowledge, the reminder unit can set a simple and easy-to-understand reminder. Furthermore, the reminder unit can set a reminder that uses appropriate technical terminology according to the user's level of expertise. For example, if the user has technical knowledge, the reminder unit can set a reminder that uses a lot of technical terminology. Furthermore, if the user does not have technical knowledge, the reminder unit can set a simple and easy-to-understand reminder. Furthermore, the reminder unit can set a reminder that uses appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the notification according to the user's level of expertise, it is possible to provide a notification that is easy for the user to understand. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or without AI. For example, the reminder unit can input information about the user's level of expertise into the generation AI and have the generation AI use specialized terminology.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] In addition to analyzing the email body, the analysis unit can also analyze the contents of email attachments. For example, the analysis unit can analyze attachments such as PDFs and Word documents to extract request details and deadlines. The analysis unit can also analyze image files to extract text from the images. Furthermore, the analysis unit can analyze audio files to extract request details from the audio. This allows for more accurate information extraction by considering not only the email body but also the contents of attachments.

[0060] The analysis unit can extract the date and time of receipt of an email in addition to the sender and subject of the email. For example, the analysis unit can extract the date and time of receipt from the "Date" field in the email header. The analysis unit can also extract date and time information written in the email body. Furthermore, the analysis unit can set the priority of the request content based on the date and time of receipt of the email. This allows for more appropriate management of task priorities by taking the date and time of receipt of the email into consideration.

[0061] The calendar adding unit can refer to the user's past calendar usage history when adding an event to the calendar based on the extracted information. For example, the calendar adding unit can learn the patterns of events set by the user in the past and add events in a similar pattern. The calendar adding unit can also add events by referring to the calendar display format that the user preferred in the past. Furthermore, the calendar adding unit can automatically adjust the event reminder settings based on the user's past calendar usage history. This makes it easier to add events by taking the user's past usage history into consideration.

[0062] In addition to providing a means for notifying a reminder, the reminder unit can also adjust the timing of the notification based on the user's schedule. For example, the reminder unit can set a reminder for a time when the user is free based on the user's calendar. The reminder unit can also set the optimal notification timing by referring to the user's past reminder usage history. Furthermore, the reminder unit can also notify the user of a reminder at an appropriate time by taking into account the user's current location information. This allows for more effective reminder notifications by taking into account the user's schedule and location information.

[0063] The reminder unit can customize the content of the notification in addition to the means of notification of the reminder according to the user's preferences. For example, the reminder unit can set the notification sound that the user prefers. The reminder unit can also set the format of the notification message that the user prefers. Furthermore, the reminder unit can also customize the content of the notification by referring to the user's past reminder usage history. This makes it possible to provide reminder notifications according to the user's preferences.

[0064] The receiving unit can analyze the user's past email reception history and select an appropriate reception method. For example, it can analyze the patterns of emails that the user has previously marked as important and prioritize the reception of similar emails. Also, if the user has frequently checked email during a specific time period in the past, it can concentrate the reception of emails during that time period. Furthermore, it can learn the characteristics of emails that the user has previously determined to be spam and filter out similar emails. In this way, by analyzing the user's past email reception history, it is possible to provide the user with the optimal email reception method.

[0065] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, emails related to projects currently underway by the user can be preferentially received. Emails related to areas in which the user is interested can also be preferentially received. Furthermore, emails related to topics in which the user has shown interest in the past can be filtered and received. In this way, important emails can be preferentially received by filtering emails based on the user's current projects and areas of interest.

[0066] When receiving an email, the receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user prefers voice input, the contents of the email can be read aloud. If the user prefers text input, the email can be displayed in text format. Furthermore, if the user prefers images, the contents of the email can be displayed as images. This improves user convenience by selecting the optimal receiving means depending on the user's input method.

[0067] The processing flow of the first embodiment will be briefly explained below.

[0068] Step 1: The receiving unit receives email. The receiving unit can obtain email from a mail server using a protocol such as POP3 or IMAP. The receiving unit can also operate when triggered by receiving email. Step 2: The analysis unit analyzes the email body received by the receiving unit and extracts the request content or deadline. The analysis unit can analyze the email body using natural language processing technology, for example, and extract the request content and deadline. The analysis unit can also extract important information from the email body using a keyword extraction algorithm. For example, from the email body that says, "Please submit the report by next Friday," the request content "submit the report" and the deadline "next Friday" can be extracted. Step 3: The calendar adding unit adds the event to the calendar based on the information extracted by the analyzing unit. The calendar adding unit can add the event to a calendar app such as Google Calendar or Outlook Calendar. The calendar adding unit can also include the sender and subject of an email in the calendar event. For example, the calendar adding unit can add an event called "Submit report" to "Next Friday" and include the sender and subject of the email in the event. Step 4: The reminder unit sets a reminder based on the event added by the calendar addition unit. For example, the reminder unit can set a reminder the day before a deadline and notify the user. The reminder unit can also include the sender and subject of an email in the reminder. For example, a reminder for an event called "Submit a report" can be set the day before the deadline, and the sender and subject of the email can be included in the reminder.

[0069] (Example 2) A system according to an embodiment of the present invention automatically updates a calendar with requests and deadlines from the body of received emails. When an email is received, AI analyzes the email body and extracts the request and deadline. Next, the system automatically adds an event to the calendar based on the extracted information. By including the email sender and subject in the calendar event, it is possible to clearly identify who made the request and what the subject is. This allows the system to efficiently manage a large number of emails without overlooking the request or deadline. Furthermore, reminders allow the user to confirm who made the request and what the subject is, ensuring that requests are not forgotten.

[0070] The calendar update system according to the embodiment includes a receiving unit, an analyzing unit, a calendar adding unit, and a reminder unit. The receiving unit receives emails. The receiving unit can retrieve emails from a mail server using protocols such as POP3 and IMAP. The receiving unit can also be triggered by receiving emails. The analyzing unit analyzes the email body received by the receiving unit and extracts the request content or deadline. The analyzing unit can analyze the email body using natural language processing technology, for example, and extract the request content and deadline. The analyzing unit can also extract important information from the email body using a keyword extraction algorithm. For example, the analyzing unit can extract the request content "submit the report" and the deadline "next Friday" from the email body saying "Please submit the report by next Friday." The calendar adding unit adds an event to the calendar based on the information extracted by the analyzing unit. The calendar adding unit can add the event to a calendar app, such as Google Calendar or Outlook Calendar. The calendar adding unit can also include the sender and subject of the email in the calendar event. For example, the calendar adding unit can add an event titled "Submit report" to "Next Friday" and include the sender and subject of the email in the event. The reminder unit sets a reminder based on the event added by the calendar adding unit. The reminder unit can set a reminder, for example, the day before the deadline and notify the user. The reminder unit can also include the sender and subject of the email in the reminder. For example, the reminder unit can set a reminder for the event titled "Submit report" the day before the deadline and include the sender and subject of the email in the reminder. As a result, the calendar reflection system according to the embodiment enables efficient task management by automatically reflecting the request content and deadline from the email body in the calendar and setting reminders.

[0071] The analysis unit can extract the sender or subject of an email. For example, the analysis unit can analyze email header information and extract the sender's email address or name. The analysis unit can also extract the subject from the email body. For example, the analysis unit can extract the sender's email address from the "From" field of the email header and extract the subject from the "Subject" field. By extracting the email sender and subject, information about the appointment added to the calendar becomes clear. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input email header information into a generation AI and have the generation AI extract the sender and subject.

[0072] The calendar adding unit can include the email sender or subject when adding an event to a calendar based on the extracted information. The calendar adding unit can include the email sender and subject when adding an event to a calendar app such as Google Calendar or Outlook Calendar. For example, the calendar adding unit can add an event titled "Submit Report" to "Next Friday" and include the email sender and subject in the event. By including the email sender and subject in the event added to the calendar, the origin of the event becomes clear. Some or all of the above-described processing in the calendar adding unit can be performed using, or without, AI. For example, the calendar adding unit can input the sender and subject information extracted by the analysis unit into a generation AI and cause the generation AI to perform processing to include the sender and subject when adding an event to a calendar.

[0073] The reminder unit can set a reminder based on an event added to the calendar and notify the user. For example, the reminder unit can set a reminder the day before a deadline and notify the user. The reminder unit can notify the user by methods such as push notification, email notification, or alarm. The reminder unit can also include the sender and subject of an email in the reminder. For example, the reminder unit can set a reminder for an event such as "Submit a report" the day before the deadline and include the sender and subject of the email in the reminder. This allows the user to remember to attend to an important event by setting a reminder and notifying the user. Some or all of the above-described processing in the reminder unit may be performed using, or without, AI. For example, the reminder unit can input information about an event added to the calendar into a generation AI and have the generation AI set the reminder and notify the user.

[0074] The reminder unit can set a reminder notification method. The reminder unit can set a reminder notification method, such as push notification, email notification, or alarm. For example, the reminder unit can set a reminder notification method to be sent by push notification according to the user's preference. The reminder unit can also set a reminder notification method to be sent by email notification. Furthermore, the reminder unit can also set a reminder notification method to be sent by alarm. This allows notifications to be sent according to the user's preference by setting the reminder notification method. Some or all of the above-mentioned processing in the reminder unit can be performed using, for example, AI, or can be performed without using AI. For example, the reminder unit can input the user's preferred notification method into the generation AI and have the generation AI set the reminder notification method.

[0075] The receiving unit can estimate the user's emotions and adjust the timing of receiving emails based on the estimated user emotions. For example, the receiving unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the receiving unit can calculate an emotion score based on changes in facial expressions. The receiving unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the receiving unit can analyze the tone and speed of the voice and calculate an emotion score. The receiving unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the receiving unit can calculate an emotion score based on heart rate fluctuations. This allows the user to adjust the timing of receiving emails based on the user's emotions, thereby reducing stress and enabling efficient email management. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit may input user emotion data into the generating AI and have the generating AI adjust the timing of receiving emails based on the emotion.

[0076] The receiving unit can analyze the user's past email reception history and select an appropriate reception method. For example, the receiving unit can analyze the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. If the user has frequently checked email during a specific time period in the past, the receiving unit can concentrate on receiving emails during that time period. Furthermore, the receiving unit can learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. For example, the receiving unit can analyze the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. If the user has frequently checked email during a specific time period in the past, the receiving unit can concentrate on receiving emails during that time period. Furthermore, the receiving unit can learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. By analyzing the past email reception history, the optimal email reception method can be provided to the user. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without AI. For example, the receiving unit can input the user's past email reception history into a generation AI and have the generation AI select the optimal reception method.

[0077] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, the receiving unit can prioritize receiving emails related to the user's current projects. The receiving unit can also prioritize receiving emails related to areas of interest to the user. Furthermore, the receiving unit can filter and receive emails related to topics in which the user has previously shown interest. For example, the receiving unit can prioritize receiving emails related to the user's current projects. The receiving unit can also prioritize receiving emails related to areas of interest to the user. Furthermore, the receiving unit can filter and receive emails related to topics in which the user has previously shown interest. In this way, by filtering emails based on the user's current projects and areas of interest, important emails can be preferentially received. Some or all of the above-described processing in the receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the receiving unit can input information about the user's current projects and areas of interest to the generation AI and have the generation AI perform the filtering.

[0078] When receiving an email, the receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user prefers voice input, the receiving unit can read out the contents of the email aloud. Furthermore, if the user prefers text input, the receiving unit can display the email in text format. Furthermore, if the user prefers images, the receiving unit can display the contents of the email as an image. For example, if the user prefers voice input, the receiving unit can read out the contents of the email aloud. Furthermore, if the user prefers text input, the receiving unit can display the contents of the email as a text format. Furthermore, if the user prefers images, the receiving unit can display the contents of the email as an image. By selecting the optimal receiving means depending on the user's input method, user convenience is improved. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input information about the user's input method into a generation AI and cause the generation AI to select the optimal receiving means.

[0079] The receiving unit can estimate the user's emotions and set the priority of received emails based on the estimated user emotions. For example, when the user is stressed, the receiving unit can prioritize displaying only important emails. Furthermore, when the user is relaxed, the receiving unit can immediately display all emails. Furthermore, when the user is busy, the receiving unit can display non-urgent emails later. For example, when the user is stressed, the receiving unit can prioritize displaying only important emails. Furthermore, when the user is relaxed, the receiving unit can immediately display all emails. Furthermore, when the user is busy, the receiving unit can later display non-urgent emails. In this way, by determining the priority of emails according to the user's emotions, important emails can be checked preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the receiving unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the receiving unit can input the user's emotional data into the generation AI and have the generation AI set email priorities based on the emotions.

[0080] When receiving emails, the receiving unit can prioritize receiving relevant emails by taking into account the user's geographical location information. For example, when the user is in a specific location, the receiving unit can prioritize receiving emails related to that location. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the home. For example, when the user is in a specific location, the receiving unit can prioritize receiving emails related to the location. Furthermore, when the user is traveling, the receiving unit can prioritize receiving emails related to the travel destination. Furthermore, when the user is at home, the receiving unit can prioritize receiving emails related to the home. In this way, by taking the user's geographical location information into account, highly relevant emails can be received preferentially. Some or all of the above-described processing in the receiving unit may be performed using AI, for example, or may be performed without using AI. For example, the receiving unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize receiving highly relevant emails.

[0081] The receiving unit can analyze the user's social media activity when receiving emails and receive relevant emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also prioritize receiving emails related to topics the user has shown interest in on social media. Furthermore, the receiving unit can analyze the user's social media activity and filter and receive relevant emails. For example, the receiving unit can prioritize receiving emails from accounts the user follows on social media. The receiving unit can also prioritize receiving emails related to topics the user has shown interest in on social media. Furthermore, the receiving unit can analyze the user's social media activity and filter and receive relevant emails. In this way, by analyzing the user's social media activity, relevant emails can be preferentially received. Some or all of the above-described processing in the receiving unit may be performed using, for example, AI, or may be performed without using AI. For example, the receiving unit can input data on the user's social media activity into the generation AI and cause the generation AI to receive relevant emails.

[0082] When receiving email, the receiving unit can adjust the receiving method by reflecting the user's past feedback. For example, the receiving unit can learn the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. The receiving unit can also learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. The receiving unit can also learn the format of emails that the user has previously preferred and prioritize receiving emails with similar formats. For example, the receiving unit can learn the patterns of emails that the user has previously marked as important and prioritize receiving similar emails. The receiving unit can also learn the characteristics of emails that the user has previously determined to be spam and filter similar emails. The receiving unit can also learn the format of emails that the user has previously preferred and prioritize receiving emails with similar formats. In this way, by reflecting the user's past feedback, an optimal receiving method can be provided. Some or all of the above-mentioned processing in the receiving unit may be performed, for example, using AI or without AI. For example, the receiving unit can input data of the user's past feedback into the generating AI and cause the generating AI to adjust the receiving method.

[0083] The analysis unit can estimate the user's emotions and adjust the means of presentation of the analysis based on the estimated user's emotions. For example, if the user is stressed, the analysis unit can display a concise and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, if the user is busy, the analysis unit can highlight and display only important information. For example, if the user is stressed, the analysis unit can display a concise and to-the-point analysis result. Furthermore, if the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, if the user is busy, the analysis unit can highlight and display only important information. By adjusting the way the analysis is presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the means of expression of the analysis based on the emotion.

[0084] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the email. For example, the analysis unit can display detailed analysis results for important emails. The analysis unit can also display brief analysis results for non-important emails. Furthermore, the analysis unit can quickly display analysis results for urgent emails. For example, the analysis unit can display detailed analysis results for important emails. The analysis unit can also display brief analysis results for non-important emails. Furthermore, the analysis unit can quickly display analysis results for urgent emails. In this way, by adjusting the level of detail of the analysis based on the importance of the email, important information can be provided preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the importance of the email to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0085] During analysis, the analysis unit can apply a different analysis algorithm depending on the category of the email. For example, in the case of business email, the analysis unit can apply a business analysis algorithm. Furthermore, in the case of private email, the analysis unit can apply a private analysis algorithm. Furthermore, in the case of spam email, the analysis unit can apply an analysis algorithm for spam filtering. For example, in the case of business email, the analysis unit can apply a business analysis algorithm. Furthermore, in the case of private email, the analysis unit can apply a private analysis algorithm. Furthermore, in the case of spam email, the analysis unit can apply an analysis algorithm for spam filtering. In this way, by applying an appropriate analysis algorithm depending on the category of the email, the analysis accuracy is improved. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the category of the email to the generation AI and cause the generation AI to apply an appropriate analysis algorithm.

[0086] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit can learn the characteristics of emails that the user previously determined to be important and prioritize analyzing similar emails. The analysis unit can also learn the characteristics of emails that the user previously determined to be spam and filter similar emails. Furthermore, the analysis unit can learn the format of analysis results that the user previously preferred and display the analysis results in a similar format. For example, the analysis unit can learn the characteristics of emails that the user previously determined to be important and prioritize analyzing similar emails. The analysis unit can also learn the characteristics of emails that the user previously determined to be spam and filter similar emails. Furthermore, the analysis unit can learn the format of analysis results that the user previously preferred and display the analysis results in a similar format. By referring to the user's past analysis results, the accuracy of the analysis is improved. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data on the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotions and adjust the analysis time based on the estimated user emotions. For example, if the user is stressed, the analysis unit can display a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, if the user is busy, the analysis unit can highlight and display only important information. For example, if the user is stressed, the analysis unit can display a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can display a detailed analysis result. Furthermore, if the user is busy, the analysis unit can highlight and display only important information. By adjusting the length of the analysis according to the user's emotions, optimal analysis results can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the analysis time based on the emotion.

[0088] During analysis, the analysis unit can set analysis priorities based on the time of receipt of the email. For example, the analysis unit can give top priority to analysis of urgent emails. The analysis unit can also give priority to analysis of important emails. Furthermore, the analysis unit can postpone analysis of non-important emails. For example, the analysis unit gives top priority to analysis of urgent emails. The analysis unit can also give priority to analysis of important emails. Furthermore, the analysis unit can postpone analysis of non-important emails. In this way, by determining the analysis priority based on the time of receipt of the emails, urgent emails can be analyzed quickly. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information about the time of receipt of the emails to the generation AI and have the generation AI set the analysis priorities.

[0089] During analysis, the analysis unit can set the analysis order based on the relevance of emails. For example, the analysis unit can prioritize analyzing emails related to a project currently underway by the user. The analysis unit can also prioritize analyzing emails related to fields in which the user is interested. Furthermore, the analysis unit can prioritize analyzing emails related to topics in which the user has previously shown interest. For example, the analysis unit prioritizes analyzing emails related to a project currently underway by the user. The analysis unit can also prioritize analyzing emails related to fields in which the user is interested. Furthermore, the analysis unit can prioritize analyzing emails related to topics in which the user has previously shown interest. In this way, by adjusting the analysis order based on the relevance of emails, important emails can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input information on the relevance of emails to the generation AI and cause the generation AI to set the analysis order.

[0090] During analysis, the analysis unit can set the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can display analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can display concise and easy-to-understand analysis results. Furthermore, the analysis unit can display analysis results that use appropriate technical terms according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can display analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can display analysis results that are concise and easy-to-understand. Furthermore, the analysis unit can display analysis results that use appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input information about the user's level of expertise to the generation AI and cause the generation AI to use technical terms.

[0091] The calendar addition unit can estimate the user's emotions and adjust the calendar addition method based on the estimated user's emotions. For example, when the user is stressed, the calendar addition unit can provide a concise and to-the-point calendar addition method. Furthermore, when the user is relaxed, the calendar addition unit can provide a detailed calendar addition method. Furthermore, when the user is busy, the calendar addition unit can emphasize and add only important information to the calendar. For example, when the user is stressed, the calendar addition unit can provide a concise and to-the-point calendar addition method. Furthermore, when the user is relaxed, the calendar addition unit can provide a detailed calendar addition method. Furthermore, when the user is busy, the calendar addition unit can emphasize and add only important information to the calendar. By adjusting the calendar addition method according to the user's emotions, it is possible to provide an optimal calendar addition method for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calendar adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar adding unit may input user emotion data to the generating AI and cause the generating AI to adjust the calendar adding means based on the emotion.

[0092] The calendar addition unit can adjust the level of detail added based on the importance of the email when adding the email to the calendar. For example, the calendar addition unit can add detailed information to the calendar for important emails. Furthermore, the calendar addition unit can add brief information to the calendar for non-important emails. Furthermore, the calendar addition unit can quickly add urgent emails to the calendar. For example, the calendar addition unit can add detailed information to the calendar for important emails. Furthermore, the calendar addition unit can add brief information to the calendar for non-important emails. Furthermore, the calendar addition unit can quickly add urgent emails to the calendar. In this way, by adjusting the level of detail added to the calendar based on the importance of the email, important information can be preferentially added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information on the importance of the email to the generation AI and cause the generation AI to adjust the level of detail added.

[0093] When adding an email to the calendar, the calendar adding unit can apply a different adding algorithm depending on the email category. For example, in the case of business email, the calendar adding unit can apply a business adding algorithm. Furthermore, in the case of private email, the calendar adding unit can also apply a private adding algorithm. Furthermore, in the case of spam email, the calendar adding unit can apply a spam filtering adding algorithm. For example, in the case of business email, the calendar adding unit can apply a business adding algorithm. Furthermore, in the case of private email, the calendar adding unit can also apply a private adding algorithm. Furthermore, in the case of spam email, the calendar adding unit can apply a spam filtering adding algorithm. In this way, by applying an appropriate adding algorithm depending on the email category, the accuracy of calendar addition is improved. Some or all of the above-mentioned processing in the calendar adding unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar adding unit can input email category information to the generation AI and cause the generation AI to apply an appropriate adding algorithm.

[0094] When adding an item to the calendar, the calendar adding unit can improve the accuracy of the addition by referring to the user's past calendar addition results. The calendar adding unit, for example, can learn the characteristics of events that the user previously determined to be important and prioritize adding similar events to the calendar. The calendar adding unit can also learn the characteristics of events that the user previously determined to be spam and filter similar events. The calendar adding unit can also learn the calendar addition format that the user previously preferred and add similar events to the calendar in a similar format. For example, the calendar adding unit can learn the characteristics of events that the user previously determined to be important and prioritize adding similar events to the calendar. The calendar adding unit can also learn the characteristics of events that the user previously determined to be spam and filter similar events. The calendar adding unit can also learn the calendar addition format that the user previously preferred and add similar events to the calendar in a similar format. In this way, by referring to the user's past calendar addition results, the accuracy of the calendar addition is improved. Some or all of the above-mentioned processing in the calendar adding unit may be performed, for example, using AI or without AI. For example, the calendar addition unit can input data of the user's past calendar addition results into the generation AI and cause the generation AI to improve the accuracy of the additions.

[0095] The calendar addition unit can estimate the user's emotions and adjust the time for adding to the calendar based on the estimated user's emotions. For example, if the user is stressed, the calendar addition unit can provide a short and concise method for adding to the calendar. Furthermore, if the user is relaxed, the calendar addition unit can provide a detailed method for adding to the calendar. Furthermore, if the user is busy, the calendar addition unit can emphasize only important information when adding to the calendar. For example, if the user is stressed, the calendar addition unit can provide a short and concise method for adding to the calendar. Furthermore, if the user is relaxed, the calendar addition unit can provide a detailed method for adding to the calendar. Furthermore, if the user is busy, the calendar addition unit can emphasize only important information when adding to the calendar. This allows the length of the calendar addition to be adjusted according to the user's emotions, thereby providing an optimal method for adding to the calendar for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the calendar adding unit may be performed using, for example, AI, or may be performed without using AI. For example, the calendar adding unit may input user emotion data to the generating AI and cause the generating AI to adjust the calendar addition time based on the emotion.

[0096] The calendar addition unit can set a priority for adding emails to the calendar based on the time of receipt of the email when adding the email. For example, the calendar addition unit can add urgent emails to the calendar with the highest priority. The calendar addition unit can also add important emails to the calendar with a higher priority. The calendar addition unit can also add non-important emails to the calendar at a later date. For example, the calendar addition unit can add urgent emails to the calendar with the highest priority. The calendar addition unit can also add important emails to the calendar with a higher priority. The calendar addition unit can also add non-important emails to the calendar at a later date. In this way, by determining the priority for adding emails to the calendar based on the time of receipt of the emails, urgent emails can be quickly added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information about the time of receipt of the email to the generation AI and cause the generation AI to set the priority for adding the email.

[0097] The calendar addition unit can set the order of emails to be added to the calendar based on the relevance of the emails when adding them to the calendar. For example, the calendar addition unit can prioritize adding emails related to a project currently underway by the user to the calendar. The calendar addition unit can also prioritize adding emails related to areas of interest to the calendar. Furthermore, the calendar addition unit can prioritize adding emails related to topics in which the user has previously shown interest to the calendar. For example, the calendar addition unit can prioritize adding emails related to a project currently underway by the user to the calendar. The calendar addition unit can also prioritize adding emails related to areas of interest to the calendar. Furthermore, the calendar addition unit can prioritize adding emails related to topics in which the user has previously shown interest to the calendar. In this way, by adjusting the order of emails to be added to the calendar based on the relevance of the emails, important emails can be prioritized to be added to the calendar. Some or all of the above-described processing in the calendar addition unit may be performed using AI, for example, or may be performed without using AI. For example, the calendar addition unit can input information about the relevance of the emails to a generation AI and cause the generation AI to set the order of addition.

[0098] The calendar adding unit can set the use of additional technical terms according to the user's level of expertise when adding a calendar. For example, if the user has technical expertise, the calendar adding unit can provide a calendar adding method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the calendar adding unit can provide a concise and easy-to-understand calendar adding method. Furthermore, the calendar adding unit can provide a calendar adding method that uses appropriate technical terms according to the user's level of expertise. For example, if the user has technical expertise, the calendar adding unit can provide a calendar adding method that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the calendar adding unit can provide a concise and easy-to-understand calendar adding method. Furthermore, the calendar adding unit can provide a calendar adding method that uses appropriate technical terms according to the user's level of expertise. In this way, by adjusting the use of additional technical terms according to the user's level of expertise, a calendar adding method that is easy for the user to understand can be provided. Some or all of the above-described processing in the calendar adding unit may be performed using, for example, AI, or may be performed without AI. For example, the calendar adding unit can input information about the user's level of expertise to a generation AI and cause the generation AI to use technical terms.

[0099] The reminder unit can estimate the user's emotions and adjust the reminder notification method based on the estimated user emotions. For example, if the user is feeling stressed, the reminder unit can notify the user with a gentle sound. Also, if the user is relaxed, the reminder unit can notify the user with a bright sound. Furthermore, if the user is busy, the reminder unit can notify the user with a concise and to-the-point reminder. For example, if the user is feeling stressed, the reminder unit can notify the user with a gentle sound. Also, if the user is relaxed, the reminder unit can notify the user with a bright sound. Furthermore, if the user is busy, the reminder unit can notify the user with a concise and to-the-point reminder. In this way, by adjusting the reminder notification method according to the user's emotions, it is possible to provide the optimal notification method for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder unit may be performed using AI, or may be performed without using AI. For example, the reminder unit may input user emotion data into the generation AI and cause the generation AI to adjust the reminder notification means based on the emotion.

[0100] When setting a reminder, the reminder unit can adjust the level of detail in the notification based on the importance of the email. For example, the reminder unit can set a detailed reminder for an important email. The reminder unit can also set a brief reminder for a non-important email. Furthermore, the reminder unit can also set a quick reminder for an urgent email. For example, the reminder unit can set a detailed reminder for an important email. The reminder unit can also set a brief reminder for a non-important email. Furthermore, the reminder unit can also set a quick reminder for an urgent email. In this way, by adjusting the level of detail in the notification based on the importance of the email, important information can be notified preferentially. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input information about the importance of the email to the generation AI and cause the generation AI to adjust the level of detail in the notification.

[0101] When setting a reminder, the reminder unit can apply a different notification algorithm depending on the category of the email. For example, in the case of a business email, the reminder unit can apply a business notification algorithm. Furthermore, in the case of a private email, the reminder unit can apply a private notification algorithm. Furthermore, in the case of spam email, the reminder unit can apply a spam filtering notification algorithm. For example, in the case of a business email, the reminder unit can apply a business notification algorithm. Furthermore, in the case of a private email, the reminder unit can apply a private notification algorithm. Furthermore, in the case of spam email, the reminder unit can apply a spam filtering notification algorithm. In this way, by applying an appropriate notification algorithm depending on the category of the email, the accuracy of notifications is improved. Some or all of the above-mentioned processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input email category information to the generation AI and cause the generation AI to apply an appropriate notification algorithm.

[0102] When setting a reminder, the reminder unit can improve the accuracy of notifications by referring to the user's past reminder results. For example, the reminder unit can learn the characteristics of reminders that the user previously determined to be important and prioritize setting similar reminders. The reminder unit can also learn the characteristics of reminders that the user previously determined to be spam and filter similar reminders. Furthermore, the reminder unit can learn the reminder formats that the user previously preferred and set reminders in a similar format. For example, the reminder unit can learn the characteristics of reminders that the user previously determined to be important and prioritize setting similar reminders. The reminder unit can also learn the characteristics of reminders that the user previously determined to be spam and filter similar reminders. Furthermore, the reminder unit can learn the reminder formats that the user previously preferred and set reminders in a similar format. This improves the accuracy of notifications by referring to the user's past reminder results. Some or all of the above-described processing in the reminder unit may be performed using AI, for example, or may be performed without using AI. For example, the reminder unit may input data on the user's past reminder results into the generation AI and cause the generation AI to improve the accuracy of notifications.

[0103] The reminder unit can estimate the user's emotions and adjust the reminder notification time based on the estimated user's emotions. For example, if the user is feeling stressed, the reminder unit can notify the user of a reminder at a gentle timing. Furthermore, if the user is relaxed, the reminder unit can notify the user of a reminder at an appropriate timing. Furthermore, if the user is busy, the reminder unit can notify the user of a reminder at an important timing. For example, if the user is feeling stressed, the reminder unit can notify the user of a reminder at a gentle timing. Furthermore, if the user is relaxed, the reminder unit can notify the user of a reminder at an appropriate timing. Furthermore, if the user is busy, the reminder unit can notify the user of a reminder at an important timing. In this way, by adjusting the timing of the reminder notification according to the user's emotions, the notification can be made at the optimal timing for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reminder unit may be performed using AI, or may be performed without using AI. For example, the reminder unit may input user emotion data into the generation AI and cause the generation AI to adjust the reminder notification time based on the emotion.

[0104] When setting a reminder, the reminder unit can set the priority of notifications based on the time of receipt of the email. For example, in the case of an urgent email, the reminder unit can set a reminder as the highest priority. The reminder unit can also set a reminder as a priority for an important email. Furthermore, the reminder unit can set a reminder for a later date for a non-important email. For example, the reminder unit can set a reminder as the highest priority for an urgent email. The reminder unit can also set a reminder as a priority for an important email. Furthermore, the reminder unit can set a reminder for a later date for a non-important email. In this way, by determining the priority of notifications based on the time of receipt of the email, urgent emails can be notified promptly. Some or all of the above-described processing in the reminder unit may be performed using, for example, AI, or may be performed without using AI. For example, the reminder unit can input information about the time of receipt of the email to the generation AI and have the generation AI set the priority of notifications.

[0105] When setting a reminder, the reminder unit can set the order of notifications based on the relevance of the emails. For example, the reminder unit can prioritize reminders for emails related to a project the user is currently working on. The reminder unit can also prioritize reminders for emails related to fields in which the user is interested. Furthermore, the reminder unit can also prioritize reminders for emails related to topics in which the user has previously shown interest. For example, the reminder unit prioritizes reminders for emails related to a project the user is currently working on. The reminder unit can also prioritize reminders for emails related to fields in which the user is interested. Furthermore, the reminder unit can also prioritize reminders for emails related to topics in which the user has previously shown interest. In this way, by adjusting the order of notifications based on the relevance of the emails, important emails can be prioritized. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or without AI. For example, the reminder unit can input email relevance information into the generation AI and have the generation AI set the notification order.

[0106] When setting a reminder, the reminder unit can set the use of technical terminology in the notification according to the user's level of expertise. For example, if the user has technical knowledge, the reminder unit can set a reminder that uses a lot of technical terminology. Furthermore, if the user does not have technical knowledge, the reminder unit can set a simple and easy-to-understand reminder. Furthermore, the reminder unit can set a reminder that uses appropriate technical terminology according to the user's level of expertise. For example, if the user has technical knowledge, the reminder unit can set a reminder that uses a lot of technical terminology. Furthermore, if the user does not have technical knowledge, the reminder unit can set a simple and easy-to-understand reminder. Furthermore, the reminder unit can set a reminder that uses appropriate technical terminology according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the notification according to the user's level of expertise, it is possible to provide a notification that is easy for the user to understand. Some or all of the above-described processing in the reminder unit may be performed, for example, using AI or without AI. For example, the reminder unit can input information about the user's level of expertise into the generation AI and have the generation AI use specialized terminology. === Hard Collateral 1-1 === Each of the above-described elements, including the receiving unit, analyzing unit, calendar adding unit, and reminder unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the receiving unit can receive emails using the communication I / F 44 of the smart device 14 and retrieve emails from a mail server via the communication I / F 26 of the data processing device 12. The analyzing unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the email body using natural language processing technology to extract request content and deadlines. The calendar adding unit is implemented by the control unit 46A of the smart device 14 and adds an event to the calendar based on the extracted information. The reminder unit is implemented by the specific processing unit 290 of the data processing device 12 and sets a reminder based on the added event. === Hard Collateral 1-2 === Each of the multiple elements, including the receiving unit, analyzing unit, calendar adding unit, and reminder unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the receiving unit can receive email using the communication I / F 44 of the smart glasses 214 and obtain email from a mail server via the communication I / F 26 of the data processing device 12. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the email body using natural language processing technology to extract request content and deadlines. The calendar adding unit is realized by the control unit 46A of the smart glasses 214 and adds an appointment to the calendar based on the extracted information. The reminder unit is realized by the specific processing unit 290 of the data processing device 12 and sets a reminder based on the added appointment. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, calendar adding unit, and reminder unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the receiving unit can receive email using the communication I / F 44 of the headset type terminal 314 and obtain email from a mail server via the communication I / F 26 of the data processing device 12. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the email body using natural language processing technology to extract request content and deadlines. The calendar adding unit is realized by the control unit 46A of the headset type terminal 314 and adds an event to the calendar based on the extracted information. The reminder unit is realized by the specific processing unit 290 of the data processing device 12 and sets a reminder based on the added event. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned receiving unit, analyzing unit, calendar adding unit, and reminder unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the receiving unit can receive email using the communication I / F 44 of the robot 414 and obtain email from a mail server via the communication I / F 26 of the data processing device 12. The analyzing unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the email body using natural language processing technology to extract request content and deadlines. The calendar adding unit is realized by the control unit 46A of the robot 414 and adds an event to the calendar based on the extracted information. The reminder unit is realized by the specific processing unit 290 of the data processing device 12 and sets a reminder based on the added event.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] In addition to analyzing the email body, the analysis unit can also analyze the contents of email attachments. For example, the analysis unit can analyze attachments such as PDFs and Word documents to extract request details and deadlines. The analysis unit can also analyze image files to extract text from the images. Furthermore, the analysis unit can analyze audio files to extract request details from the audio. This allows for more accurate information extraction by considering not only the email body but also the contents of attachments.

[0109] The analysis unit can extract the date and time of receipt of an email in addition to the sender and subject of the email. For example, the analysis unit can extract the date and time of receipt from the "Date" field in the email header. The analysis unit can also extract date and time information written in the email body. Furthermore, the analysis unit can set the priority of the request content based on the date and time of receipt of the email. This allows for more appropriate management of task priorities by taking the date and time of receipt of the email into consideration.

[0110] The calendar adding unit can refer to the user's past calendar usage history when adding an event to the calendar based on the extracted information. For example, the calendar adding unit can learn the patterns of events set by the user in the past and add events in a similar pattern. The calendar adding unit can also add events by referring to the calendar display format that the user preferred in the past. Furthermore, the calendar adding unit can automatically adjust the event reminder settings based on the user's past calendar usage history. This makes it easier to add events by taking the user's past usage history into consideration.

[0111] In addition to providing a means for notifying a reminder, the reminder unit can also adjust the timing of the notification based on the user's schedule. For example, the reminder unit can set a reminder for a time when the user is free based on the user's calendar. The reminder unit can also set the optimal notification timing by referring to the user's past reminder usage history. Furthermore, the reminder unit can also notify the user of a reminder at an appropriate time by taking into account the user's current location information. This allows for more effective reminder notifications by taking into account the user's schedule and location information.

[0112] The reminder unit can customize the content of the notification in addition to the means of notification of the reminder according to the user's preferences. For example, the reminder unit can set the notification sound that the user prefers. The reminder unit can also set the format of the notification message that the user prefers. Furthermore, the reminder unit can also customize the content of the notification by referring to the user's past reminder usage history. This makes it possible to provide reminder notifications according to the user's preferences.

[0113] The receiving unit can estimate the user's emotions and adjust the email reception method based on the estimated user emotions. For example, if the user is feeling stressed, it can be set to receive only important emails. Alternatively, if the user is relaxed, it can be set to receive all emails. Furthermore, if the user is busy, it can be set to receive non-urgent emails later. In this way, adjusting the email reception method according to the user's emotions can reduce stress and enable efficient email management.

[0114] The receiving unit can analyze the user's past email reception history and select an appropriate reception method. For example, it can analyze the patterns of emails that the user has previously marked as important and prioritize the reception of similar emails. Also, if the user has frequently checked email during a specific time period in the past, it can concentrate the reception of emails during that time period. Furthermore, it can learn the characteristics of emails that the user has previously determined to be spam and filter out similar emails. In this way, by analyzing the user's past email reception history, it is possible to provide the user with the optimal email reception method.

[0115] When receiving emails, the receiving unit can filter them based on the user's current projects and areas of interest. For example, emails related to projects currently underway by the user can be preferentially received. Emails related to areas in which the user is interested can also be preferentially received. Furthermore, emails related to topics in which the user has shown interest in the past can be filtered and received. In this way, important emails can be preferentially received by filtering emails based on the user's current projects and areas of interest.

[0116] When receiving an email, the receiving unit can select an appropriate receiving means depending on the user's input method. For example, if the user prefers voice input, the contents of the email can be read aloud. If the user prefers text input, the email can be displayed in text format. Furthermore, if the user prefers images, the contents of the email can be displayed as images. This improves user convenience by selecting the optimal receiving means depending on the user's input method.

[0117] The receiving unit can estimate the user's emotions and set the priority of received emails based on the estimated user emotions. For example, if the user is feeling stressed, only important emails can be displayed preferentially. If the user is relaxed, all emails can be displayed immediately. Furthermore, if the user is busy, non-urgent emails can be displayed later. In this way, by determining the priority of emails according to the user's emotions, important emails can be checked preferentially.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The receiving unit receives email. The receiving unit can obtain email from a mail server using a protocol such as POP3 or IMAP. The receiving unit can also operate when triggered by receiving email. Step 2: The analysis unit analyzes the email body received by the receiving unit and extracts the request content or deadline. The analysis unit can analyze the email body using natural language processing technology, for example, and extract the request content and deadline. The analysis unit can also extract important information from the email body using a keyword extraction algorithm. For example, from the email body that says, "Please submit the report by next Friday," the request content "submit the report" and the deadline "next Friday" can be extracted. Step 3: The calendar adding unit adds the event to the calendar based on the information extracted by the analyzing unit. The calendar adding unit can add the event to a calendar app such as Google Calendar or Outlook Calendar. The calendar adding unit can also include the sender and subject of an email in the calendar event. For example, the calendar adding unit can add an event called "Submit report" to "Next Friday" and include the sender and subject of the email in the event. Step 4: The reminder unit sets a reminder based on the event added by the calendar addition unit. For example, the reminder unit can set a reminder the day before a deadline and notify the user. The reminder unit can also include the sender and subject of an email in the reminder. For example, a reminder for an event called "Submit a report" can be set the day before the deadline, and the sender and subject of the email can be included in the reminder.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a receiving unit for receiving emails; an analysis unit that analyzes the email body received by the receiving unit and extracts the request content or deadline; a calendar adding unit that adds events to a calendar based on the information extracted by the analyzing unit; a reminder unit that sets a reminder based on the schedule added by the calendar adding unit. A system characterized by:

2. The analysis unit Extract the sender or subject of an email 2. The system of claim 1.

3. The calendar adding unit Include email sender or subject when adding an event to your calendar based on extracted information 2. The system of claim 1.

4. The reminder unit Set reminders to notify users based on events added to the calendar 2. The system of claim 1.

5. The reminder unit Set reminder notification method 2. The system of claim 1.

6. The receiving unit Estimate the user's emotions and adjust the timing of receiving emails based on the estimated user emotions 2. The system of claim 1.

7. The receiving unit Analyze the user's past email reception history and select the appropriate reception method 2. The system of claim 1.

8. The receiving unit Filter incoming emails based on your current projects and interests 2. The system of claim 1.

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