System

Abstract

To provide a system allowing a user to flexibly customize an automatic response mail and set a response according to a specific condition.SOLUTION: A system according to an embodiment comprises a custom message setting unit, a schedule setting unit, an inhibition unit, a transfer unit, and a signature unit. The custom message setting unit sets a user-specific message included in an automatic response mail. The schedule setting unit sets an automatic response message which differs according to a day of the week and a time zone. The inhibition unit inhibits an automatic response to a mail from a specific contact address. The transfer unit transfers a received mail to a preset destination. The signature unit adds a custom signature to the mail to be automatically responded.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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback that it is difficult for users to flexibly customize automatic response emails, and responses cannot be tailored to specific conditions.

[0005] The system according to the embodiment aims to allow users to flexibly customize auto-reply emails and set responses according to specific conditions. [Means for solving the problem]

[0006] The system according to the embodiment includes a custom message setting unit, a schedule setting unit, a prohibition unit, a forwarding unit, and a signature unit. The custom message setting unit sets a user-specific message to be included in the auto-reply email. The schedule setting unit sets different auto-reply messages depending on the day of the week and the time of day. The prohibition unit prohibits auto-replies to emails from specific contacts. The forwarding unit forwards received emails to a pre-set destination. The signature unit adds a custom signature to the auto-reply email. Effect of the Invention

[0007] The system according to the embodiment allows users to flexibly customize auto-reply emails and set responses according to specific conditions. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to 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 Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process 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 the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[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 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The business email autoresponder system AI assistant according to the embodiment of the present invention is a system that provides a customization function that allows users to set their own greetings and messages. The system provides a time schedule function that allows users to set different autoresponder messages according to the week and date. For example, a specific message can be displayed on holidays and non-business hours. In addition, an exception function for important contacts is provided that allows users to set not to send autoresponders to emails from certain important contacts. In addition, a message forwarding function is provided that forwards received emails to specified email addresses or team members. A custom signature function is also provided that allows users to add their own custom signature to the autoresponder message. Supports a schedule notification function that automatically sends emails when notified of a scheduled event or meeting. A repeat message function is provided that allows users to set autoresponders for the same request or inquiry, reducing repetitive work. An email classification function is provided that assigns received emails to different autoresponders according to categories. Finally, a complete log function is provided that records detailed logs of sent autoresponder messages and received emails and allows them to be checked later. This allows the business email autoresponder system AI assistant to flexibly customize the content of autoresponder emails by users.

[0029] The AI ​​assistant system for automatic response of business email according to the embodiment includes a custom message setting unit, a time schedule setting unit, a prohibition unit, a forwarding unit, and a signature unit. The custom message setting unit sets a user-specific message to be included in the automatic response email. The user-specific message includes, but is not limited to, a text message, an image, a link, etc., for example. The custom message setting unit provides, for example, a function that allows the user to set a unique greeting or message. The time schedule setting unit sets different automatic response messages according to the day of the week and the time period. The automatic response messages according to the day of the week and the time period include, but are not limited to, for example, weekdays and weekends, during business hours and outside business hours, etc., for example. The time schedule setting unit provides, for example, a function to display a specific message on holidays and non-business hours. The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, for example, email addresses, domains, contact lists, etc., for example. The prohibition unit provides, for example, a function to set so that automatic responses are not sent to emails from specific important contacts. The forwarding unit forwards the received email to a preset destination. The forwarding unit provides, for example, a function of forwarding a received email to a specified email address or team member. The signature unit adds a custom signature to an automatically responded email. Custom signatures include, for example, a text signature, an image signature, a signature with a link, and the like, but are not limited to such examples. The signature unit provides, for example, a function of adding a user's custom signature to an auto-response message. This allows the AI ​​assistant in the business email auto-response system according to the embodiment to flexibly customize the content of the auto-response email by the user.

[0030] The custom message setting unit sets a user-specific message to be included in the auto-response email. Examples of the user-specific message include, but are not limited to, a text message, an image, a link, and the like. The custom message setting unit provides, for example, a function that allows the user to set an original greeting or message. Specifically, the user can freely input text and add images and links through a dedicated interface. This interface is designed to allow intuitive operation and to allow the user to easily create a custom message. Furthermore, the custom message setting unit also provides a function that allows the user to save multiple message templates and quickly switch between them as needed. For example, the user can create message templates in advance for different situations, such as for business use, private use, and events, and select an appropriate template depending on the situation. The custom message setting unit also has a function that uses AI to analyze the user's past message history and suggest the optimal message content. This allows the user to efficiently create messages and maintain consistent communication with the recipient. Furthermore, the custom message setting unit provides a message preview function, allowing the user to check the content of the message that will actually be sent in advance. This reduces the risk of sending a message with incorrect content and improves user satisfaction.

[0031] The time schedule setting unit sets different auto-response messages according to the day of the week and the time period. Auto-response messages according to the day of the week and the time period include, but are not limited to, weekdays and weekends, during business hours and outside business hours, for example. The time schedule setting unit provides a function to display a specific message on holidays and non-business hours, for example. Specifically, a user can easily set a message according to a specific day of the week and time period using a calendar-style interface. This interface supports drag-and-drop operations and is designed to allow a user to intuitively set a schedule. Furthermore, the time schedule setting unit also provides a function to change the auto-response message based on the schedule of a specific event or meeting. For example, when a user adds a meeting schedule to the calendar, a meeting message is automatically set for that time period. In addition, the time schedule setting unit also has a function to learn the user's past schedule patterns using AI and propose an optimal message schedule. This allows the user to efficiently manage the schedule and send an appropriate message to the recipient. Furthermore, the time schedule setting unit also provides a function to save multiple schedule patterns and quickly switch between them as needed. This allows the user to flexibly manage schedules according to different situations.

[0032] The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, email addresses, domains, and contact lists. The prohibition unit provides, for example, a function for setting to not send automatic responses to emails from specific important contacts. Specifically, the user can input the email address or domain of the contact to be prohibited through a dedicated interface and add it to the list. This interface is designed to allow management of the prohibition list with simple operations. Furthermore, the prohibition unit also has a function for automatically detecting spam emails and phishing emails using AI and prohibiting automatic responses to these emails. This allows the user to prevent automatic responses to unnecessary emails and to provide appropriate responses only to important emails. The prohibition unit also provides a function for setting rules to prohibit automatic responses based on specific conditions. For example, automatic responses can be prohibited for emails containing specific keywords or emails received during a specific time period. This allows the user to flexibly set prohibition rules and efficiently manage emails. Furthermore, the prohibition unit provides a function for exporting and importing the prohibition list, and the prohibition list can be shared in cooperation with other systems and devices. This allows users to achieve consistent email management and improves the security of the entire system.

[0033] The forwarding unit forwards received emails to a preset destination. The forwarding unit provides, for example, a function to forward received emails to a specified email address or team member. Specifically, the user can set the destination email address or group through a dedicated interface. This interface is designed to allow the user to set forwarding settings with simple operations. In addition, the forwarding unit also has a function to analyze the contents of received emails using AI and automatically suggest the optimal forwarding destination. For example, emails containing specific keywords or phrases can be set to be forwarded to specific team members. The forwarding unit also provides a function to set forwarding rules based on specific conditions. For example, emails from specific senders or emails received during specific time periods can be automatically forwarded. This allows users to flexibly set forwarding rules and efficiently manage emails. In addition, the forwarding unit also provides a function to record forwarding history and allow users to check past forwarding status. This allows users to track forwarding and review forwarding settings as necessary. The forwarding unit also has a function to automatically send a notification when the forwarding destination email address is changed, allowing users to maintain the latest forwarding destination information. This allows the forwarding unit to forward emails efficiently and reliably, thereby improving the work efficiency of the user.

[0034] The signature unit adds a custom signature to an auto-reply email. Examples of custom signatures include, but are not limited to, a text signature, an image signature, and a signature with a link. The signature unit provides, for example, a function for adding a user's custom signature to an auto-reply message. Specifically, the user can freely edit the content of the signature and add text, an image, and a link through a dedicated interface. This interface is designed to allow intuitive operation and to allow the user to easily create a custom signature. Furthermore, the signature unit also provides a function for saving multiple signature templates and quickly switching between them as needed. For example, the user can create signature templates in advance for different situations, such as for business use, private use, and events, and select an appropriate template depending on the situation. The signature unit also has a function for analyzing the user's past signature history using AI and proposing the optimal signature content. This allows the user to efficiently create signatures and maintain consistent communication with the recipient. Furthermore, the signature unit provides a signature preview function, allowing the user to check the content of the signature that will actually be sent in advance. This reduces the risk of sending a signature with incorrect content and improves user satisfaction.

[0035] The filter unit can assign received emails to different automatic responses depending on the category. The filter unit can, for example, appropriately classify received emails depending on the category and perform an automatic response. The categories include, for example, work-related, personal-related, project-specific, and the like, but are not limited to such examples. The filter unit provides, for example, a function of assigning received emails to different automatic responses depending on the category. This allows received emails to be appropriately classified depending on the category and an automatic response to be performed. A part or all of the above-mentioned processing in the filter unit may be performed using, for example, AI, or may be performed without using AI. For example, the filter unit can input the contents of received emails to AI and have AI perform category classification.

[0036] The notification unit can notify of a scheduled event or meeting. The notification unit can, for example, automatically notify of an event or meeting. The event or meeting includes, but is not limited to, an internal meeting, an external event, an online meeting, and the like. The notification unit, for example, provides a function for notifying of a scheduled event or meeting. This allows the notification of an event or meeting to be automatically performed. A part or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or can be performed without using AI. For example, the notification unit can input schedule information to AI and cause AI to generate notification content.

[0037] The log unit can record logs of the sent automatic response message and the received email so that they can be viewed. The log unit can, for example, record detailed logs of the sent automatic response message and the received email, and can check them later. The log includes, for example, the sending date and time, the receiving date and time, and the message content, but is not limited to such examples. The log unit provides, for example, a function of recording logs of the sent automatic response message and the received email so that they can be viewed. This allows detailed logs of the sent automatic response message and the received email to be recorded, and can be checked later. Some or all of the above-mentioned processing in the log unit can be performed, for example, using AI, or can be performed without using AI. For example, the log unit can input the sending and receiving data of the email to AI, and have the AI ​​execute the recording of the log.

[0038] The time schedule setting unit can automatically update the schedule by synchronizing with the user's calendar app. The time schedule setting unit, for example, synchronizes with the user's calendar app, thereby automating the schedule update. The calendar app includes, for example, Google (registered trademark) Calendar, Outlook (registered trademark) Calendar, etc., but is not limited to such examples. The time schedule setting unit, for example, provides a function of automatically updating the schedule by synchronizing with the user's calendar app. As a result, the schedule update is automated by synchronizing with the user's calendar app. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data of the calendar app to AI and cause AI to execute schedule update.

[0039] The prohibition unit can prohibit an automatic response even to an email containing a specific keyword. The specific keyword includes, for example, a spam keyword, a project name, and the like, but is not limited to such examples. The prohibition unit provides, for example, a function for prohibiting an automatic response to an email containing a specific keyword. This makes it possible to prohibit an automatic response even to an email containing a specific keyword. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the contents of the email into AI and cause the AI ​​to detect the keyword.

[0040] The prohibition unit can prohibit automatic responses even during a specific time period set by the user. The specific time period includes, but is not limited to, business hours, break times, and nighttime. The prohibition unit provides, for example, a function for prohibiting automatic responses during a specific time period set by the user. This makes it possible to prohibit automatic responses even during a specific time period set by the user. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data of the time period into AI and cause AI to execute the prohibition of automatic responses.

[0041] The notification unit can adjust the notification content based on the location of the event or meeting. The location includes, but is not limited to, for example, an office, a conference room, an online platform, and the like. The notification unit provides, for example, a function for adjusting the notification content based on the location of the event or meeting. This allows the notification content to be adjusted based on the location of the event or meeting. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input location data into AI and cause AI to adjust the notification content.

[0042] The forwarding unit can automatically forward the received email to the project team if the content of the received email is related to a specific project. The forwarding unit can automatically forward, for example, emails related to a specific project to the project team. The specific project includes, for example, a project name, a project ID, and the like, but is not limited to such examples. The forwarding unit provides, for example, a function of automatically forwarding the received email to the project team if the content of the received email is related to a specific project. This makes it possible to automatically forward emails related to a specific project to the project team. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input the content of the email into AI and have the AI ​​execute a project-related judgment.

[0043] The custom message setting unit can analyze the user's past message history and suggest the optimal custom message. The past message history includes, for example, the sending date and time, the message content, the reply status, etc., but is not limited to such examples. The custom message setting unit, for example, automatically displays phrases that the user has frequently used in the past as candidates. The tone and content of the message sent by the user to a specific person are analyzed, and a similar message is suggested. A message suitable for a specific situation is predicted and suggested from the user's past message history. This makes it possible to analyze the user's past message history and suggest the optimal custom message. A part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input the past message history into AI and have the AI ​​execute the message suggestion.

[0044] The custom message setting unit can customize a message based on the user's current project or field of interest when setting a custom message. The current project or field of interest includes, for example, a project name, a topic of interest, and the like, but is not limited to such examples. The custom message setting unit, for example, proposes a message including information related to a project that the user is currently working on. Based on the user's field of interest, the custom message setting unit proposes a message including related topics and news. Based on the user's field of interest, the custom message setting unit proposes a message including information related to an event or conference that the user is participating in. This makes it possible to customize a message based on the user's current project or field of interest. A part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input data on the project or field of interest into AI and have the AI ​​execute message customization.

[0045] The custom message setting unit can prioritize highly relevant messages in consideration of the geographical location information of the user when setting the custom message. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the custom message setting unit proposes a message including information related to the area. When the user is traveling, the custom message setting unit proposes a message including information related to the travel destination. When the user is participating in a specific event, the custom message setting unit proposes a message including information related to the event. This makes it possible to prioritize highly relevant messages in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input geographical location information to AI and cause AI to execute message priority setting.

[0046] The custom message setting unit can analyze the user's social media activity and suggest related messages when setting the custom message. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The custom message setting unit can suggest related messages based on, for example, information shared by the user on social media. The custom message setting unit can suggest related messages based on accounts the user follows on social media. The custom message setting unit can suggest related messages based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related messages. A part or all of the above-mentioned processing in the custom message setting unit can be performed, for example, using AI or can be performed without using AI. For example, the custom message setting unit can input data on social media activity into AI and have the AI ​​execute message suggestions.

[0047] The time schedule setting unit can analyze the user's past schedule history and propose an optimal schedule when setting the time schedule. The past schedule history includes, for example, the date and time of the plan, the contents, and the attendees, but is not limited to such examples. The time schedule setting unit proposes an optimal schedule, for example, based on activities that the user frequently performed in the past. From the user's past schedule history, an activity suitable for a specific time period is proposed. The user's past schedule history is analyzed and an efficient schedule is proposed. In this way, the user's past schedule history can be analyzed and an optimal schedule can be proposed. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input the past schedule history to AI and have the AI ​​execute the schedule proposal.

[0048] The time schedule setting unit can customize the schedule based on the user's current living situation and areas of interest when setting the time schedule. The current living situation and areas of interest include, for example, lifestyle habits, hobbies, and topics of interest, but are not limited to such examples. The time schedule setting unit, for example, proposes a schedule related to a project that the user is currently working on. Based on the user's areas of interest, a schedule including related activities is proposed. A flexible schedule is proposed according to the user's living situation. This makes it possible to customize the schedule based on the user's current living situation and areas of interest. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data on the living situation and areas of interest into AI and have the AI ​​execute the customization of the schedule.

[0049] The time schedule setting unit can prioritize a highly relevant schedule in consideration of the geographical location information of the user when setting the time schedule. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the time schedule setting unit incorporates activities that can be done in that area into the schedule. When the user is traveling, the time schedule setting unit incorporates activities that can be done at the travel destination into the schedule. When the user is participating in a specific event, the time schedule setting unit incorporates activities related to the event into the schedule. This makes it possible to prioritize a highly relevant schedule in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input geographical location information to AI and cause AI to execute priority setting of the schedule.

[0050] The time schedule setting unit can analyze the social media activity of the user and propose a related schedule when setting the time schedule. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The time schedule setting unit incorporates related activities into the schedule, for example, based on information shared by the user on social media. The time schedule setting unit incorporates related activities into the schedule based on accounts the user follows on social media. The time schedule setting unit incorporates related activities into the schedule based on groups and events the user participates in on social media. This makes it possible to analyze the social media activity of the user and propose a related schedule. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data of social media activity into AI and have the AI ​​execute the schedule proposal.

[0051] When setting the prohibition, the prohibition unit can analyze the user's past email history and propose the optimal prohibition setting. The past email history includes, for example, the sending date and time, the email content, the reply status, etc., but is not limited to such examples. The prohibition unit proposes the prohibition setting based on, for example, contacts with which the user has frequently exchanged in the past. From the user's past email history, the prohibition unit proposes the prohibition setting for emails containing a specific keyword. The user's past email history is analyzed and the prohibition setting for a specific time period is proposed. In this way, the user's past email history can be analyzed and the optimal prohibition setting can be proposed. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the past email history to AI and have the AI ​​execute the proposal of the prohibition setting.

[0052] The prohibition unit can customize the prohibition setting based on the user's current project and field of interest when setting the prohibition. The current project and field of interest include, for example, a project name, a topic of interest, and the like, but are not limited to such examples. For example, the prohibition unit proposes a prohibition setting for emails related to a project that the user is currently working on. The prohibition unit proposes a prohibition setting for related emails based on the user's field of interest. The prohibition unit proposes a prohibition setting for emails related to an event or conference that the user is participating in. This allows the prohibition setting to be customized based on the user's current project and field of interest. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data on the project and field of interest into AI and cause AI to customize the prohibition setting.

[0053] When setting the prohibition, the prohibition unit can give priority to the prohibition setting with high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the prohibition unit proposes the prohibition setting for emails related to the area. When the user is traveling, the prohibition unit proposes the prohibition setting for emails related to the travel destination. When the user is participating in a specific event, the prohibition unit proposes the prohibition setting for emails related to the event. In this way, the prohibition setting with high relevance can be given priority in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the geographical location information to AI and cause AI to execute the priority setting of the prohibition setting.

[0054] The prohibition unit can analyze the user's social media activity at the time of prohibition setting and propose related prohibition settings. Social media activities include, for example, the content of posts, the number of likes, the number of followers, etc., but are not limited to such examples. The prohibition unit, for example, proposes prohibition settings for related emails based on information shared by the user on social media. Proposes prohibition settings for related emails based on accounts the user follows on social media. Proposes prohibition settings for related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activities and propose related prohibition settings. Part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data on social media activities into AI and cause AI to execute the proposal of prohibition settings.

[0055] The forwarding unit can analyze the user's past email history and propose optimal forwarding settings when setting forwarding. The past email history includes, for example, the sending date and time, email content, and reply status, but is not limited to such examples. The forwarding unit, for example, automatically displays email addresses that the user has frequently forwarded in the past as candidates. From the user's past email history, proposes settings for automatically forwarding emails related to a specific project. The user's past email history is analyzed and forwarding settings for a specific time period are proposed. In this way, the user's past email history can be analyzed and optimal forwarding settings can be proposed. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input the past email history into AI and have the AI ​​execute the proposal of forwarding settings.

[0056] The forwarding unit can customize the forwarding settings based on the user's current project and field of interest at the time of forwarding settings. The current project and field of interest include, but are not limited to, for example, a project name and a topic of interest. The forwarding unit, for example, proposes a setting to automatically forward emails related to a project that the user is currently working on. The forwarding unit proposes a setting to automatically forward related emails based on the user's field of interest. The forwarding unit proposes a setting to automatically forward emails related to an event or conference that the user is participating in. This makes it possible to customize the forwarding settings based on the user's current project and field of interest. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI or may be performed without using AI. For example, the forwarding unit can input data on the project and field of interest into AI and have the AI ​​execute customization of the forwarding settings.

[0057] The forwarding unit can prioritize forwarding settings that are highly relevant in consideration of the geographical location information of the user when setting forwarding. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, when the user is in a specific area, the forwarding unit prioritizes forwarding emails related to the area. When the user is traveling, the forwarding unit prioritizes forwarding emails related to the travel destination. When the user is participating in a specific event, the forwarding unit prioritizes forwarding emails related to the event. This allows for prioritized forwarding settings that are highly relevant in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input geographical location information to AI and cause AI to execute the priority setting of forwarding settings.

[0058] The forwarding unit can analyze the user's social media activity at the time of forwarding setting and propose related forwarding settings. Social media activities include, but are not limited to, for example, the contents of posts, the number of likes, and the number of followers. The forwarding unit proposes settings for forwarding related emails based on, for example, information shared by the user on social media. The forwarding unit proposes settings for forwarding related emails based on accounts the user follows on social media. The forwarding unit proposes settings for forwarding related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and propose related forwarding settings. Part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input data on social media activities into AI and have the AI ​​execute the proposal of forwarding settings.

[0059] The signature unit can analyze the user's past signature history and propose an optimal signature when setting a signature. The past signature history includes, but is not limited to, the signature content, frequency of use, and change history. The signature unit, for example, automatically displays signatures that the user has frequently used in the past as candidates. The signature unit analyzes the tone and content of a signature sent by the user to a specific person and proposes a similar signature. A signature suitable for a specific situation is predicted and proposed from the user's past signature history. This makes it possible to analyze the user's past signature history and propose an optimal signature. A part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input the past signature history into AI and have the AI ​​execute the signature proposal.

[0060] The signature unit can customize the signature based on the user's current project and field of interest when setting the signature. The current project and field of interest include, but are not limited to, for example, a project name, a topic of interest, and the like. The signature unit, for example, proposes a signature including information related to a project that the user is currently working on. Based on the user's field of interest, the signature unit proposes a signature including related topics and news. Based on the user's field of interest, the signature unit proposes a signature including information related to an event or conference that the user is participating in. This makes it possible to customize the signature based on the user's current project and field of interest. Part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input data on the project and field of interest into AI and have the AI ​​execute the customization of the signature.

[0061] The signature unit can prioritize a highly relevant signature in consideration of the geographical location information of the user when setting the signature. The geographical location information includes, but is not limited to, for example, GPS data, IP address, etc. For example, when the user is in a specific area, the signature unit proposes a signature including information related to the area. When the user is traveling, the signature unit proposes a signature including information related to the travel destination. When the user is participating in a specific event, the signature unit proposes a signature including information related to the event. This makes it possible to prioritize a highly relevant signature in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input geographical location information to AI and cause AI to execute the priority setting of the signature.

[0062] The signature unit can analyze the user's social media activity and suggest a related signature when setting a signature. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The signature unit can, for example, suggest a related signature based on information shared by the user on social media. The signature unit can suggest a related signature based on accounts the user follows on social media. The signature unit can suggest a related signature based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest a related signature. Some or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input data on social media activity into AI and have the AI ​​execute the signature suggestion.

[0063] The classification unit can analyze the user's past email history at the time of classification setting to propose optimal classification criteria. The past email history includes, for example, the sending date and time, email content, and reply status, but is not limited to such examples. The classification unit proposes classification criteria, for example, based on contacts with which the user has frequently communicated in the past. From the user's past email history, a classification criterion is proposed for emails containing a specific keyword. The user's past email history is analyzed, and classification criteria for a specific time period are proposed. In this way, the user's past email history can be analyzed to propose optimal classification criteria. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input the past email history to AI and have the AI ​​execute the proposal of classification criteria.

[0064] The classification unit can customize the classification criteria based on the user's current project or field of interest at the time of classification setting. The current project or field of interest includes, for example, a project name, a topic of interest, and the like, but is not limited to such examples. For example, the classification unit preferentially classifies emails related to a project that the user is currently working on. Based on the user's field of interest, the classification unit preferentially classifies related emails. Based on the user's field of interest, the classification unit preferentially classifies emails related to an event or conference in which the user is participating. This allows the classification criteria to be customized based on the user's current project or field of interest. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input data on projects and fields of interest to AI and cause AI to customize the classification criteria.

[0065] The classification unit can prioritize classification criteria with high relevance in consideration of the geographical location information of the user when setting the classification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the classification unit prioritizes classifying emails related to the area. When the user is traveling, the classification unit prioritizes classifying emails related to the travel destination. When the user is participating in a specific event, the classification unit prioritizes classifying emails related to the event. This makes it possible to prioritize classification criteria with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input geographical location information to AI and cause AI to execute priority setting of classification criteria.

[0066] The classification unit can analyze the user's social media activity at the time of classification setting and propose relevant classification criteria. Social media activities include, for example, the content of posts, the number of likes, the number of followers, etc., but are not limited to such examples. The classification unit proposes criteria for classifying related emails based on, for example, information shared by the user on social media. Proposes criteria for classifying related emails based on accounts the user follows on social media. Proposes criteria for classifying related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and propose relevant classification criteria. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input data of social media activities to AI and have the AI ​​execute the proposal of classification criteria.

[0067] The notification unit can analyze the user's past schedule history and propose optimal notification content when setting notifications. The past schedule history includes, for example, the date and time of the schedule, the content, and the attendees, but is not limited to such examples. The notification unit proposes optimal notification content, for example, based on activities that the user frequently performed in the past. Proposes notification content suitable for a specific time period from the user's past schedule history. Analyzes the user's past schedule history and proposes efficient notification content. This makes it possible to analyze the user's past schedule history and propose optimal notification content. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the past schedule history into AI and have the AI ​​execute the proposal of notification content.

[0068] The notification unit can customize the notification contents based on the user's current living situation and areas of interest when setting the notification. The current living situation and areas of interest include, for example, lifestyle habits, hobbies, and topics of interest, but are not limited to such examples. The notification unit, for example, proposes notification contents related to a project that the user is currently working on. Based on the user's areas of interest, the notification unit proposes notification contents including related activities. Flexible notification contents are proposed according to the user's living situation. This makes it possible to customize the notification contents based on the user's current living situation and areas of interest. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on the living situation and areas of interest into AI and have the AI ​​execute customization of the notification contents.

[0069] The notification unit can prioritize highly relevant notification contents in consideration of the geographical location information of the user when setting the notification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the notification unit includes activities that can be done in that area in the notification contents. When the user is traveling, the notification unit includes activities that can be done at the travel destination in the notification contents. When the user is participating in a specific event, the notification unit includes activities related to the event in the notification contents. This makes it possible to prioritize highly relevant notification contents in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input geographical location information to AI and cause AI to execute priority setting of notification contents.

[0070] The notification unit can analyze the user's social media activity and suggest related notification content when setting notifications. Social media activity includes, but is not limited to, for example, post content, number of likes, number of followers, etc. The notification unit can suggest related notification content based on, for example, information shared by the user on social media. The notification unit can suggest related notification content based on accounts the user follows on social media. The notification unit can suggest related notification content based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related notification content. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on social media activity into AI and have the AI ​​execute the suggestion of notification content.

[0071] The recording unit can analyze the user's past email history at the time of recording setting and propose optimal recording contents. The past email history includes, for example, the sending date and time, email contents, and reply status, but is not limited to such examples. The recording unit proposes recording contents based on, for example, contacts with which the user has frequently communicated in the past. From the user's past email history, the recording unit proposes recording contents for emails containing specific keywords. The user's past email history is analyzed and recording contents for a specific time period are proposed. This makes it possible to analyze the user's past email history and propose optimal recording contents. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the past email history to AI and have the AI ​​execute the proposal of recording contents.

[0072] The recording unit can customize the recorded contents based on the user's current project and field of interest at the time of recording setting. The current project and field of interest include, for example, a project name, a topic of interest, and the like, but are not limited to such examples. For example, the recording unit preferentially records emails related to a project that the user is currently working on. Based on the user's field of interest, related emails are preferentially recorded. Emails related to an event or conference in which the user is participating are preferentially recorded. This allows the recorded contents to be customized based on the user's current project and field of interest. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data on the project and field of interest into AI and have the AI ​​execute customization of the recorded contents.

[0073] The recording unit can prioritize highly relevant recorded contents in consideration of the geographical location information of the user at the time of recording setting. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the recording unit prioritizes recording of emails related to the area. When the user is traveling, the recording unit prioritizes recording of emails related to the travel destination. When the user is participating in a specific event, the recording unit prioritizes recording of emails related to the event. This makes it possible to prioritize highly relevant recorded contents in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input geographical location information to AI and cause AI to execute priority setting of recorded contents.

[0074] The recording unit can analyze the user's social media activity at the time of recording setting and suggest related recording contents. Social media activities include, for example, posting contents, number of likes, number of followers, etc., but are not limited to such examples. The recording unit, for example, proposes criteria for recording related emails based on information shared by the user on social media. Proposes criteria for recording related emails based on accounts the user follows on social media. Proposes criteria for recording related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activities and suggest related recording contents. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data on social media activities into AI and have the AI ​​execute the suggestion of recording contents.

[0075] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0076] The custom message setting unit can analyze the user's past message history and suggest the optimal custom message. For example, phrases that the user has frequently used in the past can be automatically displayed as candidates. The tone and content of messages sent by the user to specific recipients can be analyzed and similar messages can be suggested. It is also possible to predict and suggest messages suitable for specific situations from the user's past message history. In this way, the optimal custom message can be suggested by analyzing the user's past message history.

[0077] The notification unit can adjust the notification contents taking into account the geographical location information of the user. For example, when the user is in a specific area, a notification including information related to the area is sent. When the user is traveling, a notification including information related to the travel destination is sent. When the user is participating in a specific event, a notification including information related to the event can also be sent. This makes it possible to adjust the notification contents taking into account the geographical location information of the user.

[0078] The time schedule setting unit can customize the schedule based on the user's current life situation and areas of interest. For example, a schedule related to a project the user is currently working on is proposed. A schedule including related activities is proposed based on the user's areas of interest. A flexible schedule can also be proposed according to the user's life situation. This allows the schedule to be customized based on the user's current life situation and areas of interest.

[0079] The notification unit can analyze the user's social media activity and suggest related notification content. For example, the notification unit can suggest related notification content based on information shared by the user on social media. The notification unit can suggest related notification content based on accounts the user follows on social media. The notification unit can also suggest related notification content based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related notification content.

[0080] The custom message setting unit can prioritize highly relevant messages in consideration of the geographical location information of the user. For example, if the user is in a specific area, a message including information related to the area is suggested. If the user is traveling, a message including information related to the travel destination is suggested. If the user is participating in a specific event, a message including information related to the event can also be suggested. This makes it possible to prioritize highly relevant messages in consideration of the geographical location information of the user.

[0081] The process flow of the first embodiment will be briefly described below.

[0082] Step 1: The custom message setting unit sets a user-specific message to be included in the auto-response email. The user-specific message may include, but is not limited to, a text message, an image, a link, etc. The custom message setting unit provides, for example, a function that allows the user to set their own greeting or message. Step 2: The schedule setting unit sets different auto-reply messages according to the day of the week and the time period. The auto-reply messages according to the day of the week and the time period include, but are not limited to, weekdays and weekends, business hours and non-business hours, etc. The schedule setting unit provides a function to display a specific message on holidays and non-business hours, for example. Step 3: The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, email addresses, domains, contact lists, etc. The prohibition unit provides a function to set, for example, not to send automatic responses to emails from specific important contacts. Step 4: The forwarding unit forwards the received email to a preset destination. The forwarding unit provides, for example, a function for forwarding the received email to a specified email address or team member. Step 5: The signature section adds a custom signature to the auto-reply email. The custom signature may include, but is not limited to, a text signature, an image signature, a signature with a link, etc. The signature section provides, for example, a function to add a user's custom signature to the auto-reply message.

[0083] (Example 2) The business email autoresponder system AI assistant according to the embodiment of the present invention is a system that provides a customization function that allows users to set their own greetings and messages. The system provides a time schedule function that allows users to set different autoresponder messages according to the week and date. For example, a specific message can be displayed on holidays and non-business hours. In addition, an exception function for important contacts is provided that allows users to set not to send autoresponders to emails from certain important contacts. In addition, a message forwarding function is provided that forwards received emails to specified email addresses or team members. A custom signature function is also provided that allows users to add their own custom signature to the autoresponder message. Supports a schedule notification function that automatically sends emails when notified of a scheduled event or meeting. A repeat message function is provided that allows users to set autoresponders for the same request or inquiry, reducing repetitive work. An email classification function is provided that assigns received emails to different autoresponders according to categories. Finally, a complete log function is provided that records detailed logs of sent autoresponder messages and received emails and allows them to be checked later. This allows the business email autoresponder system AI assistant to flexibly customize the content of autoresponder emails by users.

[0084] The AI ​​assistant system for automatic response of business email according to the embodiment includes a custom message setting unit, a time schedule setting unit, a prohibition unit, a forwarding unit, and a signature unit. The custom message setting unit sets a user-specific message to be included in the automatic response email. The user-specific message includes, but is not limited to, a text message, an image, a link, etc., for example. The custom message setting unit provides, for example, a function that allows the user to set a unique greeting or message. The time schedule setting unit sets different automatic response messages according to the day of the week and the time period. The automatic response messages according to the day of the week and the time period include, but are not limited to, for example, weekdays and weekends, during business hours and outside business hours, etc., for example. The time schedule setting unit provides, for example, a function to display a specific message on holidays and non-business hours. The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, for example, email addresses, domains, contact lists, etc., for example. The prohibition unit provides, for example, a function to set so that automatic responses are not sent to emails from specific important contacts. The forwarding unit forwards the received email to a preset destination. The forwarding unit provides, for example, a function of forwarding a received email to a specified email address or team member. The signature unit adds a custom signature to an automatically responded email. Custom signatures include, for example, a text signature, an image signature, a signature with a link, and the like, but are not limited to such examples. The signature unit provides, for example, a function of adding a user's custom signature to an auto-response message. This allows the AI ​​assistant in the business email auto-response system according to the embodiment to flexibly customize the content of the auto-response email by the user.

[0085] The custom message setting unit sets a user-specific message to be included in the auto-response email. Examples of the user-specific message include, but are not limited to, a text message, an image, a link, and the like. The custom message setting unit provides, for example, a function that allows the user to set an original greeting or message. Specifically, the user can freely input text and add images and links through a dedicated interface. This interface is designed to allow intuitive operation and to allow the user to easily create a custom message. Furthermore, the custom message setting unit also provides a function that allows the user to save multiple message templates and quickly switch between them as needed. For example, the user can create message templates in advance for different situations, such as for business use, private use, and events, and select an appropriate template depending on the situation. The custom message setting unit also has a function that uses AI to analyze the user's past message history and suggest the optimal message content. This allows the user to efficiently create messages and maintain consistent communication with the recipient. Furthermore, the custom message setting unit provides a message preview function, allowing the user to check the content of the message that will actually be sent in advance. This reduces the risk of sending a message with incorrect content and improves user satisfaction.

[0086] The time schedule setting unit sets different auto-response messages according to the day of the week and the time period. Auto-response messages according to the day of the week and the time period include, but are not limited to, weekdays and weekends, during business hours and outside business hours, for example. The time schedule setting unit provides a function to display a specific message on holidays and non-business hours, for example. Specifically, a user can easily set a message according to a specific day of the week and time period using a calendar-style interface. This interface supports drag-and-drop operations and is designed to allow a user to intuitively set a schedule. Furthermore, the time schedule setting unit also provides a function to change the auto-response message based on the schedule of a specific event or meeting. For example, when a user adds a meeting schedule to the calendar, a meeting message is automatically set for that time period. In addition, the time schedule setting unit also has a function to learn the user's past schedule patterns using AI and propose an optimal message schedule. This allows the user to efficiently manage the schedule and send an appropriate message to the recipient. Furthermore, the time schedule setting unit also provides a function to save multiple schedule patterns and quickly switch between them as needed. This allows the user to flexibly manage schedules according to different situations.

[0087] The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, email addresses, domains, and contact lists. The prohibition unit provides, for example, a function for setting to not send automatic responses to emails from specific important contacts. Specifically, the user can input the email address or domain of the contact to be prohibited through a dedicated interface and add it to the list. This interface is designed to allow management of the prohibition list with simple operations. Furthermore, the prohibition unit also has a function for automatically detecting spam emails and phishing emails using AI and prohibiting automatic responses to these emails. This allows the user to prevent automatic responses to unnecessary emails and to provide appropriate responses only to important emails. The prohibition unit also provides a function for setting rules to prohibit automatic responses based on specific conditions. For example, automatic responses can be prohibited for emails containing specific keywords or emails received during a specific time period. This allows the user to flexibly set prohibition rules and efficiently manage emails. Furthermore, the prohibition unit provides a function for exporting and importing the prohibition list, and the prohibition list can be shared in cooperation with other systems and devices. This allows users to achieve consistent email management and improves the security of the entire system.

[0088] The forwarding unit forwards received emails to a preset destination. The forwarding unit provides, for example, a function to forward received emails to a specified email address or team member. Specifically, the user can set the destination email address or group through a dedicated interface. This interface is designed to allow the user to set forwarding settings with simple operations. In addition, the forwarding unit also has a function to analyze the contents of received emails using AI and automatically suggest the optimal forwarding destination. For example, emails containing specific keywords or phrases can be set to be forwarded to specific team members. The forwarding unit also provides a function to set forwarding rules based on specific conditions. For example, emails from specific senders or emails received during specific time periods can be automatically forwarded. This allows users to flexibly set forwarding rules and efficiently manage emails. In addition, the forwarding unit also provides a function to record forwarding history and allow users to check past forwarding status. This allows users to track forwarding and review forwarding settings as necessary. The forwarding unit also has a function to automatically send a notification when the forwarding destination email address is changed, allowing users to maintain the latest forwarding destination information. This allows the forwarding unit to forward emails efficiently and reliably, thereby improving the work efficiency of the user.

[0089] The signature unit adds a custom signature to an auto-reply email. Examples of custom signatures include, but are not limited to, a text signature, an image signature, and a signature with a link. The signature unit provides, for example, a function for adding a user's custom signature to an auto-reply message. Specifically, the user can freely edit the content of the signature and add text, an image, and a link through a dedicated interface. This interface is designed to allow intuitive operation and to allow the user to easily create a custom signature. Furthermore, the signature unit also provides a function for saving multiple signature templates and quickly switching between them as needed. For example, the user can create signature templates in advance for different situations, such as for business use, private use, and events, and select an appropriate template depending on the situation. The signature unit also has a function for analyzing the user's past signature history using AI and proposing the optimal signature content. This allows the user to efficiently create signatures and maintain consistent communication with the recipient. Furthermore, the signature unit provides a signature preview function, allowing the user to check the content of the signature that will actually be sent in advance. This reduces the risk of sending a signature with incorrect content and improves user satisfaction.

[0090] The filter unit can assign received emails to different automatic responses depending on the category. The filter unit can, for example, appropriately classify received emails depending on the category and perform an automatic response. The categories include, for example, work-related, personal-related, project-specific, and the like, but are not limited to such examples. The filter unit provides, for example, a function of assigning received emails to different automatic responses depending on the category. This allows received emails to be appropriately classified depending on the category and an automatic response to be performed. A part or all of the above-mentioned processing in the filter unit may be performed using, for example, AI, or may be performed without using AI. For example, the filter unit can input the contents of received emails to AI and have AI perform category classification.

[0091] The notification unit can notify of a scheduled event or meeting. The notification unit can, for example, automatically notify of an event or meeting. The event or meeting includes, but is not limited to, an internal meeting, an external event, an online meeting, and the like. The notification unit, for example, provides a function for notifying of a scheduled event or meeting. This allows the notification of an event or meeting to be automatically performed. A part or all of the above-mentioned processing in the notification unit can be performed, for example, using AI or can be performed without using AI. For example, the notification unit can input schedule information to AI and cause AI to generate notification content.

[0092] The log unit can record logs of the sent automatic response message and the received email so that they can be viewed. The log unit can, for example, record detailed logs of the sent automatic response message and the received email, and can check them later. The log includes, for example, the sending date and time, the receiving date and time, and the message content, but is not limited to such examples. The log unit provides, for example, a function of recording logs of the sent automatic response message and the received email so that they can be viewed. This allows detailed logs of the sent automatic response message and the received email to be recorded, and can be checked later. Some or all of the above-mentioned processing in the log unit can be performed, for example, using AI, or can be performed without using AI. For example, the log unit can input the sending and receiving data of the email to AI, and have the AI ​​execute the recording of the log.

[0093] The time schedule setting unit can automatically update the schedule by synchronizing with the user's calendar app. The time schedule setting unit, for example, synchronizes with the user's calendar app, thereby automating the schedule update. The calendar app includes, for example, Google Calendar, Outlook Calendar, etc., but is not limited to such examples. The time schedule setting unit, for example, provides a function of automatically updating the schedule by synchronizing with the user's calendar app. As a result, the schedule update is automated by synchronizing with the user's calendar app. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data of the calendar app to AI and cause AI to execute schedule update.

[0094] The prohibition unit can prohibit an automatic response even to an email containing a specific keyword. The specific keyword includes, for example, a spam keyword, a project name, and the like, but is not limited to such examples. The prohibition unit provides, for example, a function for prohibiting an automatic response to an email containing a specific keyword. This makes it possible to prohibit an automatic response even to an email containing a specific keyword. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the contents of the email into AI and cause the AI ​​to detect the keyword.

[0095] The prohibition unit can prohibit automatic responses even during a specific time period set by the user. The specific time period includes, but is not limited to, business hours, break times, and nighttime. The prohibition unit provides, for example, a function for prohibiting automatic responses during a specific time period set by the user. This makes it possible to prohibit automatic responses even during a specific time period set by the user. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data of the time period into AI and cause AI to execute the prohibition of automatic responses.

[0096] The notification unit can adjust the notification content based on the location of the event or meeting. The location includes, but is not limited to, for example, an office, a conference room, an online platform, and the like. The notification unit provides, for example, a function for adjusting the notification content based on the location of the event or meeting. This allows the notification content to be adjusted based on the location of the event or meeting. Some or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input location data into AI and cause AI to adjust the notification content.

[0097] The forwarding unit can automatically forward the received email to the project team if the content of the received email is related to a specific project. The forwarding unit can automatically forward, for example, emails related to a specific project to the project team. The specific project includes, for example, a project name, a project ID, and the like, but is not limited to such examples. The forwarding unit provides, for example, a function of automatically forwarding the received email to the project team if the content of the received email is related to a specific project. This makes it possible to automatically forward emails related to a specific project to the project team. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input the content of the email into AI and have the AI ​​execute a project-related judgment.

[0098] The custom message setting unit can estimate the user's emotion and adjust the content of the custom message based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, fatigue, etc., but is not limited to such examples. For example, when the user is stressed, the custom message setting unit proposes a message with gentle language that relaxes the user. When the user is happy, the custom message setting unit proposes a bright message that further enhances the positive emotion. When the user is tired, the custom message setting unit proposes a concise and less burdensome message. This makes it possible to adjust the content of the custom message based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the custom message setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the custom message setting unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the message.

[0099] The custom message setting unit can analyze the user's past message history and suggest the optimal custom message. The past message history includes, for example, the sending date and time, the message content, the reply status, etc., but is not limited to such examples. The custom message setting unit, for example, automatically displays phrases that the user has frequently used in the past as candidates. The tone and content of the message sent by the user to a specific person are analyzed, and a similar message is suggested. A message suitable for a specific situation is predicted and suggested from the user's past message history. This makes it possible to analyze the user's past message history and suggest the optimal custom message. A part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input the past message history into AI and have the AI ​​execute the message suggestion.

[0100] The custom message setting unit can customize a message based on the user's current project or field of interest when setting a custom message. The current project or field of interest includes, for example, a project name, a topic of interest, and the like, but is not limited to such examples. The custom message setting unit, for example, proposes a message including information related to a project that the user is currently working on. Based on the user's field of interest, the custom message setting unit proposes a message including related topics and news. Based on the user's field of interest, the custom message setting unit proposes a message including information related to an event or conference that the user is participating in. This makes it possible to customize a message based on the user's current project or field of interest. A part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input data on the project or field of interest into AI and have the AI ​​execute message customization.

[0101] The custom message setting unit can estimate the user's emotion and determine the priority of the custom message based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, fatigue, etc., but is not limited to such examples. For example, when the user is stressed, the custom message setting unit preferentially displays a relaxing message. When the user is happy, the custom message setting unit preferentially displays a positive message. When the user is tired, the custom message setting unit preferentially displays a concise and less burdensome message. This makes it possible to determine the priority of the custom message based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the custom message setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the custom message setting unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of the messages.

[0102] The custom message setting unit can prioritize highly relevant messages in consideration of the geographical location information of the user when setting the custom message. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the custom message setting unit proposes a message including information related to the area. When the user is traveling, the custom message setting unit proposes a message including information related to the travel destination. When the user is participating in a specific event, the custom message setting unit proposes a message including information related to the event. This makes it possible to prioritize highly relevant messages in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the custom message setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the custom message setting unit can input geographical location information to AI and cause AI to execute message priority setting.

[0103] The custom message setting unit can analyze the user's social media activity and suggest related messages when setting the custom message. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The custom message setting unit can suggest related messages based on, for example, information shared by the user on social media. The custom message setting unit can suggest related messages based on accounts the user follows on social media. The custom message setting unit can suggest related messages based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related messages. A part or all of the above-mentioned processing in the custom message setting unit can be performed, for example, using AI or can be performed without using AI. For example, the custom message setting unit can input data on social media activity into AI and have the AI ​​execute message suggestions.

[0104] The time schedule setting unit can estimate the user's emotion and adjust the setting of the time schedule based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, tiredness, etc., but is not limited to such examples. For example, when the user is stressed, the time schedule setting unit proposes a schedule that allows the user to relax. When the user is happy, the time schedule setting unit proposes a schedule that further enhances positive emotions. When the user is tired, the time schedule setting unit proposes a schedule that prioritizes rest. This makes it possible to adjust the setting of the time schedule based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the time schedule setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the time schedule setting unit can input the user's emotion data to an AI and cause the AI ​​to adjust the schedule.

[0105] The time schedule setting unit can analyze the user's past schedule history and propose an optimal schedule when setting the time schedule. The past schedule history includes, for example, the date and time of the plan, the contents, and the attendees, but is not limited to such examples. The time schedule setting unit proposes an optimal schedule, for example, based on activities that the user frequently performed in the past. From the user's past schedule history, an activity suitable for a specific time period is proposed. The user's past schedule history is analyzed and an efficient schedule is proposed. In this way, the user's past schedule history can be analyzed and an optimal schedule can be proposed. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input the past schedule history to AI and have the AI ​​execute the schedule proposal.

[0106] The time schedule setting unit can customize the schedule based on the user's current living situation and areas of interest when setting the time schedule. The current living situation and areas of interest include, for example, lifestyle habits, hobbies, and topics of interest, but are not limited to such examples. The time schedule setting unit, for example, proposes a schedule related to a project that the user is currently working on. Based on the user's areas of interest, a schedule including related activities is proposed. A flexible schedule is proposed according to the user's living situation. This makes it possible to customize the schedule based on the user's current living situation and areas of interest. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data on the living situation and areas of interest into AI and have the AI ​​execute the customization of the schedule.

[0107] The time schedule setting unit can estimate the user's emotion and determine the priority of the time schedule based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, tiredness, etc., but is not limited to such examples. For example, when the user is feeling stressed, the time schedule setting unit preferentially incorporates relaxing activities into the schedule. When the user is happy, the time schedule setting unit preferentially incorporates positive activities into the schedule. When the user is tired, the time schedule setting unit preferentially incorporates rest into the schedule. This makes it possible to determine the priority of the time schedule based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the time schedule setting unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the time schedule setting unit can input the user's emotion data into the AI ​​and cause the AI ​​to execute the determination of the priority of the schedule.

[0108] The time schedule setting unit can prioritize a highly relevant schedule in consideration of the geographical location information of the user when setting the time schedule. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the time schedule setting unit incorporates activities that can be done in that area into the schedule. When the user is traveling, the time schedule setting unit incorporates activities that can be done at the travel destination into the schedule. When the user is participating in a specific event, the time schedule setting unit incorporates activities related to the event into the schedule. This makes it possible to prioritize a highly relevant schedule in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input geographical location information to AI and cause AI to execute priority setting of the schedule.

[0109] The time schedule setting unit can analyze the social media activity of the user and propose a related schedule when setting the time schedule. The social media activity includes, for example, the content of posts, the number of likes, the number of followers, and the like, but is not limited to such examples. The time schedule setting unit incorporates related activities into the schedule, for example, based on information shared by the user on social media. The time schedule setting unit incorporates related activities into the schedule based on accounts the user follows on social media. The time schedule setting unit incorporates related activities into the schedule based on groups and events the user participates in on social media. This makes it possible to analyze the social media activity of the user and propose a related schedule. A part or all of the above-mentioned processing in the time schedule setting unit may be performed, for example, using AI, or may be performed without using AI. For example, the time schedule setting unit can input data of social media activity into AI and have the AI ​​execute the schedule proposal.

[0110] The prohibition unit can estimate the user's emotion and adjust the prohibition setting of the automatic response based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, fatigue, etc., but is not limited to such examples. For example, when the user is stressed, the prohibition unit prohibits automatic response to emails from important contacts. When the user is relaxed, the prohibition unit allows automatic response to emails from specific contacts. When the user is tired, the prohibition unit prohibits automatic response during a specific time period. This makes it possible to adjust the prohibition setting of the automatic response based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prohibition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prohibition unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the adjustment of the prohibition setting.

[0111] When setting the prohibition, the prohibition unit can analyze the user's past email history and propose the optimal prohibition setting. The past email history includes, for example, the sending date and time, the email content, the reply status, etc., but is not limited to such examples. The prohibition unit proposes the prohibition setting based on, for example, contacts with which the user has frequently exchanged in the past. From the user's past email history, the prohibition unit proposes the prohibition setting for emails containing a specific keyword. The user's past email history is analyzed and the prohibition setting for a specific time period is proposed. In this way, the user's past email history can be analyzed and the optimal prohibition setting can be proposed. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the past email history to AI and have the AI ​​execute the proposal of the prohibition setting.

[0112] The prohibition unit can customize the prohibition setting based on the user's current project and field of interest when setting the prohibition. The current project and field of interest include, for example, a project name, a topic of interest, and the like, but are not limited to such examples. For example, the prohibition unit proposes a prohibition setting for emails related to a project that the user is currently working on. The prohibition unit proposes a prohibition setting for related emails based on the user's field of interest. The prohibition unit proposes a prohibition setting for emails related to an event or conference that the user is participating in. This allows the prohibition setting to be customized based on the user's current project and field of interest. A part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data on the project and field of interest into AI and cause AI to customize the prohibition setting.

[0113] The prohibition unit can estimate the user's emotion and determine the prohibition priority of the automatic response based on the estimated user's emotion. The user's emotion includes, for example, stress, relaxation, fatigue, etc., but is not limited to such examples. For example, when the user is stressed, the prohibition unit preferentially prohibits automatic responses to emails from important contacts. When the user is relaxed, the prohibition unit allows automatic responses to emails from specific contacts. When the user is tired, the prohibition unit preferentially prohibits automatic responses during a specific time period. This makes it possible to determine the prohibition priority of the automatic response based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the prohibition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the prohibition unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the prohibition priority.

[0114] When setting the prohibition, the prohibition unit can give priority to the prohibition setting with high relevance in consideration of the geographical location information of the user. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the prohibition unit proposes the prohibition setting for emails related to the area. When the user is traveling, the prohibition unit proposes the prohibition setting for emails related to the travel destination. When the user is participating in a specific event, the prohibition unit proposes the prohibition setting for emails related to the event. In this way, the prohibition setting with high relevance can be given priority in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input the geographical location information to AI and cause AI to execute the priority setting of the prohibition setting.

[0115] The prohibition unit can analyze the user's social media activity at the time of prohibition setting and propose related prohibition settings. Social media activities include, for example, the content of posts, the number of likes, the number of followers, etc., but are not limited to such examples. The prohibition unit, for example, proposes prohibition settings for related emails based on information shared by the user on social media. Proposes prohibition settings for related emails based on accounts the user follows on social media. Proposes prohibition settings for related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activities and propose related prohibition settings. Part or all of the above-mentioned processing in the prohibition unit may be performed, for example, using AI, or may be performed without using AI. For example, the prohibition unit can input data on social media activities into AI and cause AI to execute the proposal of prohibition settings.

[0116] The forwarding unit can estimate the user's emotion and adjust the email forwarding settings based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the forwarding unit proposes a setting to prioritize forwarding of important emails. When the user is relaxed, the forwarding unit maintains the normal forwarding settings. When the user is tired, the forwarding unit proposes a setting to suspend email forwarding during a specific time period. This makes it possible to adjust the email forwarding settings based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the forwarding unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the forwarding unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the forwarding settings.

[0117] The forwarding unit can analyze the user's past email history and propose optimal forwarding settings when setting forwarding. The past email history includes, for example, the sending date and time, email content, and reply status, but is not limited to such examples. The forwarding unit, for example, automatically displays email addresses that the user has frequently forwarded in the past as candidates. From the user's past email history, proposes settings for automatically forwarding emails related to a specific project. The user's past email history is analyzed and forwarding settings for a specific time period are proposed. In this way, the user's past email history can be analyzed and optimal forwarding settings can be proposed. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input the past email history into AI and have the AI ​​execute the proposal of forwarding settings.

[0118] The forwarding unit can customize the forwarding settings based on the user's current project and field of interest at the time of forwarding settings. The current project and field of interest include, but are not limited to, for example, a project name and a topic of interest. The forwarding unit, for example, proposes a setting to automatically forward emails related to a project that the user is currently working on. The forwarding unit proposes a setting to automatically forward related emails based on the user's field of interest. The forwarding unit proposes a setting to automatically forward emails related to an event or conference that the user is participating in. This makes it possible to customize the forwarding settings based on the user's current project and field of interest. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI or may be performed without using AI. For example, the forwarding unit can input data on the project and field of interest into AI and have the AI ​​execute customization of the forwarding settings.

[0119] The forwarding unit can estimate the user's emotion and determine the priority of email forwarding based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the forwarding unit prioritizes forwarding important emails. When the user is relaxed, the forwarding unit maintains normal forwarding settings. When the user is tired, the forwarding unit suspends email forwarding during a specific time period. This allows the priority of email forwarding to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the forwarding unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the forwarding priority.

[0120] The forwarding unit can prioritize forwarding settings that are highly relevant in consideration of the geographical location information of the user when setting forwarding. The geographical location information includes, for example, GPS data, IP address, and the like, but is not limited to such examples. For example, when the user is in a specific area, the forwarding unit prioritizes forwarding emails related to the area. When the user is traveling, the forwarding unit prioritizes forwarding emails related to the travel destination. When the user is participating in a specific event, the forwarding unit prioritizes forwarding emails related to the event. This allows for prioritized forwarding settings that are highly relevant in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input geographical location information to AI and cause AI to execute the priority setting of forwarding settings.

[0121] The forwarding unit can analyze the user's social media activity at the time of forwarding setting and propose related forwarding settings. Social media activities include, but are not limited to, for example, the contents of posts, the number of likes, and the number of followers. The forwarding unit proposes settings for forwarding related emails based on, for example, information shared by the user on social media. The forwarding unit proposes settings for forwarding related emails based on accounts the user follows on social media. The forwarding unit proposes settings for forwarding related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and propose related forwarding settings. Part or all of the above-mentioned processing in the forwarding unit may be performed, for example, using AI, or may be performed without using AI. For example, the forwarding unit can input data on social media activities into AI and have the AI ​​execute the proposal of forwarding settings.

[0122] The signature unit can estimate the user's emotion and adjust the content of the custom signature based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, tiredness, etc., but is not limited to such examples. For example, when the user is stressed, the signature unit proposes a signature with gentle language that relaxes the user. When the user is happy, the signature unit proposes a bright signature that further enhances the positive emotion. When the user is tired, the signature unit proposes a simple and less burdensome signature. This makes it possible to adjust the content of the custom signature based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the signature unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the signature unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the signature.

[0123] The signature unit can analyze the user's past signature history and propose an optimal signature when setting a signature. The past signature history includes, but is not limited to, the signature content, frequency of use, and change history. The signature unit, for example, automatically displays signatures that the user has frequently used in the past as candidates. The signature unit analyzes the tone and content of a signature sent by the user to a specific person and proposes a similar signature. A signature suitable for a specific situation is predicted and proposed from the user's past signature history. This makes it possible to analyze the user's past signature history and propose an optimal signature. A part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input the past signature history into AI and have the AI ​​execute the signature proposal.

[0124] The signature unit can customize the signature based on the user's current project and field of interest when setting the signature. The current project and field of interest include, but are not limited to, for example, a project name, a topic of interest, and the like. The signature unit, for example, proposes a signature including information related to a project that the user is currently working on. Based on the user's field of interest, the signature unit proposes a signature including related topics and news. Based on the user's field of interest, the signature unit proposes a signature including information related to an event or conference that the user is participating in. This makes it possible to customize the signature based on the user's current project and field of interest. Part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input data on the project and field of interest into AI and have the AI ​​execute the customization of the signature.

[0125] The signature unit can estimate the user's emotion and determine the priority of custom signatures based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, joy, fatigue, etc. For example, when the user is stressed, the signature unit preferentially displays a signature that allows the user to relax. When the user is happy, the signature unit preferentially displays a positive signature. When the user is tired, the signature unit preferentially displays a simple and less burdensome signature. This allows the priority of custom signatures to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the signature unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the signature unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the priority of signatures.

[0126] The signature unit can prioritize a highly relevant signature in consideration of the geographical location information of the user when setting the signature. The geographical location information includes, but is not limited to, for example, GPS data, IP address, etc. For example, when the user is in a specific area, the signature unit proposes a signature including information related to the area. When the user is traveling, the signature unit proposes a signature including information related to the travel destination. When the user is participating in a specific event, the signature unit proposes a signature including information related to the event. This makes it possible to prioritize a highly relevant signature in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input geographical location information to AI and cause AI to execute the priority setting of the signature.

[0127] The signature unit can analyze the user's social media activity and suggest a related signature when setting a signature. Social media activity includes, but is not limited to, for example, the content of posts, the number of likes, and the number of followers. The signature unit can, for example, suggest a related signature based on information shared by the user on social media. The signature unit can suggest a related signature based on accounts the user follows on social media. The signature unit can suggest a related signature based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest a related signature. Some or all of the above-mentioned processing in the signature unit may be performed, for example, using AI, or may be performed without using AI. For example, the signature unit can input data on social media activity into AI and have the AI ​​execute the signature suggestion.

[0128] The classification unit can estimate the user's emotion and adjust the classification criteria of the email based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the classification unit prioritizes classification of important emails. When the user is relaxed, the classification unit maintains the normal classification criteria. When the user is tired, the classification of emails is suspended during a specific time period. This makes it possible to adjust the classification criteria of the email based on the user's emotion. The estimation of the emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the classification criteria.

[0129] The classification unit can analyze the user's past email history at the time of classification setting to propose optimal classification criteria. The past email history includes, for example, the sending date and time, email content, and reply status, but is not limited to such examples. The classification unit proposes classification criteria, for example, based on contacts with which the user has frequently communicated in the past. From the user's past email history, a classification criterion is proposed for emails containing a specific keyword. The user's past email history is analyzed, and classification criteria for a specific time period are proposed. In this way, the user's past email history can be analyzed to propose optimal classification criteria. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input the past email history to AI and have the AI ​​execute the proposal of classification criteria.

[0130] The classification unit can customize the classification criteria based on the user's current project or field of interest at the time of classification setting. The current project or field of interest includes, for example, a project name, a topic of interest, and the like, but is not limited to such examples. For example, the classification unit preferentially classifies emails related to a project that the user is currently working on. Based on the user's field of interest, the classification unit preferentially classifies related emails. Based on the user's field of interest, the classification unit preferentially classifies emails related to an event or conference in which the user is participating. This allows the classification criteria to be customized based on the user's current project or field of interest. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, AI, or may be performed without using AI. For example, the classification unit can input data on projects and fields of interest to AI and cause AI to customize the classification criteria.

[0131] The classification unit can estimate the user's emotion and determine the classification priority of emails based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the classification unit prioritizes classification of important emails. When the user is relaxed, the classification unit maintains the normal classification standard. When the user is tired, the classification of emails is suspended during a specific time period. This makes it possible to determine the classification priority of emails based on the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the classification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the classification unit can input the user's emotion data to the AI ​​and cause the AI ​​to execute the determination of the classification priority.

[0132] The classification unit can prioritize classification criteria with high relevance in consideration of the geographical location information of the user when setting the classification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the classification unit prioritizes classifying emails related to the area. When the user is traveling, the classification unit prioritizes classifying emails related to the travel destination. When the user is participating in a specific event, the classification unit prioritizes classifying emails related to the event. This makes it possible to prioritize classification criteria with high relevance in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input geographical location information to AI and cause AI to execute priority setting of classification criteria.

[0133] The classification unit can analyze the user's social media activity at the time of classification setting and propose relevant classification criteria. Social media activity includes, for example, the content of posts, the number of likes, the number of followers, etc., but is not limited to such examples. The classification unit proposes criteria for classifying related emails based on, for example, information shared by the user on social media. Proposes criteria for classifying related emails based on accounts the user follows on social media. Proposes criteria for classifying related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and propose relevant classification criteria. Part or all of the above-mentioned processing in the classification unit may be performed, for example, using AI, or may be performed without using AI. For example, the classification unit can input data of social media activity to AI and have the AI ​​execute the proposal of classification criteria.

[0134] The notification unit can estimate the user's emotion and adjust the notification content based on the estimated user's emotion. The user's emotion includes, for example, stress, joy, fatigue, etc., but is not limited to such examples. For example, when the user is stressed, the notification unit proposes notification content that allows the user to relax. When the user is happy, the notification unit proposes notification content that further enhances positive emotions. When the user is tired, the notification unit proposes notification content that is simple and less burdensome. This makes it possible to adjust the notification content based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to the AI ​​and cause the AI ​​to adjust the notification content.

[0135] The notification unit can analyze the user's past schedule history and propose optimal notification content when setting notifications. The past schedule history includes, for example, the date and time of the schedule, the content, and the attendees, but is not limited to such examples. The notification unit proposes optimal notification content, for example, based on activities that the user frequently performed in the past. Proposes notification content suitable for a specific time period from the user's past schedule history. Analyzes the user's past schedule history and proposes efficient notification content. This makes it possible to analyze the user's past schedule history and propose optimal notification content. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input the past schedule history into AI and have the AI ​​execute the proposal of notification content.

[0136] The notification unit can customize the notification contents based on the user's current living situation and areas of interest when setting the notification. The current living situation and areas of interest include, for example, lifestyle habits, hobbies, and topics of interest, but are not limited to such examples. The notification unit, for example, proposes notification contents related to a project that the user is currently working on. Based on the user's areas of interest, the notification unit proposes notification contents including related activities. Flexible notification contents are proposed according to the user's living situation. This makes it possible to customize the notification contents based on the user's current living situation and areas of interest. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on the living situation and areas of interest into AI and have the AI ​​execute customization of the notification contents.

[0137] The notification unit can estimate the user's emotion and determine the priority of notifications based on the estimated user's emotion. The user's emotion includes, but is not limited to, stress, joy, fatigue, and the like. For example, when the user is stressed, the notification unit preferentially displays notifications that can relax the user. When the user is happy, the notification unit preferentially displays positive notifications. When the user is tired, the notification unit preferentially displays simple notifications that are less burdensome. This allows the priority of notifications to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the notification unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the notification unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the notification priority.

[0138] The notification unit can prioritize highly relevant notification contents in consideration of the geographical location information of the user when setting the notification. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the notification unit includes activities that can be done in that area in the notification contents. When the user is traveling, the notification unit includes activities that can be done at the travel destination in the notification contents. When the user is participating in a specific event, the notification unit includes activities related to the event in the notification contents. This makes it possible to prioritize highly relevant notification contents in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input geographical location information to AI and cause AI to execute priority setting of notification contents.

[0139] The notification unit can analyze the user's social media activity and suggest related notification content when setting notifications. Social media activity includes, but is not limited to, for example, post content, number of likes, number of followers, etc. The notification unit can suggest related notification content based on, for example, information shared by the user on social media. The notification unit can suggest related notification content based on accounts the user follows on social media. The notification unit can suggest related notification content based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related notification content. Part or all of the above-mentioned processing in the notification unit may be performed, for example, using AI, or may be performed without using AI. For example, the notification unit can input data on social media activity into AI and have the AI ​​execute the suggestion of notification content.

[0140] The recording unit can estimate the user's emotion and adjust the recorded content based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the recording unit prioritizes recording of important emails. When the user is relaxed, the recording unit maintains normal recording settings. When the user is tired, the recording unit pauses recording during a specific time period. This allows the recorded content to be adjusted based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data to an AI and cause the AI ​​to adjust the recorded content.

[0141] The recording unit can analyze the user's past email history at the time of recording setting and propose optimal recording contents. The past email history includes, for example, the sending date and time, email contents, and reply status, but is not limited to such examples. The recording unit proposes recording contents based on, for example, contacts with which the user has frequently communicated in the past. From the user's past email history, the recording unit proposes recording contents for emails containing specific keywords. The user's past email history is analyzed and recording contents for a specific time period are proposed. This makes it possible to analyze the user's past email history and propose optimal recording contents. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input the past email history to AI and have the AI ​​execute the proposal of recording contents.

[0142] The recording unit can customize the recorded contents based on the user's current project and field of interest at the time of recording setting. The current project and field of interest include, for example, a project name, a topic of interest, and the like, but are not limited to such examples. For example, the recording unit preferentially records emails related to a project that the user is currently working on. Based on the user's field of interest, related emails are preferentially recorded. Emails related to an event or conference in which the user is participating are preferentially recorded. This allows the recorded contents to be customized based on the user's current project and field of interest. A part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data on the project and field of interest into AI and have the AI ​​execute customization of the recorded contents.

[0143] The recording unit can estimate the user's emotion and determine the priority of recording based on the estimated user's emotion. The user's emotion includes, but is not limited to, for example, stress, relaxation, fatigue, etc. For example, when the user is stressed, the recording unit prioritizes recording of important emails. When the user is relaxed, the recording unit maintains normal recording settings. When the user is tired, the recording unit pauses recording during a specific time period. This allows the recording priority to be determined based on the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the recording unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the recording unit can input the user's emotion data to an AI and cause the AI ​​to execute the determination of the recording priority.

[0144] The recording unit can prioritize highly relevant recorded contents in consideration of the geographical location information of the user at the time of recording setting. The geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. For example, when the user is in a specific area, the recording unit prioritizes recording of emails related to the area. When the user is traveling, the recording unit prioritizes recording of emails related to the travel destination. When the user is participating in a specific event, the recording unit prioritizes recording of emails related to the event. This makes it possible to prioritize highly relevant recorded contents in consideration of the geographical location information of the user. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input geographical location information to AI and cause AI to execute priority setting of recorded contents.

[0145] The recording unit can analyze the user's social media activity at the time of recording setting and suggest related recording contents. Social media activities include, for example, posting contents, number of likes, number of followers, etc., but are not limited to such examples. The recording unit, for example, proposes criteria for recording related emails based on information shared by the user on social media. Proposes criteria for recording related emails based on accounts the user follows on social media. Proposes criteria for recording related emails based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activities and suggest related recording contents. Part or all of the above-mentioned processing in the recording unit may be performed, for example, using AI, or may be performed without using AI. For example, the recording unit can input data on social media activities into AI and have the AI ​​execute the suggestion of recording contents.

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

[0147] The custom message setting unit can analyze the user's past message history and suggest the optimal custom message. For example, phrases that the user has frequently used in the past can be automatically displayed as candidates. The tone and content of messages sent by the user to specific recipients can be analyzed and similar messages can be suggested. It is also possible to predict and suggest messages suitable for specific situations from the user's past message history. In this way, the optimal custom message can be suggested by analyzing the user's past message history.

[0148] The filter unit can estimate the user's emotions and adjust the email classification criteria based on the estimated user's emotions. For example, if the user is stressed, important emails are prioritized for classification. If the user is relaxed, normal classification criteria are maintained. If the user is tired, email classification can be suspended during a specific time period. This makes it possible to adjust the email classification criteria based on the user's emotions.

[0149] The notification unit can adjust the notification contents taking into account the geographical location information of the user. For example, when the user is in a specific area, a notification including information related to the area is sent. When the user is traveling, a notification including information related to the travel destination is sent. When the user is participating in a specific event, a notification including information related to the event can also be sent. This makes it possible to adjust the notification contents taking into account the geographical location information of the user.

[0150] The log unit can estimate the user's emotions and adjust the recording contents based on the estimated user's emotions. For example, if the user is stressed, important emails are recorded with priority. If the user is relaxed, normal recording settings are maintained. If the user is tired, recording can be paused during specific time periods. This makes it possible to adjust the recording contents based on the user's emotions.

[0151] The time schedule setting unit can customize the schedule based on the user's current life situation and areas of interest. For example, a schedule related to a project the user is currently working on is proposed. A schedule including related activities is proposed based on the user's areas of interest. A flexible schedule can also be proposed according to the user's life situation. This allows the schedule to be customized based on the user's current life situation and areas of interest.

[0152] The prohibition unit can estimate the user's emotions and adjust the prohibition setting of the automatic response based on the estimated user's emotions. For example, when the user is stressed, the prohibition unit prohibits automatic responses to emails from important contacts. When the user is relaxed, the prohibition unit permits automatic responses to emails from specific contacts. When the user is tired, the prohibition unit can also prohibit automatic responses during specific time periods. In this way, the prohibition setting of the automatic response can be adjusted based on the user's emotions.

[0153] The notification unit can analyze the user's social media activity and suggest related notification content. For example, the notification unit can suggest related notification content based on information shared by the user on social media. The notification unit can suggest related notification content based on accounts the user follows on social media. The notification unit can also suggest related notification content based on groups and events the user participates in on social media. This makes it possible to analyze the user's social media activity and suggest related notification content.

[0154] The forwarding unit can estimate the user's emotions and adjust the email forwarding settings based on the estimated user's emotions. For example, if the user is stressed, the forwarding unit can suggest settings to prioritize forwarding of important emails. If the user is relaxed, the forwarding unit can maintain normal forwarding settings. If the user is tired, the forwarding unit can also suggest settings to suspend email forwarding during specific time periods. This makes it possible to adjust the email forwarding settings based on the user's emotions.

[0155] The signature unit can estimate the user's emotions and adjust the content of the custom signature based on the estimated user's emotions. For example, if the user is feeling stressed, a signature using gentle language that relaxes the user is suggested. If the user is happy, a cheerful signature that further enhances positive emotions is suggested. If the user is tired, a simple and less burdensome signature can also be suggested. In this way, the content of the custom signature can be adjusted based on the user's emotions.

[0156] The custom message setting unit can prioritize highly relevant messages in consideration of the geographical location information of the user. For example, if the user is in a specific area, a message including information related to the area is suggested. If the user is traveling, a message including information related to the travel destination is suggested. If the user is participating in a specific event, a message including information related to the event can also be suggested. This makes it possible to prioritize highly relevant messages in consideration of the geographical location information of the user.

[0157] The process flow of the second embodiment will be briefly described below.

[0158] Step 1: The custom message setting unit sets a user-specific message to be included in the auto-response email. The user-specific message may include, but is not limited to, a text message, an image, a link, etc. The custom message setting unit provides, for example, a function that allows the user to set their own greeting or message. Step 2: The schedule setting unit sets different auto-reply messages according to the day of the week and the time period. The auto-reply messages according to the day of the week and the time period include, but are not limited to, weekdays and weekends, business hours and non-business hours, etc. The schedule setting unit provides a function to display a specific message on holidays and non-business hours, for example. Step 3: The prohibition unit prohibits automatic responses to emails from specific contacts. The specific contacts include, but are not limited to, email addresses, domains, contact lists, etc. The prohibition unit provides a function to set, for example, not to send automatic responses to emails from specific important contacts. Step 4: The forwarding unit forwards the received email to a preset destination. The forwarding unit provides, for example, a function for forwarding the received email to a specified email address or team member. Step 5: The signature section adds a custom signature to the auto-reply email. The custom signature may include, but is not limited to, a text signature, an image signature, a signature with a link, etc. The signature section provides, for example, a function to add a user's custom signature to the auto-reply message.

[0159] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0160] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0161] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0162] For example, the custom message setting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the time schedule setting unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the prohibition unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the signature unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0164] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0165] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0166] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0167] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0168] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0169] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0170] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0171] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0172] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0173] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0174] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0175] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0176] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0177] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0178] For example, the custom message setting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the time schedule setting unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the prohibition unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the forwarding unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the signature unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0180] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0181] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0182] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0183] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0184] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0185] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0186] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0187] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0188] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

[0190] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0191] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for 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.

[0192] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0193] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0194] For example, the custom message setting unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the time schedule setting unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the prohibition unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the forwarding unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the signature unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

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

[0196] 7, 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.

[0197] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0198] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0199] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0200] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0201] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0202] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0203] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0204] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0206] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0207] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0208] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0209] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0210] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0211] For example, the custom message setting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the time schedule setting unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the prohibition unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the transfer unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the signature unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0212] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0213] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0214] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0215] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0216] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0217] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0218] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0219] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0220] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0221] In addition, 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 upon request from the data processing device 12.

[0222] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0223] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0224] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0225] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0226] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0227] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0228] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0229] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0230] (Appendix 1) A custom message setting section for setting a user-specific message to be included in the auto-response email; A schedule setting section for setting different auto-reply messages according to the day of the week and the time of day; A prohibition section for prohibiting automatic responses to emails from specific contacts; a forwarding unit for forwarding received e-mail to a preset destination; A signature section for adding a custom signature to auto-reply emails. A system characterized by: (Appendix 2) Equipped with a filter section that assigns different automatic responses to incoming emails depending on their category 2. The system of claim 1, (Appendix 3) Has a notification section that notifies you of scheduled events or meetings 2. The system of claim 1, (Appendix 4) Equipped with a log section that records and allows viewing of logs of sent auto-reply messages and received emails 2. The system of claim 1, (Appendix 5) The time schedule setting unit, Sync with the user's calendar app to automatically update the schedule 2. The system of claim 1, (Appendix 6) The prohibition unit is Prevent automatic replies from being sent to emails containing specific keywords 2. The system of claim 1, (Appendix 7) The prohibition unit is Prevent automatic replies during specific user-specified hours 2. The system of claim 1, (Appendix 8) The notification unit is Tailor notifications based on the location of the event or meeting 4. The system of claim 3. (Appendix 9) The transfer unit is Automatically forward incoming emails to project teams if the content is relevant to a specific project 2. The system of claim 1, (Appendix 10) The custom message setting unit includes: Infer user sentiment and tailor custom message content based on the inferred user sentiment 2. The system of claim 1, (Appendix 11) The custom message setting unit includes: Analyze the user's past message history and suggest the best custom message 2. The system of claim 1, (Appendix 12) The custom message setting unit includes: Custom Messages When configured, messages can be tailored based on the user's current project or area of ​​interest. 2. The system of claim 1, (Appendix 13) The custom message setting unit includes: Infer user sentiment and prioritize custom messages based on the inferred user sentiment 2. The system of claim 1, (Appendix 14) The custom message setting unit includes: When setting custom messages, prioritize relevant messages by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 15) The custom message setting unit includes: Analyze users' social media activity to suggest relevant messages when setting custom messages 2. The system of claim 1, (Appendix 16) The time schedule setting unit, To estimate a user's emotion and adjust a time schedule setting based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The time schedule setting unit, When setting a time schedule, the system analyzes the user's past schedule history and suggests the optimal schedule. 2. The system of claim 1, (Appendix 18) The time schedule setting unit, Customize the schedule based on the user's current life situation and interests while setting the time schedule 2. The system of claim 1, (Appendix 19) The time schedule setting unit, To estimate a user's emotions and determine a priority of a time schedule based on the estimated user's emotions. 2. The system of claim 1, (Appendix 20) The time schedule setting unit, When setting time schedules, prioritize relevant schedules by considering the user's geographic location information 2. The system of claim 1, (Appendix 21) The time schedule setting unit, Analyze users' social media activity and suggest relevant schedules when setting time schedules 2. The system of claim 1, (Appendix 22) The prohibition unit is Inferring user sentiment and adjusting auto-reply prohibition settings based on the estimated user sentiment 2. The system of claim 1, (Appendix 23) The prohibition unit is When setting a ban, the system analyzes the user's past email history and suggests optimal ban settings. 2. The system of claim 1, (Appendix 24) The prohibition unit is Customize bans based on the user's current projects and interests when setting bans 2. The system of claim 1, (Appendix 25) The prohibition unit is To estimate a user's emotion and determine a ban priority of an automatic response based on the estimated user's emotion. 2. The system of claim 1, (Appendix 26) The prohibition unit is When setting bans, the geographic location of the user is taken into account to prioritize the most relevant bans 2. The system of claim 1, (Appendix 27) The prohibition unit is When setting a ban, analyze the user's social media activity and suggest relevant bans 2. The system of claim 1, (Appendix 28) The transfer unit is Infer user sentiment and adjust email forwarding settings based on the inferred user sentiment 2. The system of claim 1, (Appendix 29) The transfer unit is When setting up forwarding, the system analyzes the user's past email history and suggests optimal forwarding settings. 2. The system of claim 1, (Appendix 30) The transfer unit is Customize transfer settings based on the user's current projects and interests while setting up the transfer 2. The system of claim 1, (Appendix 31) The transfer unit is Infer user sentiment and prioritize email forwarding based on the inferred sentiment 2. The system of claim 1, (Appendix 32) The transfer unit is When setting up forwarding, the geographic location of the user is taken into consideration to prioritize the most relevant forwarding settings. 2. The system of claim 1, (Appendix 33) The transfer unit is When setting up forwarding, analyze the user's social media activity and suggest relevant forwarding settings 2. The system of claim 1, (Appendix 34) The signature portion is Infer user sentiment and tailor custom signature content based on the inferred user sentiment 2. The system of claim 1, (Appendix 35) The signature portion is When setting a signature, the system analyzes the user's past signature history and suggests the most suitable signature. 2. The system of claim 1, (Appendix 36) The signature portion is Customize your signature based on your current projects and interests while setting up your signature 2. The system of claim 1, (Appendix 37) The signature portion is Infer user sentiment and prioritize custom signatures based on the inferred user sentiment 2. The system of claim 1, (Appendix 38) The signature portion is When setting a signature, the system takes into account the user's geographic location information and prioritizes the most relevant signature. 2. The system of claim 1, (Appendix 39) The signature portion is Analyzes your social media activity and suggests relevant signatures when you set up a signature 2. The system of claim 1, (Appendix 40) The classification unit includes: Infer user sentiment and adjust email classification criteria based on the inferred user sentiment 3. The system of claim 2, (Appendix 41) The classification unit includes: When setting classification, the system analyzes the user's past email history to suggest optimal classification criteria. 3. The system of claim 2, (Appendix 42) The classification unit includes: Customize classification criteria based on your current projects or interests when setting up classifications 3. The system of claim 2, (Appendix 43) The classification unit includes: Inferring user sentiment and determining email classification priorities based on the inferred user sentiment 3. The system of claim 2, (Appendix 44) The classification unit includes: When setting classifications, the system takes into account the user's geographic location to prioritize classification criteria that are more relevant to them. 3. The system of claim 2, (Appendix 45) The classification unit includes: During classification, we analyze users' social media activity and suggest relevant classification criteria. 3. The system of claim 2, (Appendix 46) The notification unit is Estimate user emotions and adjust notification content based on the estimated user emotions 4. The system of claim 3. (Appendix 47) The notification unit is When setting notifications, the system analyzes the user's past schedule history to suggest optimal notification content. 4. The system of claim 3. (Appendix 48) The notification unit is When setting up notifications, customize the content of notifications based on the user's current life situation and interests 4. The system of claim 3. (Appendix 49) The notification unit is Infer user emotions and prioritize notifications based on the inferred user emotions 4. The system of claim 3. (Appendix 50) The notification unit is When setting notifications, the app takes into account the user's geographic location to prioritize more relevant notifications. 4. The system of claim 3. (Appendix 51) The notification unit is When you set up notifications, we analyze your social media activity to suggest relevant notification content. 4. The system of claim 3. (Appendix 52) The recording unit is Inferring a user's emotions and adjusting the recording content based on the estimated user's emotions 5. The system of claim 4. (Appendix 53) The recording unit is When setting up a record, the system analyzes the user's past email history and suggests the most appropriate record content. 5. The system of claim 4. (Appendix 54) The recording unit is Customize recording content based on the user's current projects and interests when setting up a recording 5. The system of claim 4. (Appendix 55) The recording unit is Inferring user emotions and prioritizing records based on the estimated user emotions 5. The system of claim 4. (Appendix 56) The recording unit is When setting up a recording, the system takes into account the user's geographic location to prioritize recording content that is more relevant to them 5. The system of claim 4. (Appendix 57) The recording unit is Analyze your social media activity and suggest relevant recording content when setting up a recording 5. The system of claim 4. [Explanation of symbols]

[0231] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A custom message setting section for setting a user-specific message to be included in the auto-response email; A time schedule setting section for setting different auto-reply messages according to the day of the week and the time of day; A prohibition section for prohibiting automatic responses to emails from specific contacts; a forwarding unit for forwarding received e-mail to a preset destination; A signature section for adding a custom signature to auto-reply emails. A system characterized by:

2. Equipped with a filter section that assigns different automatic responses to incoming emails depending on their category 2. The system of claim 1 .

3. Has a notification section that notifies you of scheduled events or meetings 2. The system of claim 1 .

4. Equipped with a log section that records and allows viewing of logs of sent auto-reply messages and received emails 2. The system of claim 1 .

5. The time schedule setting unit, Sync with the user's calendar app to automatically update the schedule 2. The system of claim 1 .

6. The prohibition unit is Prevent automatic replies from being sent to emails containing specific keywords 2. The system of claim 1 .

7. The prohibition unit is Prevent automatic replies during specific user-specified hours 2. The system of claim 1 .

8. The notification unit is Tailor notifications based on the location of the event or meeting 4. The system of claim 3.

9. The transfer unit is Automatically forward incoming emails to project teams if the content is relevant to a specific project 2. The system of claim 1 .

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