system

Abstract

We provide a system that automates email creation and processing, significantly improving work efficiency. [Solution] A system including means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for obtaining a new email from a receiving mail server, means for analyzing the content of the obtained email using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is determined to be necessary, means for displaying a preview of the generated reply email to the user, and means for sending a reply email based on a send instruction from the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the modern business environment, a lot of time is spent on creating and processing emails. There is a need to streamline this process and reduce the workload of the person in charge. In particular, the time required for creating standard emails, analyzing the content of received emails, and making decisions on replies requires a large amount of man-hours when done manually. This causes problems such as difficulty in responding quickly and concentrating on other important tasks. Therefore, there is a need for a system that automates email creation and processing of received emails and significantly improves work efficiency.

Means for Solving the Problems

[0005] The present invention solves the aforementioned problems with a system that includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for obtaining a new email from a receiving mail server, means for analyzing the obtained email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is determined to be necessary, means for displaying a preview of the generated reply email to the user, and means for sending a reply email based on a send instruction from the user. Furthermore, these processes can be made even more efficient by further including means for obtaining a template from a database to automate the selection of an email template, and means for keyword extraction and natural language processing in the analysis of the email content.

[0006] "Means for receiving data input from users" refers to an interface or method for users to input necessary information into a system.

[0007] "A means of selecting an email template and generating the body text based on data input" refers to a method or function that automatically selects an appropriate email template based on information entered by the user and uses that template to create the email body text.

[0008] "A means of displaying a preview of the generated email body to the user" refers to a function that visually displays the automatically generated email body to the user, allowing them to check and correct it.

[0009] "Means of sending emails based on user instructions" refers to a method or system for actually sending an email to a recipient when a user gives a sending instruction.

[0010] "Means of retrieving new emails from an incoming mail server" refers to a method or function for importing newly received emails from an external mail server into the system.

[0011] "Methods for analyzing acquired email content using AI" refers to a function that uses artificial intelligence (AI) to understand the content of acquired emails and extract specific information or keywords.

[0012] "Means for determining whether a reply is necessary based on analysis results" refers to a method or function that automatically determines whether a reply is necessary based on the content of an email analyzed by AI.

[0013] "Means for automatically generating reply emails when a reply is deemed necessary" refers to a method or function for automatically creating an appropriate reply email when a reply is deemed necessary.

[0014] "A means of displaying a generated reply email to the user in preview" refers to a function that displays an automatically generated reply email to the user so that they can check its contents.

[0015] "Means for sending reply emails based on user instructions" refers to a method or system for actually sending a reply email when a user gives a sending instruction.

[0016] "Means of retrieving templates from a database" refers to a method or function for searching for necessary email templates from a database and importing them into the system.

[0017] "Keyword extraction and natural language processing means" refers to a method or function for extracting important keywords from email content and analyzing the content using natural language processing techniques. [Brief explanation of the drawing]

[0018] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] [[ID=3�]]It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0019] 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.

[0020] First, the language used in the following description will be explained.

[0021] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0022] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0023] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0024] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0026] [First Embodiment]

[0027] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0028] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0029] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0031] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0034] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0037] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0038] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0039] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system allows users to create and send emails with minimal effort, and to analyze the content of received emails and automatically send replies.

[0040] First, the user enters data to create an email. The user enters the necessary information (e.g., customer name, order number, date, etc.) through their terminal. The server receives this input data and selects an appropriate email template from its database. Based on the selected template, the server generates the email body and displays a preview of this generated email body to the user. The user can review this preview and make corrections as needed. After the user issues a send command, the server sends the email.

[0041] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to extract specific keywords and phrases from the email body. Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. This generated reply email is also displayed to the user as a preview, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email.

[0042] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the order number and customer name. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0043] On the other hand, when processing incoming emails, for example, if an email containing an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[0044] In this way, this system automates the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[0048] Step 2:

[0049] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[0050] Step 3:

[0051] The server automatically generates the email body by embedding the user's input data into the selected template.

[0052] Step 4:

[0053] The server sends the generated email body to the terminal and displays a preview to the user. The user reviews the email content and makes corrections as needed.

[0054] Step 5:

[0055] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[0056] Step 6:

[0057] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[0058] ---

[0059] Step 1:

[0060] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[0061] Step 2:

[0062] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[0063] Step 3:

[0064] The server passes the content of the received email to the AI ​​(artificial intelligence), which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[0065] Step 4:

[0066] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[0067] Step 5:

[0068] The server sends the generated reply email to the terminal and displays a preview for the user. The user reviews the reply and makes corrections as needed.

[0069] Step 6:

[0070] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[0071] Step 7:

[0072] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[0073] (Example 1)

[0074] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0075] Traditional email creation and processing systems required users to perform many tasks manually, resulting in a significant burden of time and effort. Furthermore, the ability to accurately analyze incoming emails and automatically generate replies was insufficient. As a result, users had to dedicate a considerable amount of time to email processing, hindering their ability to work efficiently.

[0076] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0077] In this invention, the server includes means for receiving electronic information input from a user, means for selecting a communication template based on the electronic information input and generating the body text, and means for displaying the generated communication body text as a preview to the user. This enables the user to create and send emails accurately and quickly with minimal operation. The server also includes means for acquiring new communications from a receiving communication server, means for analyzing the acquired communication content using a generation AI model, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply communication if a reply is determined to be necessary, and means for displaying the generated reply communication as a preview to the user. This enables efficient and accurate content analysis of received emails and automatic replies, significantly reducing the burden on the user.

[0078] A "user" is an individual or organization that operates the system, inputs electronic information, or issues transmission instructions.

[0079] "Electronic data entry" refers to data that a user provides to the system using a terminal (e.g., order number, customer name, order date).

[0080] A "communication template" is a pre-prepared document template with a specified format and content, used to generate the body of an email.

[0081] "Preview display" is a function that displays generated communications or email body text on the screen for the user to check and edit.

[0082] A "transmit instruction" is an operation or command that a user makes to a system in order to send an electronic message.

[0083] An "electronic message" is a digital message that a user sends to another individual or organization through a system.

[0084] A "receiving communication server" is a server that stores newly received communications and makes them accessible to the system.

[0085] "Acquisition" refers to the operation in which a server retrieves new communications from a receiving communication server and makes them available for use within the system.

[0086] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and perform a specific task (e.g., natural language processing).

[0087] "Analysis" refers to the process of processing acquired communication content using a generative AI model to extract specific keywords or patterns.

[0088] "Determining whether a reply is necessary" is the process by which the system determines, based on the analysis results, whether a reply is required.

[0089] A "reply message" is an electronic message automatically generated by the server when analysis determines that a reply is necessary.

[0090] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system allows users to create and send emails with minimal effort, and to analyze the content of received emails and automatically send replies.

[0091] First, the user uses a terminal to input data to compose an email. The user accesses the input form on the terminal and enters the necessary information (e.g., order number, customer name, order date, etc.). The server receives this input data and selects an appropriate communication template from the database. Specifically, a common relational database (e.g., MySQL® or PostgreSQL) can be used. Based on the selected template, the server generates the email body. A common template engine (e.g., Handlebars.js or Mustache) can be used for template processing for generation.

[0092] The generated email body is displayed as a preview on the user's device. The user can review this preview and make corrections as needed. Once corrections are complete, the user clicks the send button to send a transmission command to the server. The server receives this transmission command and sends the email using the SMTP protocol. As a concrete example, consider the case where a user creates an "order confirmation email." The user enters the order number "12345," customer name "Customer Name," and order date "October 1, 2023" through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the customer name and order number. The generated email is displayed as a preview to the user, and once the user reviews it and clicks the send button, the server sends the email to the customer.

[0093] Next, the system also automates the processing of incoming emails. The server periodically retrieves new communications from the receiving communication server. The retrieved emails are analyzed on the server by a generative AI model. For analysis, natural language processing models such as GPT-3® and BERT can be used. These models can run on cloud platforms such as Amazon Web Services (AWS®) and Google Cloud Platform (GCP).

[0094] The AI ​​model extracts specific keywords and phrases from the email body and determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email is also displayed as a preview on the user's device, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email. As a concrete example, consider the case where an "order cancellation request" email is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." If the AI ​​model determines that a reply is necessary based on its analysis results, the server automatically generates a reply email stating "Cancellation request received." This reply email is displayed as a preview to the user, and once the user reviews it and issues a send command, the server sends the reply email to the customer.

[0095] Examples of prompt messages include: "Please enter the information needed to create an order confirmation email. Please specify the order number, customer name, and order date," and "We will analyze newly received emails and generate an automatic reply email if necessary. Please provide the content of the email to be analyzed."

[0096] Thus, the system of the present invention streamlines the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0097] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0098] System program processing flow

[0099] Step 1:

[0100] The user uses the terminal to enter the necessary data to compose an email. The user accesses the input form on the terminal and enters information such as order number, customer name, and order date. This generates the input data.

[0101] Input: Data entered by the user into the input form on the device (e.g., order number, customer name, order date)

[0102] Output: Input data sent to the system

[0103] Step 2:

[0104] The server receives the input data provided by the user. Based on the received data, the server accesses the database and selects the appropriate communication template.

[0105] Input: Input data sent by the user from their device.

[0106] Output: Selection of an appropriate communication template (e.g., "Order Confirmation Email" template)

[0107] Step 3:

[0108] The server generates the email body based on the selected communication template. During generation, it performs a process of embedding input data. Specifically, customer names, order numbers, etc., are embedded in the template's placeholders.

[0109] Input: Selected communication template, user input data

[0110] Output: Generated email body (Example: "Dear Customer Name, we have confirmed your order for order number 12345")

[0111] Step 4:

[0112] The generated email body is displayed as a preview on the user's device. The user reviews this preview and makes corrections as needed.

[0113] Input: Generated email body

[0114] Output: Email body modified by the user (if necessary)

[0115] Step 5:

[0116] When a user issues a send command, the server receives this command and sends the email. The server uses the SMTP protocol to deliver the email to the recipient.

[0117] Input: User's sending instructions, revised email body

[0118] Output: Email delivered to the recipient

[0119] Step 6:

[0120] The server periodically retrieves new communications from the receiving server. This is done using the IMAP protocol.

[0121] Input: New communication from the receiving communication server

[0122] Output: New communications acquired

[0123] Step 7:

[0124] The server analyzes the acquired communication content using a generation AI model. Natural language processing techniques are used for the analysis to extract specific keywords and phrases. For example, keywords such as "cancel" and "request" are extracted.

[0125] Input: Newly acquired communications

[0126] Output: Analysis results (specific keywords or phrases)

[0127] Step 8:

[0128] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email.

[0129] Input: Analysis results

[0130] Output: Determination of whether a reply is necessary, and a reply email generated if necessary.

[0131] Step 9:

[0132] The generated reply email is displayed as a preview on the user's device. The user reviews the content and makes corrections as needed.

[0133] Input: Generated reply email

[0134] Output: User-modified reply email (if necessary)

[0135] Step 10:

[0136] When a user issues a send command, the server receives this command and sends a reply email. The server uses the SMTP protocol to deliver the reply email to the recipient.

[0137] Input: User's submission instructions, revised reply email.

[0138] Output: Reply email delivered to the recipient

[0139] In this way, the user, terminal, and server cooperate at each step to efficiently and accurately create and process emails.

[0140] (Application Example 1)

[0141] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0142] In modern content delivery services, streamlining user support and email marketing is a critical challenge. Manual email creation and sending is time-consuming and labor-intensive, and while quick responses are required, delays can occur due to time differences and staff shortages. Furthermore, immediate responses to user inquiries are essential for improving the user experience, and automation is needed in this area as well. To solve these challenges, an automated system for email creation and sending / receiving is required.

[0143] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0144] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving mail server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for automating user support related to content distribution, means for generating the reply email body using a generation AI model, and means for creating reply content based on prompt text. This enables efficient and rapid response in both user support and email newsletter distribution.

[0145] "Means for receiving data input from users" refers to providing an interface for users to input necessary information into the system.

[0146] "A method for selecting an email template and generating the email body" refers to a process that selects an appropriate email template from a database based on the input data and automatically generates the email body using that template.

[0147] "A means of displaying a preview of the generated email body to the user" refers to a method of displaying a preview of the automatically generated email content so that the user can check and correct it before sending.

[0148] "A means of sending emails based on instructions from the user" refers to a method of receiving instructions from the user to send an email after they have reviewed and corrected it, and then sending the email based on those instructions.

[0149] "Methods for retrieving new emails from the receiving mail server" refers to the process of periodically retrieving new emails that have arrived on the receiving mail server.

[0150] "Methods for analyzing acquired email content using AI" refer to methods for analyzing received emails using artificial intelligence technology to understand their content.

[0151] "A means of determining whether a reply is necessary based on analysis results" refers to determining whether a reply is necessary based on the content of the email analyzed by AI.

[0152] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a system that automatically generates an appropriate reply email when a reply is deemed necessary.

[0153] "A means of displaying a preview of the generated reply email to the user" refers to a method of displaying a preview of the automatically generated reply email so that the user can review and modify it.

[0154] "A means of sending a reply email based on instructions from the user" refers to receiving instructions from the user to send a reply email after they have reviewed and corrected it, and then sending the reply email based on those instructions.

[0155] "Means for automating user support related to content distribution" refers to a system that automates support for user inquiries that arise in content distribution services.

[0156] "A method for generating reply email content using a generative AI model" refers to a method that uses an artificial intelligence model to automatically generate appropriate reply content for received emails.

[0157] "Means for creating a response based on a prompt" refers to a method by which an artificial intelligence model creates the most appropriate response for the user based on the prompt (input) text.

[0158] This invention provides a system that streamlines the creation, sending, and automatic analysis and replying to of emails related to a user's content delivery service. This system enables users to send and receive emails quickly and accurately with minimal effort.

[0159] This system mainly consists of the following elements:

[0160] 1. Means by which users input data: Users input necessary information (e.g., customer name, campaign information, inquiry details, etc.) through a terminal. Web forms, dedicated applications, etc., are used as the data input interface.

[0161] 2. Method for selecting an email template and generating the email body: The server receives data entered by the user and selects an appropriate email template from the database. Based on this template, the server generates the email body.

[0162] 3. Means for displaying a preview of the generated email body to the user: The generated email body will be displayed as a preview on the user's terminal. The user can review it and make corrections as needed.

[0163] 4. Means of sending emails based on user instructions: When a user issues a send instruction, the server sends the email to the specified recipient.

[0164] 5. Means of retrieving new emails from the incoming mail server: The server has a function to periodically retrieve new emails from the incoming mail server.

[0165] 6. Method for analyzing acquired email content using AI: Acquired emails are analyzed using a generative AI model implemented on the server. This model uses natural language processing technology to extract specific keywords and phrases from the email content.

[0166] 7. Means for determining whether a reply is necessary based on the analysis results: The server determines whether a reply is necessary based on the analysis results.

[0167] 8. Means for automatically generating a reply email when a reply is deemed necessary: ​​When a reply is deemed necessary, the server will automatically generate a reply email using a generation AI model based on the prompt message.

[0168] 9. Means for displaying generated reply emails to the user as a preview: Automatically generated reply emails are displayed as a preview on the user's terminal, allowing the user to review and modify the content.

[0169] 10. Means for sending reply emails based on user instructions: When a user issues a send instruction, the server sends a reply email to the specified recipient.

[0170] This system uses the following hardware and software:

[0171] Hardware: Servers (mail servers, AI computing servers), user terminals (PCs, smartphones, tablets)

[0172] Software: Python execution environment, open-source email libraries (smtplib, imaplib), generative AI model (OpenAI® API)

[0173] As a concrete example, consider a scenario where a content distribution service administrator sends an email to notify customers of new content. The administrator inputs the new content information via a terminal, and the system selects an appropriate email template based on that input and automatically generates the email body. The generated email body is previewed by the administrator, and after confirmation, it is sent in bulk.

[0174] Furthermore, the AI ​​model analyzes user inquiry emails and generates appropriate replies. An example of a prompt message is as follows:

[0175] text

[0176] Subject: Regarding new service features

[0177] Text: Please provide information about the new features of the service.

[0178] Please compose your reply email based on the above information.

[0179] This allows users to send and receive emails quickly and efficiently, significantly improving the operational efficiency of content delivery services.

[0180] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0181] Step 1:

[0182] Users enter data through their devices.

[0183] Users use their devices (PCs, smartphones, tablets) to input necessary data such as new content information, customer information, and inquiries. The entered data is then sent from the device to the server.

[0184] Step 2:

[0185] The server selects an email template and generates the email body.

[0186] The server selects an appropriate email template from the database based on the received data. It then generates the email body by embedding the user's entered information into the selected template. This process involves retrieving data from the database and embedding it into the template.

[0187] Step 3:

[0188] The server displays a preview of the generated email body to the user.

[0189] The server sends the generated email body to the user's terminal for preview. The user can review and edit the email content before sending.

[0190] Step 4:

[0191] The user issues a send command, and the server sends the email.

[0192] Once the user reviews the email content and issues a send command, the server sends the email to the specified recipient. The user is then notified of the sending result.

[0193] Step 5:

[0194] The server retrieves new emails from the incoming mail server.

[0195] The server retrieves new emails from the receiving mail server at regular intervals. This process involves accessing the receiving mail server and downloading new emails.

[0196] Step 6:

[0197] The server uses AI to analyze the email content it has retrieved.

[0198] The server inputs the retrieved email content into a generative AI model, which then analyzes the email's content. The generative AI model uses natural language processing techniques to extract keywords and important phrases from the email body. Based on these analysis results, it then performs the following processing.

[0199] Step 7:

[0200] The server will determine whether a reply is necessary based on the analysis results.

[0201] The server determines whether a reply is necessary based on the AI ​​analysis results. For example, if keywords such as "cancel" or "request" are included, it will determine that a reply is necessary.

[0202] Step 8:

[0203] The server automatically generates a reply email.

[0204] If a reply is deemed necessary, the server automatically generates a reply email using a generation AI model based on the specified prompt text. This process involves inputting the prompt text and automatically generating the reply email.

[0205] Step 9:

[0206] The server displays a preview of the generated reply email to the user.

[0207] The server sends the generated reply email to the user's terminal for preview. The user can review the reply email content and make corrections as needed.

[0208] Step 10:

[0209] The user issues a send command, and the server sends a reply email.

[0210] Once the user reviews the reply email and issues a send command, the server sends the reply email to the specified recipient. The server then notifies the user of the sending result.

[0211] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0212] This invention relates to a system that streamlines the automated processing of user-generated and received emails by incorporating an emotion engine to adapt the content and tone of emails to the user's emotions. This system enables the generation and replies of emails that take the user's emotions into consideration.

[0213] First, the user enters the necessary data (e.g., customer name, order number, date, etc.) through their terminal. This data is used to create the email. The server receives the data entered by the user and selects an appropriate email template from its database based on that data. Based on the selected template, the server automatically generates the email body.

[0214] Because an emotion engine is implemented, the server analyzes the user's emotions from their input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email body. For example, if the user is feeling grateful, the server will add expressions of gratitude to the email body. Also, if the user is feeling anxious, the server will generate reassuring sentences.

[0215] The generated email body is previewed on the user's terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[0216] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, an emotion engine analyzes the user's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer is making a complaint, the emotion engine uses the analysis results to generate a reply email expressing an apology.

[0217] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also previewed on the user's terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server actually sends the reply email using SMTP.

[0218] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0219] On the other hand, consider the processing of incoming emails, for example, when an email requesting order cancellation is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the emotion engine analyzes the customer's emotions, and if the customer is expressing disappointment or anger, it generates a reply appropriate to that emotion (e.g., an apology or explanation). Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[0220] In this way, this system automates the creation and processing of user emails, and generates and replies emails that take user emotions into consideration, thereby significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[0221] The following describes the processing flow.

[0222] Step 1:

[0223] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[0224] Step 2:

[0225] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[0226] Step 3:

[0227] The server automatically generates the email body by embedding the user's input data into the selected template.

[0228] Step 4:

[0229] Before the server displays a preview of the email body on the user's terminal, it uses an emotion engine to analyze the user's input data to determine their emotions.

[0230] Step 5:

[0231] The server dynamically adjusts the tone and content of the generated email body based on the analysis results of the emotion engine. For example, if the user has the emotion of "gratitude," words reflecting that emotion will be added to the body of the email.

[0232] Step 6:

[0233] The server displays a preview of the edited email body on the user's device. The user reviews the preview and makes corrections as needed.

[0234] Step 7:

[0235] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[0236] Step 8:

[0237] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[0238] ---

[0239] Step 1:

[0240] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[0241] Step 2:

[0242] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[0243] Step 3:

[0244] The server passes the content of the received email to the AI, which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[0245] Step 4:

[0246] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[0247] Step 5:

[0248] The server passes the generated reply email to the emotion engine, which analyzes the sender's emotions based on the content of the received email.

[0249] Step 6:

[0250] The server adjusts the tone and content of the reply email based on the analyzed sentiment. For example, if the recipient has expressed dissatisfaction, it will add an apology or explanation.

[0251] Step 7:

[0252] The server displays a preview of the adjusted reply email on the user's device, allowing the user to review the content. The user reviews the content and makes corrections as needed.

[0253] Step 8:

[0254] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[0255] Step 9:

[0256] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[0257] (Example 2)

[0258] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0259] Traditional email systems required users to manually compose email bodies and adjust the content according to their emotions, which was time-consuming and laborious. Furthermore, processing incoming emails was also done manually, resulting in long response times and a significant burden on users.

[0260] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0261] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving email server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for analyzing the user's emotions using an emotion engine and dynamically adjusting the tone and content of the email body based on the analysis results, and means for analyzing the sender's emotions from the content of the received email using an emotion engine and dynamically adjusting the content of the reply email based on the analysis results. This enables the automatic generation and sending of emails that take the user's emotions into consideration, resulting in a significant reduction in time and effort.

[0262] A "user" is a person or group that uses the system to create and manage emails.

[0263] "Means for receiving data input" refers to interfaces or functions for receiving user input data (e.g., customer name, order number, date, etc.).

[0264] "Methods for selecting email templates" refers to algorithms or processes for automatically selecting appropriate email templates from a database.

[0265] "Methods for generating the body text" refers to the process or technology of creating the email body by integrating a selected template with data entered by the user.

[0266] "Means of previewing" refers to interfaces or functions that display the generated email content to the user in a format that allows them to review it.

[0267] "Means of sending emails based on send instructions" refers to protocols and functions for sending emails to designated recipients in response to a user's send instruction.

[0268] "Methods for retrieving new emails from the receiving mail server" refers to the functions and protocols used to periodically retrieve new emails from the receiving mail server.

[0269] "Methods of analysis using AI" refers to the process and technology of analyzing email content obtained using artificial intelligence and extracting important information and emotions.

[0270] "Means for determining whether a reply is necessary" refers to algorithms or processes that determine whether a reply is necessary to an incoming email based on the results of AI analysis.

[0271] "Methods for automatically generating reply emails" refers to processes and technologies that automatically generate reply emails using templates or sentiment engines when a reply is deemed necessary.

[0272] An "emotion engine" refers to algorithms and technologies that analyze the emotions of users and received emails, and dynamically adjust the content and tone of emails based on the analysis results.

[0273] "Dynamic adjustment methods" refer to functions or processes that automatically change the wording and expression of email content based on the results of sentiment analysis.

[0274] This invention is a system for streamlining the automated processing of user-generated and received emails. By combining this system with an emotion engine, it is possible to adapt the content and tone of emails to the user's emotions, minimizing user interaction while enabling fast and accurate email sending and receiving.

[0275] The user enters the necessary data (e.g., customer name, order number, date, etc.) through the terminal. The terminal sends the entered data to the server. The server receives the data sent by the user and selects an appropriate email template from the database based on that data.

[0276] The server automatically generates email content based on the selected template. Because an emotion engine is integrated, the server performs sentiment analysis on the user's input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email content. For example, if the user expresses gratitude, it adds expressions of gratitude to the email content. If the user is feeling anxious, it generates reassuring sentences. Specifically, the emotion engine utilizes IBM Watson® Emotion Analysis, among others.

[0277] The generated email body is previewed on the terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button, the server uses SMTP to send the email to the recipient.

[0278] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the content of these retrieved emails is analyzed by AI (for example, the Google Cloud Natural Language API). The AI ​​analyzes the email body and extracts specific keywords and phrases. Furthermore, an emotion engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the reply content and tone based on the results. If a customer is making a complaint, the emotion engine uses the analysis results to generate an apologetic reply email.

[0279] Based on the analysis results, the server determines whether a reply is necessary, and if so, it automatically generates an appropriate reply email. This reply email is also previewed on the terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the transmission, the server sends the reply email using SMTP.

[0280] As a concrete example, when a user creates an order confirmation email, the user enters information such as the order number, customer name, and order date through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates an email body with the order number and customer name embedded. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0281] Examples of prompt statements include the following:

[0282] "Customer Name: Taro Tanaka", "Order Number: 123456", "Order Date: 2023-10-01"

[0283] In this way, the present invention automates the user's email creation and received email processing, and generates and replies to emails considering the user's emotions, thereby significantly reducing time and effort. The user can send and receive emails quickly and accurately with minimal operations, and communication with customers can also proceed smoothly.

[0284] The flow of the specific process in Example 2 will be described using FIG. 13.

[0285] Step 1:

[0286] The user inputs data for email creation into the terminal.

[0287] The user inputs necessary information (e.g., customer name, order number, date, etc.) into the input form of the terminal. The input data is saved in the terminal.

[0288] Input: Information such as customer name, order number, date, etc.

[0289] Output: The input data is saved in the terminal.

[0290] Specific operation: The user inputs data into the form field and clicks the send button.

[0291] Step 2:

[0292] The terminal sends the input data to the server. <​​​​​​​​​​​

[0296] Specific operation: The program of the terminal forms a request using the input data and sends a POST request to the server via HTTPS.

[0297] Step 3:

[0298] The server selects an appropriate email template.

[0299] Based on the received data, the server selects an appropriate email template from the database. A conditional matching algorithm is used for template selection.

[0300] Input: Input data sent to the server

[0301] Output: An appropriate email template is selected

[0302] Specific operation: The server's algorithm queries the database based on the input data and retrieves the corresponding template.

[0303] Step 4:

[0304] The server analyzes the user's emotion using an emotion engine.

[0305] The server inputs the input data from the user into the emotion engine and obtains the result. The emotion engine uses, for example, IBM Watson Emotion Analysis.

[0306] Input: Input data from the user

[0307] Output: Emotion analysis result

[0308] ​​​​​​

[0310] The server generates and adjusts the email body based on the analysis results.

[0311] The server dynamically generates the email body by incorporating the analysis results into the selected email template.

[0312] Input: Email template, sentiment analysis results

[0313] Output: Generated email body

[0314] Specific operation: The server integrates the template and sentiment analysis results and executes code to dynamically generate the email body.

[0315] Step 6:

[0316] The email body generated by the server is displayed as a preview on the terminal.

[0317] The generated email body will be displayed on the terminal's user interface.

[0318] Input: Generated email body

[0319] Output: The email body is displayed on the user's device.

[0320] Specific operation: The server sends the email body to the terminal, and the terminal receives it and executes code to display it on the screen.

[0321] Step 7:

[0322] The user reviews and edits the email content and then issues a send command.

[0323] The user reviews the previewed email content and makes any necessary corrections. Then, they click the send button to initiate the sending process.

[0324] Input: Previewed email body

[0325] Output: Email body after user review and modification, and sending instructions.

[0326] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[0327] Step 8:

[0328] The server sends the email via SMTP.

[0329] The server uses the SMTP protocol to send emails. A log of the sent emails is also saved.

[0330] Input: Confirmed and corrected email body, sending instructions

[0331] Output: The email is sent to the recipient, and the sending log is saved.

[0332] Specific operation: The server's SMTP client executes code to send an email to the specified recipient's address.

[0333] Step 9:

[0334] The server periodically retrieves new emails from the incoming mail server.

[0335] The server retrieves new emails from the incoming mail server at regular intervals. This interval is configurable.

[0336] Input: None (triggered by time interval)

[0337] Output: New emails are retrieved to the server.

[0338] Specific operation: The server retrieves new emails from the mail server using the POP3 / IMAP protocol.

[0339] Step 10:

[0340] The server uses AI to analyze the content of incoming emails.

[0341] The server passes the content of the received email to the AI ​​and retrieves the analysis results. The AI ​​uses the Google Cloud Natural Language API, among others.

[0342] Input: Newly received emails

[0343] Output: Analysis results of received emails

[0344] Specific operation: The server sends the received email to the AI ​​analysis engine and executes code to receive the analysis results.

[0345] Step 11:

[0346] The server will determine whether a response is needed based on the analysis results.

[0347] Based on the analysis results, the server executes an algorithm to determine whether a reply is necessary.

[0348] Input: Analysis results of received emails

[0349] Output: Result of the determination of whether a reply is needed.

[0350] Specific operation: The server's algorithm analyzes the results and executes code to determine whether a reply is necessary.

[0351] Step 12:

[0352] The server automatically generates a reply email and displays a preview on the device.

[0353] If a reply is deemed necessary, the server automatically generates a reply email and displays a preview on the user's device. The sentiment engine is also used at this stage.

[0354] Input: Email data if a reply is deemed necessary.

[0355] Output: Generated automated reply email, preview display

[0356] Specific operation: The server generates a reply email based on the reply email template, reflecting the sentiment analysis results, and executes code to send it to the terminal and display a preview.

[0357] Step 13:

[0358] The user reviews and edits the reply email and then issues the send command.

[0359] The user reviews the previewed reply email and makes any necessary corrections. Then, they click the send button to initiate submission.

[0360] Input: Previewed reply email

[0361] Output: Reply email after user confirmation and correction, and sending instructions.

[0362] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[0363] Step 14:

[0364] The server sends a reply email via SMTP.

[0365] The server uses the SMTP protocol to send a reply email. A log of the sent email is also saved.

[0366] Input: Confirmation and correction confirmation email, sending instructions

[0367] Output: A reply email is sent to the recipient, and a sending log is saved.

[0368] Specific operation: The server's SMTP client executes code to send a reply email to the specified recipient's address.

[0369] (Application Example 2)

[0370] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0371] In modern brick-and-mortar stores, efficient and effective communication with customers is essential. However, many email correspondences are time-consuming and labor-intensive, and creating replies that take emotions into account is particularly difficult. Conventional automated email generation systems struggle to create emails that reflect the emotions of users and customers, resulting in decreased customer satisfaction and reduced efficiency in responses.

[0372] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0373] In this invention, the server includes means for analyzing the user's emotions and dynamically adjusting the tone and content of the email body accordingly; means for extracting keywords from the received email content and generating a reply email based on those keywords; and means for displaying a preview of the generated email body to the user. This enables the automatic generation and sending of emails that reflect the emotions of users and customers, facilitating smoother communication with customers.

[0374] "Means for receiving data input from users" refers to an interface that imports information entered by users (e.g., customer name, order number, etc.) into the system.

[0375] "A means of selecting an email template and generating the body text based on data input" refers to a function that selects an appropriate email template from a database based on the entered data and automatically creates the body text of the email based on that template.

[0376] "A means of displaying a preview of the generated email body to the user" refers to a function that displays the content of the email generated by the system to the user in advance, allowing the user to check and modify the content.

[0377] "A means of sending emails based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user gives a sending instruction.

[0378] "Method for retrieving new emails from the receiving mail server" refers to a function that periodically retrieves newly received emails from the mail server.

[0379] "Methods for analyzing acquired email content using artificial intelligence" refers to a function that uses artificial intelligence to analyze the content of received emails and extract important keywords and sentiments.

[0380] "Means for determining whether a reply is necessary based on analysis results" refers to a function that automatically determines whether a reply is necessary based on the analyzed data.

[0381] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a function in which the system automatically creates an appropriate reply email when a reply is deemed necessary.

[0382] "A means of displaying a preview of the generated reply email to the user" refers to a function that allows the system to display the automatically generated reply email to the user in advance, enabling them to review and modify it.

[0383] "A means of sending a reply email based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user instructs the system to send a reply email.

[0384] "A means of analyzing user emotions and dynamically adjusting the tone and content of emails accordingly" refers to a function that analyzes user emotions from input data and automatically changes the tone and content of emails accordingly.

[0385] "A means of extracting keywords from the content of a received email and generating a reply email based on those keywords" refers to a function that extracts important keywords from a received email and automatically creates an appropriate reply based on those keywords.

[0386] This invention is a system that selects an email template based on data input from the user, generates the email body, and adjusts the content and tone of the email after analyzing the user's emotions. This system operates in cooperation with three parties: the server, the terminal, and the user.

[0387] First, this system provides an interface that allows users to input data via a terminal. The data entered by the user (e.g., customer name, order number, date, etc.) is sent to the server. The server receives this data, selects an appropriate email template from the database, and generates the email body based on the selected template.

[0388] The server is equipped with an emotion analysis engine that analyzes the user's emotions from their input data. Based on this analysis, the server dynamically adjusts the tone and content of the generated email body. For example, if the user is feeling grateful, the server adds expressions of gratitude to the email body. If the user is feeling anxious, the server generates reassuring sentences.

[0389] The generated email body is displayed as a preview on the terminal. The user reviews the email content and makes any necessary corrections. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[0390] Next, let's discuss the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server. The content of these retrieved emails is analyzed by artificial intelligence installed on the server. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, a sentiment analysis engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer sends a complaint, the sentiment engine uses the analysis results to generate a reply email expressing an apology.

[0391] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also displayed as a preview on the terminal, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server sends the reply email using SMTP.

[0392] Hardware and software to be used

[0393] Hardware: Smartphones, servers

[0394] Software: Python, smtplib (SMTP library), sentiment analysis engine (e.g., IBM Watson), natural language processing library (e.g., Spacy)

[0395] Specific example

[0396] Specific example of email generation: When a user creates an "order confirmation email," they input the order number, customer name, order date, etc., through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The sentiment analysis engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body.

[0397] Specific example of processing incoming emails: When an email with an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the sentiment analysis engine analyzes the customer's emotions, and if the customer is showing disappointment or anger, for example, it generates a reply appropriate to that emotion (e.g., an apology or explanation).

[0398] Example of a prompt

[0399] Please generate an email message for when a customer cancels an order. Assume the customer is angry about the cancellation.

[0400] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0401] Step 1:

[0402] The user enters data (e.g., customer name, order number, order date, etc.) through the terminal. The terminal sends the data entered by the user to the server. The entered data becomes the basic information used to generate emails.

[0403] Step 2:

[0404] Based on the data received by the server, it selects an appropriate email template from the database. The database contains various email templates, and the server extracts the template that matches the specified criteria. The email body is automatically generated by embedding the user's entered data into the selected template.

[0405] Step 3:

[0406] The emotion analysis engine installed on the server analyzes the user's input data to determine their emotions. A natural language processing library (e.g., Spacy) is used for this analysis. Based on the analyzed emotions, the server dynamically adjusts the tone and content of the generated email body. For example, if an emotion of gratitude is detected, an expression of gratitude will be added to the email body.

[0407] Step 4:

[0408] The server displays a preview of the generated email body on the user's device. The user reviews the email content through the device and makes corrections as needed.

[0409] Step 5:

[0410] When a user issues a send command on their device, the device transmits it to the server, which then uses SMTP to send the final email to the recipient. In this step, the sent email reaches the customer in its complete form.

[0411] Step 6:

[0412] In processing incoming emails, the server periodically retrieves new emails from the incoming mail server. The content of the retrieved emails is also analyzed on the server.

[0413] Step 7:

[0414] The content of received emails is analyzed by the server's artificial intelligence and sentiment analysis engine. Specifically, natural language processing technology is used to analyze the email body and extract specific keywords and phrases. At the same time, the sentiment analysis engine analyzes the sender's emotions.

[0415] Step 8:

[0416] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email includes the extracted keywords and content corresponding to the analyzed sentiment.

[0417] Step 9:

[0418] The server displays a preview of the generated reply email on the user's device. The user then reviews the content of the reply email through their device and makes any necessary corrections.

[0419] Step 10:

[0420] When a user initiates a reply email from their device, that instruction is transmitted to the server, which then uses SMTP to send the reply email. This ensures that the customer receives an appropriate and emotionally sensitive reply.

[0421] Through each of the steps described so far, the system can efficiently generate and send / receive emails that take into account the emotions of users and customers.

[0422] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0423] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0424] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0425] [Second Embodiment]

[0426] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0427] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0428] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0429] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0430] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0431] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0432] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0433] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0434] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0435] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0436] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0437] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0438] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[0439] First, the user enters data to create an email. The user enters the necessary information (e.g., customer name, order number, date, etc.) through their terminal. The server receives this input data and selects an appropriate email template from its database. Based on the selected template, the server generates the email body and displays a preview of this generated email body to the user. The user can review this preview and make corrections as needed. After the user issues a send command, the server sends the email.

[0440] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to extract specific keywords and phrases from the email body. Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. This generated reply email is also displayed to the user as a preview, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email.

[0441] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the order number and customer name. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0442] On the other hand, when processing incoming emails, for example, if an email containing an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[0443] In this way, this system automates the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[0447] Step 2:

[0448] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[0449] Step 3:

[0450] The server automatically generates the email body by embedding the user's input data into the selected template.

[0451] Step 4:

[0452] The server sends the generated email body to the terminal and displays a preview to the user. The user reviews the email content and makes corrections as needed.

[0453] Step 5:

[0454] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[0455] Step 6:

[0456] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[0457] ---

[0458] Step 1:

[0459] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[0460] Step 2:

[0461] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[0462] Step 3:

[0463] The server passes the content of the received email to the AI ​​(artificial intelligence), which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[0464] Step 4:

[0465] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[0466] Step 5:

[0467] The server sends the generated reply email to the terminal and displays a preview for the user. The user reviews the reply and makes corrections as needed.

[0468] Step 6:

[0469] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[0470] Step 7:

[0471] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[0472] (Example 1)

[0473] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0474] Traditional email creation and processing systems required users to perform many tasks manually, resulting in a significant burden of time and effort. Furthermore, the ability to accurately analyze incoming emails and automatically generate replies was insufficient. As a result, users had to dedicate a considerable amount of time to email processing, hindering their ability to work efficiently.

[0475] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0476] In this invention, the server includes means for receiving electronic information input from a user, means for selecting a communication template based on the electronic information input and generating the body text, and means for displaying the generated communication body text as a preview to the user. This enables the user to create and send emails accurately and quickly with minimal operation. The server also includes means for acquiring new communications from a receiving communication server, means for analyzing the acquired communication content using a generation AI model, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply communication if a reply is determined to be necessary, and means for displaying the generated reply communication as a preview to the user. This enables efficient and accurate content analysis of received emails and automatic replies, significantly reducing the burden on the user.

[0477] A "user" is an individual or organization that operates the system, inputs electronic information, or issues transmission instructions.

[0478] "Electronic data entry" refers to data that a user provides to the system using a terminal (e.g., order number, customer name, order date).

[0479] A "communication template" is a pre-prepared document template with a specified format and content, used to generate the body of an email.

[0480] "Preview display" is a function that displays generated communications or email body text on the screen for the user to check and edit.

[0481] A "transmit instruction" is an operation or command that a user makes to a system in order to send an electronic message.

[0482] An "electronic message" is a digital message that a user sends to another individual or organization through a system.

[0483] A "receiving communication server" is a server that stores newly received communications and makes them accessible to the system.

[0484] "Acquisition" refers to the operation in which a server retrieves new communications from a receiving communication server and makes them available for use within the system.

[0485] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and perform a specific task (e.g., natural language processing).

[0486] "Analysis" refers to the process of processing acquired communication content using a generation AI model to extract specific keywords or patterns.

[0487] "Determining whether a reply is necessary" is the process by which the system determines, based on the analysis results, whether a reply is required.

[0488] A "reply message" is an electronic message automatically generated by the server when analysis determines that a reply is necessary.

[0489] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[0490] First, the user uses a terminal to input data to compose an email. The user accesses the input form on the terminal and enters the necessary information (e.g., order number, customer name, order date, etc.). The server receives this input data and selects the appropriate communication template from the database. Specifically, a common relational database (e.g., MySQL or PostgreSQL) can be used. Based on the selected template, the server generates the email body. A common template engine (e.g., Handlebars.js or Mustache) can be used for template processing for generation.

[0491] The generated email body is displayed as a preview on the user's device. The user can review this preview and make corrections as needed. Once corrections are complete, the user clicks the send button to send a transmission command to the server. The server receives this transmission command and sends the email using the SMTP protocol. As a concrete example, consider the case where a user creates an "order confirmation email." The user enters the order number "12345," customer name "Customer Name," and order date "October 1, 2023" through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the customer name and order number. The generated email is displayed as a preview to the user, and once the user reviews it and clicks the send button, the server sends the email to the customer.

[0492] Next, the system also automates the processing of incoming emails. The server periodically retrieves new communications from the receiving communication server. The retrieved emails are analyzed on the server by a generative AI model. For analysis, natural language processing models such as GPT-3 and BERT can be used. These models can run on cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0493] The AI ​​model extracts specific keywords and phrases from the email body and determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email is also displayed as a preview on the user's device, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email. As a concrete example, consider the case where an "order cancellation request" email is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." If the AI ​​model determines that a reply is necessary based on its analysis results, the server automatically generates a reply email stating "Cancellation request received." This reply email is displayed as a preview to the user, and once the user reviews it and issues a send command, the server sends the reply email to the customer.

[0494] Examples of prompt messages include: "Please enter the information needed to create an order confirmation email. Please specify the order number, customer name, and order date," and "We will analyze newly received emails and generate an automatic reply email if necessary. Please provide the content of the email to be analyzed."

[0495] Thus, the system of the present invention streamlines the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0496] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0497] System program processing flow

[0498] Step 1:

[0499] The user uses the terminal to enter the necessary data to compose an email. The user accesses the input form on the terminal and enters information such as order number, customer name, and order date. This generates the input data.

[0500] Input: Data entered by the user into the input form on the device (e.g., order number, customer name, order date)

[0501] Output: Input data sent to the system

[0502] Step 2:

[0503] The server receives the input data provided by the user. Based on the received data, the server accesses the database and selects the appropriate communication template.

[0504] Input: Input data sent by the user from their device.

[0505] Output: Selection of an appropriate communication template (e.g., "Order Confirmation Email" template)

[0506] Step 3:

[0507] The server generates the email body based on the selected communication template. During generation, it performs a process of embedding input data. Specifically, customer names, order numbers, etc., are embedded in the template's placeholders.

[0508] Input: Selected communication template, user input data

[0509] Output: Generated email body (Example: "Dear Customer Name, we have confirmed your order for order number 12345")

[0510] Step 4:

[0511] The generated email body is displayed as a preview on the user's device. The user reviews this preview and makes corrections as needed.

[0512] Input: Generated email body

[0513] Output: Email body modified by the user (if necessary)

[0514] Step 5:

[0515] When a user issues a send command, the server receives this command and sends the email. The server uses the SMTP protocol to deliver the email to the recipient.

[0516] Input: User's sending instructions, revised email body

[0517] Output: Email delivered to the recipient

[0518] Step 6:

[0519] The server periodically retrieves new communications from the receiving server. This is done using the IMAP protocol.

[0520] Input: New communication from the receiving communication server

[0521] Output: New communications acquired

[0522] Step 7:

[0523] The server analyzes the acquired communication content using a generation AI model. Natural language processing techniques are used for the analysis to extract specific keywords and phrases. For example, keywords such as "cancel" and "request" are extracted.

[0524] Input: Newly acquired communications

[0525] Output: Analysis results (specific keywords or phrases)

[0526] Step 8:

[0527] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email.

[0528] Input: Analysis results

[0529] Output: Determination of whether a reply is necessary, and a reply email generated if necessary.

[0530] Step 9:

[0531] The generated reply email is displayed as a preview on the user's device. The user reviews the content and makes corrections as needed.

[0532] Input: Generated reply email

[0533] Output: User-modified reply email (if necessary)

[0534] Step 10:

[0535] When a user issues a send command, the server receives this command and sends a reply email. The server uses the SMTP protocol to deliver the reply email to the recipient.

[0536] Input: User's submission instructions, revised reply email.

[0537] Output: Reply email delivered to the recipient

[0538] In this way, the user, terminal, and server cooperate at each step to efficiently and accurately create and process emails.

[0539] (Application Example 1)

[0540] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0541] In modern content delivery services, streamlining user support and email marketing is a critical challenge. Manual email creation and sending is time-consuming and labor-intensive, and while quick responses are required, delays can occur due to time differences and staff shortages. Furthermore, immediate responses to user inquiries are essential for improving the user experience, and automation is needed in this area as well. To solve these challenges, an automated system for email creation and sending / receiving is required.

[0542] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0543] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving mail server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for automating user support related to content distribution, means for generating the reply email body using a generation AI model, and means for creating reply content based on prompt text. This enables efficient and rapid response in both user support and email newsletter distribution.

[0544] "Means for receiving data input from users" refers to providing an interface for users to input necessary information into the system.

[0545] "A method for selecting an email template and generating the email body" refers to a process that selects an appropriate email template from a database based on the entered data and automatically generates the email body using that template.

[0546] "A means of displaying a preview of the generated email body to the user" refers to a method of displaying a preview of the automatically generated email content so that the user can check and correct it before sending.

[0547] "A means of sending emails based on instructions from the user" refers to a method of receiving instructions from the user to send an email after they have reviewed and corrected it, and then sending the email based on those instructions.

[0548] "Methods for retrieving new emails from the receiving mail server" refers to the process of periodically retrieving new emails that have arrived on the receiving mail server.

[0549] "Methods for analyzing acquired email content using AI" refer to methods for analyzing received emails using artificial intelligence technology to understand their content.

[0550] "A means of determining whether a reply is necessary based on analysis results" refers to determining whether a reply is necessary based on the content of the email analyzed by AI.

[0551] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a system that automatically generates an appropriate reply email when a reply is deemed necessary.

[0552] "A means of displaying a preview of the generated reply email to the user" refers to a method of displaying a preview of the automatically generated reply email so that the user can review and modify it.

[0553] "A means of sending a reply email based on instructions from the user" refers to a method of receiving instructions from the user to send a reply email after they have reviewed and corrected it, and then sending the reply email based on those instructions.

[0554] "Means for automating user support related to content distribution" refers to a system that automates support for user inquiries that arise in content distribution services.

[0555] "A method for generating reply email content using a generative AI model" refers to a method that uses an artificial intelligence model to automatically generate appropriate reply content for received emails.

[0556] "Means for creating a response based on a prompt" refers to a method by which an artificial intelligence model creates the most suitable response for the user based on the prompt (input) text.

[0557] This invention provides a system that streamlines the creation, sending, and automatic analysis and replying to of emails related to a user's content delivery service. This system enables users to send and receive emails quickly and accurately with minimal effort.

[0558] This system mainly consists of the following elements:

[0559] 1. Means by which users input data: Users input necessary information (e.g., customer name, campaign information, inquiry details, etc.) through a terminal. Web forms, dedicated applications, etc., are used as the data input interface.

[0560] 2. Method for selecting an email template and generating the email body: The server receives data entered by the user and selects an appropriate email template from the database. Based on this template, the server generates the email body.

[0561] 3. Means for displaying a preview of the generated email body to the user: The generated email body will be displayed as a preview on the user's terminal. The user can review it and make corrections as needed.

[0562] 4. Means of sending emails based on user instructions: When a user issues a send instruction, the server sends the email to the specified recipient.

[0563] 5. Means of retrieving new emails from the incoming mail server: The server has a function to periodically retrieve new emails from the incoming mail server.

[0564] 6. Method for analyzing acquired email content using AI: Acquired emails are analyzed using a generative AI model implemented on the server. This model uses natural language processing technology to extract specific keywords and phrases from the email content.

[0565] 7. Means for determining whether a reply is necessary based on the analysis results: The server determines whether a reply is necessary based on the analysis results.

[0566] 8. Means for automatically generating a reply email when a reply is deemed necessary: ​​When a reply is deemed necessary, the server will automatically generate a reply email using a generation AI model based on the prompt message.

[0567] 9. Means for displaying generated reply emails to the user as a preview: Automatically generated reply emails are displayed as a preview on the user's terminal, allowing the user to review and modify the content.

[0568] 10. Means for sending reply emails based on user instructions: When a user issues a send instruction, the server sends a reply email to the specified recipient.

[0569] This system uses the following hardware and software:

[0570] Hardware: Servers (mail servers, AI computing servers), user terminals (PCs, smartphones, tablets)

[0571] Software: Python execution environment, open-source email libraries (smtplib, imaplib), generative AI models (OpenAI API)

[0572] As a concrete example, consider a scenario where a content distribution service administrator sends an email to notify customers of new content. The administrator inputs the new content information via a terminal, and the system selects an appropriate email template based on that input and automatically generates the email body. The generated email body is previewed by the administrator, and after confirmation, it is sent in bulk.

[0573] Furthermore, the AI ​​model analyzes user inquiry emails and generates appropriate replies. An example of a prompt message is as follows:

[0574] text

[0575] Subject: Regarding new service features

[0576] Text: Please provide information about the new features of the service.

[0577] Please compose your reply email based on the above information.

[0578] This allows users to send and receive emails quickly and efficiently, significantly improving the operational efficiency of content delivery services.

[0579] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0580] Step 1:

[0581] Users enter data through their devices.

[0582] Users use their devices (PCs, smartphones, tablets) to input necessary data such as new content information, customer information, and inquiries. The entered data is then sent from the device to the server.

[0583] Step 2:

[0584] The server selects an email template and generates the email body.

[0585] The server selects an appropriate email template from the database based on the received data. It then generates the email body by embedding the user's entered information into the selected template. This process involves retrieving data from the database and embedding it into the template.

[0586] Step 3:

[0587] The server displays a preview of the generated email body to the user.

[0588] The server sends the generated email body to the user's terminal for preview. The user can review and edit the email content before sending.

[0589] Step 4:

[0590] The user issues a send command, and the server sends the email.

[0591] Once the user reviews the email content and issues a send command, the server sends the email to the specified recipient. The user is then notified of the sending result.

[0592] Step 5:

[0593] The server retrieves new emails from the incoming mail server.

[0594] The server retrieves new emails from the receiving mail server at regular intervals. This process involves accessing the receiving mail server and downloading new emails.

[0595] Step 6:

[0596] The server uses AI to analyze the email content it has retrieved.

[0597] The server inputs the retrieved email content into a generative AI model, which then analyzes the email's content. The generative AI model uses natural language processing techniques to extract keywords and important phrases from the email body. Based on these analysis results, it then performs the following processing.

[0598] Step 7:

[0599] The server will determine whether a reply is necessary based on the analysis results.

[0600] The server determines whether a reply is necessary based on the AI ​​analysis results. For example, if keywords such as "cancel" or "request" are included, it will determine that a reply is necessary.

[0601] Step 8:

[0602] The server automatically generates a reply email.

[0603] If a reply is deemed necessary, the server automatically generates a reply email using a generation AI model based on the specified prompt text. This process involves inputting the prompt text and automatically generating the reply email.

[0604] Step 9:

[0605] The server displays a preview of the generated reply email to the user.

[0606] The server sends the generated reply email to the user's terminal for preview. The user can review the reply email content and make corrections as needed.

[0607] Step 10:

[0608] The user issues a send command, and the server sends a reply email.

[0609] Once the user reviews the reply email and issues a send command, the server sends the reply email to the specified recipient. The server then notifies the user of the sending result.

[0610] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0611] This invention relates to a system that streamlines the automated processing of user-generated and received emails by incorporating an emotion engine to adapt the content and tone of emails to the user's emotions. This system enables the generation and replies of emails that take the user's emotions into consideration.

[0612] First, the user enters the necessary data (e.g., customer name, order number, date, etc.) through their terminal. This data is used to create the email. The server receives the data entered by the user and selects an appropriate email template from its database based on that data. Based on the selected template, the server automatically generates the email body.

[0613] Because an emotion engine is implemented, the server analyzes the user's emotions from their input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email body. For example, if the user is feeling grateful, the server will add expressions of gratitude to the email body. Also, if the user is feeling anxious, the server will generate reassuring sentences.

[0614] The generated email body is previewed on the user's terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[0615] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, an emotion engine analyzes the user's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer is making a complaint, the emotion engine uses the analysis results to generate a reply email expressing an apology.

[0616] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also previewed on the user's terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server actually sends the reply email using SMTP.

[0617] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0618] On the other hand, consider the processing of incoming emails, for example, when an email requesting order cancellation is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the emotion engine analyzes the customer's emotions, and if the customer is expressing disappointment or anger, it generates a reply appropriate to that emotion (e.g., an apology or explanation). Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[0619] In this way, this system automates the creation and processing of user emails, and generates and replies emails that take user emotions into consideration, thereby significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[0620] The following describes the processing flow.

[0621] Step 1:

[0622] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[0623] Step 2:

[0624] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[0625] Step 3:

[0626] The server automatically generates the email body by embedding the user's input data into the selected template.

[0627] Step 4:

[0628] Before the server displays a preview of the email body on the user's terminal, it uses an emotion engine to analyze the user's input data to determine their emotions.

[0629] Step 5:

[0630] The server dynamically adjusts the tone and content of the generated email body based on the analysis results of the emotion engine. For example, if the user has the emotion of "gratitude," words reflecting that emotion will be added to the body of the email.

[0631] Step 6:

[0632] The server displays a preview of the edited email body on the user's device. The user reviews the preview and makes any necessary corrections.

[0633] Step 7:

[0634] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[0635] Step 8:

[0636] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[0637] ---

[0638] Step 1:

[0639] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[0640] Step 2:

[0641] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[0642] Step 3:

[0643] The server passes the content of the received email to the AI, which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[0644] Step 4:

[0645] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[0646] Step 5:

[0647] The server passes the generated reply email to the emotion engine, which analyzes the sender's emotions based on the content of the received email.

[0648] Step 6:

[0649] The server adjusts the tone and content of the reply email based on the analyzed sentiment. For example, if the recipient has expressed dissatisfaction, it will add an apology or explanation.

[0650] Step 7:

[0651] The server displays a preview of the adjusted reply email on the user's device, allowing the user to review the content. The user reviews the content and makes corrections as needed.

[0652] Step 8:

[0653] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[0654] Step 9:

[0655] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[0656] (Example 2)

[0657] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0658] Traditional email systems required users to manually compose emails and adjust the content according to their emotions, which was time-consuming and laborious. Furthermore, processing incoming emails was also done manually, resulting in long response times and a significant burden on users.

[0659] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0660] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving email server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for analyzing the user's emotions using an emotion engine and dynamically adjusting the tone and content of the email body based on the analysis results, and means for analyzing the sender's emotions from the content of the received email using an emotion engine and dynamically adjusting the content of the reply email based on the analysis results. This enables the automatic generation and sending of emails that take the user's emotions into consideration, resulting in a significant reduction in time and effort.

[0661] A "user" is a person or group that uses the system to create and manage emails.

[0662] "Means for receiving data input" refers to interfaces or functions for receiving user input data (e.g., customer name, order number, date, etc.).

[0663] "Methods for selecting email templates" refers to algorithms or processes for automatically selecting appropriate email templates from a database.

[0664] "Methods for generating the body text" refers to the process or technology of creating the email body by integrating a selected template with data entered by the user.

[0665] "Means of previewing" refers to interfaces or functions that display the generated email content to the user in a format that allows them to review it.

[0666] "Means of sending emails based on send instructions" refers to protocols and functions for sending emails to designated recipients in response to a user's send instruction.

[0667] "Methods for retrieving new emails from an incoming mail server" refers to the functions and protocols used to periodically retrieve new emails from an incoming mail server.

[0668] "Methods of analysis using AI" refers to the process and technology of analyzing email content obtained using artificial intelligence and extracting important information and emotions.

[0669] "Means for determining whether a reply is necessary" refers to algorithms or processes that determine whether a reply is necessary to an incoming email based on the results of AI analysis.

[0670] "Methods for automatically generating reply emails" refers to processes and technologies that automatically generate reply emails using templates or sentiment engines when a reply is deemed necessary.

[0671] An "emotion engine" refers to algorithms and technologies that analyze the emotions of users and received emails, and dynamically adjust the content and tone of emails based on the analysis results.

[0672] "Dynamic adjustment methods" refer to functions or processes that automatically change the wording and expression of email content based on the results of sentiment analysis.

[0673] This invention is a system for streamlining the automated processing of user-generated and received emails. By combining this system with an emotion engine, it is possible to adapt the content and tone of emails to the user's emotions, minimizing user interaction while enabling fast and accurate email sending and receiving.

[0674] The user enters the necessary data (e.g., customer name, order number, date, etc.) through the terminal. The terminal sends the entered data to the server. The server receives the data sent by the user and selects an appropriate email template from the database based on that data.

[0675] The server automatically generates email content based on the selected template. Because an emotion engine is integrated, the server performs sentiment analysis on the user's input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email content. For example, if the user expresses gratitude, it adds expressions of gratitude to the email content. If the user is feeling anxious, it generates reassuring sentences. Specifically, IBM Watson Emotion Analysis is used as the emotion engine.

[0676] The generated email body is previewed on the terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button, the server uses SMTP to send the email to the recipient.

[0677] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the content of these retrieved emails is analyzed by AI (for example, the Google Cloud Natural Language API). The AI ​​analyzes the email body and extracts specific keywords and phrases. Furthermore, an emotion engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the reply content and tone based on the results. If a customer is making a complaint, the emotion engine uses the analysis results to generate an apologetic reply email.

[0678] Based on the analysis results, the server determines whether a reply is necessary, and if so, it automatically generates an appropriate reply email. This reply email is also previewed on the terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the transmission, the server sends the reply email using SMTP.

[0679] As a concrete example, when a user creates an order confirmation email, the user enters information such as the order number, customer name, and order date through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates an email body with the order number and customer name embedded. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0680] An example of a prompt message is as follows:

[0681] "Customer Name: Taro Tanaka" "Order Number: 123456" "Order Date: 2023-10-01"

[0682] Thus, the present invention automates the processing of user-generated and received emails, and significantly reduces time and effort by generating and responding to emails that take user emotions into consideration. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[0683] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0684] Step 1:

[0685] The user enters data for creating an email into the device.

[0686] The user enters the necessary information (e.g., customer name, order number, date, etc.) into the input form on the device. The entered data is saved on the device.

[0687] Input: Customer name, order number, date, and other information.

[0688] Output: Input data is saved to the terminal.

[0689] Specific action: The user enters data into the form fields and clicks the submit button.

[0690] Step 2:

[0691] The terminal sends the input data to the server.

[0692] The terminal sends the data entered by the user to the server using HTTPS. This communication is encrypted.

[0693] Input: Input data saved on the device

[0694] Output: Input data is sent to the server.

[0695] Specific operation: The terminal program uses the input data to form a request and sends a POST request to the server via HTTPS.

[0696] Step 3:

[0697] The server selects the appropriate email template.

[0698] The server selects an appropriate email template from the database based on the received data. A conditional matching algorithm is used for template selection.

[0699] Input: Input data sent to the server

[0700] Output: An appropriate email template will be selected.

[0701] Specific operation: The server's algorithm queries the database based on the input data and retrieves the corresponding template.

[0702] Step 4:

[0703] The server uses an emotion engine to analyze the user's emotions.

[0704] The server inputs user data into the emotion engine and retrieves the results. The emotion engine uses, for example, IBM Watson Emotion Analysis.

[0705] Input: User input data

[0706] Output: Emotion analysis results

[0707] Specific operation: The server sends input data to the emotion engine, receives the analysis results, and saves those results to internal storage.

[0708] Step 5:

[0709] The server generates and adjusts the email body based on the analysis results.

[0710] The server dynamically generates the email body by incorporating the analysis results into the selected email template.

[0711] Input: Email template, sentiment analysis results

[0712] Output: Generated email body

[0713] Specific operation: The server integrates the template and sentiment analysis results and executes code to dynamically generate the email body.

[0714] Step 6:

[0715] The email body generated by the server is displayed as a preview on the terminal.

[0716] The generated email body will be displayed in the terminal's user interface.

[0717] Input: Generated email body

[0718] Output: The email body is displayed on the user's device.

[0719] Specific operation: The server sends the email body to the terminal, and the terminal receives it and executes code to display it on the screen.

[0720] Step 7:

[0721] The user reviews and edits the email content and then issues a send command.

[0722] The user reviews the previewed email content and makes any necessary corrections. Then, they click the send button to initiate the sending process.

[0723] Input: Previewed email body

[0724] Output: Email body after user review and modification, and sending instructions.

[0725] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[0726] Step 8:

[0727] The server sends the email via SMTP.

[0728] The server uses the SMTP protocol to send emails. A log of the sent emails is also saved.

[0729] Input: Confirmed and corrected email body, sending instructions

[0730] Output: The email is sent to the recipient, and the sending log is saved.

[0731] Specific operation: The server's SMTP client executes code to send an email to the specified recipient's address.

[0732] Step 9:

[0733] The server periodically retrieves new emails from the incoming mail server.

[0734] The server retrieves new emails from the incoming mail server at regular intervals. This interval is configurable.

[0735] Input: None (triggered by time interval)

[0736] Output: New emails are retrieved to the server.

[0737] Specific operation: The server retrieves new emails from the mail server using the POP3 / IMAP protocol.

[0738] Step 10:

[0739] The server uses AI to analyze the content of incoming emails.

[0740] The server passes the content of the received email to the AI ​​and retrieves the analysis results. The AI ​​uses the Google Cloud Natural Language API, among others.

[0741] Input: Newly received emails

[0742] Output: Analysis results of received emails

[0743] Specific operation: The server sends the received email to the AI ​​analysis engine and executes code to receive the analysis results.

[0744] Step 11:

[0745] The server will determine whether a response is needed based on the analysis results.

[0746] Based on the analysis results, the server executes an algorithm to determine whether a reply is necessary.

[0747] Input: Analysis results of received emails

[0748] Output: Result of the determination of whether a reply is needed.

[0749] Specific operation: The server's algorithm analyzes the results and executes code to determine whether a reply is necessary.

[0750] Step 12:

[0751] The server automatically generates a reply email and displays a preview on the device.

[0752] If a reply is deemed necessary, the server automatically generates a reply email and displays a preview on the user's device. The sentiment engine is also used at this stage.

[0753] Input: Email data if a reply is deemed necessary.

[0754] Output: Generated automated reply email, preview display

[0755] Specific operation: The server generates a reply email based on the reply email template, reflecting the sentiment analysis results, and executes code to send it to the terminal and display a preview.

[0756] Step 13:

[0757] The user reviews and corrects the reply email and issues a send command.

[0758] The user reviews the previewed reply email and makes any necessary corrections. Then, they click the send button to initiate submission.

[0759] Input: Previewed reply email

[0760] Output: Reply email after user confirmation and correction, and sending instructions.

[0761] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[0762] Step 14:

[0763] The server sends a reply email via SMTP.

[0764] The server uses the SMTP protocol to send a reply email. A log of the sent email is also saved.

[0765] Input: Confirmation and correction reply email, sending instructions

[0766] Output: A reply email is sent to the recipient, and a sending log is saved.

[0767] Specific operation: The server's SMTP client executes code to send a reply email to the specified recipient's address.

[0768] (Application Example 2)

[0769] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0770] In modern brick-and-mortar stores, efficient and effective communication with customers is essential. However, many email correspondences are time-consuming and labor-intensive, and creating replies that take emotions into account is particularly difficult. Conventional automated email generation systems struggle to create emails that reflect the emotions of users and customers, resulting in decreased customer satisfaction and reduced efficiency in responses.

[0771] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0772] In this invention, the server includes means for analyzing the user's emotions and dynamically adjusting the tone and content of the email body accordingly; means for extracting keywords from the received email content and generating a reply email based on those keywords; and means for displaying a preview of the generated email body to the user. This enables the automatic generation and sending of emails that reflect the emotions of users and customers, facilitating smoother communication with customers.

[0773] "Means for receiving data input from users" refers to an interface that imports information entered by users (e.g., customer name, order number, etc.) into the system.

[0774] "A means of selecting an email template and generating the body text based on data input" refers to a function that selects an appropriate email template from a database based on the entered data and automatically creates the body text of the email based on that template.

[0775] "A means of displaying a preview of the generated email body to the user" refers to a function that displays the content of the email generated by the system to the user in advance, allowing the user to check and modify the content.

[0776] "A means of sending emails based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user gives a sending instruction.

[0777] "Method for retrieving new emails from the receiving mail server" refers to a function that periodically retrieves newly received emails from the mail server.

[0778] "Methods for analyzing acquired email content using artificial intelligence" refers to a function that uses artificial intelligence to analyze the content of received emails and extract important keywords and sentiments.

[0779] "Means for determining whether a reply is necessary based on analysis results" refers to a function that automatically determines whether a reply is necessary based on the analyzed data.

[0780] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a function in which the system automatically creates an appropriate reply email when it is determined that a reply is required.

[0781] "A means of displaying a preview of the generated reply email to the user" refers to a function that allows the system to display the automatically generated reply email to the user in advance, enabling them to review and modify it.

[0782] "A means of sending a reply email based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user instructs the system to send a reply email.

[0783] "A means of analyzing user emotions and dynamically adjusting the tone and content of emails accordingly" refers to a function that analyzes user emotions from input data and automatically changes the tone and content of emails accordingly.

[0784] "A means of extracting keywords from the content of a received email and generating a reply email based on those keywords" refers to a function that extracts important keywords from a received email and automatically creates an appropriate reply based on those keywords.

[0785] This invention is a system that selects an email template based on data input from the user, generates the email body, and adjusts the content and tone of the email after analyzing the user's emotions. This system operates in cooperation with three parties: the server, the terminal, and the user.

[0786] First, this system provides an interface that allows users to input data via a terminal. The data entered by the user (e.g., customer name, order number, date, etc.) is sent to the server. The server receives this data, selects an appropriate email template from the database, and generates the email body based on the selected template.

[0787] The server is equipped with an emotion analysis engine that analyzes the user's emotions from their input data. Based on this analysis, the server dynamically adjusts the tone and content of the generated email body. For example, if the user is feeling grateful, the server adds expressions of gratitude to the email body. If the user is feeling anxious, the server generates reassuring sentences.

[0788] The generated email body is displayed as a preview on the terminal. The user reviews the email content and makes any necessary corrections. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[0789] Next, let's discuss the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server. The content of these retrieved emails is analyzed by artificial intelligence installed on the server. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, a sentiment analysis engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer sends a complaint, the sentiment engine uses the analysis results to generate a reply email expressing an apology.

[0790] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also displayed as a preview on the terminal, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server sends the reply email using SMTP.

[0791] Hardware and software to be used

[0792] Hardware: Smartphones, servers

[0793] Software: Python, smtplib (SMTP library), sentiment analysis engine (e.g., IBM Watson), natural language processing library (e.g., Spacy)

[0794] Specific example

[0795] Specific example of email generation: When a user creates an "order confirmation email," they input the order number, customer name, order date, etc., through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The sentiment analysis engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body.

[0796] Specific example of processing incoming emails: When an email with an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the sentiment analysis engine analyzes the customer's emotions, and if the customer is showing disappointment or anger, for example, it generates a reply appropriate to that emotion (e.g., an apology or explanation).

[0797] Example of a prompt

[0798] Please generate an email message for when a customer cancels an order. Assume the customer is angry about the cancellation.

[0799] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0800] Step 1:

[0801] The user enters data (e.g., customer name, order number, order date, etc.) through the terminal. The terminal sends the data entered by the user to the server. The entered data becomes the basic information used to generate emails.

[0802] Step 2:

[0803] Based on the data received by the server, it selects an appropriate email template from the database. The database contains various email templates, and the server extracts the template that matches the specified criteria. The email body is automatically generated by embedding the user's entered data into the selected template.

[0804] Step 3:

[0805] The emotion analysis engine installed on the server analyzes the user's input data to determine their emotions. A natural language processing library (e.g., Spacy) is used for this analysis. Based on the analyzed emotions, the server dynamically adjusts the tone and content of the generated email body. For example, if an emotion of gratitude is detected, an expression of gratitude will be added to the email body.

[0806] Step 4:

[0807] The server displays a preview of the generated email body on the user's device. The user reviews the email content through the device and makes corrections as needed.

[0808] Step 5:

[0809] When a user issues a send command on their device, the device transmits it to the server, which then uses SMTP to send the final email to the recipient. In this step, the sent email reaches the customer in its complete form.

[0810] Step 6:

[0811] In processing incoming emails, the server periodically retrieves new emails from the incoming mail server. The content of the retrieved emails is also analyzed on the server.

[0812] Step 7:

[0813] The content of received emails is analyzed by the server's artificial intelligence and sentiment analysis engine. Specifically, natural language processing technology is used to analyze the email body and extract specific keywords and phrases. At the same time, the sentiment analysis engine analyzes the sender's emotions.

[0814] Step 8:

[0815] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email includes the extracted keywords and content corresponding to the analyzed sentiment.

[0816] Step 9:

[0817] The server displays a preview of the generated reply email on the user's device. The user then reviews the content of the reply email through their device and makes any necessary corrections.

[0818] Step 10:

[0819] When a user initiates a reply email from their device, that instruction is transmitted to the server, which then uses SMTP to send the reply email. This ensures that the customer receives an appropriate and emotionally sensitive reply.

[0820] Through each of the steps described so far, the system can efficiently generate and send / receive emails that take into account the emotions of users and customers.

[0821] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0822] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0823] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0824] [Third Embodiment]

[0825] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0826] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0827] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0828] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0829] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0830] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0831] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0832] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0833] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0834] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0835] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0836] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0837] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[0838] First, the user enters data to create an email. The user enters the necessary information (e.g., customer name, order number, date, etc.) through their terminal. The server receives this input data and selects an appropriate email template from its database. Based on the selected template, the server generates the email body and displays a preview of this generated email body to the user. The user can review this preview and make corrections as needed. After the user issues a send command, the server sends the email.

[0839] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to extract specific keywords and phrases from the email body. Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. This generated reply email is also displayed to the user as a preview, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email.

[0840] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the order number and customer name. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[0841] On the other hand, when processing incoming emails, for example, if an email containing an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[0842] In this way, this system automates the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0843] The following describes the processing flow.

[0844] Step 1:

[0845] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[0846] Step 2:

[0847] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[0848] Step 3:

[0849] The server automatically generates the email body by embedding the user's input data into the selected template.

[0850] Step 4:

[0851] The server sends the generated email body to the terminal and displays a preview to the user. The user reviews the email content and makes corrections as needed.

[0852] Step 5:

[0853] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[0854] Step 6:

[0855] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[0856] ---

[0857] Step 1:

[0858] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[0859] Step 2:

[0860] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[0861] Step 3:

[0862] The server passes the content of the received email to the AI ​​(artificial intelligence), which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[0863] Step 4:

[0864] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[0865] Step 5:

[0866] The server sends the generated reply email to the terminal and displays a preview for the user. The user reviews the reply and makes corrections as needed.

[0867] Step 6:

[0868] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[0869] Step 7:

[0870] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[0871] (Example 1)

[0872] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0873] Traditional email creation and processing systems required users to perform many tasks manually, resulting in a significant burden of time and effort. Furthermore, the ability to accurately analyze incoming emails and automatically generate replies was insufficient. As a result, users had to dedicate a considerable amount of time to email processing, hindering their ability to work efficiently.

[0874] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0875] In this invention, the server includes means for receiving electronic information input from a user, means for selecting a communication template based on the electronic information input and generating the body text, and means for displaying the generated communication body text as a preview to the user. This enables the user to create and send emails accurately and quickly with minimal operation. The server also includes means for acquiring new communications from a receiving communication server, means for analyzing the acquired communication content using a generation AI model, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply communication if a reply is determined to be necessary, and means for displaying the generated reply communication as a preview to the user. This enables efficient and accurate content analysis of received emails and automatic replies, significantly reducing the burden on the user.

[0876] A "user" is an individual or organization that operates the system, inputs electronic information, or issues transmission instructions.

[0877] "Electronic data entry" refers to data that a user provides to the system using a terminal (e.g., order number, customer name, order date).

[0878] A "communication template" is a pre-prepared document template with a specified format and content, used to generate the body of an email.

[0879] "Preview display" is a function that displays generated communications or email body text on the screen for the user to check and edit.

[0880] A "transmit instruction" is an operation or command that a user makes to a system in order to send an electronic message.

[0881] An "electronic message" is a digital message that a user sends to another individual or organization through a system.

[0882] A "receiving communication server" is a server that stores newly received communications and makes them accessible to the system.

[0883] "Acquisition" refers to the operation in which a server retrieves new communications from a receiving communication server and makes them available for use within the system.

[0884] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and perform a specific task (e.g., natural language processing).

[0885] "Analysis" refers to the process of processing acquired communication content using a generation AI model to extract specific keywords or patterns.

[0886] "Determining whether a reply is necessary" is the process by which the system determines, based on the analysis results, whether a reply is required.

[0887] A "reply message" is an electronic message automatically generated by the server when analysis determines that a reply is necessary.

[0888] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[0889] First, the user uses a terminal to input data to compose an email. The user accesses the input form on the terminal and enters the necessary information (e.g., order number, customer name, order date, etc.). The server receives this input data and selects the appropriate communication template from the database. Specifically, a common relational database (e.g., MySQL or PostgreSQL) can be used. Based on the selected template, the server generates the email body. A common template engine (e.g., Handlebars.js or Mustache) can be used for template processing for generation.

[0890] The generated email body is displayed as a preview on the user's device. The user can review this preview and make corrections as needed. Once corrections are complete, the user clicks the send button to send a transmission command to the server. The server receives this transmission command and sends the email using the SMTP protocol. As a concrete example, consider the case where a user creates an "order confirmation email." The user enters the order number "12345," customer name "Customer Name," and order date "October 1, 2023" through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the customer name and order number. The generated email is displayed as a preview to the user, and once the user reviews it and clicks the send button, the server sends the email to the customer.

[0891] Next, the system also automates the processing of incoming emails. The server periodically retrieves new communications from the receiving communication server. The retrieved emails are analyzed on the server by a generative AI model. For analysis, natural language processing models such as GPT-3 and BERT can be used. These models can run on cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[0892] The AI ​​model extracts specific keywords and phrases from the email body and determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email is also displayed as a preview on the user's device, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email. As a concrete example, consider the case where an "order cancellation request" email is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." If the AI ​​model determines that a reply is necessary based on its analysis results, the server automatically generates a reply email stating "Cancellation request received." This reply email is displayed as a preview to the user, and once the user reviews it and issues a send command, the server sends the reply email to the customer.

[0893] Examples of prompt messages include: "Please enter the information needed to create an order confirmation email. Please specify the order number, customer name, and order date," and "We will analyze newly received emails and generate an automatic reply email if necessary. Please provide the content of the email to be analyzed."

[0894] Thus, the system of the present invention streamlines the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[0895] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0896] System program processing flow

[0897] Step 1:

[0898] The user uses the terminal to enter the necessary data to compose an email. The user accesses the input form on the terminal and enters information such as order number, customer name, and order date. This generates the input data.

[0899] Input: Data entered by the user into the input form on the device (e.g., order number, customer name, order date)

[0900] Output: Input data sent to the system

[0901] Step 2:

[0902] The server receives the input data provided by the user. Based on the received data, the server accesses the database and selects the appropriate communication template.

[0903] Input: Input data sent by the user from their device.

[0904] Output: Selection of an appropriate communication template (e.g., "Order Confirmation Email" template)

[0905] Step 3:

[0906] The server generates the email body based on the selected communication template. During generation, it performs a process of embedding input data. Specifically, customer names, order numbers, etc., are embedded in the template's placeholders.

[0907] Input: Selected communication template, user input data

[0908] Output: Generated email body (Example: "Dear Customer Name, we have confirmed your order for order number 12345")

[0909] Step 4:

[0910] The generated email body is displayed as a preview on the user's device. The user reviews this preview and makes corrections as needed.

[0911] Input: Generated email body

[0912] Output: Email body modified by the user (if necessary)

[0913] Step 5:

[0914] When a user issues a send command, the server receives this command and sends the email. The server uses the SMTP protocol to deliver the email to the recipient.

[0915] Input: User's sending instructions, revised email body

[0916] Output: Email delivered to the recipient

[0917] Step 6:

[0918] The server periodically retrieves new communications from the receiving server. This is done using the IMAP protocol.

[0919] Input: New communication from the receiving communication server

[0920] Output: New communications acquired

[0921] Step 7:

[0922] The server analyzes the acquired communication content using a generation AI model. Natural language processing techniques are used for the analysis to extract specific keywords and phrases. For example, keywords such as "cancel" and "request" are extracted.

[0923] Input: Newly acquired communications

[0924] Output: Analysis results (specific keywords or phrases)

[0925] Step 8:

[0926] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email.

[0927] Input: Analysis results

[0928] Output: Determination of whether a reply is necessary, and a reply email generated if necessary.

[0929] Step 9:

[0930] The generated reply email is displayed as a preview on the user's device. The user reviews the content and makes corrections as needed.

[0931] Input: Generated reply email

[0932] Output: User-modified reply email (if necessary)

[0933] Step 10:

[0934] When a user issues a send command, the server receives this command and sends a reply email. The server uses the SMTP protocol to deliver the reply email to the recipient.

[0935] Input: User's submission instructions, revised reply email.

[0936] Output: Reply email delivered to the recipient

[0937] In this way, the user, terminal, and server cooperate at each step to efficiently and accurately create and process emails.

[0938] (Application Example 1)

[0939] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0940] In modern content delivery services, streamlining user support and email marketing is a critical challenge. Manual email creation and sending is time-consuming and labor-intensive, and while quick responses are required, delays can occur due to time differences and staff shortages. Furthermore, immediate responses to user inquiries are essential for improving the user experience, and automation is needed in this area as well. To solve these challenges, an automated system for email creation and sending / receiving is required.

[0941] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0942] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving mail server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for automating user support related to content distribution, means for generating the reply email body using a generation AI model, and means for creating reply content based on prompt text. This enables efficient and rapid response in both user support and email newsletter distribution.

[0943] "Means for receiving data input from users" refers to providing an interface for users to input necessary information into the system.

[0944] "A method for selecting an email template and generating the email body" refers to a process that selects an appropriate email template from a database based on the entered data and automatically generates the email body using that template.

[0945] "A means of displaying a preview of the generated email body to the user" refers to a method of displaying a preview of the automatically generated email content so that the user can check and correct it before sending.

[0946] "A means of sending emails based on instructions from the user" refers to a method of receiving instructions from the user to send an email after they have reviewed and corrected it, and then sending the email based on those instructions.

[0947] "Methods for retrieving new emails from the receiving mail server" refers to the process of periodically retrieving new emails that have arrived on the receiving mail server.

[0948] "Methods for analyzing acquired email content using AI" refer to methods for analyzing received emails using artificial intelligence technology to understand their content.

[0949] "A means of determining whether a reply is necessary based on analysis results" refers to determining whether a reply is necessary based on the content of the email analyzed by AI.

[0950] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a system that automatically generates an appropriate reply email when a reply is deemed necessary.

[0951] "A means of displaying a preview of the generated reply email to the user" refers to a method of displaying a preview of the automatically generated reply email so that the user can review and modify it.

[0952] "A means of sending a reply email based on instructions from the user" refers to a method of receiving instructions from the user to send a reply email after they have reviewed and corrected it, and then sending the reply email based on those instructions.

[0953] "Means for automating user support related to content distribution" refers to a system that automates support for user inquiries that arise in content distribution services.

[0954] "A method for generating reply email content using a generative AI model" refers to a method that uses an artificial intelligence model to automatically generate appropriate reply content for received emails.

[0955] "Means for creating a response based on a prompt" refers to a method by which an artificial intelligence model creates the most suitable response for the user based on the prompt (input) text.

[0956] This invention provides a system that streamlines the creation, sending, and automatic analysis and replying to of emails related to a user's content delivery service. This system enables users to send and receive emails quickly and accurately with minimal effort.

[0957] This system mainly consists of the following elements:

[0958] 1. Means by which users input data: Users input necessary information (e.g., customer name, campaign information, inquiry details, etc.) through a terminal. Web forms, dedicated applications, etc., are used as the data input interface.

[0959] 2. Method for selecting an email template and generating the email body: The server receives data entered by the user and selects an appropriate email template from the database. Based on this template, the server generates the email body.

[0960] 3. Means for displaying a preview of the generated email body to the user: The generated email body will be displayed as a preview on the user's terminal. The user can review it and make corrections as needed.

[0961] 4. Means of sending emails based on user instructions: When a user issues a send instruction, the server sends the email to the specified recipient.

[0962] 5. Means of retrieving new emails from the incoming mail server: The server has a function to periodically retrieve new emails from the incoming mail server.

[0963] 6. Method for analyzing acquired email content using AI: Acquired emails are analyzed using a generative AI model implemented on the server. This model uses natural language processing technology to extract specific keywords and phrases from the email content.

[0964] 7. Means for determining whether a reply is necessary based on the analysis results: The server determines whether a reply is necessary based on the analysis results.

[0965] 8. Means for automatically generating a reply email when a reply is deemed necessary: ​​When a reply is deemed necessary, the server will automatically generate a reply email using a generation AI model based on the prompt message.

[0966] 9. Means for displaying generated reply emails to the user as a preview: Automatically generated reply emails are displayed as a preview on the user's terminal, allowing the user to review and modify the content.

[0967] 10. Means for sending reply emails based on user instructions: When a user issues a send instruction, the server sends a reply email to the specified recipient.

[0968] This system uses the following hardware and software:

[0969] Hardware: Servers (mail servers, AI computing servers), user terminals (PCs, smartphones, tablets)

[0970] Software: Python execution environment, open-source email libraries (smtplib, imaplib), generative AI models (OpenAI API)

[0971] As a concrete example, consider a scenario where a content distribution service administrator sends an email to notify customers of new content. The administrator inputs the new content information via a terminal, and the system selects an appropriate email template based on that input and automatically generates the email body. The generated email body is previewed by the administrator, and after confirmation, it is sent in bulk.

[0972] Furthermore, the AI ​​model analyzes user inquiry emails and generates appropriate replies. An example of a prompt message is as follows:

[0973] text

[0974] Subject: Regarding new service features

[0975] Text: Please provide information about the new features of the service.

[0976] Please compose your reply email based on the above information.

[0977] This allows users to send and receive emails quickly and efficiently, significantly improving the operational efficiency of content delivery services.

[0978] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0979] Step 1:

[0980] Users enter data through their devices.

[0981] Users use their devices (PCs, smartphones, tablets) to input necessary data such as new content information, customer information, and inquiries. The entered data is then sent from the device to the server.

[0982] Step 2:

[0983] The server selects an email template and generates the email body.

[0984] The server selects an appropriate email template from the database based on the received data. It then generates the email body by embedding the user's entered information into the selected template. This process involves retrieving data from the database and embedding it into the template.

[0985] Step 3:

[0986] The server displays a preview of the generated email body to the user.

[0987] The server sends the generated email body to the user's terminal for preview. The user can review and edit the email content before sending.

[0988] Step 4:

[0989] The user issues a send command, and the server sends the email.

[0990] Once the user reviews the email content and issues a send command, the server sends the email to the specified recipient. The user is then notified of the sending result.

[0991] Step 5:

[0992] The server retrieves new emails from the incoming mail server.

[0993] The server retrieves new emails from the receiving mail server at regular intervals. This process involves accessing the receiving mail server and downloading new emails.

[0994] Step 6:

[0995] The server uses AI to analyze the email content it has retrieved.

[0996] The server inputs the retrieved email content into a generative AI model, which then analyzes the email's content. The generative AI model uses natural language processing techniques to extract keywords and important phrases from the email body. Based on these analysis results, it then performs the following processing.

[0997] Step 7:

[0998] The server will determine whether a reply is necessary based on the analysis results.

[0999] The server determines whether a reply is necessary based on the AI ​​analysis results. For example, if keywords such as "cancel" or "request" are included, it will determine that a reply is necessary.

[1000] Step 8:

[1001] The server automatically generates a reply email.

[1002] If a reply is deemed necessary, the server automatically generates a reply email using a generation AI model based on the specified prompt text. This process involves inputting the prompt text and automatically generating the reply email.

[1003] Step 9:

[1004] The server displays a preview of the generated reply email to the user.

[1005] The server sends the generated reply email to the user's terminal for preview. The user can review the reply email content and make corrections as needed.

[1006] Step 10:

[1007] The user issues a send command, and the server sends a reply email.

[1008] Once the user reviews the reply email and issues a send command, the server sends the reply email to the specified recipient. The server then notifies the user of the sending result.

[1009] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1010] This invention relates to a system that streamlines the automated processing of user-generated and received emails by incorporating an emotion engine to adapt the content and tone of emails to the user's emotions. This system enables the generation and replies of emails that take the user's emotions into consideration.

[1011] First, the user enters the necessary data (e.g., customer name, order number, date, etc.) through their terminal. This data is used to create the email. The server receives the data entered by the user and selects an appropriate email template from its database based on that data. Based on the selected template, the server automatically generates the email body.

[1012] Because an emotion engine is implemented, the server analyzes the user's emotions from their input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email body. For example, if the user is feeling grateful, the server will add expressions of gratitude to the email body. Also, if the user is feeling anxious, the server will generate reassuring sentences.

[1013] The generated email body is previewed on the user's terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[1014] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, an emotion engine analyzes the user's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer is making a complaint, the emotion engine uses the analysis results to generate a reply email expressing an apology.

[1015] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also previewed on the user's terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server actually sends the reply email using SMTP.

[1016] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[1017] On the other hand, consider the processing of incoming emails, for example, when an email requesting order cancellation is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the emotion engine analyzes the customer's emotions, and if the customer is expressing disappointment or anger, it generates a reply appropriate to that emotion (e.g., an apology or explanation). Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[1018] In this way, this system automates the creation and processing of user emails, and generates and replies emails that take user emotions into consideration, thereby significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[1019] The following describes the processing flow.

[1020] Step 1:

[1021] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[1022] Step 2:

[1023] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[1024] Step 3:

[1025] The server automatically generates the email body by embedding the user's input data into the selected template.

[1026] Step 4:

[1027] Before the server displays a preview of the email body on the user's terminal, it uses an emotion engine to analyze the user's input data to determine their emotions.

[1028] Step 5:

[1029] The server dynamically adjusts the tone and content of the generated email body based on the analysis results of the emotion engine. For example, if the user has the emotion of "gratitude," words reflecting that emotion will be added to the body of the email.

[1030] Step 6:

[1031] The server displays a preview of the edited email body on the user's device. The user reviews the preview and makes any necessary corrections.

[1032] Step 7:

[1033] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[1034] Step 8:

[1035] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[1036] ---

[1037] Step 1:

[1038] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[1039] Step 2:

[1040] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[1041] Step 3:

[1042] The server passes the content of the received email to the AI, which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[1043] Step 4:

[1044] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[1045] Step 5:

[1046] The server passes the generated reply email to the emotion engine, which analyzes the sender's emotions based on the content of the received email.

[1047] Step 6:

[1048] The server adjusts the tone and content of the reply email based on the analyzed sentiment. For example, if the recipient has expressed dissatisfaction, it will add an apology or explanation.

[1049] Step 7:

[1050] The server displays a preview of the adjusted reply email on the user's device, allowing the user to review the content. The user reviews the content and makes corrections as needed.

[1051] Step 8:

[1052] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[1053] Step 9:

[1054] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[1055] (Example 2)

[1056] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1057] Traditional email systems required users to manually compose emails and adjust the content according to their emotions, which was time-consuming and laborious. Furthermore, processing incoming emails was also done manually, resulting in long response times and a significant burden on users.

[1058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1059] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving email server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for analyzing the user's emotions using an emotion engine and dynamically adjusting the tone and content of the email body based on the analysis results, and means for analyzing the sender's emotions from the content of the received email using an emotion engine and dynamically adjusting the content of the reply email based on the analysis results. This enables the automatic generation and sending of emails that take the user's emotions into consideration, resulting in a significant reduction in time and effort.

[1060] A "user" is a person or group that uses the system to create and manage emails.

[1061] "Means for receiving data input" refers to interfaces or functions for receiving user input data (e.g., customer name, order number, date, etc.).

[1062] "Methods for selecting email templates" refers to algorithms or processes for automatically selecting appropriate email templates from a database.

[1063] "Methods for generating the body text" refers to the process or technology of creating the email body by integrating a selected template with data entered by the user.

[1064] "Means of previewing" refers to interfaces or functions that display the generated email content to the user in a format that allows them to review it.

[1065] "Means of sending emails based on send instructions" refers to protocols and functions for sending emails to designated recipients in response to a user's send instruction.

[1066] "Methods for retrieving new emails from an incoming mail server" refers to the functions and protocols used to periodically retrieve new emails from an incoming mail server.

[1067] "Methods of analysis using AI" refers to the process and technology of analyzing email content obtained using artificial intelligence and extracting important information and emotions.

[1068] "Means for determining whether a reply is necessary" refers to algorithms or processes that determine whether a reply is necessary to an incoming email based on the results of AI analysis.

[1069] "Methods for automatically generating reply emails" refers to processes and technologies that automatically generate reply emails using templates or sentiment engines when a reply is deemed necessary.

[1070] An "emotion engine" refers to algorithms and technologies that analyze the emotions of users and received emails, and dynamically adjust the content and tone of emails based on the analysis results.

[1071] "Dynamic adjustment methods" refer to functions or processes that automatically change the wording and expression of email content based on the results of sentiment analysis.

[1072] This invention is a system for streamlining the automated processing of user-generated and received emails. By combining this system with an emotion engine, it is possible to adapt the content and tone of emails to the user's emotions, minimizing user interaction while enabling fast and accurate email sending and receiving.

[1073] The user enters the necessary data (e.g., customer name, order number, date, etc.) through the terminal. The terminal sends the entered data to the server. The server receives the data sent by the user and selects an appropriate email template from the database based on that data.

[1074] The server automatically generates email content based on the selected template. Because an emotion engine is integrated, the server performs sentiment analysis on the user's input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email content. For example, if the user expresses gratitude, it adds expressions of gratitude to the email content. If the user is feeling anxious, it generates reassuring sentences. Specifically, IBM Watson Emotion Analysis is used as the emotion engine.

[1075] The generated email body is previewed on the terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button, the server uses SMTP to send the email to the recipient.

[1076] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the content of these retrieved emails is analyzed by AI (for example, the Google Cloud Natural Language API). The AI ​​analyzes the email body and extracts specific keywords and phrases. Furthermore, an emotion engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the reply content and tone based on the results. If a customer is making a complaint, the emotion engine uses the analysis results to generate an apologetic reply email.

[1077] Based on the analysis results, the server determines whether a reply is necessary, and if so, it automatically generates an appropriate reply email. This reply email is also previewed on the terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the transmission, the server sends the reply email using SMTP.

[1078] As a concrete example, when a user creates an order confirmation email, the user enters information such as the order number, customer name, and order date through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates an email body with the order number and customer name embedded. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[1079] An example of a prompt message is as follows:

[1080] "Customer Name: Taro Tanaka" "Order Number: 123456" "Order Date: 2023-10-01"

[1081] Thus, the present invention automates the processing of user-generated and received emails, and significantly reduces time and effort by generating and responding to emails that take user emotions into consideration. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[1082] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1083] Step 1:

[1084] The user enters data for creating an email into the device.

[1085] The user enters the necessary information (e.g., customer name, order number, date, etc.) into the input form on the device. The entered data is saved on the device.

[1086] Input: Customer name, order number, date, and other information.

[1087] Output: Input data is saved to the terminal.

[1088] Specific action: The user enters data into the form fields and clicks the submit button.

[1089] Step 2:

[1090] The terminal sends the input data to the server.

[1091] The terminal sends the data entered by the user to the server using HTTPS. This communication is encrypted.

[1092] Input: Input data saved on the device

[1093] Output: Input data is sent to the server.

[1094] Specific operation: The terminal program uses the input data to form a request and sends a POST request to the server via HTTPS.

[1095] Step 3:

[1096] The server selects the appropriate email template.

[1097] The server selects an appropriate email template from the database based on the received data. A conditional matching algorithm is used for template selection.

[1098] Input: Input data sent to the server

[1099] Output: An appropriate email template will be selected.

[1100] Specific operation: The server's algorithm queries the database based on the input data and retrieves the corresponding template.

[1101] Step 4:

[1102] The server uses an emotion engine to analyze the user's emotions.

[1103] The server inputs user data into the emotion engine and retrieves the results. The emotion engine uses, for example, IBM Watson Emotion Analysis.

[1104] Input: User input data

[1105] Output: Emotion analysis results

[1106] Specific operation: The server sends input data to the emotion engine, receives the analysis results, and saves those results to internal storage.

[1107] Step 5:

[1108] The server generates and adjusts the email body based on the analysis results.

[1109] The server dynamically generates the email body by incorporating the analysis results into the selected email template.

[1110] Input: Email template, sentiment analysis results

[1111] Output: Generated email body

[1112] Specific operation: The server integrates the template and sentiment analysis results and executes code to dynamically generate the email body.

[1113] Step 6:

[1114] The email body generated by the server is displayed as a preview on the terminal.

[1115] The generated email body will be displayed in the terminal's user interface.

[1116] Input: Generated email body

[1117] Output: The email body is displayed on the user's device.

[1118] Specific operation: The server sends the email body to the terminal, and the terminal receives it and executes code to display it on the screen.

[1119] Step 7:

[1120] The user reviews and edits the email content and then issues a send command.

[1121] The user reviews the previewed email content and makes any necessary corrections. Then, they click the send button to initiate the sending process.

[1122] Input: Previewed email body

[1123] Output: Email body after user review and modification, and sending instructions.

[1124] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[1125] Step 8:

[1126] The server sends the email via SMTP.

[1127] The server uses the SMTP protocol to send emails. A log of the sent emails is also saved.

[1128] Input: Confirmed and corrected email body, sending instructions

[1129] Output: The email is sent to the recipient, and the sending log is saved.

[1130] Specific operation: The server's SMTP client executes code to send an email to the specified recipient's address.

[1131] Step 9:

[1132] The server periodically retrieves new emails from the incoming mail server.

[1133] The server retrieves new emails from the incoming mail server at regular intervals. This interval is configurable.

[1134] Input: None (triggered by time interval)

[1135] Output: New emails are retrieved to the server.

[1136] Specific operation: The server retrieves new emails from the mail server using the POP3 / IMAP protocol.

[1137] Step 10:

[1138] The server uses AI to analyze the content of incoming emails.

[1139] The server passes the content of the received email to the AI ​​and retrieves the analysis results. The AI ​​uses the Google Cloud Natural Language API, among others.

[1140] Input: Newly received emails

[1141] Output: Analysis results of received emails

[1142] Specific operation: The server sends the received email to the AI ​​analysis engine and executes code to receive the analysis results.

[1143] Step 11:

[1144] The server will determine whether a response is needed based on the analysis results.

[1145] Based on the analysis results, the server executes an algorithm to determine whether a reply is necessary.

[1146] Input: Analysis results of received emails

[1147] Output: Result of the determination of whether a reply is needed.

[1148] Specific operation: The server's algorithm analyzes the results and executes code to determine whether a reply is necessary.

[1149] Step 12:

[1150] The server automatically generates a reply email and displays a preview on the device.

[1151] If a reply is deemed necessary, the server automatically generates a reply email and displays a preview on the user's device. The sentiment engine is also used at this stage.

[1152] Input: Email data if a reply is deemed necessary.

[1153] Output: Generated automated reply email, preview display

[1154] Specific operation: The server generates a reply email based on the reply email template, reflecting the sentiment analysis results, and executes code to send it to the terminal and display a preview.

[1155] Step 13:

[1156] The user reviews and corrects the reply email and issues a send command.

[1157] The user reviews the previewed reply email and makes any necessary corrections. Then, they click the send button to initiate submission.

[1158] Input: Previewed reply email

[1159] Output: Reply email after user confirmation and correction, and sending instructions.

[1160] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[1161] Step 14:

[1162] The server sends a reply email via SMTP.

[1163] The server uses the SMTP protocol to send a reply email. A log of the sent email is also saved.

[1164] Input: Confirmation and correction reply email, sending instructions

[1165] Output: A reply email is sent to the recipient, and a sending log is saved.

[1166] Specific operation: The server's SMTP client executes code to send a reply email to the specified recipient's address.

[1167] (Application Example 2)

[1168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1169] In modern brick-and-mortar stores, efficient and effective communication with customers is essential. However, many email correspondences are time-consuming and labor-intensive, and creating replies that take emotions into account is particularly difficult. Conventional automated email generation systems struggle to create emails that reflect the emotions of users and customers, resulting in decreased customer satisfaction and reduced efficiency in responses.

[1170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1171] In this invention, the server includes means for analyzing the user's emotions and dynamically adjusting the tone and content of the email body accordingly; means for extracting keywords from the received email content and generating a reply email based on those keywords; and means for displaying a preview of the generated email body to the user. This enables the automatic generation and sending of emails that reflect the emotions of users and customers, facilitating smoother communication with customers.

[1172] "Means for receiving data input from users" refers to an interface that imports information entered by users (e.g., customer name, order number, etc.) into the system.

[1173] "A means of selecting an email template and generating the body text based on data input" refers to a function that selects an appropriate email template from a database based on the entered data and automatically creates the body text of the email based on that template.

[1174] "A means of displaying a preview of the generated email body to the user" refers to a function that displays the content of the email generated by the system to the user in advance, allowing the user to check and modify the content.

[1175] "A means of sending emails based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user gives a sending instruction.

[1176] "Method for retrieving new emails from the receiving mail server" refers to a function that periodically retrieves newly received emails from the mail server.

[1177] "Methods for analyzing acquired email content using artificial intelligence" refers to a function that uses artificial intelligence to analyze the content of received emails and extract important keywords and sentiments.

[1178] "Means for determining whether a reply is necessary based on analysis results" refers to a function that automatically determines whether a reply is necessary based on the analyzed data.

[1179] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a function in which the system automatically creates an appropriate reply email when it is determined that a reply is required.

[1180] "A means of displaying a preview of the generated reply email to the user" refers to a function that allows the system to display the automatically generated reply email to the user in advance, enabling them to review and modify it.

[1181] "A means of sending a reply email based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user instructs the system to send a reply email.

[1182] "A means of analyzing user emotions and dynamically adjusting the tone and content of emails accordingly" refers to a function that analyzes user emotions from input data and automatically changes the tone and content of emails accordingly.

[1183] "A means of extracting keywords from the content of a received email and generating a reply email based on those keywords" refers to a function that extracts important keywords from a received email and automatically creates an appropriate reply based on those keywords.

[1184] This invention is a system that selects an email template based on data input from the user, generates the email body, and adjusts the content and tone of the email after analyzing the user's emotions. This system operates in cooperation with three parties: the server, the terminal, and the user.

[1185] First, this system provides an interface that allows users to input data via a terminal. The data entered by the user (e.g., customer name, order number, date, etc.) is sent to the server. The server receives this data, selects an appropriate email template from the database, and generates the email body based on the selected template.

[1186] The server is equipped with an emotion analysis engine that analyzes the user's emotions from their input data. Based on this analysis, the server dynamically adjusts the tone and content of the generated email body. For example, if the user is feeling grateful, the server adds expressions of gratitude to the email body. If the user is feeling anxious, the server generates reassuring sentences.

[1187] The generated email body is displayed as a preview on the terminal. The user reviews the email content and makes any necessary corrections. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[1188] Next, let's discuss the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server. The content of these retrieved emails is analyzed by artificial intelligence installed on the server. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, a sentiment analysis engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer sends a complaint, the sentiment engine uses the analysis results to generate a reply email expressing an apology.

[1189] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also displayed as a preview on the terminal, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server sends the reply email using SMTP.

[1190] Hardware and software to be used

[1191] Hardware: Smartphones, servers

[1192] Software: Python, smtplib (SMTP library), sentiment analysis engine (e.g., IBM Watson), natural language processing library (e.g., Spacy)

[1193] Specific example

[1194] Specific example of email generation: When a user creates an "order confirmation email," they input the order number, customer name, order date, etc., through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The sentiment analysis engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body.

[1195] Specific example of processing incoming emails: When an email with an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the sentiment analysis engine analyzes the customer's emotions, and if the customer is showing disappointment or anger, for example, it generates a reply appropriate to that emotion (e.g., an apology or explanation).

[1196] Example of a prompt

[1197] Please generate an email message for when a customer cancels an order. Assume the customer is angry about the cancellation.

[1198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1199] Step 1:

[1200] The user enters data (e.g., customer name, order number, order date, etc.) through the terminal. The terminal sends the data entered by the user to the server. The entered data becomes the basic information used to generate emails.

[1201] Step 2:

[1202] Based on the data received by the server, it selects an appropriate email template from the database. The database contains various email templates, and the server extracts the template that matches the specified criteria. The email body is automatically generated by embedding the user's entered data into the selected template.

[1203] Step 3:

[1204] The emotion analysis engine installed on the server analyzes the user's input data to determine their emotions. A natural language processing library (e.g., Spacy) is used for this analysis. Based on the analyzed emotions, the server dynamically adjusts the tone and content of the generated email body. For example, if an emotion of gratitude is detected, an expression of gratitude will be added to the email body.

[1205] Step 4:

[1206] The server displays a preview of the generated email body on the user's device. The user reviews the email content through the device and makes corrections as needed.

[1207] Step 5:

[1208] When a user issues a send command on their device, the device transmits it to the server, which then uses SMTP to send the final email to the recipient. In this step, the sent email reaches the customer in its complete form.

[1209] Step 6:

[1210] In processing incoming emails, the server periodically retrieves new emails from the incoming mail server. The content of the retrieved emails is also analyzed on the server.

[1211] Step 7:

[1212] The content of received emails is analyzed by the server's artificial intelligence and sentiment analysis engine. Specifically, natural language processing technology is used to analyze the email body and extract specific keywords and phrases. At the same time, the sentiment analysis engine analyzes the sender's emotions.

[1213] Step 8:

[1214] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email includes the extracted keywords and content corresponding to the analyzed sentiment.

[1215] Step 9:

[1216] The server displays a preview of the generated reply email on the user's device. The user then reviews the content of the reply email through their device and makes any necessary corrections.

[1217] Step 10:

[1218] When a user initiates a reply email from their device, that instruction is transmitted to the server, which then uses SMTP to send the reply email. This ensures that the customer receives an appropriate and emotionally sensitive reply.

[1219] Through each of the steps described so far, the system can efficiently generate and send / receive emails that take into account the emotions of users and customers.

[1220] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1221] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1222] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1223] [Fourth Embodiment]

[1224] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1225] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1226] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1227] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1228] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1229] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1230] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1231] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1232] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1233] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1234] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1235] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1236] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1237] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[1238] First, the user enters data to create an email. The user enters the necessary information (e.g., customer name, order number, date, etc.) through their terminal. The server receives this input data and selects an appropriate email template from its database. Based on the selected template, the server generates the email body and displays a preview of this generated email body to the user. The user can review this preview and make corrections as needed. After the user issues a send command, the server sends the email.

[1239] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to extract specific keywords and phrases from the email body. Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. This generated reply email is also displayed to the user as a preview, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email.

[1240] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the order number and customer name. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[1241] On the other hand, when processing incoming emails, for example, if an email containing an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[1242] In this way, this system automates the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[1243] The following describes the processing flow.

[1244] Step 1:

[1245] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[1246] Step 2:

[1247] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[1248] Step 3:

[1249] The server automatically generates the email body by embedding the user's input data into the selected template.

[1250] Step 4:

[1251] The server sends the generated email body to the terminal and displays a preview to the user. The user reviews the email content and makes corrections as needed.

[1252] Step 5:

[1253] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[1254] Step 6:

[1255] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[1256] ---

[1257] Step 1:

[1258] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[1259] Step 2:

[1260] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[1261] Step 3:

[1262] The server passes the content of the received email to the AI ​​(artificial intelligence), which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[1263] Step 4:

[1264] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[1265] Step 5:

[1266] The server sends the generated reply email to the terminal and displays a preview for the user. The user reviews the reply and makes corrections as needed.

[1267] Step 6:

[1268] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[1269] Step 7:

[1270] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[1271] (Example 1)

[1272] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1273] Traditional email creation and processing systems required users to perform many tasks manually, resulting in a significant burden of time and effort. Furthermore, the ability to accurately analyze incoming emails and automatically generate replies was insufficient. As a result, users had to dedicate a considerable amount of time to email processing, hindering their ability to work efficiently.

[1274] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1275] In this invention, the server includes means for receiving electronic information input from a user, means for selecting a communication template based on the electronic information input and generating the body text, and means for displaying the generated communication body text as a preview to the user. This enables the user to create and send emails accurately and quickly with minimal operation. The server also includes means for acquiring new communications from a receiving communication server, means for analyzing the acquired communication content using a generation AI model, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply communication if a reply is determined to be necessary, and means for displaying the generated reply communication as a preview to the user. This enables efficient and accurate content analysis of received emails and automatic replies, significantly reducing the burden on the user.

[1276] A "user" is an individual or organization that operates the system, inputs electronic information, or issues transmission instructions.

[1277] "Electronic data entry" refers to data that a user provides to the system using a terminal (e.g., order number, customer name, order date).

[1278] A "communication template" is a pre-prepared document template with a specified format and content, used to generate the body of an email.

[1279] "Preview display" is a function that displays generated communications or email body text on the screen for the user to check and edit.

[1280] A "transmit instruction" is an operation or command that a user makes to a system in order to send an electronic message.

[1281] An "electronic message" is a digital message that a user sends to another individual or organization through a system.

[1282] A "receiving communication server" is a server that stores newly received communications and makes them accessible to the system.

[1283] "Acquisition" refers to the operation in which a server retrieves new communications from a receiving communication server and makes them available for use within the system.

[1284] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to analyze data and perform a specific task (e.g., natural language processing).

[1285] "Analysis" refers to the process of processing acquired communication content using a generation AI model to extract specific keywords or patterns.

[1286] "Determining whether a reply is necessary" is the process by which the system determines, based on the analysis results, whether a reply is required.

[1287] A "reply message" is an electronic message automatically generated by the server when analysis determines that a reply is necessary.

[1288] This invention is a system that streamlines the automatic processing of user-generated and received emails. This system enables users to create and send emails with minimal operation, and to analyze the content of received emails and send automatic replies.

[1289] First, the user uses a terminal to input data to compose an email. The user accesses the input form on the terminal and enters the necessary information (e.g., order number, customer name, order date, etc.). The server receives this input data and selects the appropriate communication template from the database. Specifically, a common relational database (e.g., MySQL or PostgreSQL) can be used. Based on the selected template, the server generates the email body. A common template engine (e.g., Handlebars.js or Mustache) can be used for template processing for generation.

[1290] The generated email body is displayed as a preview on the user's device. The user can review this preview and make corrections as needed. Once corrections are complete, the user clicks the send button to send a transmission command to the server. The server receives this transmission command and sends the email using the SMTP protocol. As a concrete example, consider the case where a user creates an "order confirmation email." The user enters the order number "12345," customer name "Customer Name," and order date "October 1, 2023" through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by filling in the customer name and order number. The generated email is displayed as a preview to the user, and once the user reviews it and clicks the send button, the server sends the email to the customer.

[1291] Next, the system also automates the processing of incoming emails. The server periodically retrieves new communications from the receiving communication server. The retrieved emails are analyzed on the server by a generative AI model. For analysis, natural language processing models such as GPT-3 and BERT can be used. These models can run on cloud platforms such as Amazon Web Services (AWS) and Google Cloud Platform (GCP).

[1292] The AI ​​model extracts specific keywords and phrases from the email body and determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email is also displayed as a preview on the user's device, allowing the user to review the content and make any necessary corrections. Finally, when the user issues a send command, the server sends the reply email. As a concrete example, consider the case where an "order cancellation request" email is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." If the AI ​​model determines that a reply is necessary based on its analysis results, the server automatically generates a reply email stating "Cancellation request received." This reply email is displayed as a preview to the user, and once the user reviews it and issues a send command, the server sends the reply email to the customer.

[1293] Examples of prompt messages include: "Please enter the information needed to create an order confirmation email. Please specify the order number, customer name, and order date," and "We will analyze newly received emails and generate an automatic reply email if necessary. Please provide the content of the email to be analyzed."

[1294] Thus, the system of the present invention streamlines the processing of emails created and received by users, significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation.

[1295] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1296] System program processing flow

[1297] Step 1:

[1298] The user uses the terminal to enter the necessary data to compose an email. The user accesses the input form on the terminal and enters information such as order number, customer name, and order date. This generates the input data.

[1299] Input: Data entered by the user into the input form on the device (e.g., order number, customer name, order date)

[1300] Output: Input data sent to the system

[1301] Step 2:

[1302] The server receives the input data provided by the user. Based on the received data, the server accesses the database and selects the appropriate communication template.

[1303] Input: Input data sent by the user from their device.

[1304] Output: Selection of an appropriate communication template (e.g., "Order Confirmation Email" template)

[1305] Step 3:

[1306] The server generates the email body based on the selected communication template. During generation, it performs a process of embedding input data. Specifically, customer names, order numbers, etc., are embedded in the template's placeholders.

[1307] Input: Selected communication template, user input data

[1308] Output: Generated email body (Example: "Dear Customer Name, we have confirmed your order for order number 12345")

[1309] Step 4:

[1310] The generated email body is displayed as a preview on the user's device. The user reviews this preview and makes corrections as needed.

[1311] Input: Generated email body

[1312] Output: Email body modified by the user (if necessary)

[1313] Step 5:

[1314] When a user issues a send command, the server receives this command and sends the email. The server uses the SMTP protocol to deliver the email to the recipient.

[1315] Input: User's sending instructions, revised email body

[1316] Output: Email delivered to the recipient

[1317] Step 6:

[1318] The server periodically retrieves new communications from the receiving server. This is done using the IMAP protocol.

[1319] Input: New communication from the receiving communication server

[1320] Output: New communications acquired

[1321] Step 7:

[1322] The server analyzes the acquired communication content using a generation AI model. Natural language processing techniques are used for the analysis to extract specific keywords and phrases. For example, keywords such as "cancel" and "request" are extracted.

[1323] Input: Newly acquired communications

[1324] Output: Analysis results (specific keywords or phrases)

[1325] Step 8:

[1326] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email.

[1327] Input: Analysis results

[1328] Output: Determination of whether a reply is necessary, and a reply email generated if necessary.

[1329] Step 9:

[1330] The generated reply email is displayed as a preview on the user's device. The user reviews the content and makes corrections as needed.

[1331] Input: Generated reply email

[1332] Output: User-modified reply email (if necessary)

[1333] Step 10:

[1334] When a user issues a send command, the server receives this command and sends a reply email. The server uses the SMTP protocol to deliver the reply email to the recipient.

[1335] Input: User's submission instructions, revised reply email.

[1336] Output: Reply email delivered to the recipient

[1337] In this way, the user, terminal, and server cooperate at each step to efficiently and accurately create and process emails.

[1338] (Application Example 1)

[1339] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1340] In modern content delivery services, streamlining user support and email marketing is a critical challenge. Manual email creation and sending is time-consuming and labor-intensive, and while quick responses are required, delays can occur due to time differences and staff shortages. Furthermore, immediate responses to user inquiries are essential for improving the user experience, and automation is needed in this area as well. To solve these challenges, an automated system for email creation and sending / receiving is required.

[1341] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1342] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving mail server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for automating user support related to content distribution, means for generating the reply email body using a generation AI model, and means for creating reply content based on prompt text. This enables efficient and rapid response in both user support and email newsletter distribution.

[1343] "Means for receiving data input from users" refers to providing an interface for users to input necessary information into the system.

[1344] "A method for selecting an email template and generating the email body" refers to a process that selects an appropriate email template from a database based on the entered data and automatically generates the email body using that template.

[1345] "A means of displaying a preview of the generated email body to the user" refers to a method of displaying a preview of the automatically generated email content so that the user can check and correct it before sending.

[1346] "A means of sending emails based on instructions from the user" refers to a method of receiving instructions from the user to send an email after they have reviewed and corrected it, and then sending the email based on those instructions.

[1347] "Methods for retrieving new emails from the receiving mail server" refers to the process of periodically retrieving new emails that have arrived on the receiving mail server.

[1348] "Methods for analyzing acquired email content using AI" refer to methods for analyzing received emails using artificial intelligence technology to understand their content.

[1349] "A means of determining whether a reply is necessary based on analysis results" refers to determining whether a reply is necessary based on the content of the email analyzed by AI.

[1350] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a system that automatically generates an appropriate reply email when a reply is deemed necessary.

[1351] "A means of displaying a preview of the generated reply email to the user" refers to a method of displaying a preview of the automatically generated reply email so that the user can review and modify it.

[1352] "A means of sending a reply email based on instructions from the user" refers to a method of receiving instructions from the user to send a reply email after they have reviewed and corrected it, and then sending the reply email based on those instructions.

[1353] "Means for automating user support related to content distribution" refers to a system that automates support for user inquiries that arise in content distribution services.

[1354] "A method for generating reply email content using a generative AI model" refers to a method that uses an artificial intelligence model to automatically generate appropriate reply content for received emails.

[1355] "Means for creating a response based on a prompt" refers to a method by which an artificial intelligence model creates the most suitable response for the user based on the prompt (input) text.

[1356] This invention provides a system that streamlines the creation, sending, and automatic analysis and replying to of emails related to a user's content delivery service. This system enables users to send and receive emails quickly and accurately with minimal effort.

[1357] This system mainly consists of the following elements:

[1358] 1. Means by which users input data: Users input necessary information (e.g., customer name, campaign information, inquiry details, etc.) through a terminal. Web forms, dedicated applications, etc., are used as the data input interface.

[1359] 2. Method for selecting an email template and generating the email body: The server receives data entered by the user and selects an appropriate email template from the database. Based on this template, the server generates the email body.

[1360] 3. Means for displaying a preview of the generated email body to the user: The generated email body will be displayed as a preview on the user's terminal. The user can review it and make corrections as needed.

[1361] 4. Means of sending emails based on user instructions: When a user issues a send instruction, the server sends the email to the specified recipient.

[1362] 5. Means of retrieving new emails from the incoming mail server: The server has a function to periodically retrieve new emails from the incoming mail server.

[1363] 6. Method for analyzing acquired email content using AI: Acquired emails are analyzed using a generative AI model implemented on the server. This model uses natural language processing technology to extract specific keywords and phrases from the email content.

[1364] 7. Means for determining whether a reply is necessary based on the analysis results: The server determines whether a reply is necessary based on the analysis results.

[1365] 8. Means for automatically generating a reply email when a reply is deemed necessary: ​​When a reply is deemed necessary, the server will automatically generate a reply email using a generation AI model based on the prompt message.

[1366] 9. Means for displaying generated reply emails to the user as a preview: Automatically generated reply emails are displayed as a preview on the user's terminal, allowing the user to review and modify the content.

[1367] 10. Means for sending reply emails based on user instructions: When a user issues a send instruction, the server sends a reply email to the specified recipient.

[1368] This system uses the following hardware and software:

[1369] Hardware: Servers (mail servers, AI computing servers), user terminals (PCs, smartphones, tablets)

[1370] Software: Python execution environment, open-source email libraries (smtplib, imaplib), generative AI models (OpenAI API)

[1371] As a concrete example, consider a scenario where a content distribution service administrator sends an email to notify customers of new content. The administrator inputs the new content information via a terminal, and the system selects an appropriate email template based on that input and automatically generates the email body. The generated email body is previewed by the administrator, and after confirmation, it is sent in bulk.

[1372] Furthermore, the AI ​​model analyzes user inquiry emails and generates appropriate replies. An example of a prompt message is as follows:

[1373] text

[1374] Subject: Regarding new service features

[1375] Text: Please provide information about the new features of the service.

[1376] Please compose your reply email based on the above information.

[1377] This allows users to send and receive emails quickly and efficiently, significantly improving the operational efficiency of content delivery services.

[1378] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1379] Step 1:

[1380] Users enter data through their devices.

[1381] Users use their devices (PCs, smartphones, tablets) to input necessary data such as new content information, customer information, and inquiries. The entered data is then sent from the device to the server.

[1382] Step 2:

[1383] The server selects an email template and generates the email body.

[1384] The server selects an appropriate email template from the database based on the received data. It then generates the email body by embedding the user's entered information into the selected template. This process involves retrieving data from the database and embedding it into the template.

[1385] Step 3:

[1386] The server displays a preview of the generated email body to the user.

[1387] The server sends the generated email body to the user's terminal for preview. The user can review and edit the email content before sending.

[1388] Step 4:

[1389] The user issues a send command, and the server sends the email.

[1390] Once the user reviews the email content and issues a send command, the server sends the email to the specified recipient. The user is then notified of the sending result.

[1391] Step 5:

[1392] The server retrieves new emails from the incoming mail server.

[1393] The server retrieves new emails from the receiving mail server at regular intervals. This process involves accessing the receiving mail server and downloading new emails.

[1394] Step 6:

[1395] The server uses AI to analyze the email content it has retrieved.

[1396] The server inputs the retrieved email content into a generative AI model, which then analyzes the email's content. The generative AI model uses natural language processing techniques to extract keywords and important phrases from the email body. Based on these analysis results, it then performs the following processing.

[1397] Step 7:

[1398] The server will determine whether a reply is necessary based on the analysis results.

[1399] The server determines whether a reply is necessary based on the AI ​​analysis results. For example, if keywords such as "cancel" or "request" are included, it will determine that a reply is necessary.

[1400] Step 8:

[1401] The server automatically generates a reply email.

[1402] If a reply is deemed necessary, the server automatically generates a reply email using a generation AI model based on the specified prompt text. This process involves inputting the prompt text and automatically generating the reply email.

[1403] Step 9:

[1404] The server displays a preview of the generated reply email to the user.

[1405] The server sends the generated reply email to the user's terminal for preview. The user can review the reply email content and make corrections as needed.

[1406] Step 10:

[1407] The user issues a send command, and the server sends a reply email.

[1408] Once the user reviews the reply email and issues a send command, the server sends the reply email to the specified recipient. The server then notifies the user of the sending result.

[1409] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1410] This invention relates to a system that streamlines the automated processing of user-generated and received emails by incorporating an emotion engine to adapt the content and tone of emails to the user's emotions. This system enables the generation and replies of emails that take the user's emotions into consideration.

[1411] First, the user enters the necessary data (e.g., customer name, order number, date, etc.) through their terminal. This data is used to create the email. The server receives the data entered by the user and selects an appropriate email template from its database based on that data. Based on the selected template, the server automatically generates the email body.

[1412] Because an emotion engine is implemented, the server analyzes the user's emotions from their input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email body. For example, if the user is feeling grateful, the server will add expressions of gratitude to the email body. Also, if the user is feeling anxious, the server will generate reassuring sentences.

[1413] The generated email body is previewed on the user's terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[1414] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the AI ​​analyzes the content of these retrieved emails. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, an emotion engine analyzes the user's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer is making a complaint, the emotion engine uses the analysis results to generate a reply email expressing an apology.

[1415] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also previewed on the user's terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server actually sends the reply email using SMTP.

[1416] As a concrete example, consider a case where a user creates an "order confirmation email." The user enters information such as the order number, customer name, and order date through their terminal. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[1417] On the other hand, consider the processing of incoming emails, for example, when an email requesting order cancellation is received. The server retrieves this email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the emotion engine analyzes the customer's emotions, and if the customer is expressing disappointment or anger, it generates a reply appropriate to that emotion (e.g., an apology or explanation). Based on this analysis, the AI ​​determines that a reply is necessary, and the server automatically generates an appropriate reply email. This reply email is displayed as a preview to the user, and once the user confirms and submits it, the server sends the reply email.

[1418] In this way, this system automates the creation and processing of user emails, and generates and replies emails that take user emotions into consideration, thereby significantly reducing time and effort. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[1419] The following describes the processing flow.

[1420] Step 1:

[1421] The user enters the necessary data (e.g., customer name, order number, date, etc.) through their device. This data is used to create the email.

[1422] Step 2:

[1423] The server receives data entered by the user and selects an appropriate email template from the database based on that data. This template defines the structure and format of the email.

[1424] Step 3:

[1425] The server automatically generates the email body by embedding the user's input data into the selected template.

[1426] Step 4:

[1427] Before the server displays a preview of the email body on the user's terminal, it uses an emotion engine to analyze the user's input data to determine their emotions.

[1428] Step 5:

[1429] The server dynamically adjusts the tone and content of the generated email body based on the analysis results of the emotion engine. For example, if the user has the emotion of "gratitude," words reflecting that emotion will be added to the body of the email.

[1430] Step 6:

[1431] The server displays a preview of the edited email body on the user's device. The user reviews the preview and makes any necessary corrections.

[1432] Step 7:

[1433] The user checks the email content on their device, and once they have finished making corrections, they click the send button to initiate the sending process.

[1434] Step 8:

[1435] The server, based on the user's sending instruction, actually sends the email to the recipient using SMTP (Simple Mail Transfer Protocol).

[1436] ---

[1437] Step 1:

[1438] The server accesses the incoming mail server to check for new mail. This check is performed periodically.

[1439] Step 2:

[1440] The server retrieves new emails from the incoming mail server. The retrieved emails are then stored as is.

[1441] Step 3:

[1442] The server passes the content of the received email to the AI, which then begins the analysis. The AI ​​uses natural language processing (NLP) techniques to analyze the email body and extract specific keywords and phrases.

[1443] Step 4:

[1444] The server determines whether a reply is necessary based on the results analyzed by the AI. If a reply is deemed necessary, the server automatically generates an appropriate reply email.

[1445] Step 5:

[1446] The server passes the generated reply email to the emotion engine, which analyzes the sender's emotions based on the content of the received email.

[1447] Step 6:

[1448] The server adjusts the tone and content of the reply email based on the analyzed sentiment. For example, if the recipient has expressed dissatisfaction, it will add an apology or explanation.

[1449] Step 7:

[1450] The server displays a preview of the adjusted reply email on the user's device, allowing the user to review the content. The user reviews the content and makes corrections as needed.

[1451] Step 8:

[1452] The user reviews the reply on their device, and once they have finished making corrections, they click the send button to initiate submission.

[1453] Step 9:

[1454] The server actually sends the reply email using SMTP, based on the user's sending instruction.

[1455] (Example 2)

[1456] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1457] Traditional email systems required users to manually compose emails and adjust the content according to their emotions, which was time-consuming and laborious. Furthermore, processing incoming emails was also done manually, resulting in long response times and a significant burden on users.

[1458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1459] In this invention, the server includes means for receiving data input from a user, means for selecting an email template based on the data input and generating the email body, means for displaying a preview of the generated email body to the user, means for sending an email based on a send instruction from the user, means for acquiring new emails from a receiving email server, means for analyzing the acquired email content using AI, means for determining whether a reply is necessary based on the analysis results, means for automatically generating a reply email if a reply is deemed necessary, means for displaying a preview of the generated reply email to the user, means for sending a reply email based on a send instruction from the user, means for analyzing the user's emotions using an emotion engine and dynamically adjusting the tone and content of the email body based on the analysis results, and means for analyzing the sender's emotions from the content of the received email using an emotion engine and dynamically adjusting the content of the reply email based on the analysis results. This enables the automatic generation and sending of emails that take the user's emotions into consideration, resulting in a significant reduction in time and effort.

[1460] A "user" is a person or group that uses the system to create and manage emails.

[1461] "Means for receiving data input" refers to interfaces or functions for receiving user input data (e.g., customer name, order number, date, etc.).

[1462] "Methods for selecting email templates" refers to algorithms or processes for automatically selecting appropriate email templates from a database.

[1463] "Methods for generating the body text" refers to the process or technology of creating the email body by integrating a selected template with data entered by the user.

[1464] "Means of previewing" refers to interfaces or functions that display the generated email content to the user in a format that allows them to review it.

[1465] "Means of sending emails based on send instructions" refers to protocols and functions for sending emails to designated recipients in response to a user's send instruction.

[1466] "Methods for retrieving new emails from an incoming mail server" refers to the functions and protocols used to periodically retrieve new emails from an incoming mail server.

[1467] "Methods of analysis using AI" refers to the process and technology of analyzing email content obtained using artificial intelligence and extracting important information and emotions.

[1468] "Means for determining whether a reply is necessary" refers to algorithms or processes that determine whether a reply is necessary to an incoming email based on the results of AI analysis.

[1469] "Methods for automatically generating reply emails" refers to processes and technologies that automatically generate reply emails using templates or sentiment engines when a reply is deemed necessary.

[1470] An "emotion engine" refers to algorithms and technologies that analyze the emotions of users and received emails, and dynamically adjust the content and tone of emails based on the analysis results.

[1471] "Dynamic adjustment methods" refer to functions or processes that automatically change the wording and expression of email content based on the results of sentiment analysis.

[1472] This invention is a system for streamlining the automated processing of user-generated and received emails. By combining this system with an emotion engine, it is possible to adapt the content and tone of emails to the user's emotions, minimizing user interaction while enabling fast and accurate email sending and receiving.

[1473] The user enters the necessary data (e.g., customer name, order number, date, etc.) through the terminal. The terminal sends the entered data to the server. The server receives the data sent by the user and selects an appropriate email template from the database based on that data.

[1474] The server automatically generates email content based on the selected template. Because an emotion engine is integrated, the server performs sentiment analysis on the user's input data. Based on this analysis, the server can dynamically adjust the tone and content of the generated email content. For example, if the user expresses gratitude, it adds expressions of gratitude to the email content. If the user is feeling anxious, it generates reassuring sentences. Specifically, IBM Watson Emotion Analysis is used as the emotion engine.

[1475] The generated email body is previewed on the terminal by the server. The user can review the email content and make corrections as needed. After the user clicks the send button, the server uses SMTP to send the email to the recipient.

[1476] Next, the system also automates the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server, and the content of these retrieved emails is analyzed by AI (for example, the Google Cloud Natural Language API). The AI ​​analyzes the email body and extracts specific keywords and phrases. Furthermore, an emotion engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the reply content and tone based on the results. If a customer is making a complaint, the emotion engine uses the analysis results to generate an apologetic reply email.

[1477] Based on the analysis results, the server determines whether a reply is necessary, and if so, it automatically generates an appropriate reply email. This reply email is also previewed on the terminal by the server, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the transmission, the server sends the reply email using SMTP.

[1478] As a concrete example, when a user creates an order confirmation email, the user enters information such as the order number, customer name, and order date through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates an email body with the order number and customer name embedded. The emotion engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body. The generated email is displayed as a preview to the user, and once the user confirms and sends it, the server sends the email to the customer.

[1479] An example of a prompt message is as follows:

[1480] "Customer Name: Taro Tanaka" "Order Number: 123456" "Order Date: 2023-10-01"

[1481] Thus, the present invention automates the processing of user-generated and received emails, and significantly reduces time and effort by generating and responding to emails that take user emotions into consideration. Users can send and receive emails quickly and accurately with minimal operation, and communication with customers can proceed smoothly.

[1482] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1483] Step 1:

[1484] The user enters data for creating an email into the device.

[1485] The user enters the necessary information (e.g., customer name, order number, date, etc.) into the input form on the device. The entered data is saved on the device.

[1486] Input: Customer name, order number, date, and other information.

[1487] Output: Input data is saved to the terminal.

[1488] Specific action: The user enters data into the form fields and clicks the submit button.

[1489] Step 2:

[1490] The terminal sends the input data to the server.

[1491] The terminal sends the data entered by the user to the server using HTTPS. This communication is encrypted.

[1492] Input: Input data saved on the device

[1493] Output: Input data is sent to the server.

[1494] Specific operation: The terminal program uses the input data to form a request and sends a POST request to the server via HTTPS.

[1495] Step 3:

[1496] The server selects the appropriate email template.

[1497] The server selects an appropriate email template from the database based on the received data. A conditional matching algorithm is used for template selection.

[1498] Input: Input data sent to the server

[1499] Output: An appropriate email template will be selected.

[1500] Specific operation: The server's algorithm queries the database based on the input data and retrieves the corresponding template.

[1501] Step 4:

[1502] The server uses an emotion engine to analyze the user's emotions.

[1503] The server inputs user data into the emotion engine and retrieves the results. The emotion engine uses, for example, IBM Watson Emotion Analysis.

[1504] Input: User input data

[1505] Output: Emotion analysis results

[1506] Specific operation: The server sends input data to the emotion engine, receives the analysis results, and saves those results to internal storage.

[1507] Step 5:

[1508] The server generates and adjusts the email body based on the analysis results.

[1509] The server dynamically generates the email body by incorporating the analysis results into the selected email template.

[1510] Input: Email template, sentiment analysis results

[1511] Output: Generated email body

[1512] Specific operation: The server integrates the template and sentiment analysis results and executes code to dynamically generate the email body.

[1513] Step 6:

[1514] The email body generated by the server is displayed as a preview on the terminal.

[1515] The generated email body will be displayed in the terminal's user interface.

[1516] Input: Generated email body

[1517] Output: The email body is displayed on the user's device.

[1518] Specific operation: The server sends the email body to the terminal, and the terminal receives it and executes code to display it on the screen.

[1519] Step 7:

[1520] The user reviews and edits the email content and then issues a send command.

[1521] The user reviews the previewed email content and makes any necessary corrections. Then, they click the send button to initiate the sending process.

[1522] Input: Previewed email body

[1523] Output: Email body after user review and modification, and sending instructions.

[1524] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[1525] Step 8:

[1526] The server sends the email via SMTP.

[1527] The server uses the SMTP protocol to send emails. A log of the sent emails is also saved.

[1528] Input: Confirmed and corrected email body, sending instructions

[1529] Output: The email is sent to the recipient, and the sending log is saved.

[1530] Specific operation: The server's SMTP client executes code to send an email to the specified recipient's address.

[1531] Step 9:

[1532] The server periodically retrieves new emails from the incoming mail server.

[1533] The server retrieves new emails from the incoming mail server at regular intervals. This interval is configurable.

[1534] Input: None (triggered by time interval)

[1535] Output: New emails are retrieved to the server.

[1536] Specific operation: The server retrieves new emails from the mail server using the POP3 / IMAP protocol.

[1537] Step 10:

[1538] The server uses AI to analyze the content of incoming emails.

[1539] The server passes the content of the received email to the AI ​​and retrieves the analysis results. The AI ​​uses the Google Cloud Natural Language API, among others.

[1540] Input: Newly received emails

[1541] Output: Analysis results of received emails

[1542] Specific operation: The server sends the received email to the AI ​​analysis engine and executes code to receive the analysis results.

[1543] Step 11:

[1544] The server will determine whether a response is needed based on the analysis results.

[1545] Based on the analysis results, the server executes an algorithm to determine whether a reply is necessary.

[1546] Input: Analysis results of received emails

[1547] Output: Result of the determination of whether a reply is needed.

[1548] Specific operation: The server's algorithm analyzes the results and executes code to determine whether a reply is necessary.

[1549] Step 12:

[1550] The server automatically generates a reply email and displays a preview on the device.

[1551] If a reply is deemed necessary, the server automatically generates a reply email and displays a preview on the user's device. The sentiment engine is also used at this stage.

[1552] Input: Email data if a reply is deemed necessary.

[1553] Output: Generated automated reply email, preview display

[1554] Specific operation: The server generates a reply email based on the reply email template, reflecting the sentiment analysis results, and executes code to send it to the terminal and display a preview.

[1555] Step 13:

[1556] The user reviews and corrects the reply email and issues a send command.

[1557] The user reviews the previewed reply email and makes any necessary corrections. Then, they click the send button to initiate submission.

[1558] Input: Previewed reply email

[1559] Output: Reply email after user confirmation and correction, and sending instructions.

[1560] Specific action: The user makes corrections in the preview screen and clicks the submit button.

[1561] Step 14:

[1562] The server sends a reply email via SMTP.

[1563] The server uses the SMTP protocol to send a reply email. A log of the sent email is also saved.

[1564] Input: Confirmation and correction reply email, sending instructions

[1565] Output: A reply email is sent to the recipient, and a sending log is saved.

[1566] Specific operation: The server's SMTP client executes code to send a reply email to the specified recipient's address.

[1567] (Application Example 2)

[1568] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1569] In modern brick-and-mortar stores, efficient and effective communication with customers is essential. However, many email correspondences are time-consuming and labor-intensive, and creating replies that take emotions into account is particularly difficult. Conventional automated email generation systems struggle to create emails that reflect the emotions of users and customers, resulting in decreased customer satisfaction and reduced efficiency in responses.

[1570] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[1571] In this invention, the server includes means for analyzing the user's emotions and dynamically adjusting the tone and content of the email body accordingly; means for extracting keywords from the received email content and generating a reply email based on those keywords; and means for displaying a preview of the generated email body to the user. This enables the automatic generation and sending of emails that reflect the emotions of users and customers, facilitating smoother communication with customers.

[1572] "Means for receiving data input from users" refers to an interface that imports information entered by users (e.g., customer name, order number, etc.) into the system.

[1573] "A means of selecting an email template and generating the body text based on data input" refers to a function that selects an appropriate email template from a database based on the entered data and automatically creates the body text of the email based on that template.

[1574] "A means of displaying a preview of the generated email body to the user" refers to a function that displays the content of the email generated by the system to the user in advance, allowing the user to check and modify the content.

[1575] "A means of sending emails based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user gives a sending instruction.

[1576] "Method for retrieving new emails from the receiving mail server" refers to a function that periodically retrieves newly received emails from the mail server.

[1577] "Methods for analyzing acquired email content using artificial intelligence" refers to a function that uses artificial intelligence to analyze the content of received emails and extract important keywords and sentiments.

[1578] "Means for determining whether a reply is necessary based on analysis results" refers to a function that automatically determines whether a reply is necessary based on the analyzed data.

[1579] "A means of automatically generating a reply email when a reply is deemed necessary" refers to a function in which the system automatically creates an appropriate reply email when it is determined that a reply is required.

[1580] "A means of displaying a preview of the generated reply email to the user" refers to a function that allows the system to display the automatically generated reply email to the user in advance, enabling them to review and modify it.

[1581] "A means of sending a reply email based on a user's sending instruction" refers to a function that sends an email to a specified recipient when a user instructs the system to send a reply email.

[1582] "A means of analyzing user emotions and dynamically adjusting the tone and content of emails accordingly" refers to a function that analyzes user emotions from input data and automatically changes the tone and content of emails accordingly.

[1583] "A means of extracting keywords from the content of a received email and generating a reply email based on those keywords" refers to a function that extracts important keywords from a received email and automatically creates an appropriate reply based on those keywords.

[1584] This invention is a system that selects an email template based on data input from the user, generates the email body, and adjusts the content and tone of the email after analyzing the user's emotions. This system operates in cooperation with three parties: the server, the terminal, and the user.

[1585] First, this system provides an interface that allows users to input data via a terminal. The data entered by the user (e.g., customer name, order number, date, etc.) is sent to the server. The server receives this data, selects an appropriate email template from the database, and generates the email body based on the selected template.

[1586] The server is equipped with an emotion analysis engine that analyzes the user's emotions from their input data. Based on this analysis, the server dynamically adjusts the tone and content of the generated email body. For example, if the user is feeling grateful, the server adds expressions of gratitude to the email body. If the user is feeling anxious, the server generates reassuring sentences.

[1587] The generated email body is displayed as a preview on the terminal. The user reviews the email content and makes any necessary corrections. After the user clicks the send button to initiate sending, the server uses SMTP to send the email to the recipient.

[1588] Next, let's discuss the processing of incoming emails. The server periodically retrieves new emails from the incoming mail server. The content of these retrieved emails is analyzed by artificial intelligence installed on the server. The AI ​​uses natural language processing technology to analyze the email body and extract specific keywords and phrases. In addition, a sentiment analysis engine analyzes the sender's emotions from the content of the incoming email and dynamically adjusts the content and tone of the reply. For example, if a customer sends a complaint, the sentiment engine uses the analysis results to generate a reply email expressing an apology.

[1589] Once the analysis is complete, the server determines whether a reply is necessary based on the analysis results. If a reply is deemed necessary, the server automatically generates an appropriate reply email. This generated reply email is also displayed as a preview on the terminal, allowing the user to review the content and make any necessary corrections. Finally, when the user clicks the send button to initiate the sending process, the server sends the reply email using SMTP.

[1590] Hardware and software to be used

[1591] Hardware: Smartphones, servers

[1592] Software: Python, smtplib (SMTP library), sentiment analysis engine (e.g., IBM Watson), natural language processing library (e.g., Spacy)

[1593] Specific example

[1594] Specific example of email generation: When a user creates an "order confirmation email," they input the order number, customer name, order date, etc., through their device. The server receives this information, retrieves an "order confirmation email" template from the database, and generates the email body by embedding the order number and customer name. The sentiment analysis engine analyzes the user's emotions, and if, for example, the user feels grateful to the customer, it adds words of gratitude reflecting that emotion to the email body.

[1595] Specific example of processing incoming emails: When an email with an "order cancellation request" is received, the server retrieves the email, and the AI ​​extracts keywords such as "cancel" and "request." Furthermore, the sentiment analysis engine analyzes the customer's emotions, and if the customer is showing disappointment or anger, for example, it generates a reply appropriate to that emotion (e.g., an apology or explanation).

[1596] Example of a prompt

[1597] Please generate an email message for when a customer cancels an order. Assume the customer is angry about the cancellation.

[1598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1599] Step 1:

[1600] The user enters data (e.g., customer name, order number, order date, etc.) through the terminal. The terminal sends the data entered by the user to the server. The entered data becomes the basic information used to generate emails.

[1601] Step 2:

[1602] Based on the data received by the server, it selects an appropriate email template from the database. The database contains various email templates, and the server extracts the template that matches the specified criteria. The email body is automatically generated by embedding the user's entered data into the selected template.

[1603] Step 3:

[1604] The emotion analysis engine installed on the server analyzes the user's input data to determine their emotions. A natural language processing library (e.g., Spacy) is used for this analysis. Based on the analyzed emotions, the server dynamically adjusts the tone and content of the generated email body. For example, if an emotion of gratitude is detected, an expression of gratitude will be added to the email body.

[1605] Step 4:

[1606] The server displays a preview of the generated email body on the user's device. The user reviews the email content through the device and makes corrections as needed.

[1607] Step 5:

[1608] When a user issues a send command on their device, the device transmits it to the server, which then uses SMTP to send the final email to the recipient. In this step, the sent email reaches the customer in its complete form.

[1609] Step 6:

[1610] In processing incoming emails, the server periodically retrieves new emails from the incoming mail server. The content of the retrieved emails is also analyzed on the server.

[1611] Step 7:

[1612] The content of received emails is analyzed by the server's artificial intelligence and sentiment analysis engine. Specifically, natural language processing technology is used to analyze the email body and extract specific keywords and phrases. At the same time, the sentiment analysis engine analyzes the sender's emotions.

[1613] Step 8:

[1614] Based on the analysis results, the server determines whether a reply is necessary. If a reply is deemed necessary, the server automatically generates a reply email. The generated reply email includes the extracted keywords and content corresponding to the analyzed sentiment.

[1615] Step 9:

[1616] The server displays a preview of the generated reply email on the user's device. The user then reviews the content of the reply email through their device and makes any necessary corrections.

[1617] Step 10:

[1618] When a user initiates a reply email from their device, that instruction is transmitted to the server, which then uses SMTP to send the reply email. This ensures that the customer receives an appropriate and emotionally sensitive reply.

[1619] Through each of the steps described so far, the system can efficiently generate and send / receive emails that take into account the emotions of users and customers.

[1620] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1621] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1622] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1623] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1624] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1625] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1626] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1627] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1628] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1629] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1630] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1631] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1632] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1633] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1634] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1635] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1636] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1637] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1638] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1639] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1640] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1641] The following is further disclosed regarding the embodiments described above.

[1642] (Claim 1)

[1643] A means of receiving data input from users,

[1644] A means of selecting an email template based on input and generating the email body,

[1645] A means of displaying a preview of the generated email body to the user,

[1646] A means of sending emails based on a send instruction from the user,

[1647] A method for retrieving new emails from the receiving mail server,

[1648] A method for analyzing the acquired email content using AI,

[1649] A means of determining whether a reply is necessary based on the analysis results,

[1650] A means of automatically generating a reply email when it is determined that a reply is necessary,

[1651] A means of displaying the generated reply email to the user in preview,

[1652] A means of sending a reply email based on a user's sending instruction,

[1653] A system that includes this.

[1654] (Claim 2)

[1655] The system according to claim 1, further comprising means for retrieving a template from a database in order to automate the selection of an email template.

[1656] (Claim 3)

[1657] The system according to claim 1, further comprising means for keyword extraction and natural language processing in analyzing the content of an email.

[1658] "Example 1"

[1659] (Claim 1)

[1660] A means of receiving electronic information input from users,

[1661] A means for selecting a communication template based on electronic information input and generating the main text,

[1662] A means of displaying the generated communication body to the user as a preview,

[1663] A means of sending an electronic message based on a transmission instruction from the user,

[1664] A means of obtaining new communications from the receiving communication server,

[1665] A means of analyzing acquired communication content using a generation AI model,

[1666] A means of determining whether a reply is necessary based on the analysis results,

[1667] A means of automatically generating a reply message when it is determined that a reply is necessary,

[1668] A means of displaying the generated reply communication to the user in preview,

[1669] A means for sending a reply communication based on a transmission instruction from the user,

[1670] A system that includes this.

[1671] (Claim 2)

[1672] The system according to claim 1, further comprising means for retrieving a template from data storage in order to automate the selection of a communication t...

Claims

1. A means of receiving data input from users, A means of selecting an email template based on input and generating the email body, A means of displaying a preview of the generated email body to the user, A means of sending emails based on a send instruction from the user, A method for retrieving new emails from the receiving mail server, A method for analyzing the acquired email content using AI, A means of determining whether a reply is necessary based on the analysis results, A means of automatically generating a reply email when it is determined that a reply is necessary, A means of displaying the generated reply email to the user in preview, A means of sending a reply email based on a user's sending instruction, A system that includes this.

2. The system according to claim 1, further comprising means for retrieving a template from a database in order to automate the selection of an email template.

3. The system according to claim 1, further comprising means for keyword extraction and natural language processing in analyzing the content of an email.

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