System

Abstract

To provide a system.SOLUTION: A system includes first means for detecting technical terms from the contents of a received e-mail, second means for converting the detected technical terms into appropriate English expressions using a generative AI model, and third means for transmitting a result of converting the technical terms into English expressions to a terminal of a recipient of the e-mail.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Although people want to learn English, they have no opportunities to use English on a daily basis and do not do business with overseas companies, making it difficult to study efficiently. [Means for solving the problem]

[0005] We provide a system that, upon receiving an e-mail, converts it into English and displays it to the recipient, and when the recipient replies in English, converts it into Japanese and sends it back. This system increases opportunities to use English on a daily basis, enabling efficient English learning. Furthermore, the learning function properly handles technical terms, allowing for smooth communication in business situations. [Brief explanation of the drawings]

[0006] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0017] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0018] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0024] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0025] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] The system of the present invention includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply. Specifically, the received email is automatically converted into English and displayed to the recipient. The recipient replies in English to the displayed English email. The reply is automatically converted into Japanese by the system and sent.

[0029] "Example 2"

[0030] The system of the present invention has a learning function for appropriately converting technical terms. Specifically, the system learns technical terms in received emails and converts them into appropriate English expressions. For example, the IT industry term "cloud" becomes "Cloud" in English. In this way, the system learns technical terms and converts them appropriately, enabling professional communication.

[0031] "Example 3"

[0032] The system of the present invention has a first language of Japanese and a second language of English. Specifically, received emails are in Japanese and converted into English. Also, replies from recipients are in English and converted into Japanese. In this way, communication between Japanese and English is supported.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The system receives an email from the user.

[0036] Step 2: The received email will be automatically converted into English by the system.

[0037] Step 3: The converted English email is displayed to the user.

[0038] Step 4: The user responds to the displayed email in English in English.

[0039] Step 5: The user's reply is automatically translated into Japanese by the system.

[0040] Step 6: The converted Japanese reply will be sent.

[0041] "Example 2"

[0042] Step 1: The system learns the terminology in the emails it receives.

[0043] Step 2: The system converts the learned terminology into appropriate English expressions.

[0044] Step 3: The converted English email is displayed to the user.

[0045] Step 4: The user responds to the displayed email in English in English.

[0046] Step 5: The user's reply is automatically translated into Japanese by the system.

[0047] Step 6: The converted Japanese reply will be sent.

[0048] "Example 3"

[0049] Step 1: The system receives an email in Japanese from the user.

[0050] Step 2: The system automatically converts the received Japanese email into English.

[0051] Step 3: The converted English email is displayed to the user.

[0052] Step 4: The user responds to the displayed email in English in English.

[0053] Step 5: The user's English reply is automatically converted into Japanese by the system.

[0054] Step 6: The converted Japanese reply will be sent.

[0055] Example 1

[0056] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0057] Language barriers often become an obstacle to communication when exchanging emails between users who speak different languages. Rapid and accurate translation is especially important in business and international communications, but manual translation requires time and effort and carries a high risk of mistranslation. Furthermore, the difficulty of translating technical terms and specific contexts makes efficient communication difficult. To solve these issues, a system is needed that can automatically translate emails and send and receive them in the appropriate language.

[0058] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0059] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, means for using a translation service in converting the emails, means for temporarily saving the translation results from the translation service, and means for using a communication protocol in sending and receiving the emails. This enables emails to be exchanged automatically and accurately between users who speak different languages, enabling smooth communication across language barriers.

[0060] "Email" means a digital message sent or received over the Internet or other communications network.

[0061] "Means of receiving" refers to the functions and protocols (e.g., IMAP, POP3) used to import email into the server.

[0062] "Means for converting into a second language" refers to a function or service (e.g., a translation API) for translating the content of received email into another language.

[0063] "Means for displaying to the recipient" refers to the function or application (e.g., email client) for displaying the converted email on the user's terminal.

[0064] "Means for translating replies into the primary language" refers to a function or service (e.g., a translation API) that translates replies from recipients into the primary language.

[0065] "Means for sending the translated reply" refers to the functionality and protocols (e.g., SMTP) used to send the translated reply to the original sender.

[0066] "Means of using translation services" refers to the function of calling external translation APIs or services to translate the contents of emails.

[0067] "Means for temporarily storing translation results" refers to a database or memory for temporarily storing translated content.

[0068] "Means of using communication protocols" refers to the communication rules and procedures required for sending and receiving email (e.g., IMAP, POP3, SMTP).

[0069] MODE FOR CARRYING OUT THE INVENTION

[0070] The present invention relates to a system that automatically translates e-mail messages exchanged between users who speak different languages, enabling them to be sent and received in the appropriate language. A specific embodiment of this system will be described below.

[0071] System configuration

[0072] This system consists of three main components: a server, a terminal, and a user. The server is responsible for receiving, translating, and sending emails, and a terminal is a device that users use to view and reply to emails. A user is the entity that sends and receives emails.

[0073] Hardware and software used

[0074] Server: Use a high-performance computer server and install mail server software (e.g., general mail server software).

[0075] Translation Services: We use external translation services such as Google® Cloud Translation API and DeepL® API.

[0076] Database: A database (e.g., MySQL (registered trademark), PostgreSQL (registered trademark)) is used to temporarily store the translation results.

[0077] Communication protocols: Communication protocols such as IMAP, POP3, and SMTP are used to send and receive email.

[0078] System Operation

[0079] 1. Receiving emails

[0080] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol, for example by connecting to a general mail server and checking for new emails.

[0081] 2. Translating emails into a second language

[0082] The server sends the text of the received email to the Google Cloud Translation API, retrieves the translation results returned by the API, and temporarily stores the translated text in a database.

[0083] 3. View the converted email

[0084] The device receives the English email sent from the server and displays it to User B. User B then opens an email client and checks the English email. For example, it is displayed as "New Email" in a typical email app.

[0085] 4. Translating recipient replies into the recipient's primary language

[0086] The server receives the email in which User B replies in English. It sends the body of the received email to the DeepL API and retrieves the translation result returned by the API. The email body, converted into Japanese, is temporarily stored in a database.

[0087] 5. Send the converted reply

[0088] The server sends the converted Japanese reply email to User A. Using the SMTP protocol, the reply email in Japanese is sent to User A's email address. For example, the email is sent through a general mail server.

[0089] Specific examples

[0090] User A sends an email in Japanese saying "Hello, how are you?"

[0091] The server receives the email and uses the Google Cloud Translation API to translate it into "Hello, how are you?"

[0092] The terminal displays "Hello, how are you?" to User B.

[0093] User B replies, "I am fine, thank you."

[0094] The server receives the reply and translates it using the DeepL API to "I'm fine, thanks."

[0095] The server sends the translated reply to User A.

[0096] Prompt Sentence Examples

[0097] "Design a system that translates emails written in Japanese into English, and translates replies in English into Japanese and sends them. The translation APIs used are Google Cloud Translation API and DeepL API."

[0098] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol. The input is User A's email account information and a request for new emails, and the output is the received email. Specifically, the server connects to the mail server, checks for new emails, and retrieves the email body and attachments.

[0102] Step 2:

[0103] The server sends the body of the received email to the Google Cloud Translation API. The input is the received Japanese email body, and the output is the translated English email body. Specifically, the server calls the API, translates the email body from Japanese to English, and temporarily stores the translation result in a database.

[0104] Step 3:

[0105] The terminal receives the English email sent from the server and displays it to User B. The input is the English email body sent from the server, and the output is the English email displayed in User B's email client. Specifically, the terminal opens the email client and displays the English email as a "new email."

[0106] Step 4:

[0107] User B composes and sends a reply in English. The input is the reply email in English composed by User B, and the output is the reply email in English sent to the server. Specifically, User B composes a reply using an email client and clicks the send button.

[0108] Step 5:

[0109] The server receives the email with User B's reply in English and sends it to the DeepL API. The input is the English reply email received from User B, and the output is the translated reply email in Japanese. Specifically, the server calls the API, translates the email body from English to Japanese, and temporarily stores the translation result in a database.

[0110] Step 6:

[0111] The server sends the converted reply email in Japanese to User A. The input is the translated reply email in Japanese, and the output is the reply email in Japanese sent to User A. Specifically, the server uses the SMTP protocol to send the reply email in Japanese to User A's email address.

[0112] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[0113] (Application example 1)

[0114] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0115] In virtual stores, there is a problem of insufficient communication between customers and support staff who speak different languages. In particular, customer inquiries and support staff replies are often not conveyed accurately, which can lead to lower customer satisfaction and reduced operational efficiency.

[0116] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0117] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for automatically translating an inquiry from a customer and displaying it to a support staff member, and means for automatically translating the support staff member's reply into the customer's language and sending it. This facilitates communication between customers and support staff who speak different languages, making it possible to improve customer satisfaction and business efficiency.

[0118] "Means for receiving e-mail" refers to a function for capturing e-mail sent via the Internet into a server or device.

[0119] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another specified language.

[0120] The "means for displaying the translated email to the recipient" is a function for displaying the content of the translated email in a format that can be viewed by the recipient.

[0121] The "means for converting a reply from a recipient into the first language" is a function for automatically translating the content of a reply from a recipient into the original language.

[0122] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[0123] "Means for automatically translating customer inquiries and displaying them to support personnel" is a function for automatically translating customer inquiries and displaying the translation results to support personnel.

[0124] The "means for automatically translating the support representative's reply into the customer's language and sending it" is a function for automatically translating the reply from the support representative into the customer's language and sending the translation result to the customer.

[0125] To implement the present invention, the following system configuration and program are required.

[0126] System Configuration

[0127] The system uses the following hardware and software:

[0128] Hardware: Smartphones, servers

[0129] Software: Google Cloud Translation API, Python (registered trademark)

[0130] Program processing explanation

[0131] The server first receives inquiries from customers, which are then translated into the language of the support representative using the Google Cloud Translation API, and the translated inquiries are displayed on the support representative's terminal.

[0132] Once the agent types a reply, it's translated into the customer's language using the Google Cloud Translation API, and the translated reply is sent to the customer's device.

[0133] Specific examples

[0134] For example, a customer may ask in Japanese, "Hello, do you have the product in stock?" This inquiry is received by the server and translated into English using the Google Cloud Translation API. The translated message, "Hello, do you have the product in stock?" is then displayed to the support agent.

[0135] The support representative replies, "Yes, the product is in stock." The reply is again translated into Japanese using the Google Cloud Translation API. The translated message, "Yes, the product is in stock," is sent to the customer.

[0136] Prompt Sentence Examples

[0137] Below are some example prompts to input to the generative AI model:

[0138] Translate the following Japanese text to English: "Hello, do you have the item in stock?"

[0139]

[0140] Translate the following English text to Japanese: "Yes, the product is in stock."

[0141] In this way, communication between customers and support personnel who speak different languages ​​can be facilitated.

[0142] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0143] Step 1:

[0144] The server receives inquiries from customers.

[0145] Input: Customer inquiry message (e.g. "Hello, do you have this item in stock?")

[0146] Data processing: Save received messages in text format.

[0147] Output: The saved query message

[0148] Step 2:

[0149] The server uses the Google Cloud Translation API to translate the received inquiry message into the language of the support representative.

[0150] Input: Customer inquiry message (Japanese)

[0151] Data calculation: Call the Google Cloud Translation API to translate the inquiry message into English.

[0152] Output: Translated query message (English)

[0153] Step 3:

[0154] The server displays the translated inquiry message on the terminal of the support staff.

[0155] Input: Translated query message (English)

[0156] Data processing: The translated message is sent to the support representative's terminal and displayed.

[0157] Output: The message displayed on the support representative's terminal

[0158] Step 4:

[0159] The support person uses the terminal to input a reply message.

[0160] Input: Support representative's reply message (e.g., "Yes, the product is in stock.")

[0161] Data processing: Save reply messages in text format.

[0162] Output: Saved reply message

[0163] Step 5:

[0164] The server translates the support agent's reply message into the customer's language using the Google Cloud Translation API.

[0165] Input: Support representative's reply message (English)

[0166] Data calculation: Call the Google Cloud Translation API to translate the reply message into Japanese.

[0167] Output: Translated reply message (Japanese)

[0168] Step 6:

[0169] The server sends the translated reply message to the customer's terminal.

[0170] Input: Translated reply message (Japanese)

[0171] Data processing: Translated messages are sent to the customer's device and displayed.

[0172] Output: Reply message displayed on the customer's device

[0173] Example 2

[0174] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] In today's business environment, communication between users who speak different languages ​​is on the rise. However, accurately translating electronic communications containing technical terms is difficult, and misunderstandings are likely to occur, especially in technical fields. To solve this problem, a system is needed that can appropriately translate technical terms and ensure accurate communication.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0177] In this invention, the server includes means for receiving electronic communications, means for analyzing the content of the received electronic communications and extracting technical terms, means for converting the extracted technical terms into appropriate second language expressions using a generative AI model, means for rearranging the converted technical terms into their original context and reconstructing the converted electronic communications, means for displaying the converted electronic communications to a recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply, thereby enabling accurate translation of electronic communications containing technical terms and accurate communication between users who speak different languages.

[0178] "Electronic communication" means a message or information sent or received by electronic means.

[0179] A "second language" is a language that is the subject of translation or conversion, and is usually a language that the recipient can understand.

[0180] "Recipient" means a person or system that receives an electronic communication.

[0181] A "reply" is a response message sent by a recipient to a received electronic communication.

[0182] "Primary language" means the language in which the original electronic communication is written, which is usually the language spoken by the sender.

[0183] "Analysis" is the process of examining the content of electronic communications to extract specific information or patterns.

[0184] "Jargon" refers to the specific words and expressions used in a particular field or industry.

[0185] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text.

[0186] "Context" refers to the situation or background in which a particular word or expression is used.

[0187] "Reconstruction" refers to the process of rearranging the converted terminology into the context of the original electronic communication to create a new electronic communication.

[0188] The present invention is a system for ensuring accurate communication between users of different languages, specifically involving a process for analyzing electronic communications containing technical terms and converting them into an appropriate second language.

[0189] Server Roles

[0190] The server receives electronic communications sent by users. The received electronic communications are analyzed using a natural language processing library (e.g., spaCy®). The purpose of the analysis is to extract technical terms contained within the electronic communications.

[0191] The extracted terminology is input to a generative AI model (e.g., OpenAI's GPT-4®) and converted into an appropriate second-language expression. The prompt sentence used is "Please translate the following Japanese terminology into English: cloud, big data, AI."

[0192] The converted terminology is then rearranged to fit the context of the original electronic communication using Python string manipulation functions, and the reconstructed electronic communication is sent from the server to the recipient's device.

[0193] Device Role

[0194] The terminal sends user-generated electronic communications to the server using email client software (e.g., Microsoft® Outlook® or Mozilla® Thunderbird).

[0195] The terminal receiving the converted electronic communication from the server displays the content of the electronic communication to the user, who can verify that the technical terminology has been properly converted into the second language.

[0196] User Roles

[0197] The user creates an electronic communication including technical terms and transmits it to the server via the terminal, for example, an electronic communication stating, "I plan to use the cloud service."

[0198] Users receiving the converted electronic communication will verify that the technical terminology has been appropriately converted to second-language expressions, for example, "We plan to use cloud services" has been converted to "We plan to use cloud services."

[0199] Specific examples

[0200] A user creates an electronic communication containing the term "cloud" and sends it from their device to the server. The server analyzes the electronic communication, extracts "cloud" and converts it to "Cloud." The converted electronic communication is then sent back to the device and displayed to the user.

[0201] An example of a prompt sentence to be input to the generative AI model is "Please translate the following Japanese technical terms into English: Cloud, Big Data, AI." Based on this prompt sentence, the generative AI model returns an appropriate second-language expression such as "Cloud, Big Data, AI."

[0202] The above is an embodiment of the present invention.

[0203] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0204] Step 1:

[0205] The server receives electronic communications sent by users. The input is the electronic communication sent by the user through the terminal, and the output is the received electronic communication. In particular, the server receives electronic communications using the SMTP protocol.

[0206] Step 2:

[0207] The server analyzes the content of the received electronic communication. The input is the received electronic communication and the output is the extracted terminology. Specifically, the server uses a natural language processing library (e.g., spaCy) to analyze the electronic communication and extract the terminology.

[0208] Step 3:

[0209] The server inputs the extracted terminology into a generative AI model and converts it into an appropriate second-language expression. The input is the extracted terminology, and the output is the converted terminology. Specifically, the server converts the terminology using a prompt sentence such as "Please convert the following Japanese terminology into English: cloud, big data, AI" to the generative AI model (e.g., OpenAI's GPT-4).

[0210] Step 4:

[0211] The server rearranges the converted terminology into the context of the original electronic communication and reconstructs the converted electronic communication. The input is the converted terminology and the original electronic communication, and the output is the reconstructed electronic communication. Specifically, the server uses Python string manipulation functions to rearrange the converted terminology and reconstruct the electronic communication.

[0212] Step 5:

[0213] The server transmits the reconstructed electronic communication to the recipient's terminal. The input is the reconstructed electronic communication, and the output is the electronic communication sent to the recipient's terminal. In particular, the server transmits the reconstructed electronic communication using the SMTP protocol.

[0214] Step 6:

[0215] The terminal receives the converted electronic communication sent from the server and displays it to the user. The input is the electronic communication sent from the server, and the output is the electronic communication displayed to the user. In particular, the terminal receives and displays the electronic communication using email client software (e.g., Microsoft Outlook or Mozilla Thunderbird).

[0216] Step 7:

[0217] The user reviews the converted electronic communication and, if necessary, creates a reply. The input is the converted electronic communication, and the output is a reply created by the user. Specifically, the user creates the reply using email client software.

[0218] Step 8:

[0219] The terminal sends a reply created by the user to the server. The input is the reply created by the user, and the output is the reply sent to the server. In concrete terms, the terminal uses the SMTP protocol to send the reply to the server.

[0220] Step 9:

[0221] The server converts the received reply into the first language and sends it to the sender. The input is the received reply, and the output is the reply converted into the first language. Specifically, the server uses a generative AI model to convert the reply into the first language and sends it to the sender using the SMTP protocol.

[0222] (Application example 2)

[0223] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0224] Modern content distribution services often use a lot of technical terms, making it difficult for viewers to understand the content. While proper translation of technical terms between different languages ​​is particularly required, real-time translation has been difficult with conventional systems. This has led to the issue of viewers being unable to fully understand the content when watching specialized content.

[0225] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion results on a user interface. This enables viewers to understand specialized content in real time.

[0226] "Means for receiving e-mail" means a combination of hardware and software for receiving e-mail sent via the Internet.

[0227] "Means for converting received email into a second language" refers to software and algorithms for translating the content of received email into a specified second language.

[0228] "Means for displaying the translated email to the recipient" refers to the interface and software for displaying the translated email content on the recipient's device.

[0229] "Means for translating a reply from a recipient into a first language" means software and algorithms for translating a reply composed by a recipient in a second language into the first language.

[0230] A "means for sending a translated reply" is a combination of hardware and software for sending a translated reply to a sender.

[0231] The "means for detecting technical terms in content in real time" refers to natural language processing techniques and algorithms for identifying technical terms contained in the content being viewed in real time.

[0232] The "means for translating detected terminology into appropriate second language expressions" is a generative AI model and algorithm for translating detected terminology into appropriate second language expressions.

[0233] "Means for displaying the translation results in a user interface" refers to the interface and software for displaying the translated terminology results on the user's device.

[0234] An embodiment of the present invention will be described below. The system includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion result on a user interface.

[0235] Hardware and software used

[0236] Hardware: Smartphone, head-mounted display

[0237] Software: Python, TENSORFLOW®, natural language processing libraries (e.g., spaCy)

[0238] Data processing and calculation

[0239] Server Processing

[0240] The server receives emails sent via the Internet. The content of the received emails is analyzed using a natural language processing library (e.g., spaCy) to detect technical terms. The detected technical terms are converted into appropriate second-language expressions using a generative AI model (e.g., GPT-3®). The converted content is displayed to the recipient through a user interface.

[0241] Terminal handling

[0242] The device (smartphone or head-mounted display) receives the translation results sent from the server and displays them in the user interface. When the user composes a reply, the device translates the reply into the user's primary language and sends it to the server.

[0243] User operations

[0244] The user receives the email through the terminal and checks the displayed content. If technical terms are included, the technical terms are converted into appropriate second language expressions in real time and displayed. The user then creates a reply and sends it through the terminal.

[0245] Specific examples

[0246] For example, if the technical term "cloud" appears in an IT-related video a user is watching, the system will detect it in real time and translate it as "Cloud," allowing users to understand specialized content in real time.

[0247] Prompt Sentence Examples

[0248] Please translate the following Japanese technical terms into appropriate English expressions.

[0249] Terminology: Cloud

[0250] In this way, the system can translate specialized content in real time and provide it to users.

[0251] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0252] Step 1:

[0253] The server receives emails sent via the Internet. The input is the email data, and the output is the content of the received email. Specifically, the server retrieves emails from the mail server and analyzes their content.

[0254] Step 2:

[0255] The server analyzes the content of received emails using a natural language processing library (e.g., spaCy) to detect technical terms. The input is the content of the received email, and the output is a list of detected technical terms. Specifically, the server tokenizes the text of the email and identifies technical terms.

[0256] Step 3:

[0257] The server converts the detected terminology into an appropriate second-language representation using a generative AI model (e.g., GPT-3). The input is a list of terminology, and the output is a list of converted terminology. Specifically, the server inputs the terminology as a prompt sentence into the generative AI model to obtain an appropriate translation.

[0258] Step 4:

[0259] The server displays the converted content to the recipient through a user interface. The input is a list of converted technical terms, and the output is the translation result displayed on the user interface. Specifically, the server sends the conversion result to the terminal in HTML or JSON format.

[0260] Step 5:

[0261] The terminal receives the conversion results sent from the server and displays them on the user interface. The input is the conversion results sent from the server, and the output is the content displayed on the user interface. Specifically, the terminal analyzes the received data and displays it on the screen.

[0262] Step 6:

[0263] The user receives the email through the terminal and checks the displayed content. The input is the translation result displayed on the terminal, and the output is the user's understanding. Specifically, the user reads and understands the displayed content.

[0264] Step 7:

[0265] The user creates a reply and sends it through the terminal. The input is the reply content created by the user, and the output is the reply content entered into the terminal. Specifically, the user enters the reply content and presses the send button.

[0266] Step 8:

[0267] The terminal converts the reply created by the user into the first language and sends it to the server. The input is the reply content created by the user, and the output is the reply content converted into the first language. Specifically, the terminal translates the reply content using a natural language processing library and sends it to the server.

[0268] Step 9:

[0269] The server sends the converted reply to the sender. The input is the reply content converted into the first language, and the output is the reply content sent to the sender. Specifically, the server sends the converted reply to the sender through the mail server.

[0270] Example 3

[0271] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0272] In today's global business environment, communication between users who speak different languages ​​is important. However, due to language barriers, exchanging emails between users who speak different languages ​​is difficult. Accurate translation is especially required when there are differences in technical terms and nuances. Furthermore, translation work can take a long time and the quality of the translation can be low. To solve these problems, a system that provides efficient and high-quality translation is needed.

[0273] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0274] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for sending the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for performing language conversion using a generative AI model, and means for generating a prompt sentence and inputting it into the generative AI model, thereby enabling efficient and high-quality email exchange between users who speak different languages.

[0275] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[0276] The "means for converting received e-mail into a second language" is a function for translating the contents of received e-mail into another language.

[0277] The "means for sending the translated email to the recipient" is a function for sending the translated email to the specified recipient.

[0278] The "means for converting a reply from a recipient into the first language" is a function for translating a reply email sent from a recipient into the original language.

[0279] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[0280] "Means for performing language conversion using a generative AI model" refers to a function that uses an artificial intelligence model to translate the content of an email into a different language.

[0281] "Means for generating prompt sentences and inputting them to a generative AI model" refers to a function for generating input sentences that will cause the generative AI model to perform appropriate translations, and for providing the input sentences to the AI ​​model.

[0282] The present invention is a system for efficiently and efficiently exchanging e-mails with high quality between users who speak different languages. A specific embodiment of this system will be described below.

[0283] Server Roles

[0284] The server receives emails sent by users. These emails are often written in Japanese. The server uses a generative AI model (e.g., GPT-4) to translate the Japanese emails into English. When translating, the server generates a prompt sentence and inputs it into the generative AI model. An example of a prompt sentence is, "Please translate the following Japanese email into English: Hello, how is the project going?"

[0285] The translated English email is sent from the server to the recipient. When the recipient replies in English, the email is sent back to the server. The server again uses the generative AI model to translate the recipient's English email into Japanese. An example of a translation prompt is, "Please translate the following English email into Japanese: The project is going well, thank you." The translated Japanese email is then sent back to the original user from the server.

[0286] Device Role

[0287] The terminal is used by the user to compose and send emails in Japanese. When the user composes an email and clicks the send button, the terminal sends the email to the server. When the translated English email is received from the server, the terminal displays the email. When the user receives an English email, the terminal displays the email and provides an interface for the user to compose a reply email.

[0288] User Roles

[0289] A user uses a terminal to compose and send an email in Japanese. For example, they type "Hello, how is the project going?" When the user receives a reply in English from the recipient, the email is translated into Japanese by the server and displayed on the user's terminal. The user then checks the translated Japanese email and replies again if necessary.

[0290] Hardware and software used

[0291] This system uses a server, a terminal, and a generative AI model. The server includes hardware and software for receiving, sending, and translating emails. The terminal is a device on which users compose and send emails, and uses email client software (e.g., Outlook, Gmail (registered trademark)). The generative AI model, such as OpenAI's GPT-4, provides high-quality translations when a prompt sentence is input.

[0292] Specific examples

[0293] User A composes the following email in Japanese:

[0294] "Hi, how's the project going?"

[0295] User A's device sends this email to the server, which uses a generative AI model (e.g., GPT-4) to translate the email into English:

[0296] "Hello, how is the progress of the project?"

[0297] The server sends the translated email to recipient B. Recipient B replies in English:

[0298] "The project is going well, thank you."

[0299] The server receives recipient B's email and translates it into Japanese using the generative AI model:

[0300] "The project is progressing smoothly, thank you very much."

[0301] The server sends the translated email to User A's terminal, and User A receives the email.

[0302] In this way, e-mails can be exchanged efficiently and with high quality between users who use different languages. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0303] Step 1:

[0304] The server receives emails sent by users.

[0305] Input: Japanese email sent by the user from the terminal

[0306] Specific operation: The server receives email using the SMTP protocol.

[0307] Output: Received email in Japanese

[0308] Step 2:

[0309] The server analyzes the contents of the received email and extracts the Japanese text portion.

[0310] Input: Received email in Japanese

[0311] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[0312] Output: Extracted Japanese text

[0313] Step 3:

[0314] The server uses a generative AI model to translate Japanese text into English.

[0315] Input: Extracted Japanese text

[0316] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[0317] Example prompt: "Please translate the following Japanese email into English: Hello, how is the project going?"

[0318] Output: Translated English text

[0319] Step 4:

[0320] The server sends the translated English email to the recipient.

[0321] Input: Translated English text

[0322] Specific operation: The server uses the SMTP protocol to send the email to the recipient's email address.

[0323] Output: English email sent to recipient

[0324] Step 5:

[0325] The server receives a reply email in English from the recipient.

[0326] Input: Reply email in English sent by the recipient from their device

[0327] Specific operation: The server receives email using the SMTP protocol.

[0328] Output: Received reply email in English

[0329] Step 6:

[0330] The server analyzes the contents of the received reply email and extracts the English text portion.

[0331] Input: Received reply email in English

[0332] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[0333] Output: Extracted English text

[0334] Step 7:

[0335] The server uses a generative AI model to translate English text into Japanese.

[0336] Input: Extracted English text

[0337] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[0338] Example prompt: "Please translate the following English email into Japanese: The project is going well, thank you."

[0339] Output: Translated Japanese text

[0340] Step 8:

[0341] The server sends the translated Japanese email to the original user.

[0342] Input: Translated Japanese text

[0343] Specific operation: The server uses the SMTP protocol to send an email to the user's email address.

[0344] Output: Japanese email sent to the original user

[0345] (Application example 3)

[0346] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0347] In international e-commerce transactions, communication between customers and support staff who speak different languages ​​is often not smooth. This can lead to lower customer satisfaction and a decrease in transaction efficiency. Furthermore, misunderstandings and problems can occur due to inaccurate translation of technical terms and specific expressions.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for receiving an inquiry from a customer, translating it, and displaying it, and means for translating a reply created by a support staff member and sending it to the customer. This enables smooth communication between customers and support staff who speak different languages, improving customer satisfaction and streamlining transactions.

[0349] "Means for receiving e-mail" refers to a device or software that has the function of capturing e-mail sent via the Internet into a server or device.

[0350] The "means for converting received e-mail into a second language" refers to a device or software that has the function of automatically translating the content of a received e-mail into a specified second language.

[0351] The "means for displaying the translated email to the recipient" refers to a device or software that has the function of displaying the translated content of the email in a format that the recipient can understand.

[0352] The "means for translating a reply from a recipient into a first language" is a device or software that has the function of automatically translating a reply written by a recipient in a second language into the first language.

[0353] A "means for sending a translated reply" is a device or software capable of sending a translated reply to the original sender.

[0354] The "means for receiving, translating, and displaying inquiries from customers" refers to a device or software that has the function of receiving inquiries sent from customers, translating the content of the inquiries, and displaying the translated content to the support staff.

[0355] The "means for translating replies written by support staff and sending them to the customer" refers to a device or software that has the function of translating replies written by support staff and sending the translation results to the customer.

[0356] As an embodiment of the present invention, the following system can be constructed.

[0357] First, the server must have a means for receiving emails. This means has the function of importing emails sent via the Internet into the server. Next, a means for converting received emails into a second language is required. This means has the function of automatically translating the contents of received emails into the specified second language. A translation service such as Google Cloud Translation API can be used for the translation.

[0358] The translated email is then displayed to the recipient through a display mechanism that displays the translated email content in a format that the recipient can understand. When the recipient composes a reply, the reply will be written in the second language, so a mechanism is required to translate this into the first language. This mechanism is also automatically translated using the Google Cloud Translation API or similar.

[0359] The translated reply is sent to the original sender through a sending means, which has the function of sending the translated reply to the original sender.

[0360] Furthermore, a means for receiving inquiries from customers and translating and displaying them is required. This means has the function of receiving inquiries sent from customers, translating the contents of the inquiries, and displaying them to the support staff. Replies created by the support staff are sent via a means for translating and sending to the customer. This means has the function of translating the replies created by the support staff and sending the translation results to the customer.

[0361] As a concrete example, if a customer inquires in Japanese, "Please tell me the shipping status of the product," the system translates this inquiry into English as, "Could you please tell me the shipping status of the product?" The support staff receives this English inquiry and creates a reply in English, "The product has been shipped and is expected to arrive in 3 days." The system then translates this reply into Japanese as, "The product has been shipped and is expected to arrive within 3 days," and sends it to the customer.

[0362] An example of a prompt is as follows:

[0363] Please translate the Japanese text into English:

[0364] Please let me know the delivery status of the product.

[0365] Please translate the English text into Japanese:

[0366] "The product has been shipped and is expected to arrive in 3 days."

[0367] In this way, smooth communication between customers and support staff who speak different languages ​​becomes possible, improving customer satisfaction and increasing transaction efficiency.

[0368] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0369] Step 1:

[0370] The server receives emails via the Internet. The input is emails in Japanese sent by customers. The server stores these emails in a database.

[0371] Step 2:

[0372] The server translates the received Japanese email into English using the Google Cloud Translation API. The input is the Japanese email received in step 1, and the output is the translated English email. The server stores the translated English email in a database.

[0373] Step 3:

[0374] The server displays the translated English email on the support staff's terminal. The input is the English email translated in step 2, and the output is the English email displayed on the support staff's terminal. The support staff confirms this English email.

[0375] Step 4:

[0376] The support staff composes a reply in English and sends it to the server. The input is the English reply composed by the support staff, and the output is the English reply sent to the server. The server stores this English reply in a database.

[0377] Step 5:

[0378] The server translates the English reply received from the support staff into Japanese using the Google Cloud Translation API. The input is the English reply received in step 4, and the output is the translated Japanese reply. The server stores the translated Japanese reply in a database.

[0379] Step 6:

[0380] The server sends the translated Japanese reply to the customer. The input is the Japanese reply translated in step 5, and the output is the Japanese reply sent to the customer. The customer receives and confirms this Japanese reply.

[0381] In this way, smooth communication between the server, the terminal, and the user is realized.

[0382] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0383] "Example 1"

[0384] One embodiment of the present invention is an email conversion system incorporating an emotion engine. This system receives emails from users and recognizes the user's emotions from the text. The emotion engine analyzes specific words, phrases, emojis, etc. in the text to determine the emotions expressed by the user. For example, if the text contains words such as "fun" or "happy" or a smiling emoji, it is determined that the user is expressing positive emotions.

[0385] "Example 2"

[0386] Furthermore, the emotion engine has the ability to tailor email responses based on the user's emotions. For example, if the user expresses negative emotions, the system will use that information to generate a more appropriate response. Specifically, if the user expresses emotions such as "troubled" or "sad," the system will take those emotions into account and generate a reply with a more polite tone.

[0387] "Example 3"

[0388] The emotion engine also has the ability to adjust the timing of email sending based on the user's emotions. For example, if a user is expressing anger or frustration, the system can take that emotion into account and delay sending a reply. This gives the user time to calm down and avoids emotional interactions.

[0389] The processing flow of each embodiment will be described below.

[0390] "Example 1"

[0391] Step 1: Receive an email from the user.

[0392] Step 2: Analyze the text of the received email for specific words, phrases, emojis, etc.

[0393] Step 3: Based on the analysis results, determine the emotion expressed by the user.

[0394] "Example 2"

[0395] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[0396] Step 2: Tailor your email responses based on user sentiment.

[0397] Step 3: Send the tailored response to the user.

[0398] "Example 3"

[0399] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[0400] Step 2: Adjust the timing of your emails based on user sentiment.

[0401] Step 3: Send emails to users at the scheduled times.

[0402] Example 1

[0403] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] Conventional email systems have difficulty communicating between different languages, especially when it comes to accurately conveying technical terms and emotional nuances. Furthermore, when the recipient replies, they have to translate the message again, which is time-consuming. Furthermore, they lack the ability to recognize the user's emotions from the content of the email, which makes it impossible to improve the quality of communication.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0406] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, and means for recognizing emotions from the content of the received emails. This enables smooth communication between different languages ​​and allows technical terms and emotional nuances to be accurately conveyed. It also reduces the effort required for the recipient to reply, improving the quality of communication.

[0407] "Means for receiving e-mail" refers to a function that enables the server to receive e-mail sent via the network.

[0408] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another language.

[0409] The "means for displaying the translated email to the recipient" is a function for displaying the translated email on the recipient's terminal.

[0410] The "means for converting a reply from a recipient into the first language" is a function for automatically translating an email replied by a recipient into the original language.

[0411] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[0412] The "means for recognizing emotions from the content of received e-mails" is a function for analyzing the text content of e-mails and determining the emotions of the sender.

[0413] The "learning function for appropriate translation of technical terms" is a function that uses past translation data and specific dictionaries to learn in order to accurately translate technical terms used in specific fields or industries.

[0414] The present invention is a system for smoothly exchanging e-mails between different languages. A specific embodiment of this system will be described below.

[0415] System configuration

[0416] 1. Receiving emails

[0417] The server receives emails sent by users. To receive emails, mail server software such as Postfix (registered trademark) or Sendmail (registered trademark) is used. Specifically, the server receives emails using the SMTP protocol and stores them in a mail queue.

[0418] 2. Translating emails into a second language

[0419] The server automatically translates received emails into a second language (English) using translation services such as the Google Cloud Translation API and DeepL API. Specifically, the server extracts the body of the email and sends an API request to retrieve the translation result.

[0420] 3. View the converted email

[0421] The device then displays the converted English email to the recipient using email client software such as Microsoft Outlook or Gmail. Specifically, the device retrieves the email from the server using the IMAP protocol and displays it in the user interface.

[0422] 4. Translating recipient replies into the recipient's primary language

[0423] The user replies to the displayed English email in English. The server automatically converts the reply into the user's primary language (Japanese). This conversion again uses a translation service such as Google Cloud Translation API or DeepL API. Specifically, the server extracts the body of the reply email and sends an API request to obtain the translation result.

[0424] 5. Send the converted reply

[0425] The server then sends the translated Japanese reply to the original sender using the SMTP protocol and mail server software such as Postfix or Sendmail. Specifically, the server adds the translated email to the mail queue and sends it using the SMTP protocol.

[0426] Incorporating an emotion engine

[0427] 1. Emotional awareness

[0428] The server receives emails from users and recognizes their emotions from the text. The emotion engine uses natural language processing services such as IBM Watson® Natural Language Understanding and Microsoft Azure® Text Analytics. Specifically, the server extracts the body of the email and sends an API request to obtain the sentiment analysis results.

[0429] 2. Emotional Judgment

[0430] The server determines the emotion expressed by the user based on the analysis results obtained from the emotion engine. Specifically, the server extracts emotion scores such as positive, negative, and neutral from the analysis results, and determines the emotion if it exceeds a certain threshold.

[0431] Specific examples

[0432] For example, if a user sends an email saying "I'm having so much fun today 😊", the following processing will occur:

[0433] 1. The server receives an email from the user.

[0434] 2. The server sends the email body, "Today is so much fun 😊," to the Google Cloud Translation API and translates it into English.

[0435] 3. The device will display the translated email "I am very happy today 😊" to the recipient.

[0436] 4. The user replies to the displayed email with "Thank you for your message!"

[0437] 5. The server sends the reply email to the Google Cloud Translation API and translates it into Japanese.

[0438] 6. The server sends the translated reply "Thanks for your message!" to the original sender.

[0439] 7. The server uses an emotion engine to analyze the sentiment of the email "I'm having so much fun today 😊" and determines that it expresses a positive emotion.

[0440] Prompt Sentence Examples

[0441] Examples of prompts to be input to a generative AI model include:

[0442] Receive emails from users and translate their content into English. Then, display the translated English email to the recipient, translate the recipient's reply into Japanese, and send it to the original sender. Also, analyze the user's sentiment from the email content to determine whether it expresses positive emotions.

[0443] Using this prompt, the generative AI model can understand the system's processing and generate the appropriate output.

[0444] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0445] Step 1:

[0446] The server receives emails sent by users. The input is the email sent over the network, and the output is the email stored in the mail queue within the server. Specifically, the server receives emails using the SMTP protocol and stores them in the mail queue.

[0447] Step 2:

[0448] The server translates received emails into a second language (English). The input is the email stored in the mail queue, and the output is the translated English email. Specifically, the server extracts the email body and sends an API request to the Google Cloud Translation API or DeepL API to obtain the translation result.

[0449] Step 3:

[0450] The terminal displays the translated English email to the recipient. The input is the translated English email retrieved from the server, and the output is the email displayed in the recipient's email client. Specifically, the terminal retrieves the email from the server using the IMAP protocol and displays it in its user interface.

[0451] Step 4:

[0452] The user replies to the displayed English email in English. The input is the reply entered by the recipient, and the output is the reply email sent to the server. In concrete terms, the user composes and sends the reply email using an email client.

[0453] Step 5:

[0454] The server converts the reply from the recipient into the primary language (Japanese). The input is the reply email in English sent by the recipient, and the output is the translated reply email in Japanese. Specifically, the server extracts the body of the reply email and sends an API request to the Google Cloud Translation API or DeepL API to obtain the translation result.

[0455] Step 6:

[0456] The server sends the translated Japanese reply to the original sender. The input is the translated Japanese reply email, and the output is the reply email sent to the original sender. Specifically, the server adds the translated email to the mail queue and sends it using the SMTP protocol.

[0457] Step 7:

[0458] The server recognizes emotions from the content of received emails. The input is the body of the received email, and the output is the sentiment analysis results. Specifically, the server extracts the email body and sends an API request to IBM Watson Natural Language Understanding or Microsoft Azure Text Analytics to obtain the sentiment analysis results.

[0459] Step 8:

[0460] The server determines the emotion expressed by the user based on the analysis results obtained from the emotion engine. The input is the emotion analysis result, and the output is the emotion judgment result. Specifically, the server extracts emotion scores such as positive, negative, and neutral from the analysis results, and judges the emotion if it exceeds a certain threshold.

[0461] (Application example 1)

[0462] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0463] Conventional email systems have difficulty communicating between different languages, especially when it comes to accurately conveying emotional nuances. Furthermore, they lack the ability to translate technical terms appropriately or suggest replies based on emotions, placing a significant burden on users.

[0464] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for analyzing emotions from text, and means for suggesting templates and emoticons appropriate for the reply content based on the emotion analysis results. This enables smooth communication between different languages ​​and accurately conveys emotional nuances. Furthermore, the appropriate translation of technical terms and suggestion of reply content appropriate to the emotion are provided, reducing the burden on the user.

[0465] "Means for receiving e-mail" refers to a function for receiving e-mail sent via the Internet at a server or a client device.

[0466] The "means for converting received e-mail into a second language" is a function that automatically translates the content of received e-mail from the original language into another specified language.

[0467] The "means for displaying the translated email to the recipient" is a function for displaying the translated content of the email on the recipient's device.

[0468] The "means for converting a reply from a recipient into the first language" is a function that automatically translates the content of a reply from a recipient from the language of the replyer into the original language.

[0469] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[0470] "Text emotion analysis" is a function that analyzes the text content of emails and determines the user's emotions based on specific words, phrases, emojis, etc.

[0471] "Means for suggesting templates and emojis suitable for reply content based on the results of emotion analysis" is a function that suggests templates and emojis suitable for reply content to users based on the results of emotion analysis.

[0472] A system for carrying out the present invention has the following configuration: A server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for analyzing emotions from text, and means for suggesting templates and emoticons suitable for the reply content based on the results of the emotion analysis.

[0473] Hardware and Software Configuration

[0474] Hardware: Smartphones, servers

[0475] Software: Python, Google Translate API, TextBlob (registered trademark)

[0476] Data processing and calculation

[0477] 1. Receiving emails:

[0478] The server receives emails sent via the Internet and stores them in the server.

[0479] 2. Email Translation:

[0480] Received emails are automatically translated from the original language to a second language (e.g. Japanese to English) using the Google Translate API.

[0481] 3. Emotion analysis:

[0482] The translated email text is then sentiment analyzed using TextBlob to determine the user's sentiment based on specific words, phrases, emojis, etc.

[0483] 4. View the translated email:

[0484] The translated email will be displayed on the recipient's smartphone, where the recipient can check the translated content.

[0485] 5. Reply Translation:

[0486] When the recipient replies, the reply is automatically translated from the second language to the first language (e.g., English to Japanese) using the Google Translate API.

[0487] 6. Emotion-based suggestions:

[0488] Based on the results of sentiment analysis, templates and emojis suitable for the reply content are suggested to the recipient, making it easy for the recipient to create an appropriate reply.

[0489] 7. Sending a Reply:

[0490] The translated reply is then sent via the server to the original sender.

[0491] Specific examples

[0492] For example, if a recipient receives an email in Japanese saying "Hello, how are you?", the email will be translated into English as "Hello, how are you?" The translated email will be displayed on the recipient's smartphone. If the recipient replies in English with "I'm doing well, thank you!", this reply will be translated into Japanese as "I'm doing well, thank you!" and sent to the original sender.

[0493] Prompt Sentence Examples

[0494] "Translate the following Japanese text to English and analyze the sentiment: 'Hello, how are you?'"

[0495] This allows for smooth communication across different languages, accurately conveying emotional nuances, and reduces the burden on users by providing appropriate translations of technical terms and suggesting replies based on emotions.

[0496] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0497] Step 1:

[0498] The server receives emails sent over the Internet. The input is the email from the sender, and the output is the email stored in the server. In this step, the email header information and body are parsed and saved in the appropriate folder.

[0499] Step 2:

[0500] The server automatically translates received emails into a second language (e.g., Japanese to English) using the Google Translate API. The input is the email body stored in the server, and the output is the translated email body. In this step, you send an API request and receive the translation result.

[0501] Step 3:

[0502] The server performs sentiment analysis on the translated email text using TextBlob. The input is the translated email body and the output is the sentiment analysis result (e.g. positive, negative, neutral). In this step, the text is analyzed for words and phrases and a sentiment score is calculated.

[0503] Step 4:

[0504] The server displays the translated email on the recipient's smartphone. The input is the translated email body and the sentiment analysis results, and the output is the email displayed on the recipient's smartphone. In this step, the data is sent to the recipient's device and converted into a format for display.

[0505] Step 5:

[0506] The user creates a reply to a received email. The input is the reply entered by the user, and the output is the reply saved on the user's smartphone. In this step, the user enters text and presses the send button.

[0507] Step 6:

[0508] The server automatically translates the reply from the user into the primary language (e.g., English to Japanese) using the Google Translate API. The input is the reply entered by the user, and the output is the translated reply. In this step, the API request is sent and the translation result is received.

[0509] Step 7:

[0510] The server performs sentiment analysis on the translated reply using TextBlob. The input is the translated reply and the output is the sentiment analysis result. In this step, the text is analyzed for words and phrases and a sentiment score is calculated.

[0511] Step 8:

[0512] The server proposes templates and emojis suitable for the reply content to the user based on the emotion analysis results. The input is the emotion analysis results, and the output is the proposed templates and emojis. In this step, the server selects appropriate templates and emojis based on the emotion scores and displays them to the user.

[0513] Step 9:

[0514] The server sends the translated reply to the original sender. The input is the translated reply, and the output is the reply sent to the original sender. In this step, the reply is sent using an email transmission protocol.

[0515] Example 2

[0516] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0517] Conventional email systems have difficulty in properly converting technical terms and generating responses based on the user's emotions, which can lead to misunderstandings and inappropriate responses, especially when communicating between different languages. Furthermore, insufficient conversion of technical terms can make it difficult to smoothly carry out technical communication.

[0518] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0519] In this invention, the server includes means for receiving emails, means for extracting technical terms from the received emails and converting them into an appropriate second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into the first language, means for analyzing the user's emotions and adjusting the reply content based on the analysis results, and means for sending the converted replies, thereby enabling appropriate conversion of technical terms and generation of responses based on the user's emotions.

[0520] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[0521] The "means for extracting technical terms and converting them into an appropriate second language" is a function for identifying technical terms from the content of received e-mails and converting them into an appropriate second language.

[0522] The "means for displaying the converted e-mail to the recipient" is a function for visually displaying the contents of the converted e-mail to the recipient.

[0523] The "means for converting a reply from a recipient into a first language" is a function for converting the content of a reply from a recipient into a first language.

[0524] The "means for analyzing the user's emotions and adjusting the reply content based on the analysis results" is a function for analyzing the user's emotions and appropriately adjusting the reply content based on the analysis results.

[0525] The "means for sending the converted reply" is a function for sending the converted reply content to the sender.

[0526] The system of the present invention has the function of appropriately converting technical terms and adjusting email responses based on the user's emotions. A specific embodiment of this system will be described below.

[0527] System Overview

[0528] The server receives the email, analyzes its content to extract technical terms, converts it into the appropriate second language, analyzes the user's sentiment and tailors the reply accordingly, and finally sends the converted reply.

[0529] Hardware and software used

[0530] The server uses the following hardware and software:

[0531] Hardware: General purpose server computer

[0532] Software: Python, NLTK library, TextBlob library, Hugging Face® Transformers, IMAP library, SMTP library

[0533] Data processing and calculation

[0534] The server processes and calculates data in the following procedure.

[0535] 1. Receiving emails:

[0536] The server receives email using the IMAP protocol, analyzes the content of the received email, and extracts it as text data.

[0537] 2. Terminology extraction and conversion:

[0538] The server uses the NLTK library to tokenize the email content, match it against a terminology database to extract terminology, and translate the extracted terminology into the appropriate second language.

[0539] 3. Emotion analysis:

[0540] The server uses the TextBlob library to analyze the user's sentiment from the email content, obtaining a sentiment score and classifying it as negative, positive or neutral.

[0541] 4. Response Generation:

[0542] The server uses generative AI models (e.g., Hugging Face Transformers) to generate prompt sentences based on emotion scores and generate appropriate responses.

[0543] 5. Sending a reply:

[0544] The server sends the generated reply using the SMTP protocol.

[0545] Specific examples

[0546] A specific example is shown below.

[0547] Incoming email:

[0548] I'm having trouble setting up my cloud service recently. What should I do?

[0549] System Action:

[0550] 1. The server converts the technical term "cloud" to "Cloud."

[0551] 2. The server analyzes that the user has negative emotions from the expression "I'm in trouble."

[0552] 3. The server takes sentiment into account and generates a reply in a polite tone, such as:

[0553] Generated reply:

[0554] It seems you are having trouble setting up your Cloud service. If you could tell us the specific problem, we would be happy to provide you with detailed support.

[0555] Example prompt sentence:

[0556] User is feeling negative. Email content: Recently, I have been having trouble with Cloud service settings. What should I do? Generate a polite response.

[0557] The above is a specific embodiment of the system of the present invention, which enables appropriate conversion of technical terms and generation of responses based on the user's emotions.

[0558] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0559] Step 1:

[0560] Receiving email

[0561] The server receives email using the IMAP protocol. Specifically, the server connects to the IMAP server, checks for new email, and if there is new email, retrieves its contents.

[0562] Input: New emails from the IMAP server

[0563] Output: Text data of received email

[0564] Specific operation: The server logs into the IMAP server and searches for and retrieves unread emails.

[0565] Step 2:

[0566] Terminology extraction and conversion

[0567] The server extracts technical terms from the content of received emails and converts them into the appropriate second language. Specifically, the server uses the NLTK library to tokenize the content of the emails, compares them with a terminology database to extract technical terms, and converts the extracted technical terms into the appropriate second language.

[0568] Input: Text data of received email

[0569] Output: Text data of email with technical terms converted

[0570] How it works: The server breaks down the email content into words and converts each word by checking it against a terminology database.

[0571] Step 3:

[0572] Emotion analysis

[0573] The server analyzes the user's emotions from the email content. Specifically, the server uses the TextBlob library to analyze the email content and obtain an emotion score.

[0574] Input: Text data of email with technical terms converted

[0575] Output: User sentiment score (positive, negative, neutral)

[0576] Specific behavior: The server inputs the email content into a TextBlob and calculates the sentiment score.

[0577] Step 4:

[0578] Response Generation

[0579] The server generates an appropriate response based on the emotion score. Specifically, the server uses a generative AI model (e.g., Hugging Face Transformers) to generate a prompt sentence based on the emotion score and generate an appropriate response.

[0580] Input: User sentiment score, email text data with technical terms converted

[0581] Output: The generated response text data

[0582] Specific operation: The server inputs the emotion score and the email content as a prompt into the generative AI model to generate a response.

[0583] Step 5:

[0584] Sending a reply

[0585] The server sends the generated reply to the user. Specifically, the server sends the generated reply using the SMTP protocol.

[0586] Input: The text data of the generated response

[0587] Output: Reply email sent to the user

[0588] What happens: The server connects to the SMTP server and sends the generated response as an email.

[0589] (Application example 2)

[0590] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0591] Conventional email systems have difficulty in properly converting technical terms, which can easily lead to misunderstandings, especially when communicating between different languages. Furthermore, they are unable to respond in a way that takes into account the user's feelings, which can lead to a decline in the quality of communication. This makes it difficult to communicate accurately and appropriately, especially when exchanging emails containing technical content.

[0592] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0593] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into the first language, means for sending the converted replies, means for having a learning function for appropriately converting technical terms, and means for adjusting responses based on the user's emotions. This enables appropriate conversion of technical terms and responses based on the user's emotions, thereby realizing accurate and appropriate communication between different languages.

[0594] "Means for receiving e-mail" refers to a function for receiving e-mail sent via the Internet at a server or a client device.

[0595] The "means for converting received e-mail into a second language" is a function that automatically translates the content of received e-mail from the original language into another specified language.

[0596] The "means for displaying the translated email to the recipient" is a function for displaying the translated content of the email on the recipient's device.

[0597] The "means for converting a reply from a recipient into the first language" is a function for automatically translating the content of a reply from the recipient into the original language.

[0598] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[0599] "Means having a learning function for appropriately converting technical terms" is a function for learning technical terms in a specific field and converting them into appropriate translations.

[0600] The "means for adjusting a response based on a user's emotions" is a function for analyzing a user's emotions and generating a response with an appropriate tone and content based on the emotions.

[0601] A system for implementing this invention is configured as follows: First, a server has a means for receiving emails, and receives emails sent via the Internet. The received emails are then converted into a second language. Translation software using natural language processing technology is used for the conversion. Specifically, translation services such as Google Translate API and DeepL API are used.

[0602] The converted email is displayed on the recipient's device. When the recipient replies, the reply is converted back into the recipient's native language, again using translation software. The converted reply is then sent to the original sender.

[0603] Furthermore, this system has a learning function to appropriately convert technical terms. A pre-trained technical dictionary is used to convert technical terms. For example, the Japanese technical term "kuroudou" (cloud) is converted to the English technical term "Cloud." This learning function is realized using a machine learning model. Specifically, machine learning frameworks such as TensorFlow and PyTorch (registered trademark) are used.

[0604] The system also includes a way to tailor responses based on the user's emotions. The system analyzes the user's emotions using a sentiment analysis model, which uses the Hugging Face Transformers library. For example, if the user expresses negative emotions like "distressed" or "sad," the system will take that emotion into account and generate a more polite response.

[0605] As a concrete example, consider a technical blog post. If a user posts something along the lines of, "Cloud technology is very important, but many users are having trouble with it," the system works as follows:

[0606] 1. The server receives the email.

[0607] 2. Translate received emails into a second language (English).

[0608] 3. Display the converted email to the recipient.

[0609] 4. The recipient replies.

[0610] 5. Convert the reply content into the primary language (Japanese).

[0611] 6. Send the converted reply.

[0612] 7. Use the learning function to convert technical terms appropriately.

[0613] 8. Tailor your response based on user sentiment.

[0614] An example of a prompt sentence might be:

[0615] "Please translate the following text into English, adjusting the tone based on the sentiment:

[0616] Cloud technology is extremely important, but many users are struggling with it.

[0617] This way, the content posted by users will be more relevant and easier to understand.

[0618] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0619] Step 1:

[0620] The server receives the email.

[0621] Input: Email sent over the internet.

[0622] Output: Received email data.

[0623] Specific operation: The server receives emails sent over the Internet and stores their contents in a database.

[0624] Step 2:

[0625] The server converts received email into a second language.

[0626] Input: Received email data.

[0627] Output: The email data converted into the second language.

[0628] What happens: The server uses a translation service such as Google Translate API or DeepL API to convert the content of the received email into a second language (English).

[0629] Step 3:

[0630] The server displays the converted email to the recipient.

[0631] Input: Email data converted to a second language.

[0632] Output: The content of the email that will be displayed on the recipient's device.

[0633] Specific operation: The server sends the converted email content to the recipient's terminal so that it can be displayed in the recipient's email client.

[0634] Step 4:

[0635] The recipient replies.

[0636] Type: The reply typed by the recipient.

[0637] Output: Reply data.

[0638] What happens: The recipient uses their email client to type in a reply, which is then sent to the server.

[0639] Step 5:

[0640] The server translates the recipient's reply into the primary language.

[0641] Input: The data entered by the recipient in the reply.

[0642] Output: Reply data converted into the primary language.

[0643] Specific operation: The server uses a translation service such as Google Translate API or DeepL API to convert the recipient's reply content into the recipient's primary language (Japanese).

[0644] Step 6:

[0645] The server sends the transformed reply.

[0646] Input: Reply data converted into the primary language.

[0647] Output: The reply sent to the original sender.

[0648] What happens: The server sends the converted reply content to the original sender so that it is displayed in the original sender's email client.

[0649] Step 7:

[0650] The server uses a learning function to appropriately convert technical terms.

[0651] Input: Data of received emails and reply contents.

[0652] Output: Email and reply content data with appropriate terminology converted.

[0653] Specific operation: The server uses a pre-trained terminology dictionary to appropriately convert technical terms contained in emails and reply content.

[0654] Step 8:

[0655] The server adjusts the response based on the user's emotions.

[0656] Input: Data of received emails and reply contents.

[0657] Output: A response tailored based on the sentiment.

[0658] What it does: The server uses Hugging Face's Transformers library to analyze the sentiment of the email and replies, and generates a response with the appropriate tone and content based on that sentiment.

[0659] Example 3

[0660] Next, a description will be given of a third embodiment of the third embodiment. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0661] Conventional email systems have difficulty communicating between different languages, and misunderstandings are particularly common when it comes to technical terms and emotional interactions. Furthermore, replies are sent immediately without considering the user's feelings, which increases the likelihood of emotional conflicts.

[0662] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0663] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, means for analyzing the user's emotions, and means for adjusting the timing of sending the replies based on the analysis results, thereby enabling smooth communication between different languages ​​and avoiding emotional conflicts.

[0664] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[0665] The "means for converting received e-mail into a second language" is a function for translating the content of received e-mail from a first language into a second language.

[0666] The "means for displaying the translated email to the recipient" is a function for sending the translated email to the recipient's terminal and displaying it.

[0667] The "means for converting a reply from a recipient into a first language" is a function for translating an email replied by a recipient in a second language into the first language.

[0668] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[0669] The "means for analyzing user emotions" is a function for analyzing emotions from the content of emails sent by users.

[0670] The "means for adjusting the timing of sending a reply based on the analysis results" is a function for adjusting the timing of sending a reply email based on the results of sentiment analysis.

[0671] MODE FOR CARRYING OUT THE INVENTION

[0672] This invention is a system for smoothly exchanging e-mails between different languages. Specifically, an example will be described in which the first language is Japanese and the second language is English.

[0673] 1. System Configuration

[0674] The server receives Japanese emails sent by users. The received emails are translated into English using translation services such as Google Cloud Translation API and Microsoft Azure Translator. The translated emails are then sent to the recipient's device. When the recipient replies in English, the emails are sent back to the server and translated into Japanese. This translation process also uses Google Cloud Translation API and Microsoft Azure Translator.

[0675] The server then uses an emotion engine to analyze the user's emotions. Sentiment analysis uses tools such as IBM Watson's Natural Language Understanding and Microsoft Azure's Text Analytics. If the user expresses anger or dissatisfaction, the server adjusts the timing of sending replies. Specifically, it delays replies for a certain period of time to give the user time to calm down.

[0676] 2. Specific Examples

[0677] For example, if user A sends an email saying "This project is not progressing at all!", the server receives this email and translates it using the Google Cloud Translation API to "This project is not progressing at all!". The translated email is then sent to recipient B's device. If recipient B replies with "I understand your frustration, let's discuss this further in our next meeting," the server receives this email and again translates it using the Google Cloud Translation API to "I understand your frustration. Let's discuss this further in our next meeting." The translated reply is then sent to user A's device.

[0678] If the emotion engine analyzes User A's email and detects anger, the server will delay sending the reply for 30 minutes, giving User A time to calm down.

[0679] 3. Example prompts

[0680] Examples of prompts to input to a generative AI model include:

[0681] "User A sent an email expressing anger. Please adjust the timing of your response."

[0682] This system enables smooth communication between Japanese and English, and avoids emotional exchanges. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0683] Step 1:

[0684] A user sends an email in Japanese.

[0685] The user creates an email in Japanese on their own device and presses the send button. The input is the body of the email in Japanese, and the output is the email data to be sent to the server.

[0686] Step 2:

[0687] The server receives email in Japanese.

[0688] The server receives Japanese emails sent by users. The received email data is saved in a database. The input is the email data sent by the user, and the output is the email data saved in the database.

[0689] Step 3:

[0690] The server translates the email into English.

[0691] The server translates received Japanese emails into English using the Google Cloud Translation API. The translation results are temporarily stored in memory. The input is Japanese email data, and the output is the translated email data in English.

[0692] Step 4:

[0693] The server sends the translated email to the recipient's device.

[0694] The server sends the translated English email to the recipient's terminal. The recipient can check the English email in their own email client. The input is the email data translated into English, and the output is the email data sent to the recipient's terminal.

[0695] Step 5:

[0696] The recipient replies in English.

[0697] The recipient creates a reply email in English and presses the send button. The input is the reply email body in English, and the output is the reply email data to be sent to the server.

[0698] Step 6:

[0699] The server receives the reply in English.

[0700] The server receives reply emails in English sent by recipients. The contents of the reply emails are also saved in a database. The input is the reply email data sent by recipients, and the output is the reply email data saved in the database.

[0701] Step 7:

[0702] The server translates the reply into Japanese.

[0703] The server translates the received English reply email into Japanese using the Google Cloud Translation API. The translation result is temporarily stored in memory. The input is the English reply email data, and the output is the reply email data translated into Japanese.

[0704] Step 8:

[0705] The server sends the translated reply to the user.

[0706] The server sends the translated reply email in Japanese to the user's terminal. The user can check the reply email in Japanese on their own email client. The input is the reply email data translated into Japanese, and the output is the reply email data sent to the user's terminal.

[0707] Step 9:

[0708] The server analyzes the user's emotions using an emotion engine.

[0709] The server uses IBM Watson's Natural Language Understanding to analyze the sentiment of the user's email. The analysis results are stored in a database. The input is the user's email data, and the output is the sentiment analysis results.

[0710] Step 10:

[0711] The server adjusts the timing of sending replies based on the sentiment.

[0712] Based on the results of emotion analysis, the server delays sending a reply for a certain period of time if the user expresses anger or dissatisfaction. For example, by postponing the reply for 30 minutes, the server gives the user time to calm down. The input is the emotion analysis result, and the output is the adjusted reply sending timing.

[0713] (Application example 3)

[0714] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0715] Multilingual email communication requires smooth communication across language barriers. It is also necessary to adjust the timing of email transmissions to take into account the user's feelings in order to avoid emotional interactions. From a security perspective, it is also important to encrypt the contents of emails and send them safely.

[0716] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for analyzing the user's emotions, means for adjusting the timing of sending the email based on the analyzed emotions, and means for encrypting the email content. This enables smooth email communication between multiple languages, avoids emotional exchanges, and achieves more secure communications.

[0717] "Means for receiving e-mail" refers to a device or software that has the function of receiving e-mail sent via a network and storing it within the system.

[0718] The "means for converting received e-mail into a second language" refers to a device or software that has the function of translating the contents of received e-mail into another specified language.

[0719] The "means for displaying the translated email to the recipient" refers to a device or software that has the function of displaying the translated content of the email in a format that can be viewed by the recipient.

[0720] The "means for converting a reply from a recipient into a first language" refers to a device or software that has the function of translating the content of an e-mail replied by a recipient into the original language.

[0721] The "means for sending the translated reply" is a device or software that has the function of sending the translated reply email to the recipient.

[0722] The "means for analyzing user emotions" refers to a device or software that has the function of analyzing emotions from the content of emails or user input and identifying the emotional state.

[0723] The "means for adjusting the timing of sending an email based on the analyzed emotion" refers to a device or software that has the function of appropriately adjusting the timing of sending an email in accordance with the analyzed emotional state.

[0724] "Means for encrypting email content" refers to a device or software that has the function of encrypting email content and preventing unauthorized access or tampering by third parties.

[0725] A system for implementing the present invention is configured using the following hardware and software.

[0726] Hardware and software used

[0727] Hardware: Smartphone, smart glasses, head-mounted display, or robot

[0728] software:

[0729] Translation API (e.g. Google Cloud Translation API)

[0730] Emotion analysis API (e.g. IBM Watson Tone Analyzer)

[0731] Cryptography library (e.g. PyCryptodome®)

[0732] System configuration and operation

[0733] 1. Receiving emails

[0734] The server receives emails via the network and stores them in text format within the system.

[0735] 2. Email Translation

[0736] The server translates the content of the received email into a second language (English) using the Google Cloud Translation API, and the translated email is temporarily stored and displayed to the recipient.

[0737] 3. Emotion analysis

[0738] The server uses IBM Watson Tone Analyzer to analyze the user's emotions from the translated email content, and adjusts the timing of reply transmissions based on the analysis results if the user is expressing anger or dissatisfaction.

[0739] 4. Reply email translation

[0740] The recipient's reply in English is translated back into the recipient's primary language (Japanese) using the Google Cloud Translation API. The translated reply is then temporarily saved.

[0741] 5. Email Encryption

[0742] The server uses PyCryptodome to encrypt the translated reply email, and the encrypted email is sent to the recipient.

[0743] Specific examples

[0744] Prompt Sentence Examples

[0745] "I received an email in Japanese. Please translate the contents into English."

[0746] "Analyze user sentiment and adjust the timing of your replies."

[0747] Actual processing example

[0748] 1. Receiving email:

[0749] "Hello, I'd like to confirm about tomorrow's meeting."

[0750] 2. Translation:

[0751] "Hello, I would like to confirm about tomorrow's meeting."

[0752] 3. Emotion analysis:

[0753] Emotion analysis result: Neutral

[0754] 4. Reply email translation:

[0755] "Sure, the meeting is scheduled for 10 AM."

[0756] 5. Encryption:

[0757] Send encrypted emails.

[0758] In this way, the system enables smooth email communication across multiple languages, avoids emotional exchanges, and provides secure communication.

[0759] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0760] Step 1:

[0761] The server receives emails via the network. The received emails are stored in the system in text format. The input is the email received via the network, and the output is the email data in text format.

[0762] Step 2:

[0763] The server translates the content of the received email into a second language (English) using the Google Cloud Translation API. The translated email is temporarily stored and displayed to the recipient. The input is a Japanese email in text format, and the output is an English email in text format.

[0764] Step 3:

[0765] The server uses IBM Watson Tone Analyzer to analyze the user's emotions from the translated email content. Based on the analysis results, if the user expresses anger or dissatisfaction, the timing of sending a reply is adjusted. The input is a plain English email, and the output is the emotion analysis result.

[0766] Step 4:

[0767] The recipient's reply in English is translated back into the primary language (Japanese) using the Google Cloud Translation API. The translated reply is temporarily saved. The input is a plain English reply, and the output is a plain Japanese reply.

[0768] Step 5:

[0769] The server uses PyCryptodome to encrypt the translated reply email. The encrypted email is then sent to the recipient. The input is the Japanese reply email in text format, and the output is the encrypted email data.

[0770] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0771] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0772] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0773] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0774] [Second embodiment]

[0775] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0776] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0777] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0778] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0779] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0780] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0781] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0782] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0783] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0784] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0785] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0786] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0787] "Example 1"

[0788] The system of the present invention includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply. Specifically, the received email is automatically converted into English and displayed to the recipient. The recipient replies in English to the displayed English email. The reply is automatically converted into Japanese by the system and sent.

[0789] "Example 2"

[0790] The system of the present invention has a learning function for appropriately converting technical terms. Specifically, the system learns technical terms in received emails and converts them into appropriate English expressions. For example, the IT industry term "cloud" becomes "Cloud" in English. In this way, the system learns technical terms and converts them appropriately, enabling professional communication.

[0791] "Example 3"

[0792] The system of the present invention has a first language of Japanese and a second language of English. Specifically, received emails are in Japanese and converted into English. Also, replies from recipients are in English and converted into Japanese. In this way, communication between Japanese and English is supported.

[0793] The processing flow of each embodiment will be described below.

[0794] "Example 1"

[0795] Step 1: The system receives an email from the user.

[0796] Step 2: The received email will be automatically converted into English by the system.

[0797] Step 3: The converted English email is displayed to the user.

[0798] Step 4: The user responds to the displayed email in English in English.

[0799] Step 5: The user's reply is automatically translated into Japanese by the system.

[0800] Step 6: The converted Japanese reply will be sent.

[0801] "Example 2"

[0802] Step 1: The system learns the terminology in the emails it receives.

[0803] Step 2: The system converts the learned terminology into appropriate English expressions.

[0804] Step 3: The converted English email is displayed to the user.

[0805] Step 4: The user responds to the displayed email in English in English.

[0806] Step 5: The user's reply is automatically translated into Japanese by the system.

[0807] Step 6: The converted Japanese reply will be sent.

[0808] "Example 3"

[0809] Step 1: The system receives an email in Japanese from the user.

[0810] Step 2: The system automatically converts the received Japanese email into English.

[0811] Step 3: The converted English email is displayed to the user.

[0812] Step 4: The user responds to the displayed email in English in English.

[0813] Step 5: The user's English reply is automatically converted into Japanese by the system.

[0814] Step 6: The converted Japanese reply will be sent.

[0815] Example 1

[0816] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0817] Language barriers often become an obstacle to communication when exchanging emails between users who speak different languages. Rapid and accurate translation is especially important in business and international communications, but manual translation requires time and effort and carries a high risk of mistranslation. Furthermore, the difficulty of translating technical terms and specific contexts makes efficient communication difficult. To solve these issues, a system is needed that can automatically translate emails and send and receive them in the appropriate language.

[0818] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0819] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, means for using a translation service in converting the emails, means for temporarily saving the translation results from the translation service, and means for using a communication protocol in sending and receiving the emails. This enables emails to be exchanged automatically and accurately between users who speak different languages, enabling smooth communication across language barriers.

[0820] "Email" means a digital message sent or received over the Internet or other communications network.

[0821] "Means of receiving" refers to the functions and protocols (e.g., IMAP, POP3) used to import email into the server.

[0822] "Means for converting into a second language" refers to a function or service (e.g., a translation API) for translating the content of received email into another language.

[0823] "Means for displaying to the recipient" refers to the function or application (e.g., email client) for displaying the converted email on the user's terminal.

[0824] "Means for translating replies into the primary language" refers to a function or service (e.g., a translation API) that translates replies from recipients into the primary language.

[0825] "Means for sending the translated reply" refers to the functionality and protocols (e.g., SMTP) used to send the translated reply to the original sender.

[0826] "Means of using translation services" refers to the function of calling external translation APIs or services to translate the contents of emails.

[0827] "Means for temporarily storing translation results" refers to a database or memory for temporarily storing translated content.

[0828] "Means of using communication protocols" refers to the communication rules and procedures required for sending and receiving email (e.g., IMAP, POP3, SMTP).

[0829] MODE FOR CARRYING OUT THE INVENTION

[0830] The present invention relates to a system that automatically translates e-mail messages exchanged between users who speak different languages, enabling them to be sent and received in the appropriate language. A specific embodiment of this system will be described below.

[0831] System configuration

[0832] This system consists of three main components: a server, a terminal, and a user. The server is responsible for receiving, translating, and sending emails, and a terminal is a device that users use to view and reply to emails. A user is the entity that sends and receives emails.

[0833] Hardware and software used

[0834] Server: Use a high-performance computer server and install mail server software (e.g., general mail server software).

[0835] Translation Services: Use external translation services such as Google Cloud Translation API or DeepL API.

[0836] Database: Use a database (e.g., MySQL, PostgreSQL) to temporarily store the translation results.

[0837] Communication protocols: Communication protocols such as IMAP, POP3, and SMTP are used to send and receive email.

[0838] System Operation

[0839] 1. Receiving emails

[0840] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol, for example by connecting to a general mail server and checking for new emails.

[0841] 2. Translating emails into a second language

[0842] The server sends the text of the received email to the Google Cloud Translation API, retrieves the translation results returned by the API, and temporarily stores the translated text in a database.

[0843] 3. View the converted email

[0844] The device receives the English email sent from the server and displays it to User B. User B then opens an email client and checks the English email. For example, it is displayed as "New Email" in a typical email app.

[0845] 4. Translating recipient replies into the recipient's primary language

[0846] The server receives the email in which User B replies in English. It sends the body of the received email to the DeepL API and retrieves the translation result returned by the API. The email body, converted into Japanese, is temporarily stored in a database.

[0847] 5. Send the converted reply

[0848] The server sends the converted Japanese reply email to User A. Using the SMTP protocol, the reply email in Japanese is sent to User A's email address. For example, the email is sent through a general mail server.

[0849] Specific examples

[0850] User A sends an email in Japanese saying "Hello, how are you?"

[0851] The server receives the email and uses the Google Cloud Translation API to translate it into "Hello, how are you?"

[0852] The terminal displays "Hello, how are you?" to User B.

[0853] User B replies, "I am fine, thank you."

[0854] The server receives the reply and translates it using the DeepL API to "I'm fine, thanks."

[0855] The server sends the translated reply to User A.

[0856] Prompt Sentence Examples

[0857] "Design a system that translates emails written in Japanese into English, and translates replies in English into Japanese and sends them. The translation APIs used are Google Cloud Translation API and DeepL API."

[0858] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[0859] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0860] Step 1:

[0861] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol. The input is User A's email account information and a request for new emails, and the output is the received email. Specifically, the server connects to the mail server, checks for new emails, and retrieves the email body and attachments.

[0862] Step 2:

[0863] The server sends the body of the received email to the Google Cloud Translation API. The input is the received Japanese email body, and the output is the translated English email body. Specifically, the server calls the API, translates the email body from Japanese to English, and temporarily stores the translation result in a database.

[0864] Step 3:

[0865] The terminal receives the English email sent from the server and displays it to User B. The input is the English email body sent from the server, and the output is the English email displayed in User B's email client. Specifically, the terminal opens the email client and displays the English email as a "new email."

[0866] Step 4:

[0867] User B composes and sends a reply in English. The input is the reply email in English composed by User B, and the output is the reply email in English sent to the server. Specifically, User B composes a reply using an email client and clicks the send button.

[0868] Step 5:

[0869] The server receives the email with User B's reply in English and sends it to the DeepL API. The input is the English reply email received from User B, and the output is the translated reply email in Japanese. Specifically, the server calls the API, translates the email body from English to Japanese, and temporarily stores the translation result in a database.

[0870] Step 6:

[0871] The server sends the converted reply email in Japanese to User A. The input is the translated reply email in Japanese, and the output is the reply email in Japanese sent to User A. Specifically, the server uses the SMTP protocol to send the reply email in Japanese to User A's email address.

[0872] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[0873] (Application example 1)

[0874] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0875] In virtual stores, there is a problem of insufficient communication between customers and support staff who speak different languages. In particular, customer inquiries and support staff replies are often not conveyed accurately, which can lead to lower customer satisfaction and reduced operational efficiency.

[0876] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0877] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for automatically translating an inquiry from a customer and displaying it to a support staff member, and means for automatically translating the support staff member's reply into the customer's language and sending it. This facilitates communication between customers and support staff who speak different languages, making it possible to improve customer satisfaction and business efficiency.

[0878] "Means for receiving e-mail" refers to a function for capturing e-mail sent via the Internet into a server or device.

[0879] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another specified language.

[0880] The "means for displaying the translated email to the recipient" is a function for displaying the content of the translated email in a format that can be viewed by the recipient.

[0881] The "means for converting a reply from a recipient into the first language" is a function for automatically translating the content of a reply from a recipient into the original language.

[0882] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[0883] "Means for automatically translating customer inquiries and displaying them to support personnel" is a function for automatically translating customer inquiries and displaying the translation results to support personnel.

[0884] The "means for automatically translating the support representative's reply into the customer's language and sending it" is a function for automatically translating the reply from the support representative into the customer's language and sending the translation result to the customer.

[0885] To implement the present invention, the following system configuration and program are required.

[0886] System Configuration

[0887] The system uses the following hardware and software:

[0888] Hardware: Smartphones, servers

[0889] Software: Google Cloud Translation API, Python

[0890] Program processing explanation

[0891] The server first receives inquiries from customers, which are then translated into the language of the support representative using the Google Cloud Translation API, and the translated inquiries are displayed on the support representative's terminal.

[0892] Once the agent types a reply, it's translated into the customer's language using the Google Cloud Translation API, and the translated reply is sent to the customer's device.

[0893] Specific examples

[0894] For example, a customer may ask in Japanese, "Hello, do you have the product in stock?" This inquiry is received by the server and translated into English using the Google Cloud Translation API. The translated message, "Hello, do you have the product in stock?" is then displayed to the support agent.

[0895] The support representative replies, "Yes, the product is in stock." The reply is again translated into Japanese using the Google Cloud Translation API. The translated message, "Yes, the product is in stock," is sent to the customer.

[0896] Prompt Sentence Examples

[0897] Below are some example prompts to input to the generative AI model:

[0898] Translate the following Japanese text to English: "Hello, do you have the item in stock?"

[0899]

[0900] Translate the following English text to Japanese: "Yes, the product is in stock."

[0901] In this way, communication between customers and support personnel who speak different languages ​​can be facilitated.

[0902] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0903] Step 1:

[0904] The server receives inquiries from customers.

[0905] Input: Customer inquiry message (e.g. "Hello, do you have this item in stock?")

[0906] Data processing: Save received messages in text format.

[0907] Output: The saved query message

[0908] Step 2:

[0909] The server uses the Google Cloud Translation API to translate the received inquiry message into the language of the support representative.

[0910] Input: Customer inquiry message (Japanese)

[0911] Data calculation: Call the Google Cloud Translation API to translate the inquiry message into English.

[0912] Output: Translated query message (English)

[0913] Step 3:

[0914] The server displays the translated inquiry message on the terminal of the support staff.

[0915] Input: Translated query message (English)

[0916] Data processing: The translated message is sent to the support representative's terminal and displayed.

[0917] Output: The message displayed on the support representative's terminal

[0918] Step 4:

[0919] The support person uses the terminal to input a reply message.

[0920] Input: Support representative's reply message (e.g., "Yes, the product is in stock.")

[0921] Data processing: Save reply messages in text format.

[0922] Output: Saved reply message

[0923] Step 5:

[0924] The server translates the support agent's reply message into the customer's language using the Google Cloud Translation API.

[0925] Input: Support representative's reply message (English)

[0926] Data calculation: Call the Google Cloud Translation API to translate the reply message into Japanese.

[0927] Output: Translated reply message (Japanese)

[0928] Step 6:

[0929] The server sends the translated reply message to the customer's terminal.

[0930] Input: Translated reply message (Japanese)

[0931] Data processing: Translated messages are sent to the customer's device and displayed.

[0932] Output: Reply message displayed on the customer's device

[0933] Example 2

[0934] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0935] In today's business environment, communication between users who speak different languages ​​is on the rise. However, accurately translating electronic communications containing technical terms is difficult, and misunderstandings are likely to occur, especially in technical fields. To solve this problem, a system is needed that can appropriately translate technical terms and ensure accurate communication.

[0936] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0937] In this invention, the server includes means for receiving electronic communications, means for analyzing the content of the received electronic communications and extracting technical terms, means for converting the extracted technical terms into appropriate second language expressions using a generative AI model, means for rearranging the converted technical terms into their original context and reconstructing the converted electronic communications, means for displaying the converted electronic communications to a recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply, thereby enabling accurate translation of electronic communications containing technical terms and accurate communication between users who speak different languages.

[0938] "Electronic communication" means a message or information sent or received by electronic means.

[0939] A "second language" is a language that is the subject of translation or conversion, and is usually a language that the recipient can understand.

[0940] "Recipient" means a person or system that receives an electronic communication.

[0941] A "reply" is a response message sent by a recipient to a received electronic communication.

[0942] "Primary language" means the language in which the original electronic communication is written, which is usually the language spoken by the sender.

[0943] "Analysis" is the process of examining the content of electronic communications in detail to extract specific information or patterns.

[0944] "Jargon" refers to words and expressions specific to a particular field or industry.

[0945] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text.

[0946] "Context" refers to the situation or background in which a particular word or expression is used.

[0947] "Reconstruction" refers to the process of rearranging the converted terminology into the context of the original electronic communication to create a new electronic communication.

[0948] The present invention is a system for ensuring accurate communication between users of different languages, specifically involving a process for analyzing electronic communications containing technical terms and converting them into an appropriate second language.

[0949] Server Roles

[0950] The server receives electronic communications sent by users. The received electronic communications are analyzed using a natural language processing library (e.g., spaCy). The purpose of the analysis is to extract technical terms contained within the electronic communications.

[0951] The extracted terminology is input to a generative AI model (e.g., OpenAI's GPT-4) and converted into an appropriate second-language expression. The prompt sentence used is "Please translate the following Japanese terminology into English: cloud, big data, AI."

[0952] The converted terminology is then rearranged to fit the context of the original electronic communication using Python string manipulation functions, and the reconstructed electronic communication is sent from the server to the recipient's device.

[0953] Device Role

[0954] The terminal sends user-generated electronic communications to the server using email client software (e.g., Microsoft Outlook or Mozilla Thunderbird).

[0955] The terminal receiving the converted electronic communication from the server displays the content of the electronic communication to the user, who can verify that the technical terminology has been properly converted into the second language.

[0956] User Roles

[0957] The user creates an electronic communication including technical terms and transmits it to the server via the terminal, for example, an electronic communication stating, "I plan to use the cloud service."

[0958] Users receiving the converted electronic communication will verify that the technical terminology has been appropriately converted to second-language expressions, for example, "We plan to use cloud services" has been converted to "We plan to use cloud services."

[0959] Specific examples

[0960] A user creates an electronic communication containing the term "cloud" and sends it from their device to the server. The server analyzes the electronic communication, extracts "cloud" and converts it to "Cloud." The converted electronic communication is then sent back to the device and displayed to the user.

[0961] An example of a prompt sentence to be input to the generative AI model is "Please translate the following Japanese technical terms into English: Cloud, Big Data, AI." Based on this prompt sentence, the generative AI model returns an appropriate second-language expression such as "Cloud, Big Data, AI."

[0962] The above is an embodiment of the present invention.

[0963] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0964] Step 1:

[0965] The server receives electronic communications sent by users. The input is the electronic communication sent by the user through the terminal, and the output is the received electronic communication. In particular, the server receives electronic communications using the SMTP protocol.

[0966] Step 2:

[0967] The server analyzes the content of the received electronic communication. The input is the received electronic communication and the output is the extracted terminology. Specifically, the server uses a natural language processing library (e.g., spaCy) to analyze the electronic communication and extract the terminology.

[0968] Step 3:

[0969] The server inputs the extracted terminology into a generative AI model and converts it into an appropriate second-language expression. The input is the extracted terminology, and the output is the converted terminology. Specifically, the server converts the terminology using a prompt sentence such as "Please convert the following Japanese terminology into English: cloud, big data, AI" to the generative AI model (e.g., OpenAI's GPT-4).

[0970] Step 4:

[0971] The server rearranges the converted terminology into the context of the original electronic communication and reconstructs the converted electronic communication. The input is the converted terminology and the original electronic communication, and the output is the reconstructed electronic communication. Specifically, the server uses Python string manipulation functions to rearrange the converted terminology and reconstruct the electronic communication.

[0972] Step 5:

[0973] The server transmits the reconstructed electronic communication to the recipient's terminal. The input is the reconstructed electronic communication, and the output is the electronic communication sent to the recipient's terminal. In particular, the server transmits the reconstructed electronic communication using the SMTP protocol.

[0974] Step 6:

[0975] The terminal receives the converted electronic communication sent from the server and displays it to the user. The input is the electronic communication sent from the server, and the output is the electronic communication displayed to the user. In particular, the terminal receives and displays the electronic communication using email client software (e.g., Microsoft Outlook or Mozilla Thunderbird).

[0976] Step 7:

[0977] The user reviews the converted electronic communication and, if necessary, creates a reply. The input is the converted electronic communication, and the output is a reply created by the user. Specifically, the user creates the reply using email client software.

[0978] Step 8:

[0979] The terminal sends a reply created by the user to the server. The input is the reply created by the user, and the output is the reply sent to the server. In concrete terms, the terminal uses the SMTP protocol to send the reply to the server.

[0980] Step 9:

[0981] The server converts the received reply into the first language and sends it to the sender. The input is the received reply, and the output is the reply converted into the first language. Specifically, the server uses a generative AI model to convert the reply into the first language and sends it to the sender using the SMTP protocol.

[0982] (Application example 2)

[0983] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0984] Modern content distribution services often use a lot of technical terms, making it difficult for viewers to understand the content. While proper translation of technical terms between different languages ​​is particularly required, real-time translation has been difficult with conventional systems. This has led to the issue of viewers being unable to fully understand the content when watching specialized content.

[0985] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion results on a user interface. This enables viewers to understand specialized content in real time.

[0986] "Means for receiving e-mail" means a combination of hardware and software for receiving e-mail sent via the Internet.

[0987] "Means for converting received email into a second language" refers to software and algorithms for translating the content of received email into a specified second language.

[0988] "Means for displaying the translated email to the recipient" refers to the interface and software for displaying the translated email content on the recipient's device.

[0989] "Means for translating a reply from a recipient into a first language" means software and algorithms for translating a reply composed by a recipient in a second language into the first language.

[0990] A "means for sending a translated reply" is a combination of hardware and software for sending a translated reply to a sender.

[0991] The "means for detecting technical terms in content in real time" refers to natural language processing techniques and algorithms for identifying technical terms contained in the content being viewed in real time.

[0992] The "means for translating detected terminology into appropriate second language expressions" is a generative AI model and algorithm for translating detected terminology into appropriate second language expressions.

[0993] "Means for displaying the translation results in a user interface" refers to the interface and software for displaying the translated terminology results on the user's device.

[0994] An embodiment of the present invention will be described below. The system includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion result on a user interface.

[0995] Hardware and software used

[0996] Hardware: Smartphone, head-mounted display

[0997] Software: Python, TensorFlow, natural language processing libraries (e.g., spaCy)

[0998] Data processing and calculation

[0999] Server Processing

[1000] The server receives emails sent via the Internet. It analyzes the content of the received emails using a natural language processing library (e.g., spaCy) to detect technical terms. The detected technical terms are converted into appropriate second-language expressions using a generative AI model (e.g., GPT-3). The converted content is displayed to the recipient through a user interface.

[1001] Terminal handling

[1002] The device (smartphone or head-mounted display) receives the translation results sent from the server and displays them in the user interface. When the user composes a reply, the device translates the reply into the user's primary language and sends it to the server.

[1003] User operations

[1004] The user receives the email through the terminal and checks the displayed content. If technical terms are included, the technical terms are converted into appropriate second language expressions in real time and displayed. The user then creates a reply and sends it through the terminal.

[1005] Specific examples

[1006] For example, if the technical term "cloud" appears in an IT-related video a user is watching, the system will detect it in real time and translate it as "Cloud," allowing users to understand specialized content in real time.

[1007] Prompt Sentence Examples

[1008] Please translate the following Japanese technical terms into appropriate English expressions.

[1009] Terminology: Cloud

[1010] In this way, the system can translate specialized content in real time and provide it to users.

[1011] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1012] Step 1:

[1013] The server receives emails sent via the Internet. The input is the email data, and the output is the content of the received email. Specifically, the server retrieves emails from the mail server and analyzes their content.

[1014] Step 2:

[1015] The server analyzes the content of received emails using a natural language processing library (e.g., spaCy) to detect technical terms. The input is the content of the received email, and the output is a list of detected technical terms. Specifically, the server tokenizes the text of the email and identifies technical terms.

[1016] Step 3:

[1017] The server converts the detected terminology into an appropriate second-language representation using a generative AI model (e.g., GPT-3). The input is a list of terminology, and the output is a list of converted terminology. Specifically, the server inputs the terminology as a prompt sentence into the generative AI model to obtain an appropriate translation.

[1018] Step 4:

[1019] The server displays the converted content to the recipient through a user interface. The input is a list of converted technical terms, and the output is the translation result displayed on the user interface. Specifically, the server sends the conversion result to the terminal in HTML or JSON format.

[1020] Step 5:

[1021] The terminal receives the conversion results sent from the server and displays them on the user interface. The input is the conversion results sent from the server, and the output is the content displayed on the user interface. Specifically, the terminal analyzes the received data and displays it on the screen.

[1022] Step 6:

[1023] The user receives the email through the terminal and checks the displayed content. The input is the translation result displayed on the terminal, and the output is the user's understanding. Specifically, the user reads and understands the displayed content.

[1024] Step 7:

[1025] The user creates a reply and sends it through the terminal. The input is the reply content created by the user, and the output is the reply content entered into the terminal. Specifically, the user enters the reply content and presses the send button.

[1026] Step 8:

[1027] The terminal converts the reply created by the user into the first language and sends it to the server. The input is the reply content created by the user, and the output is the reply content converted into the first language. Specifically, the terminal translates the reply content using a natural language processing library and sends it to the server.

[1028] Step 9:

[1029] The server sends the converted reply to the sender. The input is the reply content converted into the first language, and the output is the reply content sent to the sender. Specifically, the server sends the converted reply to the sender through the mail server.

[1030] Example 3

[1031] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1032] In today's global business environment, communication between users who speak different languages ​​is important. However, due to language barriers, exchanging emails between users who speak different languages ​​is difficult. Accurate translation is especially required when there are differences in technical terms and nuances. Furthermore, translation work can take a long time and the quality of the translation can be low. To solve these problems, a system that provides efficient and high-quality translation is needed.

[1033] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1034] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for sending the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for performing language conversion using a generative AI model, and means for generating a prompt sentence and inputting it into the generative AI model, thereby enabling efficient and high-quality email exchange between users who speak different languages.

[1035] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[1036] The "means for converting received e-mail into a second language" is a function for translating the contents of received e-mail into another language.

[1037] The "means for sending the translated email to the recipient" is a function for sending the translated email to the specified recipient.

[1038] The "means for converting a reply from a recipient into the first language" is a function for translating a reply email sent from a recipient into the original language.

[1039] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[1040] "Means for performing language conversion using a generative AI model" refers to a function that uses an artificial intelligence model to translate the content of an email into a different language.

[1041] "Means for generating prompt sentences and inputting them to a generative AI model" refers to a function for generating input sentences that will cause the generative AI model to perform appropriate translations, and for providing the input sentences to the AI ​​model.

[1042] The present invention is a system for efficiently and efficiently exchanging e-mails with high quality between users who speak different languages. A specific embodiment of this system will be described below.

[1043] Server Roles

[1044] The server receives emails sent by users. These emails are often written in Japanese. The server uses a generative AI model (e.g., GPT-4) to translate the Japanese emails into English. When translating, the server generates a prompt sentence and inputs it into the generative AI model. An example of a prompt sentence is, "Please translate the following Japanese email into English: Hello, how is the project going?"

[1045] The translated English email is sent from the server to the recipient. When the recipient replies in English, the email is sent back to the server. The server again uses the generative AI model to translate the recipient's English email into Japanese. An example of a translation prompt is, "Please translate the following English email into Japanese: The project is going well, thank you." The translated Japanese email is then sent back to the original user from the server.

[1046] Device Role

[1047] The terminal is used by the user to compose and send emails in Japanese. When the user composes an email and clicks the send button, the terminal sends the email to the server. When the translated English email is received from the server, the terminal displays the email. When the user receives an English email, the terminal displays the email and provides an interface for the user to compose a reply email.

[1048] User Roles

[1049] A user uses a terminal to compose and send an email in Japanese. For example, they type "Hello, how is the project going?" When the user receives a reply in English from the recipient, the email is translated into Japanese by the server and displayed on the user's terminal. The user then checks the translated Japanese email and replies again if necessary.

[1050] Hardware and software used

[1051] This system uses a server, a terminal, and a generative AI model. The server includes hardware and software for receiving, sending, and translating emails. The terminal is a device on which users compose and send emails, using email client software (e.g., Outlook or Gmail). The generative AI model, such as OpenAI's GPT-4, provides high-quality translations when a prompt sentence is input.

[1052] Specific examples

[1053] User A composes the following email in Japanese:

[1054] "Hi, how's the project going?"

[1055] User A's device sends this email to the server, which uses a generative AI model (e.g., GPT-4) to translate the email into English:

[1056] "Hello, how is the progress of the project?"

[1057] The server sends the translated email to recipient B. Recipient B replies in English:

[1058] "The project is going well, thank you."

[1059] The server receives recipient B's email and translates it into Japanese using the generative AI model:

[1060] "The project is progressing smoothly, thank you very much."

[1061] The server sends the translated email to User A's terminal, and User A receives the email.

[1062] In this way, e-mails can be exchanged efficiently and with high quality between users who use different languages. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1063] Step 1:

[1064] The server receives emails sent by users.

[1065] Input: Japanese email sent by the user from the terminal

[1066] Specific operation: The server receives email using the SMTP protocol.

[1067] Output: Received email in Japanese

[1068] Step 2:

[1069] The server analyzes the contents of the received email and extracts the Japanese text portion.

[1070] Input: Received email in Japanese

[1071] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[1072] Output: Extracted Japanese text

[1073] Step 3:

[1074] The server uses a generative AI model to translate Japanese text into English.

[1075] Input: Extracted Japanese text

[1076] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[1077] Example prompt: "Please translate the following Japanese email into English: Hello, how is the project going?"

[1078] Output: Translated English text

[1079] Step 4:

[1080] The server sends the translated English email to the recipient.

[1081] Input: Translated English text

[1082] Specific operation: The server uses the SMTP protocol to send the email to the recipient's email address.

[1083] Output: English email sent to recipient

[1084] Step 5:

[1085] The server receives a reply email in English from the recipient.

[1086] Input: Reply email in English sent by the recipient from their device

[1087] Specific operation: The server receives email using the SMTP protocol.

[1088] Output: Received reply email in English

[1089] Step 6:

[1090] The server analyzes the contents of the received reply email and extracts the English text portion.

[1091] Input: Received reply email in English

[1092] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[1093] Output: Extracted English text

[1094] Step 7:

[1095] The server uses a generative AI model to translate English text into Japanese.

[1096] Input: Extracted English text

[1097] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[1098] Example prompt: "Please translate the following English email into Japanese: The project is going well, thank you."

[1099] Output: Translated Japanese text

[1100] Step 8:

[1101] The server sends the translated Japanese email to the original user.

[1102] Input: Translated Japanese text

[1103] Specific operation: The server uses the SMTP protocol to send an email to the user's email address.

[1104] Output: Japanese email sent to the original user

[1105] (Application example 3)

[1106] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1107] In international e-commerce transactions, communication between customers and support staff who speak different languages ​​is often not smooth. This can lead to lower customer satisfaction and a decrease in transaction efficiency. Furthermore, misunderstandings and problems can occur due to inaccurate translation of technical terms and specific expressions.

[1108] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for receiving an inquiry from a customer, translating it, and displaying it, and means for translating a reply created by a support staff member and sending it to the customer. This enables smooth communication between customers and support staff who speak different languages, improving customer satisfaction and streamlining transactions.

[1109] "Means for receiving e-mail" refers to a device or software that has the function of capturing e-mail sent via the Internet into a server or device.

[1110] The "means for converting received e-mail into a second language" refers to a device or software that has the function of automatically translating the content of a received e-mail into a specified second language.

[1111] The "means for displaying the translated email to the recipient" refers to a device or software that has the function of displaying the translated content of the email in a format that the recipient can understand.

[1112] The "means for translating a reply from a recipient into a first language" is a device or software that has the function of automatically translating a reply written by a recipient in a second language into the first language.

[1113] A "means for sending a translated reply" is a device or software capable of sending a translated reply to the original sender.

[1114] The "means for receiving, translating, and displaying inquiries from customers" refers to a device or software that has the function of receiving inquiries sent from customers, translating the content of the inquiries, and displaying the translated content to the support staff.

[1115] The "means for translating replies written by support staff and sending them to the customer" refers to a device or software that has the function of translating replies written by support staff and sending the translation results to the customer.

[1116] As an embodiment of the present invention, the following system can be constructed.

[1117] First, the server must have a means for receiving emails. This means has the function of importing emails sent via the Internet into the server. Next, a means for converting received emails into a second language is required. This means has the function of automatically translating the contents of received emails into the specified second language. A translation service such as Google Cloud Translation API can be used for the translation.

[1118] The translated email is then displayed to the recipient through a display mechanism that displays the translated email content in a format that the recipient can understand. When the recipient composes a reply, the reply will be written in the second language, so a mechanism is required to translate this into the first language. This mechanism is also automatically translated using the Google Cloud Translation API or similar.

[1119] The translated reply is sent to the original sender through a sending means, which has the function of sending the translated reply to the original sender.

[1120] Furthermore, a means for receiving inquiries from customers and translating and displaying them is required. This means has the function of receiving inquiries sent from customers, translating the contents of the inquiries, and displaying them to the support staff. Replies created by the support staff are sent via a means for translating and sending to the customer. This means has the function of translating the replies created by the support staff and sending the translation results to the customer.

[1121] As a concrete example, if a customer inquires in Japanese, "Please tell me the shipping status of the product," the system translates this inquiry into English as, "Could you please tell me the shipping status of the product?" The support staff receives this English inquiry and creates a reply in English, "The product has been shipped and is expected to arrive in 3 days." The system then translates this reply into Japanese as, "The product has been shipped and is expected to arrive within 3 days," and sends it to the customer.

[1122] An example of a prompt is as follows:

[1123] Please translate the Japanese text into English:

[1124] Please let me know the delivery status of the product.

[1125] Please translate the English text into Japanese:

[1126] "The product has been shipped and is expected to arrive in 3 days."

[1127] In this way, smooth communication between customers and support staff who speak different languages ​​becomes possible, improving customer satisfaction and increasing transaction efficiency.

[1128] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1129] Step 1:

[1130] The server receives emails via the Internet. The input is emails in Japanese sent by customers. The server stores these emails in a database.

[1131] Step 2:

[1132] The server translates the received Japanese email into English using the Google Cloud Translation API. The input is the Japanese email received in step 1, and the output is the translated English email. The server stores the translated English email in a database.

[1133] Step 3:

[1134] The server displays the translated English email on the support staff's terminal. The input is the English email translated in step 2, and the output is the English email displayed on the support staff's terminal. The support staff confirms this English email.

[1135] Step 4:

[1136] The support staff composes a reply in English and sends it to the server. The input is the English reply composed by the support staff, and the output is the English reply sent to the server. The server stores this English reply in a database.

[1137] Step 5:

[1138] The server translates the English reply received from the support staff into Japanese using the Google Cloud Translation API. The input is the English reply received in step 4, and the output is the translated Japanese reply. The server stores the translated Japanese reply in a database.

[1139] Step 6:

[1140] The server sends the translated Japanese reply to the customer. The input is the Japanese reply translated in step 5, and the output is the Japanese reply sent to the customer. The customer receives and confirms this Japanese reply.

[1141] In this way, smooth communication between the server, the terminal, and the user is realized.

[1142] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1143] "Example 1"

[1144] One embodiment of the present invention is an email conversion system incorporating an emotion engine. This system receives emails from users and recognizes the user's emotions from the text. The emotion engine analyzes specific words, phrases, emojis, etc. in the text to determine the emotions expressed by the user. For example, if the text contains words such as "fun" or "happy" or a smiling emoji, it is determined that the user is expressing positive emotions.

[1145] "Example 2"

[1146] Furthermore, the emotion engine has the ability to tailor email responses based on the user's emotions. For example, if the user expresses negative emotions, the system will use that information to generate a more appropriate response. Specifically, if the user expresses emotions such as "troubled" or "sad," the system will take those emotions into account and generate a reply with a more polite tone.

[1147] "Example 3"

[1148] The emotion engine also has the ability to adjust the timing of email sending based on the user's emotions. For example, if a user is expressing anger or frustration, the system can take that emotion into account and delay sending a reply. This gives the user time to calm down and avoids emotional interactions.

[1149] The processing flow of each embodiment will be described below.

[1150] "Example 1"

[1151] Step 1: Receive an email from the user.

[1152] Step 2: Analyze the text of the received email for specific words, phrases, emojis, etc.

[1153] Step 3: Based on the analysis results, determine the emotion expressed by the user.

[1154] "Example 2"

[1155] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[1156] Step 2: Tailor your email responses based on user sentiment.

[1157] Step 3: Send the tailored response to the user.

[1158] "Example 3"

[1159] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[1160] Step 2: Adjust the timing of your emails based on user sentiment.

[1161] Step 3: Send emails to users at the scheduled times.

[1162] Example 1

[1163] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1164] Conventional email systems have difficulty communicating between different languages, especially when it comes to accurately conveying technical terms and emotional nuances. Furthermore, when the recipient replies, they have to translate the message again, which is time-consuming. Furthermore, they lack the ability to recognize the user's emotions from the content of the email, which makes it impossible to improve the quality of communication.

[1165] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1166] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, and means for recognizing emotions from the content of the received emails. This enables smooth communication between different languages ​​and allows technical terms and emotional nuances to be accurately conveyed. It also reduces the effort required for the recipient to reply, improving the quality of communication.

[1167] "Means for receiving e-mail" refers to a function that enables the server to receive e-mail sent via the network.

[1168] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another language.

[1169] The "means for displaying the translated email to the recipient" is a function for displaying the translated email on the recipient's terminal.

[1170] The "means for converting a reply from a recipient into the first language" is a function for automatically translating an email replied by a recipient into the original language.

[1171] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[1172] The "means for recognizing emotions from the content of received e-mails" is a function for analyzing the text content of e-mails and determining the emotions of the sender.

[1173] The "learning function for appropriate translation of technical terms" is a function that uses past translation data and specific dictionaries to learn in order to accurately translate technical terms used in specific fields or industries.

[1174] The present invention is a system for smoothly exchanging e-mails between different languages. A specific embodiment of this system will be described below.

[1175] System configuration

[1176] 1. Receiving emails

[1177] The server receives emails sent by users. To receive emails, it uses mail server software such as Postfix or Sendmail. Specifically, the server receives emails using the SMTP protocol and stores them in a mail queue.

[1178] 2. Translating emails into a second language

[1179] The server automatically translates received emails into a second language (English) using translation services such as the Google Cloud Translation API and DeepL API. Specifically, the server extracts the body of the email and sends an API request to retrieve the translation result.

[1180] 3. View the converted email

[1181] The device then displays the converted English email to the recipient using email client software such as Microsoft Outlook or Gmail. Specifically, the device retrieves the email from the server using the IMAP protocol and displays it in the user interface.

[1182] 4. Translating recipient replies into the recipient's primary language

[1183] The user replies to the displayed English email in English. The server automatically converts the reply into the user's primary language (Japanese). This conversion again uses a translation service such as Google Cloud Translation API or DeepL API. Specifically, the server extracts the body of the reply email and sends an API request to obtain the translation result.

[1184] 5. Send the converted reply

[1185] The server then sends the translated Japanese reply to the original sender using the SMTP protocol and mail server software such as Postfix or Sendmail. Specifically, the server adds the translated email to the mail queue and sends it using the SMTP protocol.

[1186] Incorporating an emotion engine

[1187] 1. Emotional awareness

[1188] The server receives emails from users and recognizes their emotions from the text. The emotion engine uses natural language processing services such as IBM Watson Natural Language Understanding and Microsoft Azure Text Analytics. Specifically, the server extracts the body of the email and sends an API request to obtain the sentiment analysis results.

[1189] 2. Emotional Judgment

[1190] The server determines the emotion expressed by the user based on the analysis results obtained from the emotion engine. Specifically, the server extracts emotion scores such as positive, negative, and neutral from the analysis results, and determines the emotion if it exceeds a certain threshold.

[1191] Specific examples

[1192] For example, if a user sends an email saying "I'm having so much fun today 😊", the following processing will occur:

[1193] 1. The server receives an email from the user.

[1194] 2. The server sends the email body, "Today is so much fun 😊," to the Google Cloud Translation API and translates it into English.

[1195] 3. The device will display the translated email "I am very happy today 😊" to the recipient.

[1196] 4. The user replies to the displayed email with "Thank you for your message!"

[1197] 5. The server sends the reply email to the Google Cloud Translation API and translates it into Japanese.

[1198] 6. The server sends the translated reply "Thanks for your message!" to the original sender.

[1199] 7. The server uses an emotion engine to analyze the sentiment of the email "I'm having so much fun today 😊" and determines that it expresses a positive emotion.

[1200] Prompt Sentence Examples

[1201] Examples of prompts to be input to a generative AI model include:

[1202] Receive emails from users and translate their content into English. Then, display the translated English email to the recipient, translate the recipient's reply into Japanese, and send it to the original sender. Also, analyze the user's sentiment from the email content to determine whether it expresses positive emotions.

[1203] Using this prompt, the generative AI model can understand the system's processing and generate the appropriate output.

[1204] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1205] Step 1:

[1206] The server receives emails sent by users. The input is the email sent over the network, and the output is the email stored in the mail queue within the server. Specifically, the server receives emails using the SMTP protocol and stores them in the mail queue.

[1207] Step 2:

[1208] The server translates received emails into a second language (English). The input is the email stored in the mail queue, and the output is the translated English email. Specifically, the server extracts the email body and sends an API request to the Google Cloud Translation API or DeepL API to obtain the translation result.

[1209] Step 3:

[1210] The terminal displays the translated English email to the recipient. The input is the translated English email retrieved from the server, and the output is the email displayed in the recipient's email client. Specifically, the terminal retrieves the email from the server using the IMAP protocol and displays it in its user interface.

[1211] Step 4:

[1212] The user replies to the displayed English email in English. The input is the reply entered by the recipient, and the output is the reply email sent to the server. In concrete terms, the user composes and sends the reply email using an email client.

[1213] Step 5:

[1214] The server converts the reply from the recipient into the primary language (Japanese). The input is the reply email in English sent by the recipient, and the output is the translated reply email in Japanese. Specifically, the server extracts the body of the reply email and sends an API request to the Google Cloud Translation API or DeepL API to obtain the translation result.

[1215] Step 6:

[1216] The server sends the translated Japanese reply to the original sender. The input is the translated Japanese reply email, and the output is the reply email sent to the original sender. Specifically, the server adds the translated email to the mail queue and sends it using the SMTP protocol.

[1217] Step 7:

[1218] The server recognizes emotions from the content of received emails. The input is the body of the received email, and the output is the sentiment analysis results. Specifically, the server extracts the email body and sends an API request to IBM Watson Natural Language Understanding or Microsoft Azure Text Analytics to obtain the sentiment analysis results.

[1219] Step 8:

[1220] The server determines the emotion expressed by the user based on the analysis results obtained from the emotion engine. The input is the emotion analysis result, and the output is the emotion judgment result. Specifically, the server extracts emotion scores such as positive, negative, and neutral from the analysis results, and judges the emotion if it exceeds a certain threshold.

[1221] (Application example 1)

[1222] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1223] Conventional email systems have difficulty communicating between different languages, especially when it comes to accurately conveying emotional nuances. Furthermore, they lack the ability to translate technical terms appropriately or suggest replies based on emotions, placing a significant burden on users.

[1224] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for analyzing emotions from text, and means for suggesting templates and emoticons appropriate for the reply content based on the emotion analysis results. This enables smooth communication between different languages ​​and accurately conveys emotional nuances. Furthermore, the appropriate translation of technical terms and suggestion of reply content appropriate to the emotion are provided, reducing the burden on the user.

[1225] "Means for receiving e-mail" refers to a function for receiving e-mail sent via the Internet at a server or a client device.

[1226] The "means for converting received e-mail into a second language" is a function that automatically translates the content of received e-mail from the original language into another designated language.

[1227] The "means for displaying the translated email to the recipient" is a function for displaying the translated content of the email on the recipient's device.

[1228] The "means for converting a reply from a recipient into the first language" is a function that automatically translates the content of a reply from a recipient from the language of the replyer into the original language.

[1229] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[1230] "Text emotion analysis" is a function that analyzes the text content of emails and determines the user's emotions based on specific words, phrases, emojis, etc.

[1231] "Means for suggesting templates and emojis suitable for reply content based on the results of emotion analysis" is a function that suggests templates and emojis suitable for reply content to users based on the results of emotion analysis.

[1232] A system for carrying out the present invention has the following configuration: A server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for analyzing emotions from text, and means for suggesting templates and emoticons suitable for the reply content based on the results of the emotion analysis.

[1233] Hardware and Software Configuration

[1234] Hardware: Smartphones, servers

[1235] Software: Python, Google Translate API, TextBlob

[1236] Data processing and calculation

[1237] 1. Receiving emails:

[1238] The server receives emails sent via the Internet and stores them in the server.

[1239] 2. Email Translation:

[1240] Received emails are automatically translated from the original language to a second language (e.g. Japanese to English) using the Google Translate API.

[1241] 3. Emotion analysis:

[1242] The translated email text is then sentiment analyzed using TextBlob to determine the user's sentiment based on specific words, phrases, emojis, etc.

[1243] 4. View the translated email:

[1244] The translated email will be displayed on the recipient's smartphone, where the recipient can check the translated content.

[1245] 5. Reply Translation:

[1246] When the recipient replies, the reply is automatically translated from the second language to the first language (e.g., English to Japanese) using the Google Translate API.

[1247] 6. Emotion-based suggestions:

[1248] Based on the results of sentiment analysis, templates and emojis suitable for the reply content are suggested to the recipient, making it easy for the recipient to create an appropriate reply.

[1249] 7. Sending a Reply:

[1250] The translated reply is then sent via the server to the original sender.

[1251] Specific examples

[1252] For example, if a recipient receives an email in Japanese saying "Hello, how are you?", the email will be translated into English as "Hello, how are you?" The translated email will be displayed on the recipient's smartphone. If the recipient replies in English with "I'm doing well, thank you!", this reply will be translated into Japanese as "I'm doing well, thank you!" and sent to the original sender.

[1253] Prompt Sentence Examples

[1254] "Translate the following Japanese text to English and analyze the sentiment: 'Hello, how are you?'"

[1255] This allows for smooth communication across different languages, accurately conveying emotional nuances, and reduces the burden on users by providing appropriate translations of technical terms and suggesting replies based on emotions.

[1256] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1257] Step 1:

[1258] The server receives emails sent over the Internet. The input is the email from the sender, and the output is the email stored in the server. In this step, the email header information and body are parsed and saved in the appropriate folder.

[1259] Step 2:

[1260] The server automatically translates received emails into a second language (e.g., Japanese to English) using the Google Translate API. The input is the email body stored in the server, and the output is the translated email body. In this step, you send an API request and receive the translation result.

[1261] Step 3:

[1262] The server performs sentiment analysis on the translated email text using TextBlob. The input is the translated email body and the output is the sentiment analysis result (e.g. positive, negative, neutral). In this step, the text is analyzed for words and phrases and a sentiment score is calculated.

[1263] Step 4:

[1264] The server displays the translated email on the recipient's smartphone. The input is the translated email body and the sentiment analysis results, and the output is the email displayed on the recipient's smartphone. In this step, the data is sent to the recipient's device and converted into a format for display.

[1265] Step 5:

[1266] The user creates a reply to a received email. The input is the reply entered by the user, and the output is the reply saved on the user's smartphone. In this step, the user enters text and presses the send button.

[1267] Step 6:

[1268] The server automatically translates the reply from the user into the primary language (e.g., English to Japanese) using the Google Translate API. The input is the reply entered by the user, and the output is the translated reply. In this step, the API request is sent and the translation result is received.

[1269] Step 7:

[1270] The server performs sentiment analysis on the translated reply using TextBlob. The input is the translated reply and the output is the sentiment analysis result. In this step, the text is analyzed for words and phrases and a sentiment score is calculated.

[1271] Step 8:

[1272] The server proposes templates and emojis suitable for the reply content to the user based on the emotion analysis results. The input is the emotion analysis results, and the output is the proposed templates and emojis. In this step, the server selects appropriate templates and emojis based on the emotion scores and displays them to the user.

[1273] Step 9:

[1274] The server sends the translated reply to the original sender. The input is the translated reply, and the output is the reply sent to the original sender. In this step, the reply is sent using an email transmission protocol.

[1275] Example 2

[1276] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1277] Conventional email systems have difficulty in properly converting technical terms and generating responses based on the user's emotions, which can lead to misunderstandings and inappropriate responses, especially when communicating between different languages. Furthermore, insufficient conversion of technical terms can make it difficult to smoothly carry out technical communication.

[1278] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1279] In this invention, the server includes means for receiving emails, means for extracting technical terms from the received emails and converting them into an appropriate second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into the first language, means for analyzing the user's emotions and adjusting the reply content based on the analysis results, and means for sending the converted replies, thereby enabling appropriate conversion of technical terms and generation of responses based on the user's emotions.

[1280] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[1281] The "means for extracting technical terms and converting them into an appropriate second language" is a function for identifying technical terms from the content of received e-mails and converting them into an appropriate second language.

[1282] The "means for displaying the converted e-mail to the recipient" is a function for visually displaying the contents of the converted e-mail to the recipient.

[1283] The "means for converting a reply from a recipient into a first language" is a function for converting the content of a reply from a recipient into a first language.

[1284] The "means for analyzing the user's emotions and adjusting the reply content based on the analysis results" is a function for analyzing the user's emotions and appropriately adjusting the reply content based on the analysis results.

[1285] The "means for sending the converted reply" is a function for sending the converted reply content to the sender.

[1286] The system of the present invention has the function of appropriately converting technical terms and adjusting email responses based on the user's emotions. A specific embodiment of this system will be described below.

[1287] System Overview

[1288] The server receives the email, analyzes its content to extract technical terms, converts it into the appropriate second language, analyzes the user's sentiment and tailors the reply accordingly, and finally sends the converted reply.

[1289] Hardware and software used

[1290] The server uses the following hardware and software:

[1291] Hardware: General purpose server computer

[1292] Software: Python, NLTK library, TextBlob library, Hugging Face Transformers, IMAP library, SMTP library

[1293] Data processing and calculation

[1294] The server processes and calculates data in the following procedure.

[1295] 1. Receiving emails:

[1296] The server receives email using the IMAP protocol, analyzes the content of the received email, and extracts it as text data.

[1297] 2. Terminology extraction and conversion:

[1298] The server uses the NLTK library to tokenize the email content, match it against a terminology database to extract terminology, and translate the extracted terminology into the appropriate second language.

[1299] 3. Emotion analysis:

[1300] The server uses the TextBlob library to analyze the user's sentiment from the email content, obtaining a sentiment score and classifying it as negative, positive or neutral.

[1301] 4. Response Generation:

[1302] The server uses generative AI models (e.g., Hugging Face Transformers) to generate prompt sentences based on emotion scores and generate appropriate responses.

[1303] 5. Sending a reply:

[1304] The server sends the generated reply using the SMTP protocol.

[1305] Specific examples

[1306] A specific example is shown below.

[1307] Incoming email:

[1308] I'm having trouble setting up my cloud service recently. What should I do?

[1309] System Action:

[1310] 1. The server converts the technical term "cloud" to "Cloud."

[1311] 2. The server analyzes that the user has negative emotions from the expression "I'm in trouble."

[1312] 3. The server takes sentiment into account and generates a reply in a polite tone, such as:

[1313] Generated reply:

[1314] It seems you are having trouble setting up your Cloud service. If you could tell us the specific problem, we would be happy to provide you with detailed support.

[1315] Example prompt sentence:

[1316] User is feeling negative. Email content: Recently, I have been having trouble with Cloud service settings. What should I do? Generate a polite response.

[1317] The above is a specific embodiment of the system of the present invention, which enables appropriate conversion of technical terms and generation of responses based on the user's emotions.

[1318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1319] Step 1:

[1320] Receiving email

[1321] The server receives email using the IMAP protocol. Specifically, the server connects to the IMAP server, checks for new email, and if there is new email, retrieves its contents.

[1322] Input: New emails from the IMAP server

[1323] Output: Text data of received email

[1324] Specific operation: The server logs into the IMAP server and searches for and retrieves unread emails.

[1325] Step 2:

[1326] Terminology extraction and conversion

[1327] The server extracts technical terms from the content of received emails and converts them into the appropriate second language. Specifically, the server uses the NLTK library to tokenize the content of the emails, compares them with a terminology database to extract technical terms, and converts the extracted technical terms into the appropriate second language.

[1328] Input: Text data of received email

[1329] Output: Text data of email with technical terms converted

[1330] How it works: The server breaks down the email content into words and converts each word by checking it against a terminology database.

[1331] Step 3:

[1332] Emotion analysis

[1333] The server analyzes the user's emotions from the email content. Specifically, the server uses the TextBlob library to analyze the email content and obtain an emotion score.

[1334] Input: Text data of email with technical terms converted

[1335] Output: User sentiment score (positive, negative, neutral)

[1336] Specific behavior: The server inputs the email content into a TextBlob and calculates the sentiment score.

[1337] Step 4:

[1338] Response Generation

[1339] The server generates an appropriate response based on the emotion score. Specifically, the server uses a generative AI model (e.g., Hugging Face Transformers) to generate a prompt sentence based on the emotion score and generate an appropriate response.

[1340] Input: User sentiment score, email text data with technical terms converted

[1341] Output: The generated response text data

[1342] Specific operation: The server inputs the emotion score and the email content as a prompt into the generative AI model to generate a response.

[1343] Step 5:

[1344] Sending a reply

[1345] The server sends the generated reply to the user. Specifically, the server sends the generated reply using the SMTP protocol.

[1346] Input: The text data of the generated response

[1347] Output: Reply email sent to the user

[1348] What happens: The server connects to the SMTP server and sends the generated response as an email.

[1349] (Application example 2)

[1350] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1351] Conventional email systems have difficulty in properly converting technical terms, which can easily lead to misunderstandings, especially when communicating between different languages. Furthermore, they are unable to respond in a way that takes into account the user's feelings, which can lead to a decline in the quality of communication. This makes it difficult to communicate accurately and appropriately, especially when exchanging emails containing technical content.

[1352] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1353] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into the first language, means for sending the converted replies, means for having a learning function for appropriately converting technical terms, and means for adjusting responses based on the user's emotions. This enables appropriate conversion of technical terms and responses based on the user's emotions, thereby realizing accurate and appropriate communication between different languages.

[1354] "Means for receiving e-mail" refers to a function for receiving e-mail sent via the Internet at a server or a client device.

[1355] The "means for converting received e-mail into a second language" is a function that automatically translates the content of received e-mail from the original language into another specified language.

[1356] The "means for displaying the translated email to the recipient" is a function for displaying the translated content of the email on the recipient's device.

[1357] The "means for converting a reply from a recipient into the first language" is a function for automatically translating the content of a reply from the recipient into the original language.

[1358] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[1359] "Means having a learning function for appropriately converting technical terms" is a function for learning technical terms in a specific field and converting them into appropriate translations.

[1360] The "means for adjusting a response based on a user's emotions" is a function for analyzing a user's emotions and generating a response with an appropriate tone and content based on the emotions.

[1361] A system for implementing this invention is configured as follows: First, a server has a means for receiving emails, and receives emails sent via the Internet. The received emails are then converted into a second language. Translation software using natural language processing technology is used for the conversion. Specifically, translation services such as Google Translate API and DeepL API are used.

[1362] The converted email is displayed on the recipient's device. When the recipient replies, the reply is converted back into the recipient's native language, again using translation software. The converted reply is then sent to the original sender.

[1363] Furthermore, this system has a learning function to appropriately convert technical terms. A pre-trained technical dictionary is used to convert technical terms. For example, the Japanese technical term "kuroudou" (cloud) is converted to the English technical term "Cloud." This learning function is realized using a machine learning model. Specifically, machine learning frameworks such as TensorFlow and PyTorch are used.

[1364] The system also includes a way to tailor responses based on the user's emotions. The system analyzes the user's emotions using a sentiment analysis model, which uses the Hugging Face Transformers library. For example, if the user expresses negative emotions like "distressed" or "sad," the system will take that emotion into account and generate a more polite response.

[1365] As a concrete example, consider a technical blog post. If a user posts something along the lines of, "Cloud technology is very important, but many users are having trouble with it," the system works as follows:

[1366] 1. The server receives the email.

[1367] 2. Translate received emails into a second language (English).

[1368] 3. Display the converted email to the recipient.

[1369] 4. The recipient replies.

[1370] 5. Convert the reply content into the primary language (Japanese).

[1371] 6. Send the converted reply.

[1372] 7. Use the learning function to convert technical terms appropriately.

[1373] 8. Tailor your response based on user sentiment.

[1374] An example of a prompt sentence might be:

[1375] "Please translate the following text into English, adjusting the tone based on the sentiment:

[1376] Cloud technology is extremely important, but many users are struggling with it.

[1377] This way, the content posted by users will be more relevant and easier to understand.

[1378] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1379] Step 1:

[1380] The server receives the email.

[1381] Input: Email sent over the internet.

[1382] Output: Received email data.

[1383] Specific operation: The server receives emails sent over the Internet and stores their contents in a database.

[1384] Step 2:

[1385] The server converts received email into a second language.

[1386] Input: Received email data.

[1387] Output: The email data converted into the second language.

[1388] What happens: The server uses a translation service such as Google Translate API or DeepL API to convert the content of the received email into a second language (English).

[1389] Step 3:

[1390] The server displays the converted email to the recipient.

[1391] Input: Email data converted to a second language.

[1392] Output: The content of the email that will be displayed on the recipient's device.

[1393] Specific operation: The server sends the converted email content to the recipient's terminal so that it can be displayed in the recipient's email client.

[1394] Step 4:

[1395] The recipient replies.

[1396] Type: The reply typed by the recipient.

[1397] Output: Reply data.

[1398] What happens: The recipient uses their email client to type in a reply, which is then sent to the server.

[1399] Step 5:

[1400] The server translates the recipient's reply into the primary language.

[1401] Input: The data entered by the recipient in the reply.

[1402] Output: Reply data converted into the primary language.

[1403] Specific operation: The server uses a translation service such as Google Translate API or DeepL API to convert the recipient's reply content into the recipient's primary language (Japanese).

[1404] Step 6:

[1405] The server sends the transformed reply.

[1406] Input: Reply data converted into the primary language.

[1407] Output: The reply sent to the original sender.

[1408] What happens: The server sends the converted reply content to the original sender so that it is displayed in the original sender's email client.

[1409] Step 7:

[1410] The server uses a learning function to appropriately convert technical terms.

[1411] Input: Data of received emails and reply contents.

[1412] Output: Email and reply content data with appropriate terminology converted.

[1413] Specific operation: The server uses a pre-trained terminology dictionary to appropriately convert technical terms contained in emails and reply content.

[1414] Step 8:

[1415] The server adjusts the response based on the user's emotions.

[1416] Input: Data of received emails and reply contents.

[1417] Output: A response tailored based on the sentiment.

[1418] What it does: The server uses Hugging Face's Transformers library to analyze the sentiment of the email and replies, and generates a response with the appropriate tone and content based on that sentiment.

[1419] Example 3

[1420] Next, a description will be given of Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1421] Conventional email systems have difficulty communicating between different languages, and misunderstandings are particularly common when it comes to technical terms and emotional interactions. Furthermore, replies are sent instantly without considering the user's feelings, which increases the likelihood of emotional conflicts.

[1422] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1423] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, means for analyzing the user's emotions, and means for adjusting the timing of sending the replies based on the analysis results, thereby enabling smooth communication between different languages ​​and avoiding emotional conflicts.

[1424] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[1425] The "means for converting received e-mail into a second language" is a function for translating the content of received e-mail from a first language into a second language.

[1426] The "means for displaying the translated email to the recipient" is a function for sending the translated email to the recipient's terminal and displaying it.

[1427] The "means for converting a reply from a recipient into a first language" is a function for translating an email replied by a recipient in a second language into the first language.

[1428] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[1429] The "means for analyzing user emotions" is a function for analyzing emotions from the content of emails sent by users.

[1430] The "means for adjusting the timing of sending a reply based on the analysis results" is a function for adjusting the timing of sending a reply email based on the results of emotion analysis.

[1431] MODE FOR CARRYING OUT THE INVENTION

[1432] This invention is a system for smoothly exchanging e-mails between different languages. Specifically, an example will be described in which the first language is Japanese and the second language is English.

[1433] 1. System Configuration

[1434] The server receives Japanese emails sent by users. The received emails are translated into English using translation services such as Google Cloud Translation API and Microsoft Azure Translator. The translated emails are then sent to the recipient's device. When the recipient replies in English, the emails are sent back to the server and translated into Japanese. This translation process also uses Google Cloud Translation API and Microsoft Azure Translator.

[1435] The server then uses an emotion engine to analyze the user's emotions. Sentiment analysis uses tools such as IBM Watson's Natural Language Understanding and Microsoft Azure's Text Analytics. If the user expresses anger or dissatisfaction, the server adjusts the timing of sending replies. Specifically, it delays replies for a certain period of time to give the user time to calm down.

[1436] 2. Specific Examples

[1437] For example, if user A sends an email saying "This project is not progressing at all!", the server receives this email and translates it using the Google Cloud Translation API to "This project is not progressing at all!". The translated email is then sent to recipient B's device. If recipient B replies with "I understand your frustration, let's discuss this further in our next meeting," the server receives this email and again translates it using the Google Cloud Translation API to "I understand your frustration. Let's discuss this further in our next meeting." The translated reply is then sent to user A's device.

[1438] If the emotion engine analyzes User A's email and detects anger, the server will delay sending the reply for 30 minutes, giving User A time to calm down.

[1439] 3. Example prompts

[1440] Examples of prompts to input to a generative AI model include:

[1441] "User A sent an email expressing anger. Please adjust the timing of your response."

[1442] This system enables smooth communication between Japanese and English, and avoids emotional exchanges. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1443] Step 1:

[1444] A user sends an email in Japanese.

[1445] The user creates an email in Japanese on their own device and presses the send button. The input is the body of the email in Japanese, and the output is the email data to be sent to the server.

[1446] Step 2:

[1447] The server receives email in Japanese.

[1448] The server receives Japanese emails sent by users. The received email data is saved in a database. The input is the email data sent by the user, and the output is the email data saved in the database.

[1449] Step 3:

[1450] The server translates the email into English.

[1451] The server translates received Japanese emails into English using the Google Cloud Translation API. The translation results are temporarily stored in memory. The input is Japanese email data, and the output is the translated email data in English.

[1452] Step 4:

[1453] The server sends the translated email to the recipient's device.

[1454] The server sends the translated English email to the recipient's terminal. The recipient can check the English email in their own email client. The input is the email data translated into English, and the output is the email data sent to the recipient's terminal.

[1455] Step 5:

[1456] The recipient replies in English.

[1457] The recipient creates a reply email in English and presses the send button. The input is the reply email body in English, and the output is the reply email data to be sent to the server.

[1458] Step 6:

[1459] The server receives a reply in English.

[1460] The server receives reply emails in English sent by recipients. The contents of the reply emails are also saved in a database. The input is the reply email data sent by recipients, and the output is the reply email data saved in the database.

[1461] Step 7:

[1462] The server translates the reply into Japanese.

[1463] The server translates the received English reply email into Japanese using the Google Cloud Translation API. The translation result is temporarily stored in memory. The input is the English reply email data, and the output is the reply email data translated into Japanese.

[1464] Step 8:

[1465] The server sends the translated reply to the user.

[1466] The server sends the translated reply email in Japanese to the user's terminal. The user can check the reply email in Japanese on their own email client. The input is the reply email data translated into Japanese, and the output is the reply email data sent to the user's terminal.

[1467] Step 9:

[1468] The server analyzes the user's emotions using an emotion engine.

[1469] The server uses IBM Watson's Natural Language Understanding to analyze the sentiment of the user's email. The analysis results are stored in a database. The input is the user's email data, and the output is the sentiment analysis results.

[1470] Step 10:

[1471] The server adjusts the timing of sending replies based on the sentiment.

[1472] Based on the results of emotion analysis, the server delays sending a reply for a certain period of time if the user expresses anger or dissatisfaction. For example, by postponing the reply for 30 minutes, the server gives the user time to calm down. The input is the emotion analysis result, and the output is the adjusted reply sending timing.

[1473] (Application example 3)

[1474] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1475] Multilingual email communication requires smooth communication across language barriers. It is also necessary to adjust the timing of email transmissions to take into account the user's feelings in order to avoid emotional interactions. From a security perspective, it is also important to encrypt the contents of emails and send them safely.

[1476] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for analyzing the user's emotions, means for adjusting the timing of sending the email based on the analyzed emotions, and means for encrypting the email content. This enables smooth email communication between multiple languages, avoids emotional exchanges, and achieves more secure communications.

[1477] "Means for receiving e-mail" refers to a device or software that has the function of receiving e-mail sent via a network and storing it within the system.

[1478] The "means for converting received e-mail into a second language" refers to a device or software that has the function of translating the contents of received e-mail into another specified language.

[1479] The "means for displaying the translated email to the recipient" refers to a device or software that has the function of displaying the translated email content in a format that can be viewed by the recipient.

[1480] The "means for converting a reply from a recipient into a first language" refers to a device or software that has the function of translating the content of an e-mail replied by a recipient into the original language.

[1481] The "means for sending the translated reply" is a device or software that has the function of sending the translated reply email to the recipient.

[1482] The "means for analyzing user emotions" refers to a device or software that has the function of analyzing emotions from the content of emails or user input and identifying the emotional state.

[1483] The "means for adjusting the timing of sending an email based on the analyzed emotion" refers to a device or software that has the function of appropriately adjusting the timing of sending an email in accordance with the analyzed emotional state.

[1484] "Means for encrypting email content" refers to a device or software that has the function of encrypting email content and preventing unauthorized access or tampering by third parties.

[1485] A system for implementing the present invention is configured using the following hardware and software.

[1486] Hardware and software used

[1487] Hardware: smartphone, smart glasses, head-mounted display, or robot

[1488] software:

[1489] Translation API (e.g. Google Cloud Translation API)

[1490] Emotion analysis API (e.g. IBM Watson Tone Analyzer)

[1491] Cryptography libraries (e.g. PyCryptodome)

[1492] System configuration and operation

[1493] 1. Receiving emails

[1494] The server receives emails via the network and stores them in text format within the system.

[1495] 2. Email Translation

[1496] The server translates the content of the received email into a second language (English) using the Google Cloud Translation API, and the translated email is temporarily stored and displayed to the recipient.

[1497] 3. Emotion analysis

[1498] The server uses IBM Watson Tone Analyzer to analyze the user's emotions from the translated email content, and adjusts the timing of reply transmissions based on the analysis results if the user is expressing anger or dissatisfaction.

[1499] 4. Reply email translation

[1500] The recipient's reply in English is translated back into the recipient's primary language (Japanese) using the Google Cloud Translation API. The translated reply is then temporarily saved.

[1501] 5. Email Encryption

[1502] The server uses PyCryptodome to encrypt the translated reply email, and the encrypted email is sent to the recipient.

[1503] Specific examples

[1504] Prompt Sentence Examples

[1505] "I received an email in Japanese. Please translate the contents into English."

[1506] "Analyze user sentiment and adjust the timing of your replies."

[1507] Actual processing example

[1508] 1. Receiving email:

[1509] "Hello, I'd like to confirm about tomorrow's meeting."

[1510] 2. Translation:

[1511] "Hello, I would like to confirm about tomorrow's meeting."

[1512] 3. Emotion analysis:

[1513] Emotion analysis result: Neutral

[1514] 4. Reply email translation:

[1515] "Sure, the meeting is scheduled for 10 AM."

[1516] 5. Encryption:

[1517] Send encrypted emails.

[1518] In this way, the system enables smooth email communication across multiple languages, avoids emotional exchanges, and provides secure communication.

[1519] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1520] Step 1:

[1521] The server receives emails via the network. The received emails are stored in the system in text format. The input is the email received via the network, and the output is the email data in text format.

[1522] Step 2:

[1523] The server translates the content of the received email into a second language (English) using the Google Cloud Translation API. The translated email is temporarily stored and displayed to the recipient. The input is a Japanese email in text format, and the output is an English email in text format.

[1524] Step 3:

[1525] The server uses IBM Watson Tone Analyzer to analyze the user's emotions from the translated email content. Based on the analysis results, if the user expresses anger or dissatisfaction, the timing of sending a reply is adjusted. The input is a plain English email, and the output is the emotion analysis result.

[1526] Step 4:

[1527] The recipient's reply in English is translated back into the primary language (Japanese) using the Google Cloud Translation API. The translated reply is temporarily saved. The input is a plain English reply, and the output is a plain Japanese reply.

[1528] Step 5:

[1529] The server uses PyCryptodome to encrypt the translated reply email. The encrypted email is then sent to the recipient. The input is the Japanese reply email in text format, and the output is the encrypted email data.

[1530] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1531] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1532] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1533] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1534] [Third embodiment]

[1535] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1536] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1537] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1538] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1539] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1540] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1541] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1542] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1543] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1544] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1545] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1546] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1547] "Example 1"

[1548] The system of the present invention includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply. Specifically, the received email is automatically converted into English and displayed to the recipient. The recipient replies in English to the displayed English email. The reply is automatically converted into Japanese by the system and sent.

[1549] "Example 2"

[1550] The system of the present invention has a learning function for appropriately converting technical terms. Specifically, the system learns technical terms in received emails and converts them into appropriate English expressions. For example, the IT industry term "cloud" becomes "Cloud" in English. In this way, the system learns technical terms and converts them appropriately, enabling professional communication.

[1551] "Example 3"

[1552] The system of the present invention has a first language of Japanese and a second language of English. Specifically, received emails are in Japanese and converted into English. Also, replies from recipients are in English and converted into Japanese. In this way, communication between Japanese and English is supported.

[1553] The processing flow of each embodiment will be described below.

[1554] "Example 1"

[1555] Step 1: The system receives an email from the user.

[1556] Step 2: The received email will be automatically converted into English by the system.

[1557] Step 3: The converted English email is displayed to the user.

[1558] Step 4: The user responds to the displayed email in English in English.

[1559] Step 5: The user's reply is automatically translated into Japanese by the system.

[1560] Step 6: The converted Japanese reply will be sent.

[1561] "Example 2"

[1562] Step 1: The system learns the terminology in the emails it receives.

[1563] Step 2: The system converts the learned terminology into appropriate English expressions.

[1564] Step 3: The converted English email is displayed to the user.

[1565] Step 4: The user responds to the displayed email in English in English.

[1566] Step 5: The user's reply is automatically translated into Japanese by the system.

[1567] Step 6: The converted Japanese reply will be sent.

[1568] "Example 3"

[1569] Step 1: The system receives an email in Japanese from the user.

[1570] Step 2: The system automatically converts the received Japanese email into English.

[1571] Step 3: The converted English email is displayed to the user.

[1572] Step 4: The user responds to the displayed email in English in English.

[1573] Step 5: The user's English reply is automatically converted into Japanese by the system.

[1574] Step 6: The converted Japanese reply will be sent.

[1575] Example 1

[1576] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1577] Language barriers often become an obstacle to communication when exchanging emails between users who speak different languages. Rapid and accurate translation is especially important in business and international communications, but manual translation requires time and effort and carries a high risk of mistranslation. Furthermore, the difficulty of translating technical terms and specific contexts makes efficient communication difficult. To solve these issues, a system is needed that can automatically translate emails and send and receive them in the appropriate language.

[1578] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1579] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, means for using a translation service in converting the emails, means for temporarily saving the translation results from the translation service, and means for using a communication protocol in sending and receiving the emails. This enables emails to be exchanged automatically and accurately between users who speak different languages, enabling smooth communication across language barriers.

[1580] "Email" means a digital message sent or received over the Internet or other communications network.

[1581] "Means of receiving" refers to the functions and protocols (e.g., IMAP, POP3) used to import email into the server.

[1582] "Means for converting into a second language" refers to a function or service (e.g., a translation API) for translating the content of received email into another language.

[1583] "Means for displaying to the recipient" refers to the function or application (e.g., email client) for displaying the converted email on the user's terminal.

[1584] "Means for translating replies into the primary language" refers to a function or service (e.g., a translation API) that translates replies from recipients into the primary language.

[1585] "Means for sending the translated reply" refers to the functionality and protocols (e.g., SMTP) used to send the translated reply to the original sender.

[1586] "Means of using translation services" refers to the function of calling external translation APIs or services to translate the contents of emails.

[1587] "Means for temporarily storing translation results" refers to a database or memory for temporarily storing translated content.

[1588] "Means of using communication protocols" refers to the communication rules and procedures required for sending and receiving email (e.g., IMAP, POP3, SMTP).

[1589] MODE FOR CARRYING OUT THE INVENTION

[1590] The present invention relates to a system that automatically translates e-mail messages exchanged between users who speak different languages, enabling them to be sent and received in the appropriate language. A specific embodiment of this system will be described below.

[1591] System configuration

[1592] This system consists of three main components: a server, a terminal, and a user. The server is responsible for receiving, translating, and sending emails, and a terminal is a device that users use to view and reply to emails. A user is the entity that sends and receives emails.

[1593] Hardware and software used

[1594] Server: Use a high-performance computer server and install mail server software (e.g., general mail server software).

[1595] Translation Services: Use external translation services such as Google Cloud Translation API or DeepL API.

[1596] Database: Use a database (e.g., MySQL, PostgreSQL) to temporarily store the translation results.

[1597] Communication protocols: Communication protocols such as IMAP, POP3, and SMTP are used to send and receive email.

[1598] System Operation

[1599] 1. Receiving emails

[1600] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol, for example by connecting to a general mail server and checking for new emails.

[1601] 2. Translating emails into a second language

[1602] The server sends the text of the received email to the Google Cloud Translation API, retrieves the translation results returned by the API, and temporarily stores the translated text in a database.

[1603] 3. View the converted email

[1604] The device receives the English email sent from the server and displays it to User B. User B then opens an email client and checks the English email. For example, it is displayed as "New Email" in a typical email app.

[1605] 4. Translating recipient replies into the recipient's primary language

[1606] The server receives the email in which User B replies in English. It sends the body of the received email to the DeepL API and retrieves the translation result returned by the API. The email body, converted into Japanese, is temporarily stored in a database.

[1607] 5. Send the converted reply

[1608] The server sends the converted Japanese reply email to User A. Using the SMTP protocol, the reply email in Japanese is sent to User A's email address. For example, the email is sent through a general mail server.

[1609] Specific examples

[1610] User A sends an email in Japanese saying "Hello, how are you?"

[1611] The server receives the email and uses the Google Cloud Translation API to translate it into "Hello, how are you?"

[1612] The terminal displays "Hello, how are you?" to User B.

[1613] User B replies, "I am fine, thank you."

[1614] The server receives the reply and translates it using the DeepL API to "I'm fine, thanks."

[1615] The server sends the translated reply to User A.

[1616] Prompt Sentence Examples

[1617] "Design a system that translates emails written in Japanese into English, and translates replies in English into Japanese and sends them. The translation APIs used are Google Cloud Translation API and DeepL API."

[1618] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[1619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1620] Step 1:

[1621] The server retrieves new emails from User A's mailbox using the IMAP or POP3 protocol. The input is User A's email account information and a request for new emails, and the output is the received email. Specifically, the server connects to the mail server, checks for new emails, and retrieves the email body and attachments.

[1622] Step 2:

[1623] The server sends the body of the received email to the Google Cloud Translation API. The input is the received Japanese email body, and the output is the translated English email body. Specifically, the server calls the API, translates the email body from Japanese to English, and temporarily stores the translation result in a database.

[1624] Step 3:

[1625] The terminal receives the English email sent from the server and displays it to User B. The input is the English email body sent from the server, and the output is the English email displayed in User B's email client. Specifically, the terminal opens the email client and displays the English email as a "new email."

[1626] Step 4:

[1627] User B composes and sends a reply in English. The input is the reply email in English composed by User B, and the output is the reply email in English sent to the server. Specifically, User B composes a reply using an email client and clicks the send button.

[1628] Step 5:

[1629] The server receives the email with User B's reply in English and sends it to the DeepL API. The input is the English reply email received from User B, and the output is the translated reply email in Japanese. Specifically, the server calls the API, translates the email body from English to Japanese, and temporarily stores the translation result in a database.

[1630] Step 6:

[1631] The server sends the converted reply email in Japanese to User A. The input is the translated reply email in Japanese, and the output is the reply email in Japanese sent to User A. Specifically, the server uses the SMTP protocol to send the reply email in Japanese to User A's email address.

[1632] In this way, the system automates language conversion and sending / receiving of e-mails, facilitating communication between users who speak different languages.

[1633] (Application example 1)

[1634] Next, a description will be given of Application Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1635] In virtual stores, there is a problem of insufficient communication between customers and support staff who speak different languages. In particular, customer inquiries and support staff replies are often not conveyed accurately, which can lead to lower customer satisfaction and reduced operational efficiency.

[1636] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1637] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for automatically translating an inquiry from a customer and displaying it to a support staff member, and means for automatically translating the support staff member's reply into the customer's language and sending it. This facilitates communication between customers and support staff who speak different languages, making it possible to improve customer satisfaction and business efficiency.

[1638] "Means for receiving e-mail" refers to a function for capturing e-mail sent via the Internet into a server or device.

[1639] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another specified language.

[1640] The "means for displaying the translated email to the recipient" is a function for displaying the content of the translated email in a format that can be viewed by the recipient.

[1641] The "means for converting a reply from a recipient into the first language" is a function for automatically translating the content of a reply from a recipient into the original language.

[1642] The "means for sending the translated reply" is a function for sending the translated reply content to the original sender.

[1643] "Means for automatically translating customer inquiries and displaying them to support personnel" is a function for automatically translating customer inquiries and displaying the translation results to support personnel.

[1644] The "means for automatically translating the support representative's reply into the customer's language and sending it" is a function for automatically translating the reply from the support representative into the customer's language and sending the translation result to the customer.

[1645] To implement the present invention, the following system configuration and program are required.

[1646] System Configuration

[1647] The system uses the following hardware and software:

[1648] Hardware: Smartphones, servers

[1649] Software: Google Cloud Translation API, Python

[1650] Program processing explanation

[1651] The server first receives inquiries from customers, which are then translated into the language of the support representative using the Google Cloud Translation API, and the translated inquiries are displayed on the support representative's terminal.

[1652] Once the agent types a reply, it's translated into the customer's language using the Google Cloud Translation API, and the translated reply is sent to the customer's device.

[1653] Specific examples

[1654] For example, a customer may ask in Japanese, "Hello, do you have the product in stock?" This inquiry is received by the server and translated into English using the Google Cloud Translation API. The translated message, "Hello, do you have the product in stock?" is then displayed to the support agent.

[1655] The support representative replies, "Yes, the product is in stock." The reply is again translated into Japanese using the Google Cloud Translation API. The translated message, "Yes, the product is in stock," is sent to the customer.

[1656] Prompt Sentence Examples

[1657] Below are some example prompts to input to the generative AI model:

[1658] Translate the following Japanese text to English: "Hello, do you have the item in stock?"

[1659]

[1660] Translate the following English text to Japanese: "Yes, the product is in stock."

[1661] In this way, communication between customers and support personnel who speak different languages ​​can be facilitated.

[1662] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1663] Step 1:

[1664] The server receives inquiries from customers.

[1665] Input: Customer inquiry message (e.g. "Hello, do you have this item in stock?")

[1666] Data processing: Save received messages in text format.

[1667] Output: The saved query message

[1668] Step 2:

[1669] The server uses the Google Cloud Translation API to translate the received inquiry message into the language of the support representative.

[1670] Input: Customer inquiry message (Japanese)

[1671] Data calculation: Call the Google Cloud Translation API to translate the inquiry message into English.

[1672] Output: Translated query message (English)

[1673] Step 3:

[1674] The server displays the translated inquiry message on the terminal of the support staff.

[1675] Input: Translated query message (English)

[1676] Data processing: The translated message is sent to the support representative's terminal and displayed.

[1677] Output: The message displayed on the support representative's terminal

[1678] Step 4:

[1679] The support person uses the terminal to input a reply message.

[1680] Input: Support representative's reply message (e.g., "Yes, the product is in stock.")

[1681] Data processing: Save reply messages in text format.

[1682] Output: Saved reply message

[1683] Step 5:

[1684] The server translates the support agent's reply message into the customer's language using the Google Cloud Translation API.

[1685] Input: Support representative's reply message (English)

[1686] Data calculation: Call the Google Cloud Translation API to translate the reply message into Japanese.

[1687] Output: Translated reply message (Japanese)

[1688] Step 6:

[1689] The server sends the translated reply message to the customer's terminal.

[1690] Input: Translated reply message (Japanese)

[1691] Data processing: Translated messages are sent to the customer's device and displayed.

[1692] Output: Reply message displayed on the customer's device

[1693] Example 2

[1694] Next, a description will be given of Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1695] In today's business environment, communication between users who speak different languages ​​is on the rise. However, accurately translating electronic communications containing technical terms is difficult, and misunderstandings are likely to occur, especially in technical fields. To solve this problem, a system is needed that can appropriately translate technical terms and ensure accurate communication.

[1696] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1697] In this invention, the server includes means for receiving electronic communications, means for analyzing the content of the received electronic communications and extracting technical terms, means for converting the extracted technical terms into appropriate second language expressions using a generative AI model, means for rearranging the converted technical terms into their original context and reconstructing the converted electronic communications, means for displaying the converted electronic communications to a recipient, means for converting a reply from the recipient into a first language, and means for sending the converted reply, thereby enabling accurate translation of electronic communications containing technical terms and accurate communication between users who speak different languages.

[1698] "Electronic communication" means a message or information sent or received by electronic means.

[1699] A "second language" is a language that is the subject of translation or conversion, and is usually a language that the recipient can understand.

[1700] "Recipient" means a person or system that receives an electronic communication.

[1701] A "reply" is a response message sent by a recipient to a received electronic communication.

[1702] "Primary language" means the language in which the original electronic communication is written, which is usually the language spoken by the sender.

[1703] "Analysis" is the process of examining the content of electronic communications to extract specific information or patterns.

[1704] "Jargon" refers to the specific words and expressions used in a particular field or industry.

[1705] A "generative AI model" is an algorithm or system that uses artificial intelligence to generate text.

[1706] "Context" refers to the situation or background in which a particular word or expression is used.

[1707] "Reconstruction" refers to the process of rearranging the converted terminology into the context of the original electronic communication to create a new electronic communication.

[1708] The present invention is a system for ensuring accurate communication between users of different languages, specifically involving a process for analyzing electronic communications containing technical terms and converting them into an appropriate second language.

[1709] Server Roles

[1710] The server receives electronic communications sent by users. The received electronic communications are analyzed using a natural language processing library (e.g., spaCy). The purpose of the analysis is to extract technical terms contained within the electronic communications.

[1711] The extracted terminology is input to a generative AI model (e.g., OpenAI's GPT-4) and converted into an appropriate second-language expression. The prompt sentence used is "Please translate the following Japanese terminology into English: cloud, big data, AI."

[1712] The converted terminology is then rearranged to fit the context of the original electronic communication using Python string manipulation functions, and the reconstructed electronic communication is sent from the server to the recipient's device.

[1713] Device Role

[1714] The terminal sends user-generated electronic communications to the server using email client software (e.g., Microsoft Outlook or Mozilla Thunderbird).

[1715] The terminal receiving the converted electronic communication from the server displays the content of the electronic communication to the user, who can verify that the technical terminology has been properly converted into the second language.

[1716] User Roles

[1717] The user creates an electronic communication including technical terms and transmits it to the server via the terminal, for example, an electronic communication stating, "I plan to use the cloud service."

[1718] Users receiving the converted electronic communication will verify that the technical terminology has been appropriately converted to second-language expressions, for example, "We plan to use cloud services" has been converted to "We plan to use cloud services."

[1719] Specific examples

[1720] A user creates an electronic communication containing the term "cloud" and sends it from their device to the server. The server analyzes the electronic communication, extracts "cloud" and converts it to "Cloud." The converted electronic communication is then sent back to the device and displayed to the user.

[1721] An example of a prompt sentence to be input to the generative AI model is "Please translate the following Japanese technical terms into English: Cloud, Big Data, AI." Based on this prompt sentence, the generative AI model returns an appropriate second-language expression such as "Cloud, Big Data, AI."

[1722] The above is an embodiment of the present invention.

[1723] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1724] Step 1:

[1725] The server receives electronic communications sent by users. The input is the electronic communication sent by the user through the terminal, and the output is the received electronic communication. In particular, the server receives electronic communications using the SMTP protocol.

[1726] Step 2:

[1727] The server analyzes the content of the received electronic communication. The input is the received electronic communication and the output is the extracted terminology. Specifically, the server uses a natural language processing library (e.g., spaCy) to analyze the electronic communication and extract the terminology.

[1728] Step 3:

[1729] The server inputs the extracted terminology into a generative AI model and converts it into an appropriate second-language expression. The input is the extracted terminology, and the output is the converted terminology. Specifically, the server converts the terminology using a prompt sentence such as "Please convert the following Japanese terminology into English: cloud, big data, AI" to the generative AI model (e.g., OpenAI's GPT-4).

[1730] Step 4:

[1731] The server rearranges the converted terminology into the context of the original electronic communication and reconstructs the converted electronic communication. The input is the converted terminology and the original electronic communication, and the output is the reconstructed electronic communication. Specifically, the server uses Python string manipulation functions to rearrange the converted terminology and reconstruct the electronic communication.

[1732] Step 5:

[1733] The server transmits the reconstructed electronic communication to the recipient's terminal. The input is the reconstructed electronic communication, and the output is the electronic communication sent to the recipient's terminal. In particular, the server transmits the reconstructed electronic communication using the SMTP protocol.

[1734] Step 6:

[1735] The terminal receives the converted electronic communication sent from the server and displays it to the user. The input is the electronic communication sent from the server, and the output is the electronic communication displayed to the user. In particular, the terminal receives and displays the electronic communication using email client software (e.g., Microsoft Outlook or Mozilla Thunderbird).

[1736] Step 7:

[1737] The user reviews the converted electronic communication and, if necessary, creates a reply. The input is the converted electronic communication, and the output is a reply created by the user. Specifically, the user creates the reply using email client software.

[1738] Step 8:

[1739] The terminal sends a reply created by the user to the server. The input is the reply created by the user, and the output is the reply sent to the server. In concrete terms, the terminal uses the SMTP protocol to send the reply to the server.

[1740] Step 9:

[1741] The server converts the received reply into the first language and sends it to the sender. The input is the received reply, and the output is the reply converted into the first language. Specifically, the server uses a generative AI model to convert the reply into the first language and sends it to the sender using the SMTP protocol.

[1742] (Application example 2)

[1743] Next, a description will be given of Application Example 2 of Form Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1744] Modern content distribution services often use a lot of technical terms, making it difficult for viewers to understand the content. While proper translation of technical terms between different languages ​​is particularly required, real-time translation has been difficult with conventional systems. This has led to the issue of viewers being unable to fully understand the content when watching specialized content.

[1745] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into the first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion results on a user interface. This enables viewers to understand specialized content in real time.

[1746] "Means for receiving e-mail" means a combination of hardware and software for receiving e-mail sent via the Internet.

[1747] "Means for converting received email into a second language" refers to software and algorithms for translating the content of received email into a specified second language.

[1748] "Means for displaying the translated email to the recipient" refers to the interface and software for displaying the translated email content on the recipient's device.

[1749] "Means for translating a reply from a recipient into a first language" means software and algorithms for translating a reply composed by a recipient in a second language into the first language.

[1750] A "means for sending a translated reply" is a combination of hardware and software for sending a translated reply to a sender.

[1751] The "means for detecting technical terms in content in real time" refers to natural language processing techniques and algorithms for identifying technical terms contained in the content being viewed in real time.

[1752] The "means for translating detected terminology into appropriate second language expressions" is a generative AI model and algorithm for translating detected terminology into appropriate second language expressions.

[1753] "Means for displaying the translation results in a user interface" refers to the interface and software for displaying the translated terminology results on the user's device.

[1754] An embodiment of the present invention will be described below. The system includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for detecting technical terms in content in real time, means for converting the detected technical terms into appropriate second language expressions, and means for displaying the conversion result on a user interface.

[1755] Hardware and software used

[1756] Hardware: Smartphone, head-mounted display

[1757] Software: Python, TensorFlow, natural language processing libraries (e.g., spaCy)

[1758] Data processing and calculation

[1759] Server Processing

[1760] The server receives emails sent via the Internet. It analyzes the content of the received emails using a natural language processing library (e.g., spaCy) to detect technical terms. The detected technical terms are converted into appropriate second-language expressions using a generative AI model (e.g., GPT-3). The converted content is displayed to the recipient through a user interface.

[1761] Terminal handling

[1762] The device (smartphone or head-mounted display) receives the translation results sent from the server and displays them in the user interface. When the user composes a reply, the device translates the reply into the user's primary language and sends it to the server.

[1763] User operations

[1764] The user receives the email through the terminal and checks the displayed content. If technical terms are included, the technical terms are converted into appropriate second language expressions in real time and displayed. The user then creates a reply and sends it through the terminal.

[1765] Specific examples

[1766] For example, if the technical term "cloud" appears in an IT-related video a user is watching, the system will detect it in real time and translate it as "Cloud," allowing users to understand specialized content in real time.

[1767] Prompt Sentence Examples

[1768] Please translate the following Japanese technical terms into appropriate English expressions.

[1769] Terminology: Cloud

[1770] In this way, the system can translate specialized content in real time and provide it to users.

[1771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1772] Step 1:

[1773] The server receives emails sent via the Internet. The input is the email data, and the output is the content of the received email. Specifically, the server retrieves emails from the mail server and analyzes their content.

[1774] Step 2:

[1775] The server analyzes the content of received emails using a natural language processing library (e.g., spaCy) to detect technical terms. The input is the content of the received email, and the output is a list of detected technical terms. Specifically, the server tokenizes the text of the email and identifies technical terms.

[1776] Step 3:

[1777] The server converts the detected terminology into an appropriate second-language representation using a generative AI model (e.g., GPT-3). The input is a list of terminology, and the output is a list of converted terminology. Specifically, the server inputs the terminology as a prompt sentence into the generative AI model to obtain an appropriate translation.

[1778] Step 4:

[1779] The server displays the converted content to the recipient through a user interface. The input is a list of converted technical terms, and the output is the translation result displayed on the user interface. Specifically, the server sends the conversion result to the terminal in HTML or JSON format.

[1780] Step 5:

[1781] The terminal receives the conversion results sent from the server and displays them on the user interface. The input is the conversion results sent from the server, and the output is the content displayed on the user interface. Specifically, the terminal analyzes the received data and displays it on the screen.

[1782] Step 6:

[1783] The user receives the email through the terminal and checks the displayed content. The input is the translation result displayed on the terminal, and the output is the user's understanding. Specifically, the user reads and understands the displayed content.

[1784] Step 7:

[1785] The user creates a reply and sends it through the terminal. The input is the reply content created by the user, and the output is the reply content entered into the terminal. Specifically, the user enters the reply content and presses the send button.

[1786] Step 8:

[1787] The terminal converts the reply created by the user into the first language and sends it to the server. The input is the reply content created by the user, and the output is the reply content converted into the first language. Specifically, the terminal translates the reply content using a natural language processing library and sends it to the server.

[1788] Step 9:

[1789] The server sends the converted reply to the sender. The input is the reply content converted into the first language, and the output is the reply content sent to the sender. Specifically, the server sends the converted reply to the sender through the mail server.

[1790] Example 3

[1791] Next, a third embodiment of the third embodiment will be described. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1792] In today's global business environment, communication between users who speak different languages ​​is important. However, due to language barriers, exchanging emails between users who speak different languages ​​is difficult. Accurate translation is especially required when there are differences in technical terms and nuances. Furthermore, translation work can take a long time and the quality of the translation can be low. To solve these problems, a system that provides efficient and high-quality translation is needed.

[1793] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1794] In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for sending the converted email to a recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for performing language conversion using a generative AI model, and means for generating a prompt sentence and inputting it into the generative AI model, thereby enabling efficient and high-quality email exchange between users who speak different languages.

[1795] "Means for receiving e-mail" is a function that enables the server to receive e-mail sent from the user.

[1796] The "means for converting received e-mail into a second language" is a function for translating the contents of received e-mail into another language.

[1797] The "means for sending the translated email to the recipient" is a function for sending the translated email to the specified recipient.

[1798] The "means for converting a reply from a recipient into the first language" is a function for translating a reply email sent from a recipient into the original language.

[1799] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[1800] "Means for performing language conversion using a generative AI model" refers to a function that uses an artificial intelligence model to translate the content of an email into a different language.

[1801] "Means for generating prompt sentences and inputting them to a generative AI model" refers to a function for generating input sentences that will cause the generative AI model to perform appropriate translations, and for providing the input sentences to the AI ​​model.

[1802] The present invention is a system for efficiently and efficiently exchanging e-mails with high quality between users who speak different languages. A specific embodiment of this system will be described below.

[1803] Server Roles

[1804] The server receives emails sent by users. These emails are often written in Japanese. The server uses a generative AI model (e.g., GPT-4) to translate the Japanese emails into English. When translating, the server generates a prompt sentence and inputs it into the generative AI model. An example of a prompt sentence is, "Please translate the following Japanese email into English: Hello, how is the project going?"

[1805] The translated English email is sent from the server to the recipient. When the recipient replies in English, the email is sent back to the server. The server again uses the generative AI model to translate the recipient's English email into Japanese. An example of a translation prompt is, "Please translate the following English email into Japanese: The project is going well, thank you." The translated Japanese email is then sent back to the original user from the server.

[1806] Device Role

[1807] The terminal is used by the user to compose and send emails in Japanese. When the user composes an email and clicks the send button, the terminal sends the email to the server. When the translated English email is received from the server, the terminal displays the email. When the user receives an English email, the terminal displays the email and provides an interface for the user to compose a reply email.

[1808] User Roles

[1809] A user uses a terminal to compose and send an email in Japanese. For example, they type "Hello, how is the project going?" When the user receives a reply in English from the recipient, the email is translated into Japanese by the server and displayed on the user's terminal. The user then checks the translated Japanese email and replies again if necessary.

[1810] Hardware and software used

[1811] This system uses a server, a terminal, and a generative AI model. The server includes hardware and software for receiving, sending, and translating emails. The terminal is a device on which users compose and send emails, using email client software (e.g., Outlook or Gmail). The generative AI model, such as OpenAI's GPT-4, provides high-quality translations when a prompt sentence is input.

[1812] Specific examples

[1813] User A composes the following email in Japanese:

[1814] "Hi, how's the project going?"

[1815] User A's device sends this email to the server, which uses a generative AI model (e.g., GPT-4) to translate the email into English:

[1816] "Hello, how is the progress of the project?"

[1817] The server sends the translated email to recipient B. Recipient B replies in English:

[1818] "The project is going well, thank you."

[1819] The server receives recipient B's email and translates it into Japanese using the generative AI model:

[1820] "The project is progressing smoothly, thank you very much."

[1821] The server sends the translated email to User A's terminal, and User A receives the email.

[1822] In this way, e-mails can be exchanged efficiently and with high quality between users who use different languages. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1823] Step 1:

[1824] The server receives emails sent by users.

[1825] Input: Japanese email sent by the user from the terminal

[1826] Specific operation: The server receives email using the SMTP protocol.

[1827] Output: Received email in Japanese

[1828] Step 2:

[1829] The server analyzes the contents of the received email and extracts the Japanese text portion.

[1830] Input: Received email in Japanese

[1831] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[1832] Output: Extracted Japanese text

[1833] Step 3:

[1834] The server uses a generative AI model to translate Japanese text into English.

[1835] Input: Extracted Japanese text

[1836] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[1837] Example prompt: "Please translate the following Japanese email into English: Hello, how is the project going?"

[1838] Output: Translated English text

[1839] Step 4:

[1840] The server sends the translated English email to the recipient.

[1841] Input: Translated English text

[1842] Specific operation: The server uses the SMTP protocol to send the email to the recipient's email address.

[1843] Output: English email sent to recipient

[1844] Step 5:

[1845] The server receives a reply email in English from the recipient.

[1846] Input: Reply email in English sent by the recipient from their device

[1847] Specific operation: The server receives email using the SMTP protocol.

[1848] Output: Received reply email in English

[1849] Step 6:

[1850] The server analyzes the contents of the received reply email and extracts the English text portion.

[1851] Input: Received reply email in English

[1852] Specific operation: The server removes the header information and attachments from the email and obtains the text of the body.

[1853] Output: Extracted English text

[1854] Step 7:

[1855] The server uses a generative AI model to translate English text into Japanese.

[1856] Input: Extracted English text

[1857] Specific operation: The server generates a prompt sentence and inputs it into the generative AI model.

[1858] Example prompt: "Please translate the following English email into Japanese: The project is going well, thank you."

[1859] Output: Translated Japanese text

[1860] Step 8:

[1861] The server sends the translated Japanese email to the original user.

[1862] Input: Translated Japanese text

[1863] Specific operation: The server uses the SMTP protocol to send an email to the user's email address.

[1864] Output: Japanese email sent to the original user

[1865] (Application example 3)

[1866] Next, a description will be given of Application Example 3 of Form Example 3. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1867] In international e-commerce transactions, communication between customers and support staff who speak different languages ​​is often not smooth. This can lead to lower customer satisfaction and a decrease in transaction efficiency. Furthermore, misunderstandings and problems can occur due to inaccurate translation of technical terms and specific expressions.

[1868] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes means for receiving email, means for converting the received email into a second language, means for displaying the converted email to the recipient, means for converting a reply from the recipient into a first language, means for sending the converted reply, means for receiving an inquiry from a customer, translating it, and displaying it, and means for translating a reply created by a support staff member and sending it to the customer. This enables smooth communication between customers and support staff who speak different languages, improving customer satisfaction and streamlining transactions.

[1869] "Means for receiving e-mail" refers to a device or software that has the function of capturing e-mail sent via the Internet into a server or device.

[1870] The "means for converting received e-mail into a second language" refers to a device or software that has the function of automatically translating the content of a received e-mail into a specified second language.

[1871] The "means for displaying the translated email to the recipient" refers to a device or software that has the function of displaying the translated content of the email in a format that the recipient can understand.

[1872] The "means for translating a reply from a recipient into a first language" is a device or software that has the function of automatically translating a reply written by a recipient in a second language into the first language.

[1873] A "means for sending a translated reply" is a device or software capable of sending a translated reply to the original sender.

[1874] The "means for receiving, translating, and displaying inquiries from customers" refers to a device or software that has the function of receiving inquiries sent from customers, translating the content of the inquiries, and displaying the translated content to the support staff.

[1875] The "means for translating replies written by support staff and sending them to the customer" refers to a device or software that has the function of translating replies written by support staff and sending the translation results to the customer.

[1876] As an embodiment of the present invention, the following system can be constructed.

[1877] First, the server must have a means for receiving emails. This means has the function of importing emails sent via the Internet into the server. Next, a means for converting received emails into a second language is required. This means has the function of automatically translating the contents of received emails into the specified second language. A translation service such as Google Cloud Translation API can be used for the translation.

[1878] The translated email is then displayed to the recipient through a display mechanism that displays the translated email content in a format that the recipient can understand. When the recipient composes a reply, the reply will be written in the second language, so a mechanism is required to translate this into the first language. This mechanism is also automatically translated using the Google Cloud Translation API or similar.

[1879] The translated reply is sent to the original sender through a sending means, which has the function of sending the translated reply to the original sender.

[1880] Furthermore, a means for receiving inquiries from customers and translating and displaying them is required. This means has the function of receiving inquiries sent from customers, translating the contents of the inquiries, and displaying them to the support staff. Replies created by the support staff are sent via a means for translating and sending to the customer. This means has the function of translating the replies created by the support staff and sending the translation results to the customer.

[1881] As a concrete example, if a customer inquires in Japanese, "Please tell me the shipping status of the product," the system translates this inquiry into English as, "Could you please tell me the shipping status of the product?" The support staff receives this English inquiry and creates a reply in English, "The product has been shipped and is expected to arrive in 3 days." The system then translates this reply into Japanese as, "The product has been shipped and is expected to arrive within 3 days," and sends it to the customer.

[1882] An example of a prompt is as follows:

[1883] Please translate the Japanese text into English:

[1884] Please let me know the delivery status of the product.

[1885] Please translate the English text into Japanese:

[1886] "The product has been shipped and is expected to arrive in 3 days."

[1887] In this way, smooth communication between customers and support staff who speak different languages ​​becomes possible, improving customer satisfaction and increasing transaction efficiency.

[1888] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1889] Step 1:

[1890] The server receives emails via the Internet. The input is emails in Japanese sent by customers. The server stores these emails in a database.

[1891] Step 2:

[1892] The server translates the received Japanese email into English using the Google Cloud Translation API. The input is the Japanese email received in step 1, and the output is the translated English email. The server stores the translated English email in a database.

[1893] Step 3:

[1894] The server displays the translated English email on the support staff's terminal. The input is the English email translated in step 2, and the output is the English email displayed on the support staff's terminal. The support staff confirms this English email.

[1895] Step 4:

[1896] The support staff composes a reply in English and sends it to the server. The input is the English reply composed by the support staff, and the output is the English reply sent to the server. The server stores this English reply in a database.

[1897] Step 5:

[1898] The server translates the English reply received from the support staff into Japanese using the Google Cloud Translation API. The input is the English reply received in step 4, and the output is the translated Japanese reply. The server stores the translated Japanese reply in a database.

[1899] Step 6:

[1900] The server sends the translated Japanese reply to the customer. The input is the Japanese reply translated in step 5, and the output is the Japanese reply sent to the customer. The customer receives and confirms this Japanese reply.

[1901] In this way, smooth communication between the server, the terminal, and the user is realized.

[1902] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1903] "Example 1"

[1904] One embodiment of the present invention is an email conversion system incorporating an emotion engine. This system receives emails from users and recognizes the user's emotions from the text. The emotion engine analyzes specific words, phrases, emojis, etc. in the text to determine the emotions expressed by the user. For example, if the text contains words such as "fun" or "happy" or a smiling emoji, it is determined that the user is expressing positive emotions.

[1905] "Example 2"

[1906] Furthermore, the emotion engine has the ability to tailor email responses based on the user's emotions. For example, if the user expresses negative emotions, the system will use that information to generate a more appropriate response. Specifically, if the user expresses emotions such as "troubled" or "sad," the system will take those emotions into account and generate a reply with a more polite tone.

[1907] "Example 3"

[1908] The emotion engine also has the ability to adjust the timing of email sending based on the user's emotions. For example, if a user is expressing anger or frustration, the system can take that emotion into account and delay sending a reply. This gives the user time to calm down and avoids emotional interactions.

[1909] The processing flow of each embodiment will be described below.

[1910] "Example 1"

[1911] Step 1: Receive an email from the user.

[1912] Step 2: Analyze the text of the received email for specific words, phrases, emojis, etc.

[1913] Step 3: Based on the analysis results, determine the emotion expressed by the user.

[1914] "Example 2"

[1915] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[1916] Step 2: Tailor your email responses based on user sentiment.

[1917] Step 3: Send the tailored response to the user.

[1918] "Example 3"

[1919] Step 1: Receive an email from a user and recognize the user's sentiment from the text.

[1920] Step 2: Adjust the timing of your emails based on user sentiment.

[1921] Step 3: Send emails to users at the scheduled times.

[1922] Example 1

[1923] Next, a description will be given of Example 1 of Form Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1924] Conventional email systems have difficulty communicating between different languages, especially when it comes to accurately conveying technical terms and emotional nuances. Furthermore, when the recipient replies, they have to translate the message again, which is time-consuming. Furthermore, they lack the ability to recognize the user's emotions from the content of the email, which makes it impossible to improve the quality of communication.

[1925] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1926] In this invention, the server includes means for receiving emails, means for converting the received emails into a second language, means for displaying the converted emails to the recipient, means for converting replies from the recipients into a first language, means for sending the converted replies, and means for recognizing emotions from the content of the received emails. This enables smooth communication between different languages ​​and allows technical terms and emotional nuances to be accurately conveyed. It also reduces the effort required for the recipient to reply, improving the quality of communication.

[1927] "Means for receiving e-mail" refers to a function that enables the server to receive e-mail sent via the network.

[1928] The "means for converting received e-mail into a second language" is a function for automatically translating the contents of received e-mail into another language.

[1929] The "means for displaying the translated email to the recipient" is a function for displaying the translated email on the recipient's terminal.

[1930] The "means for converting a reply from a recipient into the first language" is a function for automatically translating an email replied by a recipient into the original language.

[1931] The "means for sending the translated reply" is a function for sending the translated reply email to the original sender.

[1932] The "means for recognizing emotions from the content of received e-mails" is a function for analyzing the text content of e-mails and determining the emotions of the sender.

[1933] The "learning function for appropriate translation of technical terms" is a function that uses past translation data and specific dictionaries to learn in order to accurately translate technical terms used in specific fields or industries.

[1934] The present invention is a system for smoothly exchanging e-mails between different languages. A specific embodiment of this system will be described below.

[1935] System configuration

[1936] 1. Receiving emails

[1937] The server receives emails sent by users. To receive emails, it uses mail server software such as Postfix or Sendmail. Specifically, the server receives emails using the SMTP protocol and stores them in a mail queue.

[1938] 2. Translating emails into a second language

[1939] The server automatically translates received emails into a second language (English) using translation services such as the Google Cloud Translation API and DeepL API. Specifically, the server extracts the body of the email and sends an API request to retrieve the translation result.

[1940] 3. View the converted email

[1941] The device then displays the converted English email to the recipient using email client software such as Microsoft Outlook or Gmail. Specifically, the device retrieves the email from the server using the IMAP protocol and displays it in the user interface.

[1942] 4. Translating recipient replies into the recipient's primary language

[1943] The user replies to the displayed English email in English. The server automatically converts the reply into the user's primary language (Japanese). This conversion again uses a translation service such as Google Cloud Translation API or DeepL API. Specifically, the server extracts the body of the reply email and sends an API request to obtain the translation result.

[1944] 5. Send the converted reply

[1945] The server th...

Claims

1. A first means for detecting technical terms from the content of received e-mail; A second means for converting the detected technical terminology into an appropriate English expression using a generative AI model; a third means for transmitting the conversion result of the technical term into an English expression to a terminal of a recipient of the email; A system including:

2. a fourth means for analyzing the emotion of a sender from the content of the email using an emotion engine; a fifth means for suggesting to the recipient a template or emoticons suitable for a reply to the email based on the analysis result of the sender's emotion; The system of claim 1 , comprising:

3. and a sixth means for adjusting a timing of sending a reply to the email based on a result of the analysis of the sender's emotion. The system of claim 2.

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