system

Abstract

Provide a system. A means for receiving and saving the text of an email; A means of automatically extracting important keywords from the text, means for generating subject line candidates based on the extracted keywords; means for presenting subject line suggestions to a user and applying a selected subject line to the email; A system including:

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] Setting an appropriate email subject line is extremely important in business communication. However, it is difficult to summarize the content of an email in one sentence, which can lead to important emails being postponed or overlooked. To solve this problem, a system is needed that can extract appropriate information from the email body and automatically generate an appropriate subject line based on that information. [Means for solving the problem]

[0005] The present invention relates to a system including the following means: First, a means for receiving and saving the body of an email is provided. Next, a means for automatically extracting important keywords from the body is provided. This extraction includes means for performing grammatical analysis and context analysis. Next, a means for generating subject candidates based on the keywords is provided, including means for automatically generating a subject based on a predefined template. Finally, a means for presenting the generated subject candidates to a user and applying the selected subject to the email is provided. This prevents important emails from being overlooked and enables prompt responses.

[0006] The "body of the email" is the part of the email message that contains the main content.

[0007] "Receiving" is the process by which the server retrieves newly sent mail.

[0008] "Storage" is the process of storing data such as the body of a received email in a server or database.

[0009] "Keywords" are words or phrases extracted from the email body that represent the subject or important points.

[0010] "Automatic extraction" refers to a method of extracting keywords using an algorithm without human intervention.

[0011] "Grammar analysis" is a process that analyzes the structure of words in a sentence and clarifies the role and relationships of each word.

[0012] "Context analysis" is the process of understanding the meaning and context of the entire sentence and evaluating the importance of keywords.

[0013] "Subject candidates" are multiple phrases that are appropriate as email subjects and are generated based on the extracted keywords.

[0014] A "predefined template" is a pre-defined, fixed structure or format that is used as a standard for generating subject lines.

[0015] "User" means an individual or organization that uses this system.

[0016] "Presenting" refers to displaying the generated subject candidates so that the user can check them.

[0017] "Selection" is the process by which a user chooses an appropriate subject line from the presented candidates.

[0018] "Apply" is the process of adding the selected subject to the subject field of the actual email. [Brief explanation of the drawings]

[0019] [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. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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, a 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), and an APU (Accelerated Processing Unit).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. 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."

[0040] This invention relates to a system for automatically generating email subject lines in business communications. Detailed embodiments of this system and the processing contents of the program will be described below with specific examples.

[0041] System configuration

[0042] The system consists of the following main components:

[0043] 1. A server that receives and stores email text

[0044] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0045] 3. Server and terminal that generates subject candidates and presents them to the user

[0046] 4. A device that allows users to select a subject and apply the selected subject to the email.

[0047] What the program does

[0048] Receiving and storing emails

[0049] The server stores newly received emails from email clients in an internal database.

[0050] Once the email is saved, it moves on to the next analysis step.

[0051] Text analysis and keyword extraction

[0052] The server runs the stored email body through a text analysis algorithm.

[0053] The algorithm performs grammatical analysis to understand the structure of the sentence.

[0054] Next, context analysis is performed to understand the meaning of the entire sentence.

[0055] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[0056] Auto-generated subject line suggestions

[0057] The server generates subject line candidates based on the extracted keywords using predefined templates.

[0058] For example, templates could include "Next meeting: {date and time}" and "Confirmation request: {document name}."

[0059] The generated subject suggestions are compiled into a list.

[0060] Suggesting and selecting subject lines

[0061] The server transmits the generated list of subject candidates to the terminal.

[0062] The terminal displays a list of subject suggestions in the user's email client.

[0063] The user selects the most appropriate subject from the displayed candidates.

[0064] Apply subject and send email

[0065] The terminal transmits the user's selection to the server.

[0066] The server applies the selected subject to the email's subject field.

[0067] The device will display the email with the applied subject and prepare it to send.

[0068] Specific examples

[0069] Example: Actual email body

[0070] Hello.

[0071] I will let you know the details of next week's sales meeting.

[0072] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[0073] First Quarter Sales Report

[0074] Customer Feedback Report

[0075] Analysis of the above text and subject candidate generation

[0076] 1. The server receives and stores email.

[0077] 2. The server extracts the following keywords from the body of the text: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report".

[0078] 3. The server generates the following possible subject lines:

[0079] "Sales Meeting: March 15th, 10:00 AM"

[0080] "Request for review: Q1 sales report and customer feedback report"

[0081] 4. The user selects "Sales Meeting: March 15th, 10:00 AM."

[0082] 5. The server applies the selected subject to the email and prepares it for sending.

[0083] This ensures that the subject line of your email is clear and relevant, instantly conveying to the recipient the importance of the email.

[0084] The processing flow will be explained below.

[0085] Step 1:

[0086] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[0087] Step 2:

[0088] The server stores the received emails in an internal database.

[0089] Step 3:

[0090] The server identifies the stored email as the subject of analysis and extracts the email body.

[0091] Step 4:

[0092] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[0093] Step 5:

[0094] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[0095] Step 6:

[0096] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[0097] Step 7:

[0098] Based on the extracted and weighted keywords, the server generates subject line suggestions according to predefined templates, such as "Next meeting: {date and time}" or "Confirmation request: {document name}."

[0099] Step 8:

[0100] The server transmits the generated list of subject candidates to the terminal.

[0101] Step 9:

[0102] The terminal displays a list of subject suggestions in the user's email client.

[0103] Step 10:

[0104] The user selects the most appropriate subject from the displayed subject candidates.

[0105] Step 11:

[0106] The terminal transmits the user's selection to the server.

[0107] Step 12:

[0108] The server applies the selected subject to the email's subject field.

[0109] Step 13:

[0110] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0111] Through these steps, the process is completed in which the subject of the email is automatically generated, selected by the user, and finally applied to the email.

[0112] Example 1

[0113] Next, a description will be given of 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."

[0114] In business communications, setting an appropriate subject line for an email is crucial for conveying clarity and importance to the recipient. However, manually entering an appropriate subject line is time-consuming and can sometimes be forgotten in the midst of busy daily work. This can result in emails being overlooked or misunderstood. Therefore, there is a need for a system that can automatically generate an appropriate subject line from the email body and present it to the user.

[0115] 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.

[0116] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords such as nouns, verbs, adjectives, and dates and times from the saved email body, means for generating subject candidates based on the extracted keywords, and means for presenting the generated subject candidate list to a user terminal and applying a subject selected by the user to the email. This automatically generates email subjects, allowing the user to quickly and appropriately set email subjects.

[0117] The "body of an email" is the main content part of an email message, and is the text that contains the information that the sender wants to convey to the recipient.

[0118] "Storage means" refers to a function or device that records the contents of emails in a storage system such as a database, making them accessible later.

[0119] "Keywords" are words and phrases such as important nouns, verbs, adjectives, and dates and times extracted from the body of an email and necessary for generating a subject line.

[0120] "Extraction means" refers to a function or device that uses text analysis or algorithms to identify and extract important keywords from the body of an email.

[0121] The "subject candidates" are multiple options that can be used as the subject of an email, generated based on the extracted keywords.

[0122] The "presentation means" refers to a function or device that visually displays the generated subject candidate list to the user and provides it in a selectable form.

[0123] The "means for applying" refers to a function or device for actually setting the subject selected by the user in the subject field of the email and sending it as the final email.

[0124] "Grammar analysis" is the process of analyzing the syntax and grammatical structure of a sentence and understanding the components of the sentence.

[0125] "Context analysis" is the process of understanding the meaning and context of a text as a whole and extracting relevant information from its content.

[0126] A "template" is a predefined format or skeleton, a framework into which specific information can be embedded to create a final form.

[0127] A "user terminal" is a device such as a computer, smartphone, or tablet that a user operates and interacts with the system.

[0128] overview

[0129] This invention relates to a system for automatically generating email subject lines in business communication. Specifically, this system analyzes the email body, extracts important keywords, automatically generates subject line candidates based on those keywords, and allows users to select an appropriate subject line.

[0130] System Configuration

[0131] The system consists of the following main components:

[0132] 1. Server that receives and saves the email body

[0133] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0134] 3. Server and terminal that generates subject candidates and presents them to the user

[0135] 4. User selects subject and applies selected subject to email.

[0136] Receiving and storing emails

[0137] The server stores newly received emails from email clients in an internal database. For example, when a user receives a new email using an email client such as Outlook or Gmail, the email is sent to the server and stored in a database. Here, emails are received using the SMTP protocol and stored in a MySQL (registered trademark) or PostgreSQL database.

[0138] Text analysis and keyword extraction

[0139] The server analyzes the text of the saved emails. For this analysis, it uses a natural language processing library called SpaCy. First, the server tokenizes the text and performs grammatical analysis. Then it performs context analysis to understand the meaning of the entire sentence, extracts important keywords (nouns, verbs, adjectives, dates and times, etc.), and assigns weights to them.

[0140] Auto-generated subject line suggestions

[0141] The server generates subject line candidates based on the extracted keywords, using predefined templates. For example, if there is a template called "Next Meeting: {Date and Time}", it will automatically generate a subject line such as "Next Meeting: March 15th, 10:00 AM". These subject line candidates are generated using Python template literals.

[0142] Suggesting and selecting subject lines

[0143] The server sends the generated subject candidate list to the terminal. This list is sent in JSON format, and the terminal displays the list in the user's email client. The user selects the most suitable subject from the displayed candidates. This operation is realized using JavaScript (registered trademark), HTML, and CSS.

[0144] Apply subject and send email

[0145] The terminal sends the user-selected subject to the server, which applies the selected subject to the email's subject field, saves the updated email in a database, and prepares it for sending. Finally, the terminal displays the email with the updated subject to the user and enables the send button.

[0146] Specific examples

[0147] Famous prompt sentence examples

[0148] Below is an example of an actual email body and the subject line suggestions generated from its analysis:

[0149] text

[0150] Hello.

[0151] I will let you know the details of next week's sales meeting.

[0152] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[0153] First Quarter Sales Report

[0154] Customer Feedback Report

[0155] For this email body, the server extracts the following keywords: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report". Based on these keywords, the server generates the following subject line suggestions:

[0156] "Sales Meeting: March 15th, 10:00 AM"

[0157] "Request for review: Q1 sales report and customer feedback report"

[0158] The user selects "Sales Meeting: March 15th at 10:00 AM" from these suggestions, and that subject is applied to the email.

[0159] This system allows users to quickly and efficiently set appropriate subject lines, improving the efficiency of business communications.

[0160] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0161] Step 1:

[0162] The server detects newly received emails from email clients and stores them in an internal database. At this time, the email message, including the email body, sender information, and date and time of receipt, is sent as input data. The server stores these in the database and outputs a message indicating that the data has been saved. Specifically, the server receives emails using the SMTP protocol and stores them in a MySQL or PostgreSQL database.

[0163] Step 2:

[0164] The server retrieves the email body stored in its internal database and runs it through a text analysis algorithm. The input is the email body, and the output is the parsed tokens (words and phrases). Specifically, the server uses the SpaCy library to tokenize the stored email body.

[0165] Step 3:

[0166] The server performs grammatical analysis based on the tokenized data. The input to this step is the tokens obtained in step 2, and the output is sentence structure information. Specifically, the server uses SpaCy and Python to extract syntactic elements such as noun phrases and verb phrases.

[0167] Step 4:

[0168] The server performs context analysis based on the results of grammatical analysis to extract important keywords such as nouns, verbs, adjectives, and dates and times. The input to this step is sentence structure information, and the output is important keywords and their weights. Specifically, the server uses the SpaCy library to deepen its understanding of the meaning of the sentence.

[0169] Step 5:

[0170] The server uses the extracted keywords to generate subject candidates. The input for this step is the important keywords, and the output is multiple subject candidates. Specifically, the server generates subject candidates by embedding the keywords in a predefined template, using Python template literals.

[0171] Step 6:

[0172] The server sends the generated subject candidate list to the user terminal. The input of this step is the subject candidate list, and the output is a data transmission completion message to the user terminal. Specifically, the server sends data in JSON format to the terminal and uses a web framework such as Flask or Django.

[0173] Step 7:

[0174] The terminal displays the received subject candidate list in the user's email client. The input of this step is the subject candidate list, and the output is a visual representation of the subject candidate. Specifically, the terminal uses JavaScript, HTML, and CSS to display the candidate list as a popup or drop-down menu.

[0175] Step 8:

[0176] The user selects the best subject from the displayed candidates. The input of this step is the visually displayed subject candidates, and the output is the selected subject. Specifically, the user selects a candidate subject with a mouse click or tap.

[0177] Step 9:

[0178] The terminal sends the subject selected by the user to the server. The input of this step is the subject selected by the user, and the output is a sending completion message. As a specific operation, the terminal sends the selected subject in JSON format to the server.

[0179] Step 10:

[0180] The server applies the received selected subject to the email's subject field. The input of this step is the subject selected by the user, and the output is the updated email data. Specifically, the server updates the subject field in the email database.

[0181] Step 11:

[0182] The terminal displays the email with the updated subject and prepares it to be sent. The input of this step is the updated email data, and the output is a message that it is ready to send. As a specific operation, the terminal redisplays the email with the updated subject on the user's screen along with a send button.

[0183] (Application example 1)

[0184] Next, a description will be given of Application 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."

[0185] In food delivery services, if the subject lines of order confirmation emails or delivery notification emails are inappropriate, recipients will not be able to instantly understand important information, resulting in a poor user experience. Furthermore, manually setting flexible and appropriate subject lines based on the delivery contents and the current situation is time-consuming and inefficient. Therefore, there is a need for a system that can automatically generate and apply optimal subject lines based on the user's order details.

[0186] 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.

[0187] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords from the email body, means for generating subject candidates based on the extracted keywords, means for presenting the subject candidates to the user and applying the selected subject to the email, means for receiving and analyzing the order details, means for generating subject candidates based on the analyzed order details, and means for presenting the subject candidates to the user and applying the selected subject to the order confirmation email. This enables efficient email management in a food delivery service, where an appropriate subject based on the order details is automatically generated, allowing the user to quickly grasp important information.

[0188] "Email" is data, including text and files, sent and received via electronic communication means.

[0189] "Body" refers to the primary textual content of an email, which is the user-written or system-generated message content.

[0190] "Keywords" are important words or phrases extracted from the body of an email and form the key points for understanding the content.

[0191] A "subject" is a short piece of text used to briefly indicate the subject or content of an email.

[0192] A "server" is a computer system that processes, stores, and serves information over a network.

[0193] A "template" is a predefined format for generating subject line candidates.

[0194] "User" refers to the person or account that uses the email system, and is the entity that operates the system and checks its contents.

[0195] "Order Details" means details of the items or services requested by a User for food delivery or other services.

[0196] "Analysis" is the process of extracting important elements and meaning from information in received emails and order details.

[0197] This invention relates to a system that automatically generates subject lines for order confirmation emails and delivery notification emails in food delivery services. This system is mainly composed of a server, terminals, and user operations.

[0198] System configuration

[0199] 1. Server

[0200] The server has a function of receiving and storing the contents of a user's order.

[0201] The server has a text analysis algorithm for analyzing the received order content.

[0202] The server has the function of extracting important keywords from the analysis results and generating subject candidates.

[0203] 2. Terminal

[0204] The terminal operated by the user displays the subject candidates sent from the server.

[0205] The user selects a subject suggestion and sends it to the server.

[0206] 3. Users

[0207] The user selects the best subject line from the provided suggestions and applies it to the email.

[0208] Specific examples of processing

[0209] Receiving and analyzing order details

[0210] The server receives the order details through the food delivery app. For example, if a user orders "pizza" and "pasta," the server sends these to the server as the body of an email.

[0211] The server then analyzes the stored text using a text analysis algorithm, which performs grammatical and contextual analysis to extract important keywords (e.g., pizza, pasta).

[0212] Based on the extracted keywords, subject line suggestions are generated using predefined templates, resulting in subject line suggestions such as "Order Confirmation: Pizza, Pasta" and "Shipping Notification: Pizza, Pasta."

[0213] Suggest and apply subject suggestions

[0214] The server sends the generated subject candidates as a list to the terminal.

[0215] The user can review subject line suggestions on the device and select the most appropriate one, such as "Delivery notification: Pizza, Pasta."

[0216] The selected subject is sent to the server and applied to the email.

[0217] Hardware and Software Used

[0218] Hardware: Servers, smartphones (terminals)

[0219] Software: Python®-based text analysis algorithms, email client software

[0220] Example prompt sentence:

[0221] "Extract key keywords from the following order and generate suitable subject line suggestions: 'User ordered pizza and pasta.'"

[0222] This will enable food delivery services to automatically generate appropriate email subject lines based on the order details, enabling users to quickly grasp important information and achieving efficient email management.

[0223] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0224] Step 1:

[0225] The server receives and stores the user's order details through the food delivery app.

[0226] Input: Information that the user ordered "pizza" and "pasta."

[0227] Specific Actions: The server stores this information in a database for subsequent analysis.

[0228] Step 2:

[0229] The server analyzes the body of the stored order details using a text analysis algorithm.

[0230] Input: The body of the saved order.

[0231] Specific operation: The server performs grammatical analysis and context analysis to extract keywords, and uses a generative AI model to understand the context.

[0232] Output: Important keywords (e.g., pizza, pasta) are extracted.

[0233] Step 3:

[0234] The server generates subject candidates based on the extracted keywords.

[0235] Input: Extracted keywords.

[0236] What happens: The server uses predefined templates (e.g., "Order Confirmation: {keywords}", "Shipping Notification: {keywords}") to create a list of suitable subject candidates.

[0237] Output: Subject line suggestions such as "Order Confirmation: Pizza, Pasta" or "Shipping Notification: Pizza, Pasta".

[0238] Step 4:

[0239] The server transmits the generated subject candidate list to the terminal.

[0240] Input: Subject suggestion list.

[0241] Specific operation: The server sends a list of subject candidates to the terminal using a communication protocol.

[0242] Output: A list of subject suggestions displayed on the device.

[0243] Step 5:

[0244] The user checks the subject candidates on the terminal and selects the most suitable subject.

[0245] Input: Subject suggestion list.

[0246] Specific operation: The user uses the device interface to select the most suitable subject from a list of subject candidates (e.g., "Delivery notification: Pizza, Pasta").

[0247] Output: The selected subject.

[0248] Step 6:

[0249] The server receives the user's selected subject and applies it to the email.

[0250] Input: A subject chosen by the user.

[0251] Specific Actions: The server applies the selected subject to the email's subject field.

[0252] Output: The email with the subject applied.

[0253] Step 7:

[0254] The server displays the email with the applied subject to the user and prepares it for sending.

[0255] Input: The email with the subject applied.

[0256] Specific operation: The server instructs the email client to display this email and provides a send button.

[0257] Output: A summary of the email with the applied subject will be displayed on the terminal screen.

[0258] 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.

[0259] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[0260] System configuration

[0261] The system consists of the following main components:

[0262] 1. A server that receives and stores email text

[0263] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0264] 3. A server equipped with an emotion engine that recognizes user emotions

[0265] 4. Server and terminal that generates subject candidates and presents them to the user

[0266] 5. A device that allows users to select a subject and apply the selected subject to the email.

[0267] What the program does

[0268] Receiving and storing emails

[0269] The server stores newly received emails from email clients in an internal database.

[0270] Once the email is saved, it moves on to the next analysis step.

[0271] Text analysis and keyword extraction

[0272] The server runs the stored email body through a text analysis algorithm.

[0273] The algorithm performs grammatical analysis to understand the structure of the sentence.

[0274] Next, context analysis is performed to understand the meaning and context of the entire sentence.

[0275] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[0276] User Emotion Recognition

[0277] The server recognizes the user's emotions through text analysis and voice analysis via an emotion engine.

[0278] The recognized emotion data is fed back into the subject line generation process.

[0279] Auto-generated subject line suggestions

[0280] The server generates subject line candidates using predefined templates based on the extracted keywords and the recognized user sentiment.

[0281] For example, there are templates such as "Important: Action required by {date and time}" and "Please confirm: {document name}."

[0282] The tone and phrasing of the subject line are adjusted based on sentiment data.

[0283] Suggesting and selecting subject lines

[0284] The server transmits the generated list of subject candidates to the terminal.

[0285] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[0286] The user selects the most appropriate subject from the displayed candidates.

[0287] Apply subject and send email

[0288] The terminal transmits the user's selection to the server.

[0289] The server applies the selected subject to the email's subject field.

[0290] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0291] Specific examples

[0292] Example: Actual email body

[0293] thank you for your hard work.

[0294] Share details of a project meeting scheduled for next Monday.

[0295] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[0296] Project Plan

[0297] Risk Management Report

[0298] Emotion Recognition and Subject Line Generation

[0299] 1. The server receives and stores email.

[0300] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0301] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[0302] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[0303] "Project meeting next Monday: Starts at 10:00 AM"

[0304] "Please check in advance: Project plan and risk management report"

[0305] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[0306] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[0307] 6. The server applies the selected subject to the email and prepares it for sending.

[0308] In this way, by combining an emotion engine, it is possible to automatically generate subject lines that take into account the user's emotions, thereby improving the quality of email communication.

[0309] The processing flow will be explained below.

[0310] Step 1:

[0311] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[0312] Step 2:

[0313] The server stores the received emails in an internal database.

[0314] Step 3:

[0315] The server identifies the stored email as the subject of analysis and extracts the email body.

[0316] Step 4:

[0317] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[0318] Step 5:

[0319] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[0320] Step 6:

[0321] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[0322] Step 7:

[0323] The server analyzes the user's emotions through an emotion engine, analyzing text entered by the user into an email client and recorded voice data to recognize emotions.

[0324] Step 8:

[0325] The server generates subject line candidates using predefined templates based on the extracted and weighted keywords and the user's recognized emotional data. The templates are in the form of "Next Meeting: {Date and Time}" or "Confirmation Request: {Document Name}," but the tone and phrasing of the subject line are adjusted based on the emotional data.

[0326] Step 9:

[0327] The server transmits the generated list of subject candidates to the terminal.

[0328] Step 10:

[0329] The device displays a list of candidate subject lines in the user's email client, and adjusts the order of the candidate subject lines based on the emotion data, so that they are displayed in the most appropriate order for the user.

[0330] Step 11:

[0331] The user selects the most appropriate subject from the displayed subject candidates.

[0332] Step 12:

[0333] The terminal transmits the user's selection to the server.

[0334] Step 13:

[0335] The server applies the selected subject to the email's subject field.

[0336] Step 14:

[0337] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0338] This process not only automatically generates email subject lines, but also takes into consideration the user's emotions, resulting in more appropriate and effective email communication.

[0339] Example 2

[0340] Next, a description will be given of 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."

[0341] In today's world, email is an important means of communication for both business and personal activities. However, choosing the right subject line for an email is often difficult, especially when the user's emotional state plays a significant role. This can lead to misunderstandings and communication friction. Additionally, selecting appropriate keywords and creating a subject line quickly can be challenging.

[0342] 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.

[0343] In this invention, the server includes means for receiving and saving the text of the email, means for automatically extracting important keywords from the text, and means for generating subject line candidates based on the extracted keywords and the recognized user emotions. This makes it possible to quickly generate appropriate subject lines according to the user's emotions and improve the quality of email communication.

[0344] The "body of the email" is the main part of the message contained in the email, and contains the sender's intentions and information.

[0345] "Means of storage" refers to the function of recording the body text and header information of received emails in storage such as a database, making them reusable as needed.

[0346] "Keywords" are important nouns, verbs, adjectives, dates and times, and other information extracted from the body of an email, and are useful elements for generating a subject line.

[0347] "Means of extraction" refers to the function of using a text analysis algorithm to identify and extract important keywords from the body of an email.

[0348] "User's emotions" refers to a psychological state recognized from the content of the email body and the user's situation, such as "fatigue" or "tension."

[0349] "Means for recognizing emotions" refers to the ability to use text or voice analysis to identify a user's emotions and make that information available for further processing.

[0350] "Subject candidates" are multiple options presented to the user as email subjects, and are automatically generated based on keywords and the user's emotions.

[0351] "Means for generating" refers to the function of forming subject line candidates using predefined templates based on extracted keywords and user sentiment.

[0352] "Template" means a predefined format for generating subject lines, a framework for inserting specific keywords or patterns.

[0353] "Adjustment" refers to the ability to appropriately change the tone and tone of the subject line according to the recognized user's emotions, to create a format that is optimal for the user.

[0354] The "means for presenting" refers to a function for displaying the generated subject candidates on the user's terminal and allowing the user to select the subject that seems most appropriate.

[0355] The "selected subject" refers to the subject that the user selects as the most appropriate from among multiple subject candidates.

[0356] The "means of applying" refers to a function of setting the subject selected by the user in the subject field of the actual email and preparing the email for sending.

[0357] The present invention relates to a system for recognizing a user's emotions and automatically generating email subject lines. A specific embodiment of this system is described below.

[0358] System Overview

[0359] The system includes the following main components:

[0360] 1. A server that receives and stores email text

[0361] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0362] 3. A server equipped with an emotion engine that recognizes user emotions

[0363] 4. Server and terminal that generates subject candidates and presents them to the user

[0364] 5. A device that allows users to select a subject and apply the selected subject to the email.

[0365] Receiving and storing emails

[0366] The server first detects newly received emails from the email client, for example, using the Gmail API, and stores the received emails in a database such as MongoDB.

[0367] Text analysis and keyword extraction

[0368] The text of the saved emails is then run through a text analysis algorithm on the server. For example, the SpaCy library is used for grammatical analysis to understand the structure and meaning of the sentence. Next, a context analysis model is used to understand the overall subject and theme of the text. Important nouns, verbs, adjectives, dates, and other keywords are extracted and weighted. This results in keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0369] User Emotion Recognition

[0370] The server then uses an emotion engine to recognize the user's emotions. Services such as IBM Watson® Natural Language Understanding and Google® Cloud Natural Language API are used for emotion recognition. Text and voice analysis is used to extract emotions such as "tired" or "tension," and this data is fed back into the subject line generation process.

[0371] Auto-generated subject line suggestions

[0372] The server generates subject line candidates using predefined templates based on the extracted keywords and the user's recognized emotions. For example, templates include "Please confirm: {Document name}" and "Project meeting next Monday: Starts at 10:00 AM." The tone and phrasing of the subject line are also adjusted based on the emotion data. For example, if the user is tired, a calmer subject line is generated.

[0373] Suggesting and selecting subject lines

[0374] The generated subject candidates are sent from the server to the terminal and presented to the user. The terminal displays the subject candidate list in the user's email client, and the candidate order is adjusted based on the emotion data. The user selects the most appropriate subject from this list. For example, it is displayed in the form of a window or a drop-down list.

[0375] Apply subject and send email

[0376] The selected subject is sent from the terminal to the server, which applies this subject to the subject field of the email and prepares the email for sending. The email with the applied subject is sent after the user confirms it.

[0377] Specific examples

[0378] The actual email body

[0379] plain

[0380] thank you for your hard work.

[0381] Share details of a project meeting scheduled for next Monday.

[0382] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[0383] Project Plan

[0384] Risk Management Report

[0385] The process of emotion recognition and subject line generation

[0386] 1. The server receives and stores email.

[0387] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0388] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[0389] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[0390] "Project meeting next Monday: Starts at 10:00 AM"

[0391] "Please check in advance: Project plan and risk management report"

[0392] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[0393] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[0394] 6. The server applies the selected subject to the email and prepares it for sending.

[0395] Prompt Sentence Examples

[0396] "Describe the process of a system that uses natural language analysis to extract important keywords from the body of an email and generates subject line suggestions based on the user's sentiment. As a concrete example, consider the case where a user is tired and receives an email that reads, 'I have a project meeting next Monday.'"

[0397] This system can automatically generate appropriate email subject lines that match the user's emotional state, improving the quality of email communication.

[0398] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0399] Step 1: Receiving and saving emails

[0400] The server detects newly received emails from the email client. For example, it obtains email data via the Gmail API. The email body and header information are used as input data.

[0401] The server stores the received emails in an internal database (e.g. MongoDB). The output of this step is the email data stored in the database.

[0402] Step 2: Text analysis and keyword extraction

[0403] The server runs the stored email body through a text analysis algorithm, where the input is the stored email body.

[0404] Specifically, it uses the SpaCy library to perform grammatical analysis to understand sentence structure, and then uses a custom context analysis model to understand the overall subject or theme of the text.

[0405] The output is extracted keywords such as important nouns, verbs, adjectives, dates, etc. For example, keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report" are identified.

[0406] Step 3: Recognizing user emotions

[0407] The server uses an emotion engine to recognize the user's emotions, where the input is the body of past emails and related data.

[0408] Specifically, services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used to extract emotions such as "fatigue" and "tension" from text, and the emotional data is the output.

[0409] Step 4: Auto-generate subject line suggestions

[0410] The server generates subject candidates based on the extracted keywords and sentiment data. The inputs are keywords and sentiment data.

[0411] Generate subject line suggestions using predefined templates (e.g., "Please review: {document name}" or "Project meeting next Monday: Starts at 10 AM") and adjust the tone and phrasing of subject lines based on sentiment data.

[0412] The generated subject line candidates are output, for example, "Project meeting next Monday: Start time 10:00 AM" or "Please confirm in advance: Project plan and risk management report."

[0413] Step 5: Suggest and select subject lines

[0414] The server sends the generated list of subject candidates to the terminal. The input is the subject candidates.

[0415] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[0416] The user selects the most appropriate subject from the displayed candidates, and the selected subject is output.

[0417] Step 6: Apply a subject line and send the email

[0418] The terminal sends the user's selection to the server, where the input is the selected subject.

[0419] The server applies this selected subject to the email's subject field, and the output is an email with the applied subject.

[0420] The terminal shows the email with the applied subject to the user and prepares the email for sending. As a final action, the user presses the send button to send the email.

[0421] (Application example 2)

[0422] Next, a description will be given of Application 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."

[0423] Conventional email systems require users to manually enter email subject lines, which is a time-consuming and labor-intensive task. Furthermore, there is no mechanism for generating appropriate subject lines that take the user's emotions into account, which can lead to a decline in the quality of communication. For example, when dealing with customers, it is important to respond appropriately while taking their emotions into consideration, but current systems are unable to do this. Therefore, there is a need for a system that can recognize the user's emotions and automatically generate appropriate subject lines based on them.

[0424] The specification process by the specification 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 and saving the body of an email, means for automatically extracting important keywords from the body, means for generating subject candidates based on the extracted keywords and recognizing the user's emotions, and means for presenting the subject candidates to the user and applying the selected subject to the email. This enables the automatic generation of email subjects that take the user's emotions into consideration, improving the quality of communication and reducing the burden on the user.

[0425] "Means for receiving and storing the body of an email" refers to the server receiving an email sent by a user and storing the content in a database.

[0426] "Method of automatically extracting important keywords" refers to the process of using text analysis algorithms to extract meaningful words and phrases from the email body based on context and grammar.

[0427] "Means for recognizing user emotions" refers to technologies or engines that analyze text or voice data to estimate a user's emotional state.

[0428] "Means for generating subject line candidates" refers to algorithms or templates for generating appropriate sentences as email subject lines based on extracted keywords and recognized emotion data.

[0429] "Means for presenting subject candidates to the user and applying the selected subject to the email" refers to the process of providing an interface for the user to select the most appropriate subject from multiple generated subjects and applying the selected subject to the actual email.

[0430] "Means for grammatical and contextual analysis" refers to algorithms and tools that analyze the grammatical structure and context of the email body to more accurately understand its meaning.

[0431] An "emotion engine" is software that analyzes and recognizes user emotions from text or voice, and often uses specific algorithms or models.

[0432] "Means for automatic generation based on predefined templates" refers to a mechanism for automatically creating appropriate email subject lines based on predefined phrases and formats.

[0433] "Means for adjusting the tone and phrasing of subject lines based on emotional data" refers to a system for adjusting the wording and nuance of email subject lines based on the perceived emotions of the user.

[0434] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[0435] System configuration

[0436] The system consists of the following main components:

[0437] 1. Server: Has a means to receive and store the body of email.

[0438] 2. Server: Has a means to automatically extract important keywords from the text.

[0439] 3. Server: Equipped with an emotion engine that recognizes the user's emotions and has the means to perform text analysis and voice analysis.

[0440] 4. Server: Has a means to automatically generate subject line candidates based on the extracted keywords and recognized sentiment.

[0441] 5. Terminal: Provides a means for presenting subject line suggestions to the user and applying the selected subject line to the email.

[0442] What the program does

[0443] Hardware and Software

[0444] Smart glasses: A device for recording and analyzing customers' facial expressions and voices in real time.

[0445] EmotionRecognizer: A library that analyzes emotions from customer voice data, for example, using the Google Cloud Speech-to-Text API and Emotion API.

[0446] EmailGenerator: A module that extracts keywords from the text and generates email subject lines based on sentiment data. It uses TextBlob and SpaCy for natural language processing.

[0447] Data processing and calculation

[0448] 1. The server receives audio and text from the customer and converts the audio into text.

[0449] 2. The server parses the text in TextBlob and extracts important keywords.

[0450] 3. The server uses EmotionRecognizer to obtain emotion data from the audio.

[0451] 4. The server generates subject line candidates using the EmailGenerator engine based on the extracted keywords and sentiment data.

[0452] 5. The terminal displays the generated subject candidates on the store staff's smart glasses.

[0453] 6. The user selects the most appropriate subject from the displayed subject suggestions.

[0454] For example, if a store staff member is asked in a conversation with a customer, "Do you have any business trip suits in stock?":

[0455] Incoming text: "Do you have any business trip suits in stock?"

[0456] Extracted keywords: "Business trip", "Suit", "Inventory"

[0457] Emotion data: The recognized emotion data is "interest"

[0458] Prompt Sentence Examples

[0459] A customer has a question. The keywords are "business trip", "suits", and "inventory", and the sentiment is "interest". Based on this, generate an appropriate subject line.

[0460] The above is an embodiment of the invention configured based on the claims. This system enables automatic generation of email subjects that take user emotions into consideration, improving the quality of communication and reducing the burden on users.

[0461] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0462] Step 1:

[0463] The server receives and stores the body of the email.

[0464] Specifically, the server stores newly received emails from users' email clients in an internal database, making the email body available for the next analysis step. The input is the email body, and the output is the saved email data.

[0465] Step 2:

[0466] The server automatically extracts important keywords from the body of the saved email.

[0467] Specifically, it uses TextBlob to parse text and extract nouns, verbs, and important phrases based on grammar and context. The input is the saved email body, and the output is the extracted important keywords.

[0468] Step 3:

[0469] The server recognizes the user's emotions.

[0470] Specifically, the system analyzes the user's voice data via EmotionRecognizer to obtain emotional information. The voice file is analyzed and the emotion engine recognizes emotions. The input is the user's voice data, and the output is the recognized emotional data.

[0471] Step 4:

[0472] The server automatically generates subject line candidates based on the extracted keywords and the recognized emotion data.

[0473] Specifically, the EmailGenerator engine is used to apply the extracted keywords and sentiment data to a template to generate multiple subject line candidates. The input is keywords and sentiment data, and the output is a list of subject line candidates.

[0474] Step 5:

[0475] The terminal presents the generated subject candidates to the user.

[0476] Specifically, a list of subject candidates is displayed on the store staff's smart glasses. The list is visually displayed through a user interface, allowing the user to easily select a subject. The input is the subject candidate list, and the output is the subject candidates presented to the user.

[0477] Step 6:

[0478] The user selects the most suitable subject from the presented subject candidates.

[0479] Specifically, the user selects the most appropriate subject from the displayed subject candidates using the user interface of the smart glasses. The input is the subject candidates presented to the user, and the output is the selected subject.

[0480] Step 7:

[0481] The server applies the selected subject to the email.

[0482] Specifically, it takes a user-selected subject, applies it to the subject field of the original email, and prepares the email to be sent. The input is the selected subject, and the output is the email with the subject applied.

[0483] 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.

[0484] 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> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0485] 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.

[0486] [Second embodiment]

[0487] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0488] 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.

[0489] 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).

[0490] 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.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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.

[0496] 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.

[0497] 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.

[0498] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0499] This invention relates to a system for automatically generating email subject lines in business communications. Detailed embodiments of this system and the processing contents of the program will be described below with specific examples.

[0500] System configuration

[0501] The system consists of the following main components:

[0502] 1. A server that receives and stores email text

[0503] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0504] 3. Server and terminal that generates subject candidates and presents them to the user

[0505] 4. A device that allows users to select a subject and apply the selected subject to the email.

[0506] What the program does

[0507] Receiving and storing emails

[0508] The server stores newly received emails from email clients in an internal database.

[0509] Once the email is saved, it moves on to the next analysis step.

[0510] Text analysis and keyword extraction

[0511] The server runs the stored email body through a text analysis algorithm.

[0512] The algorithm performs grammatical analysis to understand the structure of the sentence.

[0513] Next, context analysis is performed to understand the meaning of the entire sentence.

[0514] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[0515] Auto-generated subject line suggestions

[0516] The server generates subject line candidates based on the extracted keywords using predefined templates.

[0517] For example, templates could include "Next meeting: {date and time}" and "Confirmation request: {document name}."

[0518] The generated subject suggestions are compiled into a list.

[0519] Suggesting and selecting subject lines

[0520] The server transmits the generated list of subject candidates to the terminal.

[0521] The terminal displays a list of subject suggestions in the user's email client.

[0522] The user selects the most appropriate subject from the displayed candidates.

[0523] Apply subject and send email

[0524] The terminal transmits the user's selection to the server.

[0525] The server applies the selected subject to the email's subject field.

[0526] The device will display the email with the applied subject and prepare it to send.

[0527] Specific examples

[0528] Example: Actual email body

[0529] Hello.

[0530] I will let you know the details of next week's sales meeting.

[0531] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[0532] First Quarter Sales Report

[0533] Customer Feedback Report

[0534] Analysis of the above text and subject candidate generation

[0535] 1. The server receives and stores email.

[0536] 2. The server extracts the following keywords from the body of the text: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report".

[0537] 3. The server generates the following possible subject lines:

[0538] "Sales Meeting: March 15th, 10:00 AM"

[0539] "Request for review: Q1 sales report and customer feedback report"

[0540] 4. The user selects "Sales Meeting: March 15th, 10:00 AM."

[0541] 5. The server applies the selected subject to the email and prepares it for sending.

[0542] This ensures that the subject line of your email is clear and relevant, instantly conveying to the recipient the importance of the email.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[0546] Step 2:

[0547] The server stores the received emails in an internal database.

[0548] Step 3:

[0549] The server identifies the stored email as the subject of analysis and extracts the email body.

[0550] Step 4:

[0551] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[0552] Step 5:

[0553] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[0554] Step 6:

[0555] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[0556] Step 7:

[0557] Based on the extracted and weighted keywords, the server generates subject line suggestions according to predefined templates, such as "Next meeting: {date and time}" or "Confirmation request: {document name}."

[0558] Step 8:

[0559] The server transmits the generated list of subject candidates to the terminal.

[0560] Step 9:

[0561] The terminal displays a list of subject suggestions in the user's email client.

[0562] Step 10:

[0563] The user selects the most appropriate subject from the displayed subject candidates.

[0564] Step 11:

[0565] The terminal transmits the user's selection to the server.

[0566] Step 12:

[0567] The server applies the selected subject to the email's subject field.

[0568] Step 13:

[0569] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0570] Through these steps, the process is completed in which the subject of the email is automatically generated, selected by the user, and finally applied to the email.

[0571] Example 1

[0572] Next, a description will be given of 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."

[0573] In business communications, setting an appropriate subject line for an email is crucial for conveying clarity and importance to the recipient. However, manually entering an appropriate subject line is time-consuming and can sometimes be forgotten in the midst of busy daily work. This can result in emails being overlooked or misunderstood. Therefore, there is a need for a system that can automatically generate an appropriate subject line from the email body and present it to the user.

[0574] 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.

[0575] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords such as nouns, verbs, adjectives, and dates and times from the saved email body, means for generating subject candidates based on the extracted keywords, and means for presenting the generated subject candidate list to a user terminal and applying a subject selected by the user to the email. This automatically generates email subjects, allowing the user to quickly and appropriately set email subjects.

[0576] The "body of an email" is the main content part of an email message, and is the text that contains the information that the sender wants to convey to the recipient.

[0577] "Storage means" refers to a function or device that records the contents of emails in a storage system such as a database, making them accessible later.

[0578] "Keywords" are words and phrases such as important nouns, verbs, adjectives, and dates and times extracted from the body of an email and necessary for generating a subject line.

[0579] "Extraction means" refers to a function or device that uses text analysis or algorithms to identify and extract important keywords from the body of an email.

[0580] The "subject candidates" are multiple options that can be used as the subject of an email, generated based on the extracted keywords.

[0581] The "presentation means" refers to a function or device that visually displays the generated subject candidate list to the user and provides it in a selectable form.

[0582] The "means for applying" refers to a function or device for actually setting the subject selected by the user in the subject field of the email and sending it as the final email.

[0583] "Grammar analysis" is the process of analyzing the syntax and grammatical structure of a sentence and understanding the components of the sentence.

[0584] "Context analysis" is the process of understanding the meaning and context of a text as a whole and extracting relevant information from its content.

[0585] A "template" is a predefined format or skeleton, a framework into which specific information can be embedded to create a final form.

[0586] A "user terminal" is a device such as a computer, smartphone, or tablet that a user operates and interacts with the system.

[0587] overview

[0588] This invention relates to a system for automatically generating email subject lines in business communication. Specifically, this system analyzes the email body, extracts important keywords, automatically generates subject line candidates based on those keywords, and allows users to select an appropriate subject line.

[0589] System Configuration

[0590] The system consists of the following main components:

[0591] 1. Server that receives and saves the email body

[0592] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0593] 3. Server and terminal that generates subject candidates and presents them to the user

[0594] 4. User selects subject and applies selected subject to email.

[0595] Receiving and storing emails

[0596] The server stores newly received emails from email clients in its internal database. For example, when a user receives a new email using an email client such as Outlook or Gmail, the email is sent to the server and stored in its database. Here, emails are received using the SMTP protocol and stored in a MySQL or PostgreSQL database.

[0597] Text analysis and keyword extraction

[0598] The server analyzes the text of the saved emails. For this analysis, it uses a natural language processing library called SpaCy. First, the server tokenizes the text and performs grammatical analysis. Then it performs context analysis to understand the meaning of the entire sentence, extracts important keywords (nouns, verbs, adjectives, dates and times, etc.), and assigns weights to them.

[0599] Auto-generated subject line suggestions

[0600] The server generates subject line candidates based on the extracted keywords, using predefined templates. For example, if there is a template called "Next Meeting: {Date and Time}", it will automatically generate a subject line such as "Next Meeting: March 15th, 10:00 AM". These subject line candidates are generated using Python template literals.

[0601] Suggesting and selecting subject lines

[0602] The server sends the generated subject candidate list to the terminal. This list is sent in JSON format, and the terminal displays the list in the user's email client. The user selects the best subject from the displayed candidates. This operation is achieved using JavaScript, HTML, and CSS.

[0603] Apply subject and send email

[0604] The terminal sends the user-selected subject to the server, which applies the selected subject to the email's subject field, saves the updated email in a database, and prepares it for sending. Finally, the terminal displays the email with the updated subject to the user and enables the send button.

[0605] Specific examples

[0606] Famous prompt sentence examples

[0607] Below is an example of an actual email body and the subject line suggestions generated from its analysis:

[0608] text

[0609] Hello.

[0610] I will let you know the details of next week's sales meeting.

[0611] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[0612] First Quarter Sales Report

[0613] Customer Feedback Report

[0614] For this email body, the server extracts the following keywords: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report". Based on these keywords, the server generates the following subject line suggestions:

[0615] "Sales Meeting: March 15th, 10:00 AM"

[0616] "Request for review: Q1 sales report and customer feedback report"

[0617] The user selects "Sales Meeting: March 15th at 10:00 AM" from these suggestions, and that subject is applied to the email.

[0618] This system allows users to quickly and efficiently set appropriate subject lines, improving the efficiency of business communications.

[0619] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0620] Step 1:

[0621] The server detects newly received emails from email clients and stores them in an internal database. At this time, the email message, including the email body, sender information, and date and time of receipt, is sent as input data. The server stores these in the database and outputs a message indicating that the data has been saved. Specifically, the server receives emails using the SMTP protocol and stores them in a MySQL or PostgreSQL database.

[0622] Step 2:

[0623] The server retrieves the email body stored in its internal database and runs it through a text analysis algorithm. The input is the email body, and the output is the parsed tokens (words and phrases). Specifically, the server uses the SpaCy library to tokenize the stored email body.

[0624] Step 3:

[0625] The server performs grammatical analysis based on the tokenized data. The input to this step is the tokens obtained in step 2, and the output is sentence structure information. Specifically, the server uses SpaCy and Python to extract syntactic elements such as noun phrases and verb phrases.

[0626] Step 4:

[0627] The server performs context analysis based on the results of grammatical analysis to extract important keywords such as nouns, verbs, adjectives, and dates and times. The input to this step is sentence structure information, and the output is important keywords and their weights. Specifically, the server uses the SpaCy library to deepen its understanding of the meaning of the sentence.

[0628] Step 5:

[0629] The server uses the extracted keywords to generate subject candidates. The input for this step is the important keywords, and the output is multiple subject candidates. Specifically, the server generates subject candidates by embedding the keywords in a predefined template, using Python template literals.

[0630] Step 6:

[0631] The server sends the generated subject candidate list to the user terminal. The input of this step is the subject candidate list, and the output is a data transmission completion message to the user terminal. Specifically, the server sends data in JSON format to the terminal and uses a web framework such as Flask or Django.

[0632] Step 7:

[0633] The terminal displays the received subject candidate list in the user's email client. The input of this step is the subject candidate list, and the output is a visual representation of the subject candidate. Specifically, the terminal uses JavaScript, HTML, and CSS to display the candidate list as a popup or drop-down menu.

[0634] Step 8:

[0635] The user selects the best subject from the displayed candidates. The input of this step is the visually displayed subject candidates, and the output is the selected subject. Specifically, the user selects a candidate subject with a mouse click or tap.

[0636] Step 9:

[0637] The terminal sends the subject selected by the user to the server. The input of this step is the subject selected by the user, and the output is a sending completion message. As a specific operation, the terminal sends the selected subject in JSON format to the server.

[0638] Step 10:

[0639] The server applies the received selected subject to the email's subject field. The input of this step is the subject selected by the user, and the output is the updated email data. Specifically, the server updates the subject field in the email database.

[0640] Step 11:

[0641] The terminal displays the email with the updated subject and prepares it to be sent. The input of this step is the updated email data, and the output is a message that it is ready to send. As a specific operation, the terminal redisplays the email with the updated subject on the user's screen along with a send button.

[0642] (Application example 1)

[0643] Next, a description will be given of Application 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."

[0644] In food delivery services, if the subject lines of order confirmation emails or delivery notification emails are inappropriate, recipients will not be able to instantly understand important information, resulting in a poor user experience. Furthermore, manually setting flexible and appropriate subject lines based on the delivery contents and the current situation is time-consuming and inefficient. Therefore, there is a need for a system that can automatically generate and apply optimal subject lines based on the user's order details.

[0645] 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.

[0646] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords from the email body, means for generating subject candidates based on the extracted keywords, means for presenting the subject candidates to the user and applying the selected subject to the email, means for receiving and analyzing the order details, means for generating subject candidates based on the analyzed order details, and means for presenting the subject candidates to the user and applying the selected subject to the order confirmation email. This enables efficient email management in a food delivery service, where an appropriate subject based on the order details is automatically generated, allowing the user to quickly grasp important information.

[0647] "Email" is data, including text and files, sent and received via electronic communication means.

[0648] "Body" refers to the primary textual content of an email, which is the user-written or system-generated message content.

[0649] "Keywords" are important words or phrases extracted from the body of an email and form the key points for understanding the content.

[0650] A "subject" is a short piece of text used to briefly indicate the subject or content of an email.

[0651] A "server" is a computer system that processes, stores, and serves information over a network.

[0652] A "template" is a predefined format for generating subject line candidates.

[0653] "User" refers to the person or account that uses the email system, and is the entity that operates the system and checks its contents.

[0654] "Order Details" means details of the items or services requested by a User for food delivery or other services.

[0655] "Analysis" is the process of extracting important elements and meaning from information in received emails and order details.

[0656] This invention relates to a system that automatically generates subject lines for order confirmation emails and delivery notification emails in food delivery services. This system is mainly composed of a server, terminals, and user operations.

[0657] System configuration

[0658] 1. Server

[0659] The server has a function of receiving and storing the contents of a user's order.

[0660] The server has a text analysis algorithm for analyzing the received order content.

[0661] The server has the function of extracting important keywords from the analysis results and generating subject candidates.

[0662] 2. Terminal

[0663] The terminal operated by the user displays the subject candidates sent from the server.

[0664] The user selects a subject suggestion and sends it to the server.

[0665] 3. Users

[0666] The user selects the best subject line from the provided suggestions and applies it to the email.

[0667] Specific examples of processing

[0668] Receiving and analyzing order details

[0669] The server receives the order details through the food delivery app. For example, if a user orders "pizza" and "pasta," the server sends these to the server as the body of an email.

[0670] The server then analyzes the stored text using a text analysis algorithm, which performs grammatical and contextual analysis to extract important keywords (e.g., pizza, pasta).

[0671] Based on the extracted keywords, subject line suggestions are generated using predefined templates, resulting in subject line suggestions such as "Order Confirmation: Pizza, Pasta" and "Shipping Notification: Pizza, Pasta."

[0672] Suggest and apply subject suggestions

[0673] The server sends the generated subject candidates as a list to the terminal.

[0674] The user can review subject line suggestions on the device and select the most appropriate one, such as "Delivery notification: Pizza, Pasta."

[0675] The selected subject is sent to the server and applied to the email.

[0676] Hardware and Software Used

[0677] Hardware: Servers, smartphones (terminals)

[0678] Software: Python-based text analysis algorithms, email client software

[0679] Example prompt sentence:

[0680] "Extract key keywords from the following order and generate suitable subject line suggestions: 'User ordered pizza and pasta.'"

[0681] This will enable food delivery services to automatically generate appropriate email subject lines based on the order details, enabling users to quickly grasp important information and achieving efficient email management.

[0682] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0683] Step 1:

[0684] The server receives and stores the user's order details through the food delivery app.

[0685] Input: Information that the user ordered "pizza" and "pasta."

[0686] Specific Actions: The server stores this information in a database for subsequent analysis.

[0687] Step 2:

[0688] The server analyzes the body of the stored order details using a text analysis algorithm.

[0689] Input: The body of the saved order.

[0690] Specific operation: The server performs grammatical analysis and context analysis to extract keywords, and uses a generative AI model to understand the context.

[0691] Output: Important keywords (e.g., pizza, pasta) are extracted.

[0692] Step 3:

[0693] The server generates subject candidates based on the extracted keywords.

[0694] Input: Extracted keywords.

[0695] What happens: The server uses predefined templates (e.g., "Order Confirmation: {keywords}", "Shipping Notification: {keywords}") to create a list of suitable subject candidates.

[0696] Output: Subject line suggestions such as "Order Confirmation: Pizza, Pasta" or "Shipping Notification: Pizza, Pasta".

[0697] Step 4:

[0698] The server transmits the generated subject candidate list to the terminal.

[0699] Input: Subject suggestion list.

[0700] Specific operation: The server sends a list of subject candidates to the terminal using a communication protocol.

[0701] Output: A list of subject suggestions displayed on the device.

[0702] Step 5:

[0703] The user checks the subject candidates on the terminal and selects the most suitable subject.

[0704] Input: Subject suggestion list.

[0705] Specific operation: The user uses the device interface to select the most suitable subject from a list of subject candidates (e.g., "Delivery notification: Pizza, Pasta").

[0706] Output: The selected subject.

[0707] Step 6:

[0708] The server receives the user's selected subject and applies it to the email.

[0709] Input: A subject chosen by the user.

[0710] Specific Actions: The server applies the selected subject to the email's subject field.

[0711] Output: The email with the subject applied.

[0712] Step 7:

[0713] The server displays the email with the applied subject to the user and prepares it for sending.

[0714] Input: The email with the subject applied.

[0715] Specific operation: The server instructs the email client to display this email and provides a send button.

[0716] Output: A summary of the email with the applied subject will be displayed on the terminal screen.

[0717] 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.

[0718] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[0719] System configuration

[0720] The system consists of the following main components:

[0721] 1. A server that receives and stores email text

[0722] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0723] 3. A server equipped with an emotion engine that recognizes user emotions

[0724] 4. Server and terminal that generates subject candidates and presents them to the user

[0725] 5. A device that allows users to select a subject and apply the selected subject to the email.

[0726] What the program does

[0727] Receiving and storing emails

[0728] The server stores newly received emails from email clients in an internal database.

[0729] Once the email is saved, it moves on to the next analysis step.

[0730] Text analysis and keyword extraction

[0731] The server runs the stored email body through a text analysis algorithm.

[0732] The algorithm performs grammatical analysis to understand the structure of the sentence.

[0733] Next, context analysis is performed to understand the meaning and context of the entire sentence.

[0734] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[0735] User Emotion Recognition

[0736] The server recognizes the user's emotions through text analysis and voice analysis via an emotion engine.

[0737] The recognized emotion data is fed back into the subject line generation process.

[0738] Auto-generated subject line suggestions

[0739] The server generates subject line candidates using predefined templates based on the extracted keywords and the recognized user sentiment.

[0740] For example, there are templates such as "Important: Action required by {date and time}" and "Please confirm: {document name}."

[0741] The tone and phrasing of the subject line are adjusted based on sentiment data.

[0742] Suggesting and selecting subject lines

[0743] The server transmits the generated list of subject candidates to the terminal.

[0744] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[0745] The user selects the most appropriate subject from the displayed candidates.

[0746] Apply subject and send email

[0747] The terminal transmits the user's selection to the server.

[0748] The server applies the selected subject to the email's subject field.

[0749] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0750] Specific examples

[0751] Example: Actual email body

[0752] thank you for your hard work.

[0753] Share details of a project meeting scheduled for next Monday.

[0754] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[0755] Project Plan

[0756] Risk Management Report

[0757] Emotion Recognition and Subject Line Generation

[0758] 1. The server receives and stores email.

[0759] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0760] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[0761] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[0762] "Project meeting next Monday: Starts at 10:00 AM"

[0763] "Please check in advance: Project plan and risk management report"

[0764] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[0765] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[0766] 6. The server applies the selected subject to the email and prepares it for sending.

[0767] In this way, by combining an emotion engine, it is possible to automatically generate subject lines that take into account the user's emotions, thereby improving the quality of email communication.

[0768] The processing flow will be explained below.

[0769] Step 1:

[0770] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[0771] Step 2:

[0772] The server stores the received emails in an internal database.

[0773] Step 3:

[0774] The server identifies the stored email as the subject of analysis and extracts the email body.

[0775] Step 4:

[0776] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[0777] Step 5:

[0778] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[0779] Step 6:

[0780] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[0781] Step 7:

[0782] The server analyzes the user's emotions through an emotion engine, analyzing text entered by the user into an email client and recorded voice data to recognize emotions.

[0783] Step 8:

[0784] The server generates subject line candidates using predefined templates based on the extracted and weighted keywords and the user's recognized emotional data. The templates are in the form of "Next Meeting: {Date and Time}" or "Confirmation Request: {Document Name}," but the tone and phrasing of the subject line are adjusted based on the emotional data.

[0785] Step 9:

[0786] The server transmits the generated list of subject candidates to the terminal.

[0787] Step 10:

[0788] The device displays a list of candidate subject lines in the user's email client, and adjusts the order of the candidate subject lines based on the emotion data, so that they are displayed in the most appropriate order for the user.

[0789] Step 11:

[0790] The user selects the most appropriate subject from the displayed subject candidates.

[0791] Step 12:

[0792] The terminal transmits the user's selection to the server.

[0793] Step 13:

[0794] The server applies the selected subject to the email's subject field.

[0795] Step 14:

[0796] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[0797] This process not only automatically generates email subject lines, but also takes into consideration the user's emotions, resulting in more appropriate and effective email communication.

[0798] Example 2

[0799] Next, a description will be given of 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."

[0800] In today's world, email is an important means of communication for both business and personal activities. However, choosing the right subject line for an email is often difficult, especially when the user's emotional state plays a significant role. This can lead to misunderstandings and communication friction. Additionally, selecting appropriate keywords and creating a subject line quickly can be challenging.

[0801] 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.

[0802] In this invention, the server includes means for receiving and saving the text of the email, means for automatically extracting important keywords from the text, and means for generating subject line candidates based on the extracted keywords and the recognized user emotions. This makes it possible to quickly generate appropriate subject lines according to the user's emotions and improve the quality of email communication.

[0803] The "body of the email" is the main part of the message contained in the email, and contains the sender's intentions and information.

[0804] "Means of storage" refers to the function of recording the body text and header information of received emails in storage such as a database, making them reusable as needed.

[0805] "Keywords" are important nouns, verbs, adjectives, dates and times, and other information extracted from the body of an email, and are useful elements for generating a subject line.

[0806] "Means of extraction" refers to the function of using a text analysis algorithm to identify and extract important keywords from the body of an email.

[0807] "User's emotions" refers to a psychological state recognized from the content of the email body and the user's situation, such as "fatigue" or "tension."

[0808] "Means for recognizing emotions" refers to the ability to use text or voice analysis to identify a user's emotions and make that information available for further processing.

[0809] "Subject candidates" are multiple options presented to the user as email subjects, and are automatically generated based on keywords and the user's emotions.

[0810] "Means for generating" refers to the function of forming subject line candidates using predefined templates based on extracted keywords and user sentiment.

[0811] "Template" means a predefined format for generating subject lines, a framework for inserting specific keywords or patterns.

[0812] "Adjustment" refers to the ability to appropriately change the tone and tone of the subject line according to the recognized user's emotions, to create a format that is optimal for the user.

[0813] The "means for presenting" refers to a function for displaying the generated subject candidates on the user's terminal and allowing the user to select the subject that seems most appropriate.

[0814] The "selected subject" refers to the subject that the user selects as the most appropriate from among multiple subject candidates.

[0815] The "means of applying" refers to a function of setting the subject selected by the user in the subject field of the actual email and preparing the email for sending.

[0816] The present invention relates to a system for recognizing a user's emotions and automatically generating email subject lines. A specific embodiment of this system is described below.

[0817] System Overview

[0818] The system includes the following main components:

[0819] 1. A server that receives and stores email text

[0820] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0821] 3. A server equipped with an emotion engine that recognizes user emotions

[0822] 4. Server and terminal that generates subject candidates and presents them to the user

[0823] 5. A device that allows users to select a subject and apply the selected subject to the email.

[0824] Receiving and storing emails

[0825] The server first detects newly received emails from the email client, for example, using the Gmail API, and stores the received emails in a database such as MongoDB.

[0826] Text analysis and keyword extraction

[0827] The text of the saved emails is then run through a text analysis algorithm on the server. For example, the SpaCy library is used for grammatical analysis to understand the structure and meaning of the sentence. Next, a context analysis model is used to understand the overall subject and theme of the text. Important nouns, verbs, adjectives, dates, and other keywords are extracted and weighted. This results in keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0828] User Emotion Recognition

[0829] The server then uses an emotion engine to recognize the user's emotions. Services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used for emotion recognition. Text and voice analysis is used to extract emotions such as "tired" or "tension," and this data is fed back into the subject line generation process.

[0830] Auto-generated subject line suggestions

[0831] The server generates subject line candidates using predefined templates based on the extracted keywords and the user's recognized emotions. For example, templates include "Please confirm: {Document name}" and "Project meeting next Monday: Starts at 10:00 AM." The tone and phrasing of the subject line are also adjusted based on the emotion data. For example, if the user is tired, a calmer subject line is generated.

[0832] Suggesting and selecting subject lines

[0833] The generated subject candidates are sent from the server to the terminal and presented to the user. The terminal displays the subject candidate list in the user's email client, and the candidate order is adjusted based on the emotion data. The user selects the most appropriate subject from this list. For example, it is displayed in the form of a window or a drop-down list.

[0834] Apply subject and send email

[0835] The selected subject is sent from the terminal to the server, which applies this subject to the subject field of the email and prepares the email for sending. The email with the applied subject is sent after the user confirms it.

[0836] Specific examples

[0837] The actual email body

[0838] plain

[0839] thank you for your hard work.

[0840] Share details of a project meeting scheduled for next Monday.

[0841] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[0842] Project Plan

[0843] Risk Management Report

[0844] The process of emotion recognition and subject line generation

[0845] 1. The server receives and stores email.

[0846] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[0847] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[0848] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[0849] "Project meeting next Monday: Starts at 10:00 AM"

[0850] "Please check in advance: Project plan and risk management report"

[0851] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[0852] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[0853] 6. The server applies the selected subject to the email and prepares it for sending.

[0854] Prompt Sentence Examples

[0855] "Describe the process of a system that uses natural language analysis to extract important keywords from the body of an email and generates subject line suggestions based on the user's sentiment. As a concrete example, consider the case where a user is tired and receives an email that reads, 'I have a project meeting next Monday.'"

[0856] This system can automatically generate appropriate email subject lines that match the user's emotional state, improving the quality of email communication.

[0857] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0858] Step 1: Receiving and saving emails

[0859] The server detects newly received emails from the email client. For example, it obtains email data via the Gmail API. The email body and header information are used as input data.

[0860] The server stores the received emails in an internal database (e.g. MongoDB). The output of this step is the email data stored in the database.

[0861] Step 2: Text analysis and keyword extraction

[0862] The server runs the stored email body through a text analysis algorithm, where the input is the stored email body.

[0863] Specifically, it uses the SpaCy library to perform grammatical analysis to understand sentence structure, and then uses a custom context analysis model to understand the overall subject or theme of the text.

[0864] The output is extracted keywords such as important nouns, verbs, adjectives, dates, etc. For example, keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report" are identified.

[0865] Step 3: Recognizing user emotions

[0866] The server uses an emotion engine to recognize the user's emotions, where the input is the body of past emails and related data.

[0867] Specifically, services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used to extract emotions such as "fatigue" and "tension" from text, and the emotional data is the output.

[0868] Step 4: Auto-generate subject line suggestions

[0869] The server generates subject candidates based on the extracted keywords and sentiment data. The inputs are keywords and sentiment data.

[0870] Generate subject line suggestions using predefined templates (e.g., "Please review: {document name}" or "Project meeting next Monday: Starts at 10 AM") and adjust the tone and phrasing of subject lines based on sentiment data.

[0871] The generated subject line candidates are output, for example, "Project meeting next Monday: Start time 10:00 AM" or "Please confirm in advance: Project plan and risk management report."

[0872] Step 5: Suggest and select subject lines

[0873] The server sends the generated list of subject candidates to the terminal. The input is the subject candidates.

[0874] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[0875] The user selects the most appropriate subject from the displayed candidates, and the selected subject is output.

[0876] Step 6: Apply a subject line and send the email

[0877] The terminal sends the user's selection to the server, where the input is the selected subject.

[0878] The server applies this selected subject to the email's subject field, and the output is an email with the applied subject.

[0879] The terminal shows the email with the applied subject to the user and prepares the email for sending. As a final action, the user presses the send button to send the email.

[0880] (Application example 2)

[0881] Next, a description will be given of Application 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."

[0882] Conventional email systems require users to manually enter email subject lines, which is a time-consuming and labor-intensive task. Furthermore, there is no mechanism for generating appropriate subject lines that take the user's emotions into account, which can lead to a decline in the quality of communication. For example, when dealing with customers, it is important to respond appropriately while taking their emotions into consideration, but current systems are unable to do this. Therefore, there is a need for a system that can recognize the user's emotions and automatically generate appropriate subject lines based on them.

[0883] The specification process by the specification 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 and saving the body of an email, means for automatically extracting important keywords from the body, means for generating subject candidates based on the extracted keywords and recognizing the user's emotions, and means for presenting the subject candidates to the user and applying the selected subject to the email. This enables the automatic generation of email subjects that take the user's emotions into consideration, improving the quality of communication and reducing the burden on the user.

[0884] "Means for receiving and storing the body of an email" refers to the server receiving an email sent by a user and storing the content in a database.

[0885] "Method of automatically extracting important keywords" refers to the process of using text analysis algorithms to extract meaningful words and phrases from the email body based on context and grammar.

[0886] "Means for recognizing user emotions" refers to technologies or engines that analyze text or voice data to estimate a user's emotional state.

[0887] "Means for generating subject line candidates" refers to algorithms or templates for generating appropriate sentences as email subject lines based on extracted keywords and recognized emotion data.

[0888] "Means for presenting subject candidates to the user and applying the selected subject to the email" refers to the process of providing an interface for the user to select the most appropriate subject from multiple generated subjects and applying the selected subject to the actual email.

[0889] "Means for grammatical and contextual analysis" refers to algorithms and tools that analyze the grammatical structure and context of the email body to more accurately understand its meaning.

[0890] An "emotion engine" is software that analyzes and recognizes user emotions from text or voice, and often uses specific algorithms or models.

[0891] "Means for automatic generation based on predefined templates" refers to a mechanism for automatically creating appropriate email subject lines based on predefined phrases and formats.

[0892] "Means for adjusting the tone and phrasing of subject lines based on emotional data" refers to a system for adjusting the wording and nuance of email subject lines based on the perceived emotions of the user.

[0893] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[0894] System configuration

[0895] The system consists of the following main components:

[0896] 1. Server: Has a means to receive and store the body of email.

[0897] 2. Server: Has a means to automatically extract important keywords from the text.

[0898] 3. Server: Equipped with an emotion engine that recognizes the user's emotions and has the means to perform text analysis and voice analysis.

[0899] 4. Server: Has a means to automatically generate subject line candidates based on the extracted keywords and recognized sentiment.

[0900] 5. Terminal: Provides a means for presenting subject line suggestions to the user and applying the selected subject line to the email.

[0901] What the program does

[0902] Hardware and Software

[0903] Smart glasses: A device for recording and analyzing customers' facial expressions and voices in real time.

[0904] EmotionRecognizer: A library that analyzes emotions from customer voice data, for example, using the Google Cloud Speech-to-Text API and Emotion API.

[0905] EmailGenerator: A module that extracts keywords from the text and generates email subject lines based on sentiment data. It uses TextBlob and SpaCy for natural language processing.

[0906] Data processing and calculation

[0907] 1. The server receives audio and text from the customer and converts the audio into text.

[0908] 2. The server parses the text in TextBlob and extracts important keywords.

[0909] 3. The server uses EmotionRecognizer to obtain emotion data from the audio.

[0910] 4. The server generates subject line candidates using the EmailGenerator engine based on the extracted keywords and sentiment data.

[0911] 5. The terminal displays the generated subject candidates on the store staff's smart glasses.

[0912] 6. The user selects the most appropriate subject from the displayed subject suggestions.

[0913] For example, if a store staff member is asked in a conversation with a customer, "Do you have any business trip suits in stock?":

[0914] Incoming text: "Do you have any business trip suits in stock?"

[0915] Extracted keywords: "Business trip", "Suit", "Inventory"

[0916] Emotion data: The recognized emotion data is "interest"

[0917] Prompt Sentence Examples

[0918] A customer has a question. The keywords are "business trip", "suits", and "inventory", and the sentiment is "interest". Based on this, generate an appropriate subject line.

[0919] The above is an embodiment of the invention configured based on the claims. This system enables automatic generation of email subjects that take user emotions into consideration, improving the quality of communication and reducing the burden on users.

[0920] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0921] Step 1:

[0922] The server receives and stores the body of the email.

[0923] Specifically, the server stores newly received emails from users' email clients in an internal database, making the email body available for the next analysis step. The input is the email body, and the output is the saved email data.

[0924] Step 2:

[0925] The server automatically extracts important keywords from the body of the saved email.

[0926] Specifically, it uses TextBlob to parse text and extract nouns, verbs, and important phrases based on grammar and context. The input is the saved email body, and the output is the extracted important keywords.

[0927] Step 3:

[0928] The server recognizes the user's emotions.

[0929] Specifically, the system analyzes the user's voice data via EmotionRecognizer to obtain emotional information. The voice file is analyzed and the emotion engine recognizes emotions. The input is the user's voice data, and the output is the recognized emotional data.

[0930] Step 4:

[0931] The server automatically generates subject line candidates based on the extracted keywords and the recognized emotion data.

[0932] Specifically, the EmailGenerator engine is used to apply the extracted keywords and sentiment data to a template to generate multiple subject line candidates. The input is keywords and sentiment data, and the output is a list of subject line candidates.

[0933] Step 5:

[0934] The terminal presents the generated subject candidates to the user.

[0935] Specifically, a list of subject candidates is displayed on the store staff's smart glasses. The list is visually displayed through a user interface, allowing the user to easily select a subject. The input is the subject candidate list, and the output is the subject candidates presented to the user.

[0936] Step 6:

[0937] The user selects the most suitable subject from the presented subject candidates.

[0938] Specifically, the user selects the most appropriate subject from the displayed subject candidates using the user interface of the smart glasses. The input is the subject candidates presented to the user, and the output is the selected subject.

[0939] Step 7:

[0940] The server applies the selected subject to the email.

[0941] Specifically, it takes a user-selected subject, applies it to the subject field of the original email, and prepares the email to be sent. The input is the selected subject, and the output is the email with the subject applied.

[0942] 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.

[0943] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[0944] 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.

[0945] [Third embodiment]

[0946] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0947] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0948] 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).

[0949] 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.

[0950] 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.

[0951] 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).

[0952] 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.

[0953] 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.

[0954] 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.

[0955] 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.

[0956] 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.

[0957] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0958] This invention relates to a system for automatically generating email subject lines in business communications. Detailed embodiments of this system and the processing contents of the program will be described below with specific examples.

[0959] System configuration

[0960] The system consists of the following main components:

[0961] 1. A server that receives and stores email text

[0962] 2. A server with an analysis engine that automatically extracts important keywords from the text

[0963] 3. Server and terminal that generates subject candidates and presents them to the user

[0964] 4. A device that allows users to select a subject and apply the selected subject to the email.

[0965] What the program does

[0966] Receiving and storing emails

[0967] The server stores newly received emails from email clients in an internal database.

[0968] Once the email is saved, it moves on to the next analysis step.

[0969] Text analysis and keyword extraction

[0970] The server runs the stored email body through a text analysis algorithm.

[0971] The algorithm performs grammatical analysis to understand the structure of the sentence.

[0972] Next, context analysis is performed to understand the meaning of the entire sentence.

[0973] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[0974] Auto-generated subject line suggestions

[0975] The server generates subject line candidates based on the extracted keywords using predefined templates.

[0976] For example, templates could include "Next meeting: {date and time}" and "Confirmation request: {document name}."

[0977] The generated subject suggestions are compiled into a list.

[0978] Suggesting and selecting subject lines

[0979] The server transmits the generated list of subject candidates to the terminal.

[0980] The terminal displays a list of subject suggestions in the user's email client.

[0981] The user selects the most appropriate subject from the displayed candidates.

[0982] Apply subject and send email

[0983] The terminal transmits the user's selection to the server.

[0984] The server applies the selected subject to the email's subject field.

[0985] The device will display the email with the applied subject and prepare it to send.

[0986] Specific examples

[0987] Example: Actual email body

[0988] Hello.

[0989] I will let you know the details of next week's sales meeting.

[0990] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[0991] First Quarter Sales Report

[0992] Customer Feedback Report

[0993] Analysis of the above text and subject candidate generation

[0994] 1. The server receives and stores email.

[0995] 2. The server extracts the following keywords from the body of the text: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report".

[0996] 3. The server generates the following possible subject lines:

[0997] "Sales Meeting: March 15th, 10:00 AM"

[0998] "Request for review: Q1 sales report and customer feedback report"

[0999] 4. The user selects "Sales Meeting: March 15th, 10:00 AM."

[1000] 5. The server applies the selected subject to the email and prepares it for sending.

[1001] This ensures that the subject line of your email is clear and relevant, instantly conveying to the recipient the importance of the email.

[1002] The processing flow will be explained below.

[1003] Step 1:

[1004] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[1005] Step 2:

[1006] The server stores the received emails in an internal database.

[1007] Step 3:

[1008] The server identifies the stored email as the subject of analysis and extracts the email body.

[1009] Step 4:

[1010] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[1011] Step 5:

[1012] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[1013] Step 6:

[1014] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[1015] Step 7:

[1016] Based on the extracted and weighted keywords, the server generates subject line suggestions according to predefined templates, such as "Next meeting: {date and time}" or "Confirmation request: {document name}."

[1017] Step 8:

[1018] The server transmits the generated list of subject candidates to the terminal.

[1019] Step 9:

[1020] The terminal displays a list of subject suggestions in the user's email client.

[1021] Step 10:

[1022] The user selects the most appropriate subject from the displayed subject candidates.

[1023] Step 11:

[1024] The terminal transmits the user's selection to the server.

[1025] Step 12:

[1026] The server applies the selected subject to the email's subject field.

[1027] Step 13:

[1028] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1029] Through these steps, the process is completed in which the subject of the email is automatically generated, selected by the user, and finally applied to the email.

[1030] Example 1

[1031] Next, a description will be given of 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."

[1032] In business communications, setting an appropriate subject line for an email is crucial for conveying clarity and importance to the recipient. However, manually entering an appropriate subject line is time-consuming and can sometimes be forgotten in the midst of busy daily work. This can result in emails being overlooked or misunderstood. Therefore, there is a need for a system that can automatically generate an appropriate subject line from the email body and present it to the user.

[1033] 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.

[1034] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords such as nouns, verbs, adjectives, and dates and times from the saved email body, means for generating subject candidates based on the extracted keywords, and means for presenting the generated subject candidate list to a user terminal and applying a subject selected by the user to the email. This automatically generates email subjects, allowing the user to quickly and appropriately set email subjects.

[1035] The "body of an email" is the main content part of an email message, and is the text that contains the information that the sender wants to convey to the recipient.

[1036] "Storage means" refers to a function or device that records the contents of emails in a storage system such as a database, making them accessible later.

[1037] "Keywords" are words and phrases such as important nouns, verbs, adjectives, and dates and times extracted from the body of an email and necessary for generating a subject line.

[1038] "Extraction means" refers to a function or device that uses text analysis or algorithms to identify and extract important keywords from the body of an email.

[1039] The "subject candidates" are multiple options that can be used as the subject of an email, generated based on the extracted keywords.

[1040] The "presentation means" refers to a function or device that visually displays the generated subject candidate list to the user and provides it in a selectable form.

[1041] The "means for applying" refers to a function or device for actually setting the subject selected by the user in the subject field of the email and sending it as the final email.

[1042] "Grammar analysis" is the process of analyzing the syntax and grammatical structure of a sentence and understanding the components of the sentence.

[1043] "Context analysis" is the process of understanding the meaning and context of a text as a whole and extracting relevant information from its content.

[1044] A "template" is a predefined format or skeleton, a framework into which specific information can be embedded to create a final form.

[1045] A "user terminal" is a device such as a computer, smartphone, or tablet that a user operates and interacts with the system.

[1046] overview

[1047] This invention relates to a system for automatically generating email subject lines in business communication. Specifically, this system analyzes the email body, extracts important keywords, automatically generates subject line candidates based on those keywords, and allows users to select an appropriate subject line.

[1048] System Configuration

[1049] The system consists of the following main components:

[1050] 1. Server that receives and saves the email body

[1051] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1052] 3. Server and terminal that generates subject candidates and presents them to the user

[1053] 4. User selects subject and applies selected subject to email.

[1054] Receiving and storing emails

[1055] The server stores newly received emails from email clients in its internal database. For example, when a user receives a new email using an email client such as Outlook or Gmail, the email is sent to the server and stored in its database. Here, emails are received using the SMTP protocol and stored in a MySQL or PostgreSQL database.

[1056] Text analysis and keyword extraction

[1057] The server analyzes the text of the saved emails. For this analysis, it uses a natural language processing library called SpaCy. First, the server tokenizes the text and performs grammatical analysis. Then it performs context analysis to understand the meaning of the entire sentence, extracts important keywords (nouns, verbs, adjectives, dates and times, etc.), and assigns weights to them.

[1058] Auto-generated subject line suggestions

[1059] The server generates subject line candidates based on the extracted keywords, using predefined templates. For example, if there is a template called "Next Meeting: {Date and Time}", it will automatically generate a subject line such as "Next Meeting: March 15th, 10:00 AM". These subject line candidates are generated using Python template literals.

[1060] Suggesting and selecting subject lines

[1061] The server sends the generated subject candidate list to the terminal. This list is sent in JSON format, and the terminal displays the list in the user's email client. The user selects the best subject from the displayed candidates. This operation is achieved using JavaScript, HTML, and CSS.

[1062] Apply subject and send email

[1063] The terminal sends the user-selected subject to the server, which applies the selected subject to the email's subject field, saves the updated email in a database, and prepares it for sending. Finally, the terminal displays the email with the updated subject to the user and enables the send button.

[1064] Specific examples

[1065] Famous prompt sentence examples

[1066] Below is an example of an actual email body and the subject line suggestions generated from its analysis:

[1067] text

[1068] Hello.

[1069] I will let you know the details of next week's sales meeting.

[1070] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[1071] First Quarter Sales Report

[1072] Customer Feedback Report

[1073] For this email body, the server extracts the following keywords: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report". Based on these keywords, the server generates the following subject line suggestions:

[1074] "Sales Meeting: March 15th, 10:00 AM"

[1075] "Request for review: Q1 sales report and customer feedback report"

[1076] The user selects "Sales Meeting: March 15th at 10:00 AM" from these suggestions, and that subject is applied to the email.

[1077] This system allows users to quickly and efficiently set appropriate subject lines, improving the efficiency of business communications.

[1078] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1079] Step 1:

[1080] The server detects newly received emails from email clients and stores them in an internal database. At this time, the email message, including the email body, sender information, and date and time of receipt, is sent as input data. The server stores these in the database and outputs a message indicating that the data has been saved. Specifically, the server receives emails using the SMTP protocol and stores them in a MySQL or PostgreSQL database.

[1081] Step 2:

[1082] The server retrieves the email body stored in its internal database and runs it through a text analysis algorithm. The input is the email body, and the output is the parsed tokens (words and phrases). Specifically, the server uses the SpaCy library to tokenize the stored email body.

[1083] Step 3:

[1084] The server performs grammatical analysis based on the tokenized data. The input to this step is the tokens obtained in step 2, and the output is sentence structure information. Specifically, the server uses SpaCy and Python to extract syntactic elements such as noun phrases and verb phrases.

[1085] Step 4:

[1086] The server performs context analysis based on the results of grammatical analysis to extract important keywords such as nouns, verbs, adjectives, and dates and times. The input to this step is sentence structure information, and the output is important keywords and their weights. Specifically, the server uses the SpaCy library to deepen its understanding of the meaning of the sentence.

[1087] Step 5:

[1088] The server uses the extracted keywords to generate subject candidates. The input for this step is the important keywords, and the output is multiple subject candidates. Specifically, the server generates subject candidates by embedding the keywords in a predefined template, using Python template literals.

[1089] Step 6:

[1090] The server sends the generated subject candidate list to the user terminal. The input of this step is the subject candidate list, and the output is a data transmission completion message to the user terminal. Specifically, the server sends data in JSON format to the terminal and uses a web framework such as Flask or Django.

[1091] Step 7:

[1092] The terminal displays the received subject candidate list in the user's email client. The input of this step is the subject candidate list, and the output is a visual representation of the subject candidate. Specifically, the terminal uses JavaScript, HTML, and CSS to display the candidate list as a popup or drop-down menu.

[1093] Step 8:

[1094] The user selects the best subject from the displayed candidates. The input of this step is the visually displayed subject candidates, and the output is the selected subject. Specifically, the user selects a candidate subject with a mouse click or tap.

[1095] Step 9:

[1096] The terminal sends the subject selected by the user to the server. The input of this step is the subject selected by the user, and the output is a sending completion message. As a specific operation, the terminal sends the selected subject in JSON format to the server.

[1097] Step 10:

[1098] The server applies the received selected subject to the email's subject field. The input of this step is the subject selected by the user, and the output is the updated email data. Specifically, the server updates the subject field in the email database.

[1099] Step 11:

[1100] The terminal displays the email with the updated subject and prepares it to be sent. The input of this step is the updated email data, and the output is a message that it is ready to send. As a specific operation, the terminal redisplays the email with the updated subject on the user's screen along with a send button.

[1101] (Application example 1)

[1102] Next, a description will be given of Application 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."

[1103] In food delivery services, if the subject lines of order confirmation emails or delivery notification emails are inappropriate, recipients will not be able to instantly understand important information, resulting in a poor user experience. Furthermore, manually setting flexible and appropriate subject lines based on the delivery contents and the current situation is time-consuming and inefficient. Therefore, there is a need for a system that can automatically generate and apply optimal subject lines based on the user's order details.

[1104] 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.

[1105] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords from the email body, means for generating subject candidates based on the extracted keywords, means for presenting the subject candidates to the user and applying the selected subject to the email, means for receiving and analyzing the order details, means for generating subject candidates based on the analyzed order details, and means for presenting the subject candidates to the user and applying the selected subject to the order confirmation email. This enables efficient email management in a food delivery service, where an appropriate subject based on the order details is automatically generated, allowing the user to quickly grasp important information.

[1106] "Email" is data, including text and files, sent and received via electronic communication means.

[1107] "Body" refers to the primary textual content of an email, which is the user-written or system-generated message content.

[1108] "Keywords" are important words or phrases extracted from the body of an email and form the key points for understanding the content.

[1109] A "subject" is a short piece of text used to briefly indicate the subject or content of an email.

[1110] A "server" is a computer system that processes, stores, and serves information over a network.

[1111] A "template" is a predefined format for generating subject line candidates.

[1112] "User" refers to the person or account that uses the email system, and is the entity that operates the system and checks its contents.

[1113] "Order Details" means details of the items or services requested by a User for food delivery or other services.

[1114] "Analysis" is the process of extracting important elements and meaning from information in received emails and order details.

[1115] This invention relates to a system that automatically generates subject lines for order confirmation emails and delivery notification emails in food delivery services. This system is mainly composed of a server, terminals, and user operations.

[1116] System configuration

[1117] 1. Server

[1118] The server has a function of receiving and storing the contents of a user's order.

[1119] The server has a text analysis algorithm for analyzing the received order content.

[1120] The server has the function of extracting important keywords from the analysis results and generating subject candidates.

[1121] 2. Terminal

[1122] The terminal operated by the user displays the subject candidates sent from the server.

[1123] The user selects a subject suggestion and sends it to the server.

[1124] 3. Users

[1125] The user selects the best subject line from the provided suggestions and applies it to the email.

[1126] Specific examples of processing

[1127] Receiving and analyzing order details

[1128] The server receives the order details through the food delivery app. For example, if a user orders "pizza" and "pasta," the server sends these to the server as the body of an email.

[1129] The server then analyzes the stored text using a text analysis algorithm, which performs grammatical and contextual analysis to extract important keywords (e.g., pizza, pasta).

[1130] Based on the extracted keywords, subject line suggestions are generated using predefined templates, resulting in subject line suggestions such as "Order Confirmation: Pizza, Pasta" and "Shipping Notification: Pizza, Pasta."

[1131] Suggest and apply subject suggestions

[1132] The server sends the generated subject candidates as a list to the terminal.

[1133] The user can review subject line suggestions on the device and select the most appropriate one, such as "Delivery notification: Pizza, Pasta."

[1134] The selected subject is sent to the server and applied to the email.

[1135] Hardware and Software Used

[1136] Hardware: Servers, smartphones (terminals)

[1137] Software: Python-based text analysis algorithms, email client software

[1138] Example prompt sentence:

[1139] "Extract key keywords from the following order and generate suitable subject line suggestions: 'User ordered pizza and pasta.'"

[1140] This will enable food delivery services to automatically generate appropriate email subject lines based on the order details, enabling users to quickly grasp important information and achieving efficient email management.

[1141] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1142] Step 1:

[1143] The server receives and stores the user's order details through the food delivery app.

[1144] Input: Information that the user ordered "pizza" and "pasta."

[1145] Specific Actions: The server stores this information in a database for subsequent analysis.

[1146] Step 2:

[1147] The server analyzes the body of the stored order details using a text analysis algorithm.

[1148] Input: The body of the saved order.

[1149] Specific operation: The server performs grammatical analysis and context analysis to extract keywords, and uses a generative AI model to understand the context.

[1150] Output: Important keywords (e.g., pizza, pasta) are extracted.

[1151] Step 3:

[1152] The server generates subject candidates based on the extracted keywords.

[1153] Input: Extracted keywords.

[1154] What happens: The server uses predefined templates (e.g., "Order Confirmation: {keywords}", "Shipping Notification: {keywords}") to create a list of suitable subject candidates.

[1155] Output: Subject line suggestions such as "Order Confirmation: Pizza, Pasta" or "Shipping Notification: Pizza, Pasta".

[1156] Step 4:

[1157] The server transmits the generated subject candidate list to the terminal.

[1158] Input: Subject suggestion list.

[1159] Specific operation: The server sends a list of subject candidates to the terminal using a communication protocol.

[1160] Output: A list of subject suggestions displayed on the device.

[1161] Step 5:

[1162] The user checks the subject candidates on the terminal and selects the most suitable subject.

[1163] Input: Subject suggestion list.

[1164] Specific operation: The user uses the device interface to select the most suitable subject from a list of subject candidates (e.g., "Delivery notification: Pizza, Pasta").

[1165] Output: The selected subject.

[1166] Step 6:

[1167] The server receives the user's selected subject and applies it to the email.

[1168] Input: A subject chosen by the user.

[1169] Specific Actions: The server applies the selected subject to the email's subject field.

[1170] Output: The email with the subject applied.

[1171] Step 7:

[1172] The server displays the email with the applied subject to the user and prepares it for sending.

[1173] Input: The email with the subject applied.

[1174] Specific operation: The server instructs the email client to display this email and provides a send button.

[1175] Output: A summary of the email with the applied subject will be displayed on the terminal screen.

[1176] 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.

[1177] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[1178] System configuration

[1179] The system consists of the following main components:

[1180] 1. A server that receives and stores email text

[1181] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1182] 3. A server equipped with an emotion engine that recognizes user emotions

[1183] 4. Server and terminal that generates subject candidates and presents them to the user

[1184] 5. A device that allows users to select a subject and apply the selected subject to the email.

[1185] What the program does

[1186] Receiving and storing emails

[1187] The server stores newly received emails from email clients in an internal database.

[1188] Once the email is saved, it moves on to the next analysis step.

[1189] Text analysis and keyword extraction

[1190] The server runs the stored email body through a text analysis algorithm.

[1191] The algorithm performs grammatical analysis to understand the structure of the sentence.

[1192] Next, context analysis is performed to understand the meaning and context of the entire sentence.

[1193] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[1194] User Emotion Recognition

[1195] The server recognizes the user's emotions through text analysis and voice analysis via an emotion engine.

[1196] The recognized emotion data is fed back into the subject line generation process.

[1197] Auto-generated subject line suggestions

[1198] The server generates subject line candidates using predefined templates based on the extracted keywords and the recognized user sentiment.

[1199] For example, there are templates such as "Important: Action required by {date and time}" and "Please confirm: {document name}."

[1200] The tone and phrasing of the subject line are adjusted based on sentiment data.

[1201] Suggesting and selecting subject lines

[1202] The server transmits the generated list of subject candidates to the terminal.

[1203] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[1204] The user selects the most appropriate subject from the displayed candidates.

[1205] Apply subject and send email

[1206] The terminal transmits the user's selection to the server.

[1207] The server applies the selected subject to the email's subject field.

[1208] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1209] Specific examples

[1210] Example: Actual email body

[1211] thank you for your hard work.

[1212] Share details of a project meeting scheduled for next Monday.

[1213] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[1214] Project Plan

[1215] Risk Management Report

[1216] Emotion Recognition and Subject Line Generation

[1217] 1. The server receives and stores email.

[1218] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1219] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[1220] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[1221] "Project meeting next Monday: Starts at 10:00 AM"

[1222] "Please check in advance: Project plan and risk management report"

[1223] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[1224] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[1225] 6. The server applies the selected subject to the email and prepares it for sending.

[1226] In this way, by combining an emotion engine, it is possible to automatically generate subject lines that take into account the user's emotions, thereby improving the quality of email communication.

[1227] The processing flow will be explained below.

[1228] Step 1:

[1229] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[1230] Step 2:

[1231] The server stores the received emails in an internal database.

[1232] Step 3:

[1233] The server identifies the stored email as the subject of analysis and extracts the email body.

[1234] Step 4:

[1235] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[1236] Step 5:

[1237] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[1238] Step 6:

[1239] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[1240] Step 7:

[1241] The server analyzes the user's emotions through an emotion engine, analyzing text entered by the user into an email client and recorded voice data to recognize emotions.

[1242] Step 8:

[1243] The server generates subject line candidates using predefined templates based on the extracted and weighted keywords and the user's recognized emotional data. The templates are in the form of "Next Meeting: {Date and Time}" or "Confirmation Request: {Document Name}," but the tone and phrasing of the subject line are adjusted based on the emotional data.

[1244] Step 9:

[1245] The server transmits the generated list of subject candidates to the terminal.

[1246] Step 10:

[1247] The device displays a list of candidate subject lines in the user's email client, and adjusts the order of the candidate subject lines based on the emotion data, so that they are displayed in the most appropriate order for the user.

[1248] Step 11:

[1249] The user selects the most appropriate subject from the displayed subject candidates.

[1250] Step 12:

[1251] The terminal transmits the user's selection to the server.

[1252] Step 13:

[1253] The server applies the selected subject to the email's subject field.

[1254] Step 14:

[1255] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1256] This process not only automatically generates email subject lines, but also takes into consideration the user's emotions, resulting in more appropriate and effective email communication.

[1257] Example 2

[1258] Next, a description will be given of 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."

[1259] In today's world, email is an important means of communication for both business and personal activities. However, choosing the right subject line for an email is often difficult, especially when the user's emotional state plays a significant role. This can lead to misunderstandings and communication friction. Additionally, selecting appropriate keywords and creating a subject line quickly can be challenging.

[1260] 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.

[1261] In this invention, the server includes means for receiving and saving the text of the email, means for automatically extracting important keywords from the text, and means for generating subject line candidates based on the extracted keywords and the recognized user emotions. This makes it possible to quickly generate appropriate subject lines according to the user's emotions and improve the quality of email communication.

[1262] The "body of the email" is the main part of the message contained in the email, and contains the sender's intentions and information.

[1263] "Means of storage" refers to the function of recording the body text and header information of received emails in storage such as a database, making them reusable as needed.

[1264] "Keywords" are important nouns, verbs, adjectives, dates and times, and other information extracted from the body of an email, and are useful elements for generating a subject line.

[1265] "Means of extraction" refers to the function of using a text analysis algorithm to identify and extract important keywords from the body of an email.

[1266] "User's emotions" refers to a psychological state recognized from the content of the email body and the user's situation, such as "fatigue" or "tension."

[1267] "Means for recognizing emotions" refers to the ability to use text or voice analysis to identify a user's emotions and make that information available for further processing.

[1268] "Subject candidates" are multiple options presented to the user as email subjects, and are automatically generated based on keywords and the user's emotions.

[1269] "Means for generating" refers to the function of forming subject line candidates using predefined templates based on extracted keywords and user sentiment.

[1270] "Template" means a predefined format for generating subject lines, a framework for inserting specific keywords or patterns.

[1271] "Adjustment" refers to the ability to appropriately change the tone and tone of the subject line according to the recognized user's emotions, to create a format that is optimal for the user.

[1272] The "means for presenting" refers to a function for displaying the generated subject candidates on the user's terminal and allowing the user to select the subject that seems most appropriate.

[1273] The "selected subject" refers to the subject that the user selects as the most appropriate from among multiple subject candidates.

[1274] The "means of applying" refers to a function of setting the subject selected by the user in the subject field of the actual email and preparing the email for sending.

[1275] The present invention relates to a system for recognizing a user's emotions and automatically generating email subject lines. A specific embodiment of this system is described below.

[1276] System Overview

[1277] The system includes the following main components:

[1278] 1. A server that receives and stores email text

[1279] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1280] 3. A server equipped with an emotion engine that recognizes user emotions

[1281] 4. Server and terminal that generates subject candidates and presents them to the user

[1282] 5. A device that allows users to select a subject and apply the selected subject to the email.

[1283] Receiving and storing emails

[1284] The server first detects newly received emails from the email client, for example, using the Gmail API, and stores the received emails in a database such as MongoDB.

[1285] Text analysis and keyword extraction

[1286] The text of the saved emails is then run through a text analysis algorithm on the server. For example, the SpaCy library is used for grammatical analysis to understand the structure and meaning of the sentence. Next, a context analysis model is used to understand the overall subject and theme of the text. Important nouns, verbs, adjectives, dates, and other keywords are extracted and weighted. This results in keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1287] User Emotion Recognition

[1288] The server then uses an emotion engine to recognize the user's emotions. Services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used for emotion recognition. Text and voice analysis is used to extract emotions such as "tired" or "tension," and this data is fed back into the subject line generation process.

[1289] Auto-generated subject line suggestions

[1290] The server generates subject line candidates using predefined templates based on the extracted keywords and the user's recognized emotions. For example, templates include "Please confirm: {Document name}" and "Project meeting next Monday: Starts at 10:00 AM." The tone and phrasing of the subject line are also adjusted based on the emotion data. For example, if the user is tired, a calmer subject line is generated.

[1291] Suggesting and selecting subject lines

[1292] The generated subject candidates are sent from the server to the terminal and presented to the user. The terminal displays the subject candidate list in the user's email client, and the candidate order is adjusted based on the emotion data. The user selects the most appropriate subject from this list. For example, it is displayed in the form of a window or a drop-down list.

[1293] Apply subject and send email

[1294] The selected subject is sent from the terminal to the server, which applies this subject to the subject field of the email and prepares the email for sending. The email with the applied subject is sent after the user confirms it.

[1295] Specific examples

[1296] The actual email body

[1297] plain

[1298] thank you for your hard work.

[1299] Share details of a project meeting scheduled for next Monday.

[1300] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[1301] Project Plan

[1302] Risk Management Report

[1303] The process of emotion recognition and subject line generation

[1304] 1. The server receives and stores email.

[1305] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1306] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[1307] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[1308] "Project meeting next Monday: Starts at 10:00 AM"

[1309] "Please check in advance: Project plan and risk management report"

[1310] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[1311] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[1312] 6. The server applies the selected subject to the email and prepares it for sending.

[1313] Prompt Sentence Examples

[1314] "Describe the process of a system that uses natural language analysis to extract important keywords from the body of an email and generates subject line suggestions based on the user's sentiment. As a concrete example, consider the case where a user is tired and receives an email that reads, 'I have a project meeting next Monday.'"

[1315] This system can automatically generate appropriate email subject lines that match the user's emotional state, improving the quality of email communication.

[1316] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1317] Step 1: Receiving and saving emails

[1318] The server detects newly received emails from the email client. For example, it obtains email data via the Gmail API. The email body and header information are used as input data.

[1319] The server stores the received emails in an internal database (e.g. MongoDB). The output of this step is the email data stored in the database.

[1320] Step 2: Text analysis and keyword extraction

[1321] The server runs the stored email body through a text analysis algorithm, where the input is the stored email body.

[1322] Specifically, it uses the SpaCy library to perform grammatical analysis to understand sentence structure, and then uses a custom context analysis model to understand the overall subject or theme of the text.

[1323] The output is extracted keywords such as important nouns, verbs, adjectives, dates, etc. For example, keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report" are identified.

[1324] Step 3: Recognizing user emotions

[1325] The server uses an emotion engine to recognize the user's emotions, where the input is the body of past emails and related data.

[1326] Specifically, services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used to extract emotions such as "fatigue" and "tension" from text, and the emotional data is the output.

[1327] Step 4: Auto-generate subject line suggestions

[1328] The server generates subject candidates based on the extracted keywords and sentiment data. The inputs are keywords and sentiment data.

[1329] Generate subject line suggestions using predefined templates (e.g., "Please review: {document name}" or "Project meeting next Monday: Starts at 10 AM") and adjust the tone and phrasing of subject lines based on sentiment data.

[1330] The generated subject line candidates are output, for example, "Project meeting next Monday: Start time 10:00 AM" or "Please confirm in advance: Project plan and risk management report."

[1331] Step 5: Suggest and select subject lines

[1332] The server sends the generated list of subject candidates to the terminal. The input is the subject candidates.

[1333] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[1334] The user selects the most appropriate subject from the displayed candidates, and the selected subject is output.

[1335] Step 6: Apply a subject line and send the email

[1336] The terminal sends the user's selection to the server, where the input is the selected subject.

[1337] The server applies this selected subject to the email's subject field, and the output is an email with the applied subject.

[1338] The terminal shows the email with the applied subject to the user and prepares the email for sending. As a final action, the user presses the send button to send the email.

[1339] (Application example 2)

[1340] Next, a description will be given of Application 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."

[1341] Conventional email systems require users to manually enter email subject lines, which is a time-consuming and labor-intensive task. Furthermore, there is no mechanism for generating appropriate subject lines that take the user's emotions into account, which can lead to a decline in the quality of communication. For example, when dealing with customers, it is important to respond appropriately while taking their emotions into consideration, but current systems are unable to do this. Therefore, there is a need for a system that can recognize the user's emotions and automatically generate appropriate subject lines based on them.

[1342] The specification process by the specification 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 and saving the body of an email, means for automatically extracting important keywords from the body, means for generating subject candidates based on the extracted keywords and recognizing the user's emotions, and means for presenting the subject candidates to the user and applying the selected subject to the email. This enables the automatic generation of email subjects that take the user's emotions into consideration, improving the quality of communication and reducing the burden on the user.

[1343] "Means for receiving and storing the body of an email" refers to the server receiving an email sent by a user and storing the content in a database.

[1344] "Method of automatically extracting important keywords" refers to the process of using text analysis algorithms to extract meaningful words and phrases from the email body based on context and grammar.

[1345] "Means for recognizing user emotions" refers to technologies or engines that analyze text or voice data to estimate a user's emotional state.

[1346] "Means for generating subject line candidates" refers to algorithms or templates for generating appropriate sentences as email subject lines based on extracted keywords and recognized emotion data.

[1347] "Means for presenting subject candidates to the user and applying the selected subject to the email" refers to the process of providing an interface for the user to select the most appropriate subject from multiple generated subjects and applying the selected subject to the actual email.

[1348] "Means for grammatical and contextual analysis" refers to algorithms and tools that analyze the grammatical structure and context of the email body to more accurately understand its meaning.

[1349] An "emotion engine" is software that analyzes and recognizes user emotions from text or voice, and often uses specific algorithms or models.

[1350] "Means for automatic generation based on predefined templates" refers to a mechanism for automatically creating appropriate email subject lines based on predefined phrases and formats.

[1351] "Means for adjusting the tone and phrasing of subject lines based on emotional data" refers to a system for adjusting the wording and nuance of email subject lines based on the perceived emotions of the user.

[1352] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[1353] System configuration

[1354] The system consists of the following main components:

[1355] 1. Server: Has a means to receive and store the body of email.

[1356] 2. Server: Has a means to automatically extract important keywords from the text.

[1357] 3. Server: Equipped with an emotion engine that recognizes the user's emotions and has the means to perform text analysis and voice analysis.

[1358] 4. Server: Has a means to automatically generate subject line candidates based on the extracted keywords and recognized sentiment.

[1359] 5. Terminal: Provides a means for presenting subject line suggestions to the user and applying the selected subject line to the email.

[1360] What the program does

[1361] Hardware and Software

[1362] Smart glasses: A device for recording and analyzing customers' facial expressions and voices in real time.

[1363] EmotionRecognizer: A library that analyzes emotions from customer voice data, for example, using the Google Cloud Speech-to-Text API and Emotion API.

[1364] EmailGenerator: A module that extracts keywords from the text and generates email subject lines based on sentiment data. It uses TextBlob and SpaCy for natural language processing.

[1365] Data processing and calculation

[1366] 1. The server receives audio and text from the customer and converts the audio into text.

[1367] 2. The server parses the text in TextBlob and extracts important keywords.

[1368] 3. The server uses EmotionRecognizer to obtain emotion data from the audio.

[1369] 4. The server generates subject line candidates using the EmailGenerator engine based on the extracted keywords and sentiment data.

[1370] 5. The terminal displays the generated subject candidates on the store staff's smart glasses.

[1371] 6. The user selects the most appropriate subject from the displayed subject suggestions.

[1372] For example, if a store staff member is asked in a conversation with a customer, "Do you have any business trip suits in stock?":

[1373] Incoming text: "Do you have any business trip suits in stock?"

[1374] Extracted keywords: "Business trip", "Suit", "Inventory"

[1375] Emotion data: The recognized emotion data is "interest"

[1376] Prompt Sentence Examples

[1377] A customer has a question. The keywords are "business trip", "suits", and "inventory", and the sentiment is "interest". Based on this, generate an appropriate subject line.

[1378] The above is an embodiment of the invention configured based on the claims. This system enables automatic generation of email subjects that take user emotions into consideration, improving the quality of communication and reducing the burden on users.

[1379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1380] Step 1:

[1381] The server receives and stores the body of the email.

[1382] Specifically, the server stores newly received emails from users' email clients in an internal database, making the email body available for the next analysis step. The input is the email body, and the output is the saved email data.

[1383] Step 2:

[1384] The server automatically extracts important keywords from the body of the saved email.

[1385] Specifically, it uses TextBlob to parse text and extract nouns, verbs, and important phrases based on grammar and context. The input is the saved email body, and the output is the extracted important keywords.

[1386] Step 3:

[1387] The server recognizes the user's emotions.

[1388] Specifically, the system analyzes the user's voice data via EmotionRecognizer to obtain emotional information. The voice file is analyzed and the emotion engine recognizes emotions. The input is the user's voice data, and the output is the recognized emotional data.

[1389] Step 4:

[1390] The server automatically generates subject line candidates based on the extracted keywords and the recognized emotion data.

[1391] Specifically, the EmailGenerator engine is used to apply the extracted keywords and sentiment data to a template to generate multiple subject line candidates. The input is keywords and sentiment data, and the output is a list of subject line candidates.

[1392] Step 5:

[1393] The terminal presents the generated subject candidates to the user.

[1394] Specifically, a list of subject candidates is displayed on the store staff's smart glasses. The list is visually displayed through a user interface, allowing the user to easily select a subject. The input is the subject candidate list, and the output is the subject candidates presented to the user.

[1395] Step 6:

[1396] The user selects the most suitable subject from the presented subject candidates.

[1397] Specifically, the user selects the most appropriate subject from the displayed subject candidates using the user interface of the smart glasses. The input is the subject candidates presented to the user, and the output is the selected subject.

[1398] Step 7:

[1399] The server applies the selected subject to the email.

[1400] Specifically, it takes a user-selected subject, applies it to the subject field of the original email, and prepares the email to be sent. The input is the selected subject, and the output is the email with the subject applied.

[1401] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1402] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1403] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1404] [Fourth embodiment]

[1405] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1406] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1407] 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).

[1408] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1409] 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.

[1410] 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).

[1411] 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.

[1412] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1413] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1414] 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.

[1415] 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.

[1416] In the robot 414, the processor 46 performs the reception output process. 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.

[1417] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] This invention relates to a system for automatically generating email subject lines in business communications. Detailed embodiments of this system and the processing contents of the program will be described below with specific examples.

[1419] System configuration

[1420] The system consists of the following main components:

[1421] 1. A server that receives and stores email text

[1422] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1423] 3. Server and terminal that generates subject candidates and presents them to the user

[1424] 4. A device that allows users to select a subject and apply the selected subject to the email.

[1425] What the program does

[1426] Receiving and storing emails

[1427] The server stores newly received emails from email clients in an internal database.

[1428] Once the email is saved, it moves on to the next analysis step.

[1429] Text analysis and keyword extraction

[1430] The server runs the stored email body through a text analysis algorithm.

[1431] The algorithm performs grammatical analysis to understand the structure of the sentence.

[1432] Next, context analysis is performed to understand the meaning of the entire sentence.

[1433] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[1434] Auto-generated subject line suggestions

[1435] The server generates subject line candidates based on the extracted keywords using predefined templates.

[1436] For example, templates could include "Next meeting: {date and time}" and "Confirmation request: {document name}."

[1437] The generated subject suggestions are compiled into a list.

[1438] Suggesting and selecting subject lines

[1439] The server transmits the generated list of subject candidates to the terminal.

[1440] The terminal displays a list of subject suggestions in the user's email client.

[1441] The user selects the most appropriate subject from the displayed candidates.

[1442] Apply subject and send email

[1443] The terminal transmits the user's selection to the server.

[1444] The server applies the selected subject to the email's subject field.

[1445] The device will display the email with the applied subject and prepare it to send.

[1446] Specific examples

[1447] Example: Actual email body

[1448] Hello.

[1449] I will let you know the details of next week's sales meeting.

[1450] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[1451] First Quarter Sales Report

[1452] Customer Feedback Report

[1453] Analysis of the above text and subject candidate generation

[1454] 1. The server receives and stores email.

[1455] 2. The server extracts the following keywords from the body of the text: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report".

[1456] 3. The server generates the following possible subject lines:

[1457] "Sales Meeting: March 15th, 10:00 AM"

[1458] "Request for review: Q1 sales report and customer feedback report"

[1459] 4. The user selects "Sales Meeting: March 15th, 10:00 AM."

[1460] 5. The server applies the selected subject to the email and prepares it for sending.

[1461] This ensures that the subject line of your email is clear and relevant, instantly conveying to the recipient the importance of the email.

[1462] The processing flow will be explained below.

[1463] Step 1:

[1464] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[1465] Step 2:

[1466] The server stores the received emails in an internal database.

[1467] Step 3:

[1468] The server identifies the stored email as the subject of analysis and extracts the email body.

[1469] Step 4:

[1470] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[1471] Step 5:

[1472] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[1473] Step 6:

[1474] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[1475] Step 7:

[1476] Based on the extracted and weighted keywords, the server generates subject line suggestions according to predefined templates, such as "Next meeting: {date and time}" or "Confirmation request: {document name}."

[1477] Step 8:

[1478] The server transmits the generated list of subject candidates to the terminal.

[1479] Step 9:

[1480] The terminal displays a list of subject suggestions in the user's email client.

[1481] Step 10:

[1482] The user selects the most appropriate subject from the displayed subject candidates.

[1483] Step 11:

[1484] The terminal transmits the user's selection to the server.

[1485] Step 12:

[1486] The server applies the selected subject to the email's subject field.

[1487] Step 13:

[1488] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1489] Through these steps, the process is completed in which the subject of the email is automatically generated, selected by the user, and finally applied to the email.

[1490] Example 1

[1491] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1492] In business communications, setting an appropriate subject line for an email is crucial for conveying clarity and importance to the recipient. However, manually entering an appropriate subject line is time-consuming and can sometimes be forgotten in the midst of busy daily work. This can result in emails being overlooked or misunderstood. Therefore, there is a need for a system that can automatically generate an appropriate subject line from the email body and present it to the user.

[1493] 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.

[1494] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords such as nouns, verbs, adjectives, and dates and times from the saved email body, means for generating subject candidates based on the extracted keywords, and means for presenting the generated subject candidate list to a user terminal and applying a subject selected by the user to the email. This automatically generates email subjects, allowing the user to quickly and appropriately set email subjects.

[1495] The "body of an email" is the main content part of an email message, and is the text that contains the information that the sender wants to convey to the recipient.

[1496] "Storage means" refers to a function or device that records the contents of emails in a storage system such as a database, making them accessible later.

[1497] "Keywords" are words and phrases such as important nouns, verbs, adjectives, and dates and times extracted from the body of an email and necessary for generating a subject line.

[1498] "Extraction means" refers to a function or device that uses text analysis or algorithms to identify and extract important keywords from the body of an email.

[1499] The "subject candidates" are multiple options that can be used as the subject of an email, generated based on the extracted keywords.

[1500] The "presentation means" refers to a function or device that visually displays the generated subject candidate list to the user and provides it in a selectable form.

[1501] The "means for applying" refers to a function or device for actually setting the subject selected by the user in the subject field of the email and sending it as the final email.

[1502] "Grammar analysis" is the process of analyzing the syntax and grammatical structure of a sentence and understanding the components of the sentence.

[1503] "Context analysis" is the process of understanding the meaning and context of a text as a whole and extracting relevant information from its content.

[1504] A "template" is a predefined format or skeleton, a framework into which specific information can be embedded to create a final form.

[1505] A "user terminal" is a device such as a computer, smartphone, or tablet that a user operates and interacts with the system.

[1506] overview

[1507] This invention relates to a system for automatically generating email subject lines in business communication. Specifically, this system analyzes the email body, extracts important keywords, automatically generates subject line candidates based on those keywords, and allows users to select an appropriate subject line.

[1508] System Configuration

[1509] The system consists of the following main components:

[1510] 1. Server that receives and saves the email body

[1511] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1512] 3. Server and terminal that generates subject candidates and presents them to the user

[1513] 4. User selects subject and applies selected subject to email.

[1514] Receiving and storing emails

[1515] The server stores newly received emails from email clients in its internal database. For example, when a user receives a new email using an email client such as Outlook or Gmail, the email is sent to the server and stored in its database. Here, emails are received using the SMTP protocol and stored in a MySQL or PostgreSQL database.

[1516] Text analysis and keyword extraction

[1517] The server analyzes the text of the saved emails. For this analysis, it uses a natural language processing library called SpaCy. First, the server tokenizes the text and performs grammatical analysis. Then it performs context analysis to understand the meaning of the entire sentence, extracts important keywords (nouns, verbs, adjectives, dates and times, etc.), and assigns weights to them.

[1518] Auto-generated subject line suggestions

[1519] The server generates subject line candidates based on the extracted keywords, using predefined templates. For example, if there is a template called "Next Meeting: {Date and Time}", it will automatically generate a subject line such as "Next Meeting: March 15th, 10:00 AM". These subject line candidates are generated using Python template literals.

[1520] Suggesting and selecting subject lines

[1521] The server sends the generated subject candidate list to the terminal. This list is sent in JSON format, and the terminal displays the list in the user's email client. The user selects the best subject from the displayed candidates. This operation is achieved using JavaScript, HTML, and CSS.

[1522] Apply subject and send email

[1523] The terminal sends the user-selected subject to the server, which applies the selected subject to the email's subject field, saves the updated email in a database, and prepares it for sending. Finally, the terminal displays the email with the updated subject to the user and enables the send button.

[1524] Specific examples

[1525] Famous prompt sentence examples

[1526] Below is an example of an actual email body and the subject line suggestions generated from its analysis:

[1527] text

[1528] Hello.

[1529] I will let you know the details of next week's sales meeting.

[1530] The meeting will begin at 10:00 AM on March 15th. Please review the following materials:

[1531] First Quarter Sales Report

[1532] Customer Feedback Report

[1533] For this email body, the server extracts the following keywords: "Sales Meeting", "March 15th", "10:00 AM", "First Quarter Sales Report", and "Customer Feedback Report". Based on these keywords, the server generates the following subject line suggestions:

[1534] "Sales Meeting: March 15th, 10:00 AM"

[1535] "Request for review: Q1 sales report and customer feedback report"

[1536] The user selects "Sales Meeting: March 15th at 10:00 AM" from these suggestions, and that subject is applied to the email.

[1537] This system allows users to quickly and efficiently set appropriate subject lines, improving the efficiency of business communications.

[1538] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1539] Step 1:

[1540] The server detects newly received emails from email clients and stores them in an internal database. At this time, the email message, including the email body, sender information, and date and time of receipt, is sent as input data. The server stores these in the database and outputs a message indicating that the data has been saved. Specifically, the server receives emails using the SMTP protocol and stores them in a MySQL or PostgreSQL database.

[1541] Step 2:

[1542] The server retrieves the email body stored in its internal database and runs it through a text analysis algorithm. The input is the email body, and the output is the parsed tokens (words and phrases). Specifically, the server uses the SpaCy library to tokenize the stored email body.

[1543] Step 3:

[1544] The server performs grammatical analysis based on the tokenized data. The input to this step is the tokens obtained in step 2, and the output is sentence structure information. Specifically, the server uses SpaCy and Python to extract syntactic elements such as noun phrases and verb phrases.

[1545] Step 4:

[1546] The server performs context analysis based on the results of grammatical analysis to extract important keywords such as nouns, verbs, adjectives, and dates and times. The input to this step is sentence structure information, and the output is important keywords and their weights. Specifically, the server uses the SpaCy library to deepen its understanding of the meaning of the sentence.

[1547] Step 5:

[1548] The server uses the extracted keywords to generate subject candidates. The input for this step is the important keywords, and the output is multiple subject candidates. Specifically, the server generates subject candidates by embedding the keywords in a predefined template, using Python template literals.

[1549] Step 6:

[1550] The server sends the generated subject candidate list to the user terminal. The input of this step is the subject candidate list, and the output is a data transmission completion message to the user terminal. Specifically, the server sends data in JSON format to the terminal and uses a web framework such as Flask or Django.

[1551] Step 7:

[1552] The terminal displays the received subject candidate list in the user's email client. The input of this step is the subject candidate list, and the output is a visual representation of the subject candidate. Specifically, the terminal uses JavaScript, HTML, and CSS to display the candidate list as a popup or drop-down menu.

[1553] Step 8:

[1554] The user selects the best subject from the displayed candidates. The input of this step is the visually displayed subject candidates, and the output is the selected subject. Specifically, the user selects a candidate subject with a mouse click or tap.

[1555] Step 9:

[1556] The terminal sends the subject selected by the user to the server. The input of this step is the subject selected by the user, and the output is a sending completion message. As a specific operation, the terminal sends the selected subject in JSON format to the server.

[1557] Step 10:

[1558] The server applies the received selected subject to the email's subject field. The input of this step is the subject selected by the user, and the output is the updated email data. Specifically, the server updates the subject field in the email database.

[1559] Step 11:

[1560] The terminal displays the email with the updated subject and prepares it to be sent. The input of this step is the updated email data, and the output is a message that it is ready to send. As a specific operation, the terminal redisplays the email with the updated subject on the user's screen along with a send button.

[1561] (Application example 1)

[1562] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1563] In food delivery services, if the subject lines of order confirmation emails or delivery notification emails are inappropriate, recipients will not be able to instantly understand important information, resulting in a poor user experience. Furthermore, manually setting flexible and appropriate subject lines based on the delivery contents and the current situation is time-consuming and inefficient. Therefore, there is a need for a system that can automatically generate and apply optimal subject lines based on the user's order details.

[1564] 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.

[1565] In this invention, the server includes means for receiving and saving the email body, means for automatically extracting important keywords from the email body, means for generating subject candidates based on the extracted keywords, means for presenting the subject candidates to the user and applying the selected subject to the email, means for receiving and analyzing the order details, means for generating subject candidates based on the analyzed order details, and means for presenting the subject candidates to the user and applying the selected subject to the order confirmation email. This enables efficient email management in a food delivery service, where an appropriate subject based on the order details is automatically generated, allowing the user to quickly grasp important information.

[1566] "Email" is data, including text and files, sent and received via electronic communication means.

[1567] "Body" refers to the primary textual content of an email, which is the user-written or system-generated message content.

[1568] "Keywords" are important words or phrases extracted from the body of an email and form the key points for understanding the content.

[1569] A "subject" is a short piece of text used to briefly indicate the subject or content of an email.

[1570] A "server" is a computer system that processes, stores, and serves information over a network.

[1571] A "template" is a predefined format for generating subject line candidates.

[1572] "User" refers to the person or account that uses the email system, and is the entity that operates the system and checks its contents.

[1573] "Order Details" means details of the items or services requested by a User for food delivery or other services.

[1574] "Analysis" is the process of extracting important elements and meaning from information in received emails and order details.

[1575] This invention relates to a system that automatically generates subject lines for order confirmation emails and delivery notification emails in food delivery services. This system is mainly composed of a server, terminals, and user operations.

[1576] System configuration

[1577] 1. Server

[1578] The server has a function of receiving and storing the contents of a user's order.

[1579] The server has a text analysis algorithm for analyzing the received order content.

[1580] The server has the function of extracting important keywords from the analysis results and generating subject candidates.

[1581] 2. Terminal

[1582] The terminal operated by the user displays the subject candidates sent from the server.

[1583] The user selects a subject suggestion and sends it to the server.

[1584] 3. Users

[1585] The user selects the best subject line from the provided suggestions and applies it to the email.

[1586] Specific examples of processing

[1587] Receiving and analyzing order details

[1588] The server receives the order details through the food delivery app. For example, if a user orders "pizza" and "pasta," the server sends these to the server as the body of an email.

[1589] The server then analyzes the stored text using a text analysis algorithm, which performs grammatical and contextual analysis to extract important keywords (e.g., pizza, pasta).

[1590] Based on the extracted keywords, subject line suggestions are generated using predefined templates, resulting in subject line suggestions such as "Order Confirmation: Pizza, Pasta" and "Shipping Notification: Pizza, Pasta."

[1591] Suggest and apply subject suggestions

[1592] The server sends the generated subject candidates as a list to the terminal.

[1593] The user can review subject line suggestions on the device and select the most appropriate one, such as "Delivery notification: Pizza, Pasta."

[1594] The selected subject is sent to the server and applied to the email.

[1595] Hardware and Software Used

[1596] Hardware: Servers, smartphones (terminals)

[1597] Software: Python-based text analysis algorithms, email client software

[1598] Example prompt sentence:

[1599] "Extract key keywords from the following order and generate suitable subject line suggestions: 'User ordered pizza and pasta.'"

[1600] This will enable food delivery services to automatically generate appropriate email subject lines based on the order details, enabling users to quickly grasp important information and achieving efficient email management.

[1601] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1602] Step 1:

[1603] The server receives and stores the user's order details through the food delivery app.

[1604] Input: Information that the user ordered "pizza" and "pasta."

[1605] Specific Actions: The server stores this information in a database for subsequent analysis.

[1606] Step 2:

[1607] The server analyzes the body of the stored order details using a text analysis algorithm.

[1608] Input: The body of the saved order.

[1609] Specific operation: The server performs grammatical analysis and context analysis to extract keywords, and uses a generative AI model to understand the context.

[1610] Output: Important keywords (e.g., pizza, pasta) are extracted.

[1611] Step 3:

[1612] The server generates subject candidates based on the extracted keywords.

[1613] Input: Extracted keywords.

[1614] What happens: The server uses predefined templates (e.g., "Order Confirmation: {keywords}", "Shipping Notification: {keywords}") to create a list of suitable subject candidates.

[1615] Output: Subject line suggestions such as "Order Confirmation: Pizza, Pasta" or "Shipping Notification: Pizza, Pasta".

[1616] Step 4:

[1617] The server transmits the generated subject candidate list to the terminal.

[1618] Input: Subject suggestion list.

[1619] Specific operation: The server sends a list of subject candidates to the terminal using a communication protocol.

[1620] Output: A list of subject suggestions displayed on the device.

[1621] Step 5:

[1622] The user checks the subject candidates on the terminal and selects the most suitable subject.

[1623] Input: Subject suggestion list.

[1624] Specific operation: The user uses the device interface to select the most suitable subject from a list of subject candidates (e.g., "Delivery notification: Pizza, Pasta").

[1625] Output: The selected subject.

[1626] Step 6:

[1627] The server receives the user's selected subject and applies it to the email.

[1628] Input: A subject chosen by the user.

[1629] Specific Actions: The server applies the selected subject to the email's subject field.

[1630] Output: The email with the subject applied.

[1631] Step 7:

[1632] The server displays the email with the applied subject to the user and prepares it for sending.

[1633] Input: The email with the subject applied.

[1634] Specific operation: The server instructs the email client to display this email and provides a send button.

[1635] Output: A summary of the email with the applied subject will be displayed on the terminal screen.

[1636] 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.

[1637] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[1638] System configuration

[1639] The system consists of the following main components:

[1640] 1. A server that receives and stores email text

[1641] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1642] 3. A server equipped with an emotion engine that recognizes user emotions

[1643] 4. Server and terminal that generates subject candidates and presents them to the user

[1644] 5. A device that allows users to select a subject and apply the selected subject to the email.

[1645] What the program does

[1646] Receiving and storing emails

[1647] The server stores newly received emails from email clients in an internal database.

[1648] Once the email is saved, it moves on to the next analysis step.

[1649] Text analysis and keyword extraction

[1650] The server runs the stored email body through a text analysis algorithm.

[1651] The algorithm performs grammatical analysis to understand the structure of the sentence.

[1652] Next, context analysis is performed to understand the meaning and context of the entire sentence.

[1653] Important keywords such as nouns, verbs, adjectives, dates and times are extracted and weighted.

[1654] User Emotion Recognition

[1655] The server recognizes the user's emotions through text analysis and voice analysis via an emotion engine.

[1656] The recognized emotion data is fed back into the subject line generation process.

[1657] Auto-generated subject line suggestions

[1658] The server generates subject line candidates using predefined templates based on the extracted keywords and the recognized user sentiment.

[1659] For example, there are templates such as "Important: Action required by {date and time}" and "Please confirm: {document name}."

[1660] The tone and phrasing of the subject line are adjusted based on sentiment data.

[1661] Suggesting and selecting subject lines

[1662] The server transmits the generated list of subject candidates to the terminal.

[1663] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[1664] The user selects the most appropriate subject from the displayed candidates.

[1665] Apply subject and send email

[1666] The terminal transmits the user's selection to the server.

[1667] The server applies the selected subject to the email's subject field.

[1668] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1669] Specific examples

[1670] Example: Actual email body

[1671] thank you for your hard work.

[1672] Share details of a project meeting scheduled for next Monday.

[1673] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[1674] Project Plan

[1675] Risk Management Report

[1676] Emotion Recognition and Subject Line Generation

[1677] 1. The server receives and stores email.

[1678] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1679] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[1680] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[1681] "Project meeting next Monday: Starts at 10:00 AM"

[1682] "Please check in advance: Project plan and risk management report"

[1683] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[1684] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[1685] 6. The server applies the selected subject to the email and prepares it for sending.

[1686] In this way, by combining an emotion engine, it is possible to automatically generate subject lines that take into account the user's emotions, thereby improving the quality of email communication.

[1687] The processing flow will be explained below.

[1688] Step 1:

[1689] The terminal receives new emails in the email client used by the user and sends the emails to the server.

[1690] Step 2:

[1691] The server stores the received emails in an internal database.

[1692] Step 3:

[1693] The server identifies the stored email as the subject of analysis and extracts the email body.

[1694] Step 4:

[1695] The server runs text analysis algorithms, starting with grammar analysis, which analyzes the structure of a sentence and identifies clauses and phrases.

[1696] Step 5:

[1697] The server then performs contextual analysis to understand the meaning and context of the entire sentence, which also assesses the importance of each keyword.

[1698] Step 6:

[1699] The server extracts important nouns, verbs, dates, and other keywords from the text and assigns them weights, which are used to quantify the importance of the keywords.

[1700] Step 7:

[1701] The server analyzes the user's emotions through an emotion engine, analyzing text entered by the user into an email client and recorded voice data to recognize emotions.

[1702] Step 8:

[1703] The server generates subject line candidates using predefined templates based on the extracted and weighted keywords and the user's recognized emotional data. The templates are in the form of "Next Meeting: {Date and Time}" or "Confirmation Request: {Document Name}," but the tone and phrasing of the subject line are adjusted based on the emotional data.

[1704] Step 9:

[1705] The server transmits the generated list of subject candidates to the terminal.

[1706] Step 10:

[1707] The device displays a list of candidate subject lines in the user's email client, and adjusts the order of the candidate subject lines based on the emotion data, so that they are displayed in the most appropriate order for the user.

[1708] Step 11:

[1709] The user selects the most appropriate subject from the displayed subject candidates.

[1710] Step 12:

[1711] The terminal transmits the user's selection to the server.

[1712] Step 13:

[1713] The server applies the selected subject to the email's subject field.

[1714] Step 14:

[1715] The terminal will show the email with the applied subject to the user and prepare the email for sending.

[1716] This process not only automatically generates email subject lines, but also takes into consideration the user's emotions, resulting in more appropriate and effective email communication.

[1717] Example 2

[1718] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1719] In today's world, email is an important means of communication for both business and personal activities. However, choosing the right subject line for an email is often difficult, especially when the user's emotional state plays a significant role. This can lead to misunderstandings and communication friction. Additionally, selecting appropriate keywords and creating a subject line quickly can be challenging.

[1720] 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.

[1721] In this invention, the server includes means for receiving and saving the text of the email, means for automatically extracting important keywords from the text, and means for generating subject line candidates based on the extracted keywords and the recognized user emotions. This makes it possible to quickly generate appropriate subject lines according to the user's emotions and improve the quality of email communication.

[1722] The "body of the email" is the main part of the message contained in the email, and contains the sender's intentions and information.

[1723] "Means of storage" refers to the function of recording the body text and header information of received emails in storage such as a database, making them reusable as needed.

[1724] "Keywords" are important nouns, verbs, adjectives, dates and times, and other information extracted from the body of an email, and are useful elements for generating a subject line.

[1725] "Means of extraction" refers to the function of using a text analysis algorithm to identify and extract important keywords from the body of an email.

[1726] "User's emotions" refers to a psychological state recognized from the content of the email body and the user's situation, such as "fatigue" or "tension."

[1727] "Means for recognizing emotions" refers to the ability to use text or voice analysis to identify a user's emotions and make that information available for further processing.

[1728] "Subject candidates" are multiple options presented to the user as email subjects, and are automatically generated based on keywords and the user's emotions.

[1729] "Means for generating" refers to the function of forming subject line candidates using predefined templates based on extracted keywords and user sentiment.

[1730] "Template" means a predefined format for generating subject lines, a framework for inserting specific keywords or patterns.

[1731] "Adjustment" refers to the ability to appropriately change the tone and tone of the subject line according to the recognized user's emotions, to create a format that is optimal for the user.

[1732] The "means for presenting" refers to a function for displaying the generated subject candidates on the user's terminal and allowing the user to select the subject that seems most appropriate.

[1733] The "selected subject" refers to the subject that the user selects as the most appropriate from among multiple subject candidates.

[1734] The "means of applying" refers to a function of setting the subject selected by the user in the subject field of the actual email and preparing the email for sending.

[1735] The present invention relates to a system for recognizing a user's emotions and automatically generating email subject lines. A specific embodiment of this system is described below.

[1736] System Overview

[1737] The system includes the following main components:

[1738] 1. A server that receives and stores email text

[1739] 2. A server with an analysis engine that automatically extracts important keywords from the text

[1740] 3. A server equipped with an emotion engine that recognizes user emotions

[1741] 4. Server and terminal that generates subject candidates and presents them to the user

[1742] 5. A device that allows users to select a subject and apply the selected subject to the email.

[1743] Receiving and storing emails

[1744] The server first detects newly received emails from the email client, for example, using the Gmail API, and stores the received emails in a database such as MongoDB.

[1745] Text analysis and keyword extraction

[1746] The text of the saved emails is then run through a text analysis algorithm on the server. For example, the SpaCy library is used for grammatical analysis to understand the structure and meaning of the sentence. Next, a context analysis model is used to understand the overall subject and theme of the text. Important nouns, verbs, adjectives, dates, and other keywords are extracted and weighted. This results in keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1747] User Emotion Recognition

[1748] The server then uses an emotion engine to recognize the user's emotions. Services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used for emotion recognition. Text and voice analysis is used to extract emotions such as "tired" or "tension," and this data is fed back into the subject line generation process.

[1749] Auto-generated subject line suggestions

[1750] The server generates subject line candidates using predefined templates based on the extracted keywords and the user's recognized emotions. For example, templates include "Please confirm: {Document name}" and "Project meeting next Monday: Starts at 10:00 AM." The tone and phrasing of the subject line are also adjusted based on the emotion data. For example, if the user is tired, a calmer subject line is generated.

[1751] Suggesting and selecting subject lines

[1752] The generated subject candidates are sent from the server to the terminal and presented to the user. The terminal displays the subject candidate list in the user's email client, and the candidate order is adjusted based on the emotion data. The user selects the most appropriate subject from this list. For example, it is displayed in the form of a window or a drop-down list.

[1753] Apply subject and send email

[1754] The selected subject is sent from the terminal to the server, which applies this subject to the subject field of the email and prepares the email for sending. The email with the applied subject is sent after the user confirms it.

[1755] Specific examples

[1756] The actual email body

[1757] plain

[1758] thank you for your hard work.

[1759] Share details of a project meeting scheduled for next Monday.

[1760] The meeting will start at 10:00 AM. Please review the following materials beforehand:

[1761] Project Plan

[1762] Risk Management Report

[1763] The process of emotion recognition and subject line generation

[1764] 1. The server receives and stores email.

[1765] 2. The server extracts the following keywords from the text: "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report."

[1766] 3. The server uses the emotion engine to recognize that the user is feeling tired (e.g., emotion is "tired").

[1767] 4. The server generates the following subject line suggestions based on the keywords and sentiment data:

[1768] "Project meeting next Monday: Starts at 10:00 AM"

[1769] "Please check in advance: Project plan and risk management report"

[1770] *Generates subject lines with gentle expressions to take into consideration when you are tired.

[1771] 5. The user selects "Project meeting next Monday: Starts at 10:00 AM."

[1772] 6. The server applies the selected subject to the email and prepares it for sending.

[1773] Prompt Sentence Examples

[1774] "Describe the process of a system that uses natural language analysis to extract important keywords from the body of an email and generates subject line suggestions based on the user's sentiment. As a concrete example, consider the case where a user is tired and receives an email that reads, 'I have a project meeting next Monday.'"

[1775] This system can automatically generate appropriate email subject lines that match the user's emotional state, improving the quality of email communication.

[1776] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1777] Step 1: Receiving and saving emails

[1778] The server detects newly received emails from the email client. For example, it obtains email data via the Gmail API. The email body and header information are used as input data.

[1779] The server stores the received emails in an internal database (e.g. MongoDB). The output of this step is the email data stored in the database.

[1780] Step 2: Text analysis and keyword extraction

[1781] The server runs the stored email body through a text analysis algorithm, where the input is the stored email body.

[1782] Specifically, it uses the SpaCy library to perform grammatical analysis to understand sentence structure, and then uses a custom context analysis model to understand the overall subject or theme of the text.

[1783] The output is extracted keywords such as important nouns, verbs, adjectives, dates, etc. For example, keywords such as "project meeting," "Monday," "10:00 AM," "project plan," and "risk management report" are identified.

[1784] Step 3: Recognizing user emotions

[1785] The server uses an emotion engine to recognize the user's emotions, where the input is the body of past emails and related data.

[1786] Specifically, services such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API are used to extract emotions such as "fatigue" and "tension" from text, and the emotional data is the output.

[1787] Step 4: Auto-generate subject line suggestions

[1788] The server generates subject candidates based on the extracted keywords and sentiment data. The inputs are keywords and sentiment data.

[1789] Generate subject line suggestions using predefined templates (e.g., "Please review: {document name}" or "Project meeting next Monday: Starts at 10 AM") and adjust the tone and phrasing of subject lines based on sentiment data.

[1790] The generated subject line candidates are output, for example, "Project meeting next Monday: Start time 10:00 AM" or "Please confirm in advance: Project plan and risk management report."

[1791] Step 5: Suggest and select subject lines

[1792] The server sends the generated list of subject candidates to the terminal. The input is the subject candidates.

[1793] The device displays a list of subject line candidates in the user's email client, and adjusts the order of the subject line candidates based on the emotion data.

[1794] The user selects the most appropriate subject from the displayed candidates, and the selected subject is output.

[1795] Step 6: Apply a subject line and send the email

[1796] The terminal sends the user's selection to the server, where the input is the selected subject.

[1797] The server applies this selected subject to the email's subject field, and the output is an email with the applied subject.

[1798] The terminal shows the email with the applied subject to the user and prepares the email for sending. As a final action, the user presses the send button to send the email.

[1799] (Application example 2)

[1800] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1801] Conventional email systems require users to manually enter email subject lines, which is a time-consuming and labor-intensive task. Furthermore, there is no mechanism for generating appropriate subject lines that take the user's emotions into account, which can lead to a decline in the quality of communication. For example, when dealing with customers, it is important to respond appropriately while taking their emotions into consideration, but current systems are unable to do this. Therefore, there is a need for a system that can recognize the user's emotions and automatically generate appropriate subject lines based on them.

[1802] The specification process by the specification 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 and saving the body of an email, means for automatically extracting important keywords from the body, means for generating subject candidates based on the extracted keywords and recognizing the user's emotions, and means for presenting the subject candidates to the user and applying the selected subject to the email. This enables the automatic generation of email subjects that take the user's emotions into consideration, improving the quality of communication and reducing the burden on the user.

[1803] "Means for receiving and storing the body of an email" refers to the server receiving an email sent by a user and storing the content in a database.

[1804] "Method of automatically extracting important keywords" refers to the process of using text analysis algorithms to extract meaningful words and phrases from the email body based on context and grammar.

[1805] "Means for recognizing user emotions" refers to technologies or engines that analyze text or voice data to estimate a user's emotional state.

[1806] "Means for generating subject line candidates" refers to algorithms or templates for generating appropriate sentences as email subject lines based on extracted keywords and recognized emotion data.

[1807] "Means for presenting subject candidates to the user and applying the selected subject to the email" refers to the process of providing an interface for the user to select the most appropriate subject from multiple generated subjects and applying the selected subject to the actual email.

[1808] "Means for grammatical and contextual analysis" refers to algorithms and tools that analyze the grammatical structure and context of the email body to more accurately understand its meaning.

[1809] An "emotion engine" is software that analyzes and recognizes user emotions from text or voice, and often uses specific algorithms or models.

[1810] "Means for automatic generation based on predefined templates" refers to a mechanism for automatically creating appropriate email subject lines based on predefined phrases and formats.

[1811] "Means for adjusting the tone and phrasing of subject lines based on emotional data" refers to a system for adjusting the wording and nuance of email subject lines based on the perceived emotions of the user.

[1812] This invention relates to a system for automatically generating email subject lines by combining an emotion engine that recognizes user emotions. A detailed embodiment of this system and the processing contents of its program will be described below with specific examples.

[1813] System configuration

[1814] The system consists of the following main components:

[1815] 1. Server: Has a means to receive and store the body of email.

[1816] 2. Server: Has a means to automatically extract important keywords from the text.

[1817] 3. Server: Equipped with an emotion engine that recognizes the user's emotions and has the means to perform text analysis and voice analysis.

[1818] 4. Server: Has a means to automatically generate subject line candidates based on the extracted keywords and recognized sentiment.

[1819] 5. Terminal: Provides a means for presenting subject line suggestions to the user and applying the selected subject line to the email.

[1820] What the program does

[1821] Hardware and Software

[1822] Smart glasses: A device for recording and analyzing customers' facial expressions and voices in real time.

[1823] EmotionRecognizer: A library that analyzes emotions from customer voice data, for example, using the Google Cloud Speech-to-Text API and Emotion API.

[1824] EmailGenerator: A module that extracts keywords from the text and generates email subject lines based on sentiment data. It uses TextBlob and SpaCy for natural language processing.

[1825] Data processing and calculation

[1826] 1. The server receives audio and text from the customer and converts the audio into text.

[1827] 2. The server parses the text in TextBlob and extracts important keywords.

[1828] 3. The server uses EmotionRecognizer to obtain emotion data from the audio.

[1829] 4. The server generates subject line candidates using the EmailGenerator engine based on the extracted keywords and sentiment data.

[1830] 5. The terminal displays the generated subject candidates on the store staff's smart glasses.

[1831] 6. The user selects the most appropriate subject from the displayed subject suggestions.

[1832] For example, if a store staff member is asked in a conversation with a customer, "Do you have any business trip suits in stock?":

[1833] Incoming text: "Do you have any business trip suits in stock?"

[1834] Extracted keywords: "Business trip", "Suit", "Inventory"

[1835] Emotion data: The recognized emotion data is "interest"

[1836] Prompt Sentence Examples

[1837] A customer has a question. The keywords are "business trip", "suits", and "inventory", and the sentiment is "interest". Based on this, generate an appropriate subject line.

[1838] The above is an embodiment of the invention configured based on the claims. This system enables automatic generation of email subjects that take user emotions into consideration, improving the quality of communication and reducing the burden on users.

[1839] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1840] Step 1:

[1841] The server receives and stores the body of the email.

[1842] Specifically, the server stores newly received emails from users' email clients in an internal database, making the email body available for the next analysis step. The input is the email body, and the output is the saved email data.

[1843] Step 2:

[1844] The server automatically extracts important keywords from the body of the saved email.

[1845] Specifically, it uses TextBlob to parse text and extract nouns, verbs, and important phrases based on grammar and context. The input is the saved email body, and the output is the extracted important keywords.

[1846] Step 3:

[1847] The server recognizes the user's emotions.

[1848] Specifically, the system analyzes the user's voice data via EmotionRecognizer to obtain emotional information. The voice file is analyzed and the emotion engine recognizes emotions. The input is the user's voice data, and the output is the recognized emotional data.

[1849] Step 4:

[1850] The server automatically generates subject line candidates based on the extracted keywords and the recognized emotion data.

[1851] Specifically, the EmailGenerator engine is used to apply the extracted keywords and sentiment data to a template to generate multiple subject line candidates. The input is keywords and sentiment data, and the output is a list of subject line candidates.

[1852] Step 5:

[1853] The terminal presents the generated subject candidates to the user.

[1854] Specifically, a list of subject candidates is displayed on the store staff's smart glasses. The list is visually displayed through a user interface, allowing the user to easily select a subject. The input is the subject candidate list, and the output is the subject candidates presented to the user.

[1855] Step 6:

[1856] The user selects the most suitable subject from the presented subject candidates.

[1857] Specifically, the user selects the most appropriate subject from the displayed subject candidates using the user interface of the smart glasses. The input is the subject candidates presented to the user, and the output is the selected subject.

[1858] Step 7:

[1859] The server applies the selected subject to the email.

[1860] Specifically, it takes a user-selected subject, applies it to the subject field of the original email, and prepares the email to be sent. The input is the selected subject, and the output is the email with the subject applied.

[1861] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1862] 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> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) 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.

[1863] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1864] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1865] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1866] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1867] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1868] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1869] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1870] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1871] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1872] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1873] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1874] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1875] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1876] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1877] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1878] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1879] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1880] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1881] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1882] The following is further disclosed regarding the above embodiment.

[1883] (Claim 1)

[1884] A means for receiving and storing the body of the email;

[1885] A means of automatically extracting important keywords from the text,

[1886] means for generating subject line candidates based on the extracted keywords;

[1887] means for presenting subject line suggestions to a user and applying a selected subject line to the email;

[1888] A system including:

[1889] (Claim 2)

[1890] 2. The system according to claim 1, further comprising means for performing grammatical analysis and context analysis when extracting keywords from the text.

[1891] (Claim 3)

[1892] 10. The system of claim 1, further comprising means for automatically generating subject line candidates based on predefined templates.

[1893] "Example 1"

[1894] (Claim 1)

[1895] A means for receiving and storing the body of the email;

[1896] A method for automatically extracting important nouns, verbs, adjectives, dates and times, and other keywords from the text of saved emails.

[1897] means for generating subject line candidates based on the extracted keywords;

[1898] means for presenting the generated subject candidate list to a user terminal and applying a subject selected by the user to the email;

[1899] A system including:

[1900] (Claim 2)

[1901] It has the means to perform grammatical and contextual analysis when extracting keywords from the text.

[1902] 10. The system of claim 1.

[1903] (Claim 3)

[1904] It has a means to automatically generate subject candidates based on predefined templates.

[1905] 10. The system of claim 1.

[1906] "Application Example 1"

[1907] (Claim 1)

[1908] A means for receiving and storing the body of the email;

[1909] A means of automatically extracting important keywords from the text,

[1910] means for generating subject line candidates based on the extracted keywords;

[1911] means for presenting subject line suggestions to a user and applying a selected subject line to the email;

[1912] means for receiving and analyzing the order;

[1913] means for generating subject line candidates based on the parsed order content;

[1914] means for presenting subject line suggestions to a user and applying a selected subject line to an order confirmation email;

[1915] A system including:

[1916] (Claim 2)

[1917] 2. The system according to claim 1, further comprising means for performing grammatical analysis and context analysis when extracting keywords from the text.

[1918] (Claim 3)

[1919] 2. The system according to claim 1, further comprising: means for automatically generating subject candidates based on a predefined template; and means for automatically generating subject candidates based on order details.

[1920] "Example 2: Combining Emotion Engines"

[1921] (Claim 1)

[1922] A means for receiving and storing the body of the email;

[1923] A means of automatically extracting important keywords from the text,

[1924] means for generating subject line candidates based on the extracted keywords and the recognized user sentiment;

[1925] means for presenting subject line suggestions to a user and applying a selected subject line to the email;

[1926] A system including:

[1927] (Claim 2)

[1928] A means for performing grammatical analysis and context analysis when extracting keywords from the text;

[1929] 10. The system of claim 1, further comprising means for recognizing a user's emotion.

[1930] (Claim 3)

[1931] Generate subject line suggestions automatically based on predefined templates,

[1932] 10. The system of claim 1, further comprising means for adjusting the tone or inflection of the subject line based on the perceived user sentiment.

[1933] "Application example 2 when combining emotion engines"

[1934] (Claim 1)

[1935] A means for receiving and storing the body of the email;

[1936] A means of automatically extracting important keywords from the text,

[1937] means for generating subject line candidates based on the extracted keywords and user sentiment recognition;

[1938] means for presenting subject line suggestions to a user and applying a selected subject line to the email;

[1939] A system including:

[1940] (Claim 2)

[1941] 2. The system according to claim 1, further comprising means for performing grammatical analysis and context analysis and means for performing sentiment analysis using a sentiment engine when extracting keywords from the text.

[1942] (Claim 3)

[1943] 10. The system of claim 1, further comprising means for automatically generating candidate subject lines based on predefined templates and adjusting the tone and inflection of the subject lines using emotional data. [Explanation of symbols]

[1944] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for receiving and storing the body of the email; A means of automatically extracting important keywords from the text, means for generating subject line candidates based on the extracted keywords; means for presenting subject line suggestions to a user and applying a selected subject line to the email; A system including:

2. 2. The system according to claim 1, further comprising means for performing grammatical analysis and context analysis when extracting keywords from the text.

3. 2. The system of claim 1, further comprising means for automatically generating subject line candidates based on predefined templates.

Images