System

Abstract

A system is provided.SOLUTION: A system for detecting typographical errors in a mail, comprising: means for providing a user interface for inputting a body of a mail; means for receiving a typographical error check instruction from a user; means for sending the input body of the mail to a generative AI model for parsing; means for receiving a typographical error detection result and a correction suggestion from the generative AI model; and means for displaying the received correction suggestion on the user interface.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In business communication, typos and omissions in emails can undermine credibility. Especially when processing large volumes of email, visually checking every email is inefficient and prone to overlooking errors. To solve this problem, there is a need for an automated method for checking for typos and omissions, allowing for easy and accurate checking when creating emails. [Means for solving the problem]

[0005] The present invention provides a system that automatically detects typos and misspellings in emails. This system provides a user interface for users to compose emails and accepts instructions from the user to check for typos and misspellings. Furthermore, the system analyzes the body of the email based on a generative AI model, detects typos and misspellings, and generates correction suggestions. The generated correction suggestions are displayed on the user interface, allowing the user to confirm and apply them. This enables efficient and reliable email communication.

[0006] The "system for detecting typos and omissions in emails" is a system that automatically identifies typos and omissions in the body of an email and suggests corrections.

[0007] "User interface" refers to the component that provides the screen and operating means for the user to interact with the system and create, edit, and check emails.

[0008] "Instruction to check for typos" refers to an action taken by a user to have the system check for typos in an email, such as clicking a button.

[0009] A "generative AI model" refers to an algorithm or program that uses machine learning and artificial intelligence technology to learn patterns of typos and omissions from large amounts of data and analyze email content in real time.

[0010] "Means for analyzing" refers to the process or module that uses a generative AI model to analyze the body of the email sent and detect typos.

[0011] "Typo detection results" refers to information including typos and omissions discovered when the generative AI model analyzes the email body, as well as suggested corrections for those typos and omissions.

[0012] "Suggested corrections" refers to information that includes appropriate correction methods or suggestions for correcting detected typographical errors.

[0013] "Display means" refers to a method or function for visually displaying the results of typographical error detection and correction suggestions on the user interface. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention relates to a system that automatically detects typos in emails in business communications and provides correction suggestions. The system consists of four main components: a user interface, a generative AI model, a server, and a terminal.

[0036] Overall system overview

[0037] The user uses a user interface for composing an email and inputs the body of the email. When the user issues an instruction to check for typos, the device captures this instruction and sends the email body to the server. The server passes the received email body to a generative AI model, which analyzes it for typos. The generative AI model detects typos, suggests corrections, and returns the results to the server. The server sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[0038] The operation of each component of the system will be specifically described below.

[0039] User operations

[0040] The user creates a new email on the user interface and inputs the message body. For example, the user might input "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0041] The user clicks the "Check for typos" button on the email composition screen, which sends a command to check for typos to the terminal.

[0042] Terminal handling

[0043] The terminal receives instructions from the user to check for typos, converts the email body into JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[0044] json

[0045] {

[0046] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0047] }

[0048] Server Processing

[0049] The server analyzes the request received from the device, extracts the email body data, and then passes this email body to the generative AI model for analysis.

[0050] Based on the data it has learned, the generative AI model detects typos in the email body and generates correction suggestions. For example, it might generate the following correction suggestions:

[0051] "Hello, thank you for your help. Tomorrow's meeting" was changed to "Hello, thank you for your help. Tomorrow's meeting"

[0052] "I want to change the time." remains as "I want to change the time."

[0053] Returning and displaying results

[0054] The server sends the detection results and correction suggestions returned by the generative AI model back to the device. For example, it sends JSON data summarizing the correction suggestions as follows:

[0055] json

[0056] {

[0057] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0058] "suggestions": [

[0059] {"original": "Conference", "suggestion": "Meeting"}

[0060] ]

[0061] }

[0062] The terminal displays the received correction suggestions on a user interface, allowing the user to check the suggested corrections for typos and apply the corrections as necessary.

[0063] Processing of specific examples

[0064] For example, it operates as follows.

[0065] 1. The user clicks the "Check for typos" button.

[0066] 2. The device sends the email body to the server.

[0067] 3. The server requests the generated AI model to perform analysis.

[0068] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[0069] 5. The server sends the suggested revisions back to the device.

[0070] 6. The device displays the suggested corrections in the user interface.

[0071] 7. The user reviews the suggested fixes and applies them as needed.

[0072] This series of operations allows the user to efficiently and accurately create an email without any typos or errors.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0076] Step 2:

[0077] The user clicks the "Check for typos" button on the email composition screen, which triggers a command to check for typos.

[0078] Step 3:

[0079] The device catches the user's click event, obtains the email body, converts it to JSON format, and sends it to the server as an HTTP POST request.

[0080] Step 4:

[0081] The server receives the request sent from the terminal and extracts the data from the email body. For example, the received data looks like this:

[0082] json

[0083] {

[0084] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0085] }

[0086] Step 5:

[0087] The server passes the extracted email text to a generative AI model and requests it to analyze typos. The generative AI model analyzes the email text based on data it has learned in advance.

[0088] Step 6:

[0089] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It makes no changes to "I would like to change it."

[0090] Step 7:

[0091] The analysis results and suggested corrections from the generative AI model are sent back to the server. For example, the returned data looks like this:

[0092] json

[0093] {

[0094] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0095] "suggestions": [

[0096] {"original": "Conference", "suggestion": "Meeting"}

[0097] ]

[0098] }

[0099] Step 8:

[0100] The server sends the analysis results and correction suggestions received from the generative AI model to the terminal as an HTTP response.

[0101] Step 9:

[0102] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0103] Step 10:

[0104] The user reviews the suggested corrections and applies them if necessary, and finally the error-free email is ready to be sent.

[0105] This series of processing steps allows the system to efficiently and accurately detect typographical errors in emails and provide correction suggestions.

[0106] Example 1

[0107] 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."

[0108] In modern business communication, quickly creating text without typos and errors is an important challenge. However, manual proofreading is time-consuming and labor-intensive, and carries a high risk of errors remaining. To solve this problem, a system is needed that can automatically detect typos and errors in text and provide appropriate correction suggestions.

[0109] 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.

[0110] In this invention, the server includes a means for analyzing text and passing it to a generative model, a means for receiving error detection results and correction suggestions from the generative model, and a means for returning the received correction suggestions to the terminal, thereby enabling the user to accurately and quickly create text without typos or errors.

[0111] "Text" refers to a string of characters written in electronic format.

[0112] An "interface" refers to a software or hardware component that provides a screen or operating means for a user to input information into a system.

[0113] A "generative model" refers to an artificial intelligence model that learns from a variety of data in advance and performs specific analytical or generative tasks.

[0114] "Server" refers to a central computer system that receives, analyzes, and returns data.

[0115] "User" refers to an individual or group that directly operates the system and utilizes the intended functions.

[0116] An "instruction" refers to an operation or command that a user makes to request a specific action from the system.

[0117] "Analysis" refers to the operation of extracting and processing specific information based on given data.

[0118] "Suggested corrections" refers to corrections suggested by the system for detected typos.

[0119] "Visual" refers to displaying information in a way that is easily seen and understood by the user.

[0120] "Application" refers to the operation of reflecting the proposed corrections in the actual data.

[0121] This invention is a system that automatically detects typos in emails and provides correction suggestions. The system consists of four main components: a user interface, a generative model, a server, and a terminal.

[0122] Overall system overview

[0123] The user uses the interface for creating an email and inputs the body of the email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." When the user clicks the "Check for typos" button, the terminal captures this instruction and sends the body of the email to the server.

[0124] The server analyzes the received email text and passes it to a generative model to analyze typos. The generative model detects typos and generates correction suggestions and returns the results to the server. The server then sends the results back to the device, which displays them in the user interface. The user can review them and apply corrections as needed.

[0125] User Interface

[0126] The user interface provides a field for entering text and a "check for typos" button. This interface is often implemented as a browser-based application. The user enters text and clicks the "check for typos" button, which then sends the text to the device.

[0127] Generative Model

[0128] A generative model is an artificial intelligence model that analyzes input text, detects typos, and suggests corrections. This AI model has been trained on a variety of email sentences in advance, so it is able to detect typos with high accuracy.

[0129] server

[0130] The server analyzes the text data received from the device and passes it to the generative model. The server receives the typographical error detection results and correction suggestions sent back from the generative model and sends them back to the device.

[0131] Terminal

[0132] The terminal is responsible for sending user input to the server, receiving replies from the server, and displaying them on a user interface.

[0133] Specific examples

[0134] For example, if a user enters the following text and clicks the "Check for typos" button:

[0135] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0136] The device then generates the following prompt sentence and sends it to the Generative Model via the server:

[0137] "Please detect any typos in the following text and suggest appropriate corrections:

[0138] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0139] The generative model receives this prompt and performs analysis, generating a suggestion to modify, for example, "conference" to "meeting." This suggestion is sent back to the server and ultimately displayed to the user via the device.

[0140] In this way, the user can efficiently and accurately compose emails without any typos or errors.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] The user enters the email body on the email creation screen and clicks the "Check for typos" button. This action requests the system to check for typos. The email body is provided as input, and instructions to check for typos are sent to the terminal as output.

[0144] Step 2:

[0145] The terminal receives the user's instructions and converts the entered email body into JSON format. The converted data looks like this:

[0146] json

[0147] {

[0148] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0149] }

[0150] Send an HTTP POST request containing this JSON data to the server, taking the email body as input and sending the JSON data to the server as output.

[0151] Step 3:

[0152] The server parses the HTTP POST request received from the terminal and extracts the email body data. It receives JSON data as input and prepares the email body as output to be passed to the generative model. Next, the server generates and sends the following prompt to the generative model:

[0153] "Please detect any typos in the following text and suggest appropriate corrections:

[0154] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0155] It takes the email body as input and generates a prompt as output.

[0156] Step 4:

[0157] The generative model receives the prompt and analyzes the email body for typos. It takes the prompt as input and generates typo detection results and correction suggestions as output. For example, the following correction suggestions are generated:

[0158] json

[0159] {

[0160] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0161] "suggestions": [

[0162] {"original": "Conference", "suggestion": "Meeting"}

[0163] ]

[0164] }

[0165] The generative model sends the correction suggestions back to the server. It takes the prompt sentence as input and generates the correction suggestions as output.

[0166] Step 5:

[0167] The server receives the correction suggestions returned from the generative model and parses them. It takes the correction suggestions as input and returns JSON data to the device as output. For example, the following data is returned:

[0168] json

[0169] {

[0170] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0171] "suggestions": [

[0172] {"original": "Conference", "suggestion": "Meeting"}

[0173] ]

[0174] }

[0175] It receives correction suggestions as input and produces parsed data as output, which it sends back to the terminal.

[0176] Step 6:

[0177] The terminal parses the correction suggestions received from the server and displays them in the user interface. It receives JSON data from the server as input and displays the correction suggestions in the user interface as output. The user can review the results and choose whether to apply the correction suggestions.

[0178] Step 7:

[0179] The user reviews the suggested corrections displayed in the user interface and applies them as necessary. The suggested corrections displayed in the interface are taken as input, and the final, typographically correct email body is generated as output. The user selects and applies the suggested corrections.

[0180] (Application example 1)

[0181] 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."

[0182] In recent years, the quality of articles, blogs, and newsletters has become increasingly important in content distribution services. However, the presence of typos and misspellings undermines the reliability of the content and leads to a poor user experience. Traditional manual typographical error checking is time-consuming, labor-intensive, and inefficient. Furthermore, while suggestions for improvements in sentence structure and style are also desired, there is a lack of efficient means to achieve this. A system that can solve these issues and improve the quality of content distribution is needed.

[0183] 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.

[0184] In this invention, the server includes means for providing a user interface for inputting content such as articles, blogs, and newsletters, means for accepting instructions from a user to check for typos and omissions, means for transmitting the input content to a generative AI model for analysis, means for receiving typo detection results and correction suggestions from the generative AI model, and means for displaying the received correction suggestions on the user interface, thereby enabling content creators to automatically detect and correct typos and provide efficient, high-quality content.

[0185] An "article" is a piece of written text intended to convey information in the form of news, blogs, newsletters, etc.

[0186] A "blog" is a diary-style piece of writing published on the web that records the opinions, experiences, and thoughts of an individual or organization.

[0187] A "newsletter" is a regularly published informational document used by a company or organization to inform interested parties of the latest information and updates.

[0188] "User interface" refers to an operation screen or input device that allows a user to directly interact with a system.

[0189] "Typo" refers to an error where a character is typed incorrectly or a character is missing from a sentence.

[0190] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes sentences.

[0191] "Analysis" refers to the process of examining data or information in detail to understand its structure and meaning.

[0192] "Correction proposals" are proposals for appropriate corrections to detected errors or areas for improvement.

[0193] A "server" is a computer system that provides services to other computers on a network.

[0194] A specific method for implementing this invention is described below. This system detects typographical errors in articles, blogs, and newsletters in a content distribution service and suggests corrections. The system consists of a user interface, a server, a generative AI model, and a terminal.

[0195] System Overview

[0196] Users input content such as articles, blogs, and newsletters through a user interface that can be used on a variety of devices, including smartphones and PCs.

[0197] The terminal receives instructions from the user to check for typos. The input content is sent to the server as JSON format data via an HTTP POST request. For example, the following data is sent:

[0198] json

[0199] {

[0200] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0201] }

[0202] The server receives this request and requests the generative AI model to analyze it. OpenAI's GPT-4 is used as the generative AI model. This model has been trained on a variety of content texts in advance, enabling highly accurate analysis.

[0203] The generative AI model detects typos in the received content and generates correction suggestions, such as the following prompt:

[0204] "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0205] The server receives the analysis results of the generative AI model and sends them back to the device. The returned data includes the corrected text and suggested corrections:

[0206] json

[0207] {

[0208] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[0209] "suggestions": [

[0210] {"original": "camera technology", "suggestion": "camera technology"}

[0211] ]

[0212] }

[0213] The device displays the suggested corrections in a user interface, allowing the user to review the suggestions and apply or correct them, resulting in high-quality content that is free of typos and errors.

[0214] This system allows users to efficiently and accurately correct typos and distribute content. The hardware uses a high-performance computer system for the server, and the software uses Flask (a Python framework) and the OpenAI API.

[0215] Such a system and method can significantly improve the quality of content delivery.

[0216] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0217] Step 1:

[0218] The user enters a content body (e.g., "A new smartphone has been released. It has the latest camera technology, a larger battery, and a more powerful processor.") into the user interface. The content body is provided as input to the system.

[0219] Step 2:

[0220] The user clicks the "Check for typos" button. This action causes the device to receive an instruction to check for typos. The device converts the entered content body into JSON format and sends it to the server as an HTTP POST request. For example, the data sent is in the following format:

[0221] json

[0222] {

[0223] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0224] }

[0225] Step 3:

[0226] The server parses the request received from the device and extracts the content body from the JSON data. This extracted body is then provided to a generative AI model (e.g., OpenAI GPT-4) for analysis. The purpose of the analysis is to detect typos and generate correction suggestions.

[0227] Step 4:

[0228] The generative AI model analyzes the input content body, detects typos, and generates correction suggestions that are sent back to the server, such as:

[0229] json

[0230] {

[0231] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[0232] "suggestions": [

[0233] {"original": "camera technology", "suggestion": "camera technology"}

[0234] ]

[0235] }

[0236] Step 5:

[0237] The server receives the analysis results and correction suggestions from the generative AI model and sends them back to the device. The data sent from the server to the device includes the corrected text and the correction suggestions.

[0238] Step 6:

[0239] The terminal displays the correction suggestions and analysis results received from the server on a user interface. The user checks the displayed results and applies corrections as necessary. For example, the user checks the correction suggestions and selects whether to change "camera technique" to "camera technology."

[0240] Step 7:

[0241] The user then confirms the final corrections and confirms the applied content body, resulting in a high-quality content that is free of typos and errors.

[0242] This allows users to efficiently create and distribute high-quality content, and also clarifies the data generated at each step and its transformation.

[0243] 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.

[0244] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[0245] Overall system overview

[0246] The user uses a user interface for composing an email and inputs the body of the email. When the user issues instructions for checking for typos and sentiment analysis, the device receives these instructions and sends the email body to the server. The server then passes the received email body to a generative AI model and an emotion engine, which analyzes the typos and sentiment. The generative AI model and emotion engine then generate suggestions for correcting typos and sentiment-based expressions, respectively, and return the results to the server. The server then sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[0247] The operation of each component of the system will be specifically described below.

[0248] User operations

[0249] The user creates a new email on the user interface and enters the message body, for example, "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0250] The user clicks the "Check for typos and analyze emotions" button on the email composition screen, which sends instructions to the terminal to check for typos and analyze emotions.

[0251] Terminal handling

[0252] The device catches the user's click event, gets the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[0253] json

[0254] {

[0255] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0256] }

[0257] Server Processing

[0258] The server receives the request sent from the device, extracts the data from the email body, and then passes the extracted email body to the generative AI model and emotion engine for analysis.

[0259] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[0260] Returning and displaying results

[0261] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[0262] json

[0263] {

[0264] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0265] "suggestions": [

[0266] {"original": "Conference", "suggestion": "Meeting"},

[0267] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0268] ]

[0269] }

[0270] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[0271] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0272] Processing of specific examples

[0273] For example, it operates as follows.

[0274] 1. The user clicks the "Check for typos and sentiment analysis" button.

[0275] 2. The device sends the email body to the server.

[0276] 3. The server requests analysis from the generative AI model and emotion engine.

[0277] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[0278] 5. The emotion engine generates a suggestion to change "I'd like to change it" to "Could you please change it?"

[0279] 6. The server sends the suggested revisions back to the device.

[0280] 7. The device displays the suggested corrections in the user interface.

[0281] 8. The user reviews the suggested fixes and applies them as needed.

[0282] This series of operations allows the user to create an email that is free of typos and that is highly accurate and suited to the user's emotions.

[0283] The processing flow will be explained below.

[0284] Step 1:

[0285] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0286] Step 2:

[0287] The user clicks the "Check for typos and analyze sentiment" button on the email composition screen, which triggers the typo check and sentiment analysis.

[0288] Step 3:

[0289] The device catches the user's click event, retrieves the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. The data sent will be in the following format:

[0290] json

[0291] {

[0292] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0293] }

[0294] Step 4:

[0295] The server receives the request sent from the device and extracts the data from the email body, for example, extracting the text from the email field.

[0296] Step 5:

[0297] The server passes the extracted email body data to the generative AI model and emotion engine, requesting them to analyze typos and emotions, respectively.

[0298] Step 6:

[0299] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It also makes no specific suggestion for "I would like to change it."

[0300] Step 7:

[0301] The emotion engine analyzes the email body and recognizes the user's emotions. In this example, it generates a suggestion to change a phrase like "I would like to change it" to the more polite "Could you please change it?"

[0302] Step 8:

[0303] The generative AI model and emotion engine send back analysis results and suggested modifications to the server. For example, the following data may be returned:

[0304] json

[0305] {

[0306] "corrected_text": "Hello, thank you for your help. Regarding our meeting tomorrow, could we please change the time?",

[0307] "suggestions": [

[0308] {"original": "Conference", "suggestion": "Meeting"},

[0309] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0310] ]

[0311] }

[0312] Step 9:

[0313] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[0314] Step 10:

[0315] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0316] Step 11:

[0317] The user reviews the suggested corrections and applies them as necessary, for example, using "meeting" instead of the suggested "conference," and changing "I'd like to change it" to "Could you please change it?". Finally, the email is completed with no typos and in line with the user's sentiment.

[0318] This series of processing steps allows the user to efficiently create an email without any typos or mistakes, and the expressions in the email will be appropriate to the user's feelings.

[0319] Example 2

[0320] 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."

[0321] Conventional email writing support systems are limited to detecting typos and suggesting corrections, and do not provide sufficient support for users when considering the emotional expression in the email body. In particular, business emails often require appropriate emotional expression, and it is necessary to use appropriate expressions to avoid misunderstandings and rudeness. To solve this problem, a system is needed that not only detects typos but also analyzes the emotional content of the email body and suggests corrections based on that analysis.

[0322] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server is a system that detects typos and omissions in emails and performs sentiment analysis, and includes an information input means for inputting the email body, a means for accepting instructions from a user to check for typos and omissions and to analyze sentiment, a means for transmitting the input email body to an analysis device for analysis, a means for receiving the typo detection results and suggestions for correcting expressions from the analysis device, and a means for displaying the received suggestions for correction on an information display means. This enables a user not only to correct typos and omissions in the email body, but also to easily create an email that expresses appropriate sentiment.

[0323] The "mail body" is the text content that the user inputs to send as an email.

[0324] "Information input means" refers to an input device or software interface that allows a user to input the body of an email.

[0325] "User" refers to the person who operates this system and creates and sends emails.

[0326] "Typo checking" is the process of detecting mistyped or missing characters in the body of an email.

[0327] "Emotion analysis" is the process of recognizing the user's emotions and intentions from the content of the email text entered,

[0328] It is a process of proposing appropriate expressions.

[0329] The "analysis device" is part of a system that includes a generative AI model and an emotion engine, and is a device that analyzes text data and generates analysis results for typos and emotions.

[0330] "Suggested corrections" are suggestions for specific changes to improve entered text based on detected typos and analyzed sentiment.

[0331] "Information display means" refers to a device or interface for visually displaying analysis results and suggested modifications to the user.

[0332] A "prompt" is an input sentence that instructs a generative AI model to perform a specific task, such as "Please check the body of the email for typos and generate suggested corrections."

[0333] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[0334] The hardware and software required to implement this system are as follows: The server is hosted using AWS (Amazon Web Services), and NGINX is used for request routing. The generative AI model uses OpenAI GPT-4, combined with IBM Watson for the emotion engine. The user interface was developed using React.js, and the terminal is connected to a personal computer or smartphone.

[0335] User operations

[0336] The user creates a new email using the email creation user interface. For example, the user might type, "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." Next, the user clicks the "Check for typos and analyze sentiment" button on the email creation screen. This sends instructions to the terminal to check for typos and analyze sentiment.

[0337] Terminal handling

[0338] The device catches the user's click event and retrieves the email body. It converts the retrieved email body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is as follows:

[0339] json

[0340] {

[0341] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0342] }

[0343] Server Processing

[0344] The server receives the HTTP POST request sent from the device and extracts the data from the email body. Then, it passes the extracted email body to the generative AI model and emotion engine for analysis. Specifically, it gives the generative AI model the following prompt:

[0345] "Check the body of your email for typos and generate suggested corrections."

[0346] Meanwhile, the emotion engine is given the following prompt:

[0347] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[0348] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[0349] Returning and displaying results

[0350] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[0351] json

[0352] {

[0353] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0354] "suggestions": [

[0355] {"original": "Conference", "suggestion": "Meeting"},

[0356] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0357] ]

[0358] }

[0359] The server sends the analysis results and suggested revisions received from the generative AI model and emotion engine to the device as an HTTP response. The device receives the response from the server and applies the suggested revisions to the email body area. It also displays the suggested revisions in a separate display area, allowing the user to confirm the suggested revisions.

[0360] Specific examples of operation

[0361] For example, a user types "Hello, thank you for your help. We have a meeting tomorrow, but I'd like to change the time," and clicks the "Check for typos and sentiment analysis" button. The device then sends the email body to the server, which then requests analysis from the generative AI model and sentiment engine. The generative AI model generates a suggestion to change "conference" to "meeting," and the sentiment engine generates a suggestion to change "I'd like to change it" to "Could you please change it?" The server collects and organizes the suggested changes and sends them to the device. The device displays the suggested changes on the user interface, and the user can confirm and apply them.

[0362] This system allows users to easily create emails that are free of typos and that are highly accurate and reflect the user's emotions.

[0363] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0364] Step 1: User enters email content

[0365] A user uses a user interface for creating emails to input a new email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." This input is the base data for subsequent processing.

[0366] Step 2: User clicks on the "Check spelling and sentiment analysis" button

[0367] The user clicks the "Check for typos and sentiment analysis" button on the email composition screen, which triggers a request to check for typos and sentiment analysis for the entered email body.

[0368] Input: The instruction the user clicked

[0369] Output: Request for typo checking and sentiment analysis

[0370] Step 3: The device converts the email body into JSON format and sends it to the server.

[0371] The terminal converts the email body entered in step 1 into JSON format. The converted data has the following format:

[0372] json

[0373] {

[0374] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0375] }

[0376] The converted data is then sent to the server via an HTTP POST request.

[0377] Input: Email body

[0378] Output: JSON formatted data, HTTP POST request

[0379] Step 4: The server receives the JSON data and prepares it for parsing

[0380] The server receives the HTTP POST request sent from the terminal, extracts the email body from the received JSON data, and prepares it for parsing. At this time, it generates a prompt like this:

[0381] "Check the body of your email for typos and generate suggested corrections."

[0382] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[0383] Input: JSON format data

[0384] Output: Email body, prompt

[0385] Step 5: The server requests the generated AI model and emotion engine for analysis.

[0386] The server requests the generative AI model and emotion engine to analyze the extracted email text. Specifically, it requests the generative AI model to check for typos and suggest corrections, and requests the emotion engine to analyze emotions and suggest corrections to expressions.

[0387] Input: Email body, prompt text

[0388] Output: Analysis requests to generative AI models and emotion engines

[0389] Step 6: Generative AI model and emotion engine perform analysis

[0390] The generative AI model detects typos and omissions in the received email text and generates correction suggestions, while the emotion engine analyzes the email text, recognizes the user's emotions, and generates correction suggestions based on those emotions.

[0391] Input: Email body, prompt text

[0392] Output: Suggested corrections for typos and emotions

[0393] Step 7: The server receives and organizes the analysis results

[0394] The server receives the analysis results returned by the generative AI model and emotion engine. For example, it receives data such as:

[0395] json

[0396] {

[0397] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0398] "suggestions": [

[0399] {"original": "Conference", "suggestion": "Meeting"},

[0400] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0401] ]

[0402] }

[0403] The server organizes these results and formats them for transmission to the terminal.

[0404] Input: Suggestions for correcting typos and emote expressions

[0405] Output: Organized analysis results

[0406] Step 8: The server sends the analysis results to the device.

[0407] The server sends the organized analysis results to the terminal as an HTTP response.

[0408] Input: Organized analysis results

[0409] Output: Parsed results as an HTTP response

[0410] Step 9: The device receives the analysis results and displays them on the user interface.

[0411] The terminal analyzes the response received from the server, applies automatic correction suggestions to the email body area, and displays the suggestions in a separate display area to allow the user to confirm the correction suggestions.

[0412] Input: Parsed result as HTTP response

[0413] Output: Suggested fixes displayed in the user interface

[0414] Step 10: User reviews and applies suggested fixes

[0415] The user checks the suggested corrections displayed on the terminal and applies them as necessary. If the user is satisfied, the suggested corrections are applied to the original email body and the email is prepared for sending.

[0416] Input: Suggested corrections displayed in the user interface

[0417] Output: Modified email body

[0418] (Application example 2)

[0419] 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."

[0420] In today's world, it is important to improve the quality of email and message communication. However, emails and messages entered by users often contain typos, which can lead to misunderstandings and discomfort. Furthermore, especially in customer service situations, expressions that reflect appropriate emotions are required. However, correcting these errors on your own is a time-consuming and labor-intensive process, which presents a challenge.

[0421] The identification process by the identification 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 sending the email body to the generative AI model and the emotion engine for analysis, means for receiving typographical error detection results from the generative AI model and correction suggestions from the emotion engine, and means for displaying the received correction suggestions on a user interface. This makes it possible to detect typos in emails and provide users with correction suggestions that take emotions into consideration.

[0422] The "email typo and omission detection system" is a system that automatically detects typos and omissions in emails written by users and suggests corrections.

[0423] The "user interface" refers to a screen or operating means through which a user inputs the body of an email, checks for typos, and confirms the results of sentiment analysis.

[0424] The "means for accepting instructions for checking for typos and omissions and for sentiment analysis" is a function for accepting an operation from the user to request checking for typos and omissions and for sentiment analysis.

[0425] A "generative AI model" is an artificial intelligence model that learns from a variety of text data in advance and detects typos and misspellings in the body of an email.

[0426] The "emotion engine" is an analysis engine that analyzes the user's email text and suggests appropriate emotional expressions.

[0427] The "means of analysis" is a function that sends the email body to a generative AI model and emotion engine to analyze typos and emotions.

[0428] The "means for receiving correction suggestions" is a function for receiving analysis results and correction suggestions from the generative AI model and the emotion engine.

[0429] The "means for displaying the proposed revisions on the user interface" is a function for visually presenting the received analysis results and proposed revisions to the user.

[0430] To implement the present invention, the system includes the following components:

[0431] User Interface Behavior

[0432] A user creates an email using a dedicated application on a smartphone. This application includes an input field for the email body and buttons for checking for typos and sentiment analysis. For example, the user enters "Hello, thank you for your support. I would like to ask you to make a few changes to the information about tomorrow's campaign," and clicks the buttons for checking for typos and sentiment analysis.

[0433] Data transmission and analysis

[0434] The device captures the email body entered by the user, converts it to JSON format, and sends it to the server as an HTTP POST request. The server then passes this email body to the generative AI model and emotion engine, which perform typo analysis and sentiment analysis, respectively. At this time, the generative AI model detects typos and generates correction suggestions based on a large amount of text data it has previously learned. Meanwhile, the emotion engine analyzes the sentiment of the email body and generates correction suggestions that match the user's intention.

[0435] Receive and view suggested revisions

[0436] The server receives the analysis results from the generative AI model and emotion engine and sends data including suggested modifications back to the device. The device receives this data and displays it on the user interface. The user can review the suggested modifications and select and apply them as needed.

[0437] Hardware and software used

[0438] Device: Smartphone, smart glasses, or head-mounted display

[0439] Server: The server can be a cloud server and has the necessary processing power.

[0440] Software: Generative AI models, emotion engines, HTTP and JSON libraries (e.g., requests)

[0441] Data format: Serialize and deserialize data in JSON format

[0442] Adding specific examples

[0443] For example, it operates as follows.

[0444] The user enters a specific email body into the application (e.g., "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign.").

[0445] The application sends this email to the server for analysis.

[0446] The server analyzes the email using a generative AI model and an emotion engine and generates a suggested revision, such as: "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?"

[0447] The server returns the analysis results to the terminal, which displays them on a user interface.

[0448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0449] Step 1:

[0450] The user launches the dedicated application and enters the email body. For example, the user might enter, "Hello, thank you for your support. I would like to request a small change to the information about tomorrow's campaign." This email body is then displayed in the user interface.

[0451] Type: "Hello, thank you for your help. I'd like to make a small change to the information about tomorrow's campaign."

[0452] Output: The email body is displayed in the user interface.

[0453] Step 2:

[0454] The user clicks the buttons for checking for typos and sentiment analysis, and the device receives the user's instructions and captures the email body.

[0455] Input: Button click for typo checking and sentiment analysis

[0456] Output: The email body is captured in the device's memory.

[0457] Step 3:

[0458] The device converts the captured email body into JSON format and sends it to the server as an HTTP POST request. For example, it generates the following JSON data:

[0459] Input: Captured email body

[0460] Output: The email body in JSON data format is sent to the server as an HTTP POST request.

[0461] {

[0462] "email": "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign."

[0463] }

[0464] Step 4:

[0465] The server receives the JSON data sent from the device, extracts the body of the received email, and passes it to the generative AI model and emotion engine.

[0466] Input: Email body in JSON format

[0467] Output: The email body is passed to the generative AI model and sentiment engine.

[0468] Step 5:

[0469] The generative AI model detects typos and generates correction suggestions in the email body, while the sentiment engine analyzes the email body and generates sentiment-based correction suggestions.

[0470] Input: Email body

[0471] Output: Analysis results with suggested modifications

[0472] {

[0473] "corrected_text": "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?",

[0474] "suggestions": [

[0475] {"original": "I'd like to make some changes.", "suggestion": "Could you please make some changes?"}

[0476] ]

[0477] }

[0478] Step 6:

[0479] The server receives the analysis results from the generative AI model and emotion engine and sends the data back to the device.

[0480] Input: Analysis results with suggested modifications

[0481] Output: The analysis results, including suggested corrections, are sent back to the device as an HTTP response.

[0482] Step 7:

[0483] The device receives the analysis results from the server and displays them on a user interface, which visually indicates suggested modifications and allows the user to confirm and apply them.

[0484] Input: Analysis results with suggested modifications

[0485] Output: The suggested corrections are displayed in the user interface.

[0486] Step 8:

[0487] The user checks the proposed corrections and applies them as necessary. The email body with the corrections applied is then reflected in the user interface.

[0488] Enter: Confirm suggested corrections

[0489] Output: The email body with the applied corrections is reflected in the user interface.

[0490] 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.

[0491] 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.

[0492] 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.

[0493] [Second embodiment]

[0494] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0495] 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.

[0496] 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).

[0497] 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.

[0498] 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.

[0499] 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).

[0500] 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.

[0501] 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.

[0502] 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.

[0503] 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.

[0504] 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.

[0505] 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."

[0506] This invention relates to a system that automatically detects typos in emails in business communications and provides correction suggestions. The system consists of four main components: a user interface, a generative AI model, a server, and a terminal.

[0507] Overall system overview

[0508] The user uses a user interface for composing an email and inputs the body of the email. When the user issues an instruction to check for typos, the device captures this instruction and sends the email body to the server. The server passes the received email body to a generative AI model, which analyzes it for typos. The generative AI model detects typos, suggests corrections, and returns the results to the server. The server sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[0509] The operation of each component of the system will be specifically described below.

[0510] User operations

[0511] The user creates a new email on the user interface and inputs the message body. For example, the user might input "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0512] The user clicks the "Check for typos" button on the email composition screen, which sends a command to check for typos to the terminal.

[0513] Terminal handling

[0514] The terminal receives instructions from the user to check for typos, converts the email body into JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[0515] json

[0516] {

[0517] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0518] }

[0519] Server Processing

[0520] The server analyzes the request received from the device, extracts the email body data, and then passes this email body to the generative AI model for analysis.

[0521] Based on the data it has learned, the generative AI model detects typos in the email body and generates correction suggestions. For example, it might generate the following correction suggestions:

[0522] "Hello, thank you for your help. Tomorrow's meeting" was changed to "Hello, thank you for your help. Tomorrow's meeting"

[0523] "I want to change the time." remains as "I want to change the time."

[0524] Returning and displaying results

[0525] The server sends the detection results and correction suggestions returned by the generative AI model back to the device. For example, it sends JSON data summarizing the correction suggestions as follows:

[0526] json

[0527] {

[0528] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0529] "suggestions": [

[0530] {"original": "Conference", "suggestion": "Meeting"}

[0531] ]

[0532] }

[0533] The terminal displays the received correction suggestions on a user interface, allowing the user to check the suggested corrections for typos and apply the corrections as necessary.

[0534] Processing of specific examples

[0535] For example, it operates as follows.

[0536] 1. The user clicks the "Check for typos" button.

[0537] 2. The device sends the email body to the server.

[0538] 3. The server requests the generated AI model to perform analysis.

[0539] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[0540] 5. The server sends the suggested revisions back to the device.

[0541] 6. The device displays the suggested corrections in the user interface.

[0542] 7. The user reviews the suggested fixes and applies them as needed.

[0543] This series of operations allows the user to efficiently and accurately create an email without any typos or errors.

[0544] The processing flow will be explained below.

[0545] Step 1:

[0546] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0547] Step 2:

[0548] The user clicks the "Check for typos" button on the email composition screen, which triggers a command to check for typos.

[0549] Step 3:

[0550] The device catches the user's click event, obtains the email body, converts it to JSON format, and sends it to the server as an HTTP POST request.

[0551] Step 4:

[0552] The server receives the request sent from the terminal and extracts the data from the email body. For example, the received data looks like this:

[0553] json

[0554] {

[0555] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0556] }

[0557] Step 5:

[0558] The server passes the extracted email text to a generative AI model and requests it to analyze typos. The generative AI model analyzes the email text based on data it has learned in advance.

[0559] Step 6:

[0560] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It makes no changes to "I would like to change it."

[0561] Step 7:

[0562] The analysis results and suggested corrections from the generative AI model are sent back to the server. For example, the returned data looks like this:

[0563] json

[0564] {

[0565] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0566] "suggestions": [

[0567] {"original": "Conference", "suggestion": "Meeting"}

[0568] ]

[0569] }

[0570] Step 8:

[0571] The server sends the analysis results and correction suggestions received from the generative AI model to the terminal as an HTTP response.

[0572] Step 9:

[0573] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0574] Step 10:

[0575] The user reviews the suggested corrections and applies them if necessary, and finally the error-free email is ready to be sent.

[0576] This series of processing steps allows the system to efficiently and accurately detect typographical errors in emails and provide correction suggestions.

[0577] Example 1

[0578] 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."

[0579] In modern business communication, quickly creating text without typos and errors is an important challenge. However, manual proofreading is time-consuming and labor-intensive, and carries a high risk of errors remaining. To solve this problem, a system is needed that can automatically detect typos and errors in text and provide appropriate correction suggestions.

[0580] 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.

[0581] In this invention, the server includes a means for analyzing text and passing it to a generative model, a means for receiving error detection results and correction suggestions from the generative model, and a means for returning the received correction suggestions to the terminal, thereby enabling the user to accurately and quickly create text without typos or errors.

[0582] "Text" refers to a string of characters written in electronic format.

[0583] An "interface" refers to a software or hardware component that provides a screen or operating means for a user to input information into a system.

[0584] A "generative model" refers to an artificial intelligence model that learns from a variety of data in advance and performs specific analytical or generative tasks.

[0585] "Server" refers to a central computer system that receives, analyzes, and returns data.

[0586] "User" refers to an individual or group that directly operates the system and utilizes the intended functions.

[0587] An "instruction" refers to an operation or command that a user makes to request a specific action from the system.

[0588] "Analysis" refers to the operation of extracting and processing specific information based on given data.

[0589] "Suggested corrections" refers to corrections suggested by the system for detected typos.

[0590] "Visual" refers to displaying information in a way that is easily seen and understood by the user.

[0591] "Application" refers to the operation of reflecting the proposed corrections in the actual data.

[0592] This invention is a system that automatically detects typos in emails and provides correction suggestions. The system consists of four main components: a user interface, a generative model, a server, and a terminal.

[0593] Overall system overview

[0594] The user uses the interface for creating an email and inputs the body of the email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." When the user clicks the "Check for typos" button, the terminal captures this instruction and sends the body of the email to the server.

[0595] The server analyzes the received email text and passes it to a generative model to analyze typos. The generative model detects typos and generates correction suggestions and returns the results to the server. The server then sends the results back to the device, which displays them in the user interface. The user can review them and apply corrections as needed.

[0596] User Interface

[0597] The user interface provides a field for entering text and a "check for typos" button. This interface is often implemented as a browser-based application. The user enters text and clicks the "check for typos" button, which then sends the text to the device.

[0598] Generative Model

[0599] A generative model is an artificial intelligence model that analyzes input text, detects typos, and suggests corrections. This AI model has been trained on a variety of email sentences in advance, so it is able to detect typos with high accuracy.

[0600] server

[0601] The server analyzes the text data received from the device and passes it to the generative model. The server receives the typographical error detection results and correction suggestions sent back from the generative model and sends them back to the device.

[0602] Terminal

[0603] The terminal is responsible for sending user input to the server, receiving replies from the server, and displaying them on a user interface.

[0604] Specific examples

[0605] For example, if a user enters the following text and clicks the "Check for typos" button:

[0606] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0607] The device then generates the following prompt sentence and sends it to the Generative Model via the server:

[0608] "Please detect any typos in the following text and suggest appropriate corrections:

[0609] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0610] The generative model receives this prompt and performs analysis, generating a suggestion to modify, for example, "conference" to "meeting." This suggestion is sent back to the server and ultimately displayed to the user via the device.

[0611] In this way, the user can efficiently and accurately compose emails without any typos or errors.

[0612] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0613] Step 1:

[0614] The user enters the email body on the email creation screen and clicks the "Check for typos" button. This action requests the system to check for typos. The email body is provided as input, and instructions to check for typos are sent to the terminal as output.

[0615] Step 2:

[0616] The terminal receives the user's instructions and converts the entered email body into JSON format. The converted data looks like this:

[0617] json

[0618] {

[0619] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0620] }

[0621] Send an HTTP POST request containing this JSON data to the server, taking the email body as input and sending the JSON data to the server as output.

[0622] Step 3:

[0623] The server parses the HTTP POST request received from the terminal and extracts the email body data. It receives JSON data as input and prepares the email body as output to be passed to the generative model. Next, the server generates and sends the following prompt to the generative model:

[0624] "Please detect any typos in the following text and suggest appropriate corrections:

[0625] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[0626] It takes the email body as input and generates a prompt as output.

[0627] Step 4:

[0628] The generative model receives the prompt and analyzes the email body for typos. It takes the prompt as input and generates typo detection results and correction suggestions as output. For example, the following correction suggestions are generated:

[0629] json

[0630] {

[0631] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0632] "suggestions": [

[0633] {"original": "Conference", "suggestion": "Meeting"}

[0634] ]

[0635] }

[0636] The generative model sends the correction suggestions back to the server. It takes the prompt sentence as input and generates the correction suggestions as output.

[0637] Step 5:

[0638] The server receives the correction suggestions returned from the generative model and parses them. It takes the correction suggestions as input and returns JSON data to the device as output. For example, the following data is returned:

[0639] json

[0640] {

[0641] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0642] "suggestions": [

[0643] {"original": "Conference", "suggestion": "Meeting"}

[0644] ]

[0645] }

[0646] It receives correction suggestions as input and produces parsed data as output, which it sends back to the terminal.

[0647] Step 6:

[0648] The terminal parses the correction suggestions received from the server and displays them in the user interface. It receives JSON data from the server as input and displays the correction suggestions in the user interface as output. The user can review the results and choose whether to apply the correction suggestions.

[0649] Step 7:

[0650] The user reviews the suggested corrections displayed in the user interface and applies them as necessary. The suggested corrections displayed in the interface are taken as input, and the final, typographically correct email body is generated as output. The user selects and applies the suggested corrections.

[0651] (Application example 1)

[0652] 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."

[0653] In recent years, the quality of articles, blogs, and newsletters has become increasingly important in content distribution services. However, the presence of typos and misspellings undermines the reliability of the content and leads to a poor user experience. Traditional manual typographical error checking is time-consuming, labor-intensive, and inefficient. Furthermore, while suggestions for improvements in sentence structure and style are also desired, there is a lack of efficient means to achieve this. A system that can solve these issues and improve the quality of content distribution is needed.

[0654] 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.

[0655] In this invention, the server includes means for providing a user interface for inputting content such as articles, blogs, and newsletters, means for accepting instructions from a user to check for typos and omissions, means for transmitting the input content to a generative AI model for analysis, means for receiving typo detection results and correction suggestions from the generative AI model, and means for displaying the received correction suggestions on the user interface, thereby enabling content creators to automatically detect and correct typos and provide efficient, high-quality content.

[0656] An "article" is a piece of written text intended to convey information in the form of news, blogs, newsletters, etc.

[0657] A "blog" is a diary-style piece of writing published on the web that records the opinions, experiences, and thoughts of an individual or organization.

[0658] A "newsletter" is a regularly published informational document used by a company or organization to inform interested parties of the latest information and updates.

[0659] "User interface" refers to an operation screen or input device that allows a user to directly interact with a system.

[0660] "Typo" refers to an error where a character is typed incorrectly or a character is missing from a sentence.

[0661] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes sentences.

[0662] "Analysis" refers to the process of examining data or information in detail to understand its structure and meaning.

[0663] "Correction proposals" are proposals for appropriate corrections to detected errors or areas for improvement.

[0664] A "server" is a computer system that provides services to other computers on a network.

[0665] A specific method for implementing this invention is described below. This system detects typographical errors in articles, blogs, and newsletters in a content distribution service and suggests corrections. The system consists of a user interface, a server, a generative AI model, and a terminal.

[0666] System Overview

[0667] Users input content such as articles, blogs, and newsletters through a user interface that can be used on a variety of devices, including smartphones and PCs.

[0668] The terminal receives instructions from the user to check for typos. The input content is sent to the server as JSON format data via an HTTP POST request. For example, the following data is sent:

[0669] json

[0670] {

[0671] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0672] }

[0673] The server receives this request and requests the generative AI model to analyze it. OpenAI's GPT-4 is used as the generative AI model. This model has been trained on a variety of content texts in advance, enabling highly accurate analysis.

[0674] The generative AI model detects typos in the received content and generates correction suggestions, such as the following prompt:

[0675] "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0676] The server receives the analysis results of the generative AI model and sends them back to the device. The returned data includes the corrected text and suggested corrections:

[0677] json

[0678] {

[0679] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[0680] "suggestions": [

[0681] {"original": "camera technology", "suggestion": "camera technology"}

[0682] ]

[0683] }

[0684] The device displays the suggested corrections in a user interface, allowing the user to review the suggestions and apply or correct them, resulting in high-quality content that is free of typos and errors.

[0685] This system allows users to efficiently and accurately correct typos and distribute content. The hardware uses a high-performance computer system for the server, and the software uses Flask (a Python framework) and the OpenAI API.

[0686] Such a system and method can significantly improve the quality of content delivery.

[0687] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0688] Step 1:

[0689] The user enters a content body (e.g., "A new smartphone has been released. It has the latest camera technology, a larger battery, and a more powerful processor.") into the user interface. The content body is provided as input to the system.

[0690] Step 2:

[0691] The user clicks the "Check for typos" button. This action causes the device to receive an instruction to check for typos. The device converts the entered content body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is in the following format:

[0692] json

[0693] {

[0694] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[0695] }

[0696] Step 3:

[0697] The server parses the request received from the device and extracts the content body from the JSON data. This extracted body is then provided to a generative AI model (e.g., OpenAI GPT-4) for analysis. The purpose of the analysis is to detect typos and generate correction suggestions.

[0698] Step 4:

[0699] The generative AI model analyzes the input content body, detects typos, and generates correction suggestions that are sent back to the server, such as:

[0700] json

[0701] {

[0702] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[0703] "suggestions": [

[0704] {"original": "camera technology", "suggestion": "camera technology"}

[0705] ]

[0706] }

[0707] Step 5:

[0708] The server receives the analysis results and correction suggestions from the generative AI model and sends them back to the device. The data sent from the server to the device includes the corrected text and the correction suggestions.

[0709] Step 6:

[0710] The terminal displays the correction suggestions and analysis results received from the server on a user interface. The user checks the displayed results and applies corrections as necessary. For example, the user checks the correction suggestions and selects whether to change "camera technique" to "camera technology."

[0711] Step 7:

[0712] The user then confirms the final corrections and confirms the applied content body, resulting in a high-quality content that is free of typos and errors.

[0713] This allows users to efficiently create and distribute high-quality content, and also clarifies the data generated at each step and its transformation.

[0714] 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.

[0715] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[0716] Overall system overview

[0717] The user uses a user interface for composing an email and inputs the body of the email. When the user issues instructions for checking for typos and sentiment analysis, the device receives these instructions and sends the email body to the server. The server then passes the received email body to a generative AI model and an emotion engine, which analyzes the typos and sentiment. The generative AI model and emotion engine then generate suggestions for correcting typos and sentiment-based expressions, respectively, and return the results to the server. The server then sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[0718] The operation of each component of the system will be specifically described below.

[0719] User operations

[0720] The user creates a new email on the user interface and enters the message body, for example, "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0721] The user clicks the "Check for typos and analyze emotions" button on the email composition screen, which sends instructions to the terminal to check for typos and analyze emotions.

[0722] Terminal handling

[0723] The device catches the user's click event, gets the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[0724] json

[0725] {

[0726] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0727] }

[0728] Server Processing

[0729] The server receives the request sent from the device, extracts the data from the email body, and then passes the extracted email body to the generative AI model and emotion engine for analysis.

[0730] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[0731] Returning and displaying results

[0732] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[0733] json

[0734] {

[0735] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0736] "suggestions": [

[0737] {"original": "Conference", "suggestion": "Meeting"},

[0738] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0739] ]

[0740] }

[0741] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[0742] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0743] Processing of specific examples

[0744] For example, it operates as follows.

[0745] 1. The user clicks the "Check for typos and sentiment analysis" button.

[0746] 2. The device sends the email body to the server.

[0747] 3. The server requests analysis from the generative AI model and emotion engine.

[0748] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[0749] 5. The emotion engine generates a suggestion to change "I'd like to change it" to "Could you please change it?"

[0750] 6. The server sends the suggested revisions back to the device.

[0751] 7. The device displays the suggested corrections in the user interface.

[0752] 8. The user reviews the suggested fixes and applies them as needed.

[0753] This series of operations allows the user to create an email that is free of typos and that is highly accurate and suited to the user's emotions.

[0754] The processing flow will be explained below.

[0755] Step 1:

[0756] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0757] Step 2:

[0758] The user clicks the "Check for typos and analyze sentiment" button on the email composition screen, which triggers the typo check and sentiment analysis.

[0759] Step 3:

[0760] The device catches the user's click event, retrieves the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. The data sent will be in the following format:

[0761] json

[0762] {

[0763] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0764] }

[0765] Step 4:

[0766] The server receives the request sent from the device and extracts the data from the email body, for example, extracting the text from the email field.

[0767] Step 5:

[0768] The server passes the extracted email body data to the generative AI model and emotion engine, requesting them to analyze typos and emotions, respectively.

[0769] Step 6:

[0770] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It also makes no specific suggestion for "I would like to change it."

[0771] Step 7:

[0772] The emotion engine analyzes the email body and recognizes the user's emotions. In this example, it generates a suggestion to change a phrase like "I would like to change it" to the more polite "Could you please change it?"

[0773] Step 8:

[0774] The generative AI model and emotion engine send back analysis results and suggested modifications to the server. For example, the following data may be returned:

[0775] json

[0776] {

[0777] "corrected_text": "Hello, thank you for your help. Regarding our meeting tomorrow, could we please change the time?",

[0778] "suggestions": [

[0779] {"original": "Conference", "suggestion": "Meeting"},

[0780] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0781] ]

[0782] }

[0783] Step 9:

[0784] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[0785] Step 10:

[0786] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[0787] Step 11:

[0788] The user reviews the suggested corrections and applies them as necessary, for example, using "meeting" instead of the suggested "conference," and changing "I'd like to change it" to "Could you please change it?". Finally, the email is completed with no typos and in line with the user's sentiment.

[0789] This series of processing steps allows the user to efficiently create an email without any typos or mistakes, and the expressions in the email will be appropriate to the user's feelings.

[0790] Example 2

[0791] 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."

[0792] Conventional email writing support systems are limited to detecting typos and suggesting corrections, and do not provide sufficient support for users when considering the emotional expression in the email body. In particular, business emails often require appropriate emotional expression, and it is necessary to use appropriate expressions to avoid misunderstandings and rudeness. To solve this problem, a system is needed that not only detects typos but also analyzes the emotional content of the email body and suggests corrections based on that analysis.

[0793] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server is a system that detects typos and omissions in emails and performs sentiment analysis, and includes an information input means for inputting the email body, a means for accepting instructions from a user to check for typos and omissions and to analyze sentiment, a means for transmitting the input email body to an analysis device for analysis, a means for receiving the typo detection results and suggestions for correcting expressions from the analysis device, and a means for displaying the received suggestions for correction on an information display means. This enables a user not only to correct typos and omissions in the email body, but also to easily create an email that expresses appropriate sentiment.

[0794] The "mail body" is the text content that the user inputs to send as an email.

[0795] "Information input means" refers to an input device or software interface that allows a user to input the body of an email.

[0796] "User" refers to the person who operates this system and creates and sends emails.

[0797] "Typo checking" is the process of detecting mistyped or missing characters in the body of an email.

[0798] "Emotion analysis" is the process of recognizing the user's emotions and intentions from the content of the email text entered,

[0799] It is a process of proposing appropriate expressions.

[0800] The "analysis device" is part of a system that includes a generative AI model and an emotion engine, and is a device that analyzes text data and generates analysis results for typos and emotions.

[0801] "Suggested corrections" are suggestions for specific changes to improve entered text based on detected typos and analyzed sentiment.

[0802] "Information display means" refers to a device or interface for visually displaying analysis results and suggested modifications to the user.

[0803] A "prompt" is an input sentence that instructs a generative AI model to perform a specific task, such as "Please check the body of the email for typos and generate suggested corrections."

[0804] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[0805] The hardware and software required to implement this system are as follows: The server is hosted using AWS (Amazon Web Services), and NGINX is used for request routing. The generative AI model uses OpenAI GPT-4, combined with IBM Watson for the emotion engine. The user interface was developed using React.js, and the terminal is connected to a personal computer or smartphone.

[0806] User operations

[0807] The user creates a new email using the email creation user interface. For example, the user might type, "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." Next, the user clicks the "Check for typos and analyze sentiment" button on the email creation screen. This sends instructions to the terminal to check for typos and analyze sentiment.

[0808] Terminal handling

[0809] The device catches the user's click event and retrieves the email body. It converts the retrieved email body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is as follows:

[0810] json

[0811] {

[0812] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0813] }

[0814] Server Processing

[0815] The server receives the HTTP POST request sent from the device and extracts the data from the email body. Then, it passes the extracted email body to the generative AI model and emotion engine for analysis. Specifically, it gives the generative AI model the following prompt:

[0816] "Check the body of your email for typos and generate suggested corrections."

[0817] Meanwhile, the emotion engine is given the following prompt:

[0818] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[0819] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[0820] Returning and displaying results

[0821] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[0822] json

[0823] {

[0824] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0825] "suggestions": [

[0826] {"original": "Conference", "suggestion": "Meeting"},

[0827] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0828] ]

[0829] }

[0830] The server sends the analysis results and suggested revisions received from the generative AI model and emotion engine to the device as an HTTP response. The device receives the response from the server and applies the suggested revisions to the email body area. It also displays the suggested revisions in a separate display area, allowing the user to confirm the suggested revisions.

[0831] Specific examples of operation

[0832] For example, a user types "Hello, thank you for your help. We have a meeting tomorrow, but I'd like to change the time," and clicks the "Check for typos and sentiment analysis" button. The device then sends the email body to the server, which then requests analysis from the generative AI model and sentiment engine. The generative AI model generates a suggestion to change "conference" to "meeting," and the sentiment engine generates a suggestion to change "I'd like to change it" to "Could you please change it?" The server collects and organizes the suggested changes and sends them to the device. The device displays the suggested changes on the user interface, and the user can confirm and apply them.

[0833] This system allows users to easily create emails that are free of typos and that are accurate and reflect the user's emotions.

[0834] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0835] Step 1: User enters email content

[0836] A user uses a user interface for creating emails to input a new email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." This input is the base data for subsequent processing.

[0837] Step 2: User clicks on the "Check spelling and sentiment analysis" button

[0838] The user clicks the "Check for typos and sentiment analysis" button on the email composition screen, which triggers a request to check for typos and sentiment analysis for the entered email body.

[0839] Input: The instruction the user clicked

[0840] Output: Request for typo checking and sentiment analysis

[0841] Step 3: The device converts the email body into JSON format and sends it to the server.

[0842] The terminal converts the email body entered in step 1 into JSON format. The converted data has the following format:

[0843] json

[0844] {

[0845] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0846] }

[0847] The converted data is then sent to the server via an HTTP POST request.

[0848] Input: Email body

[0849] Output: JSON formatted data, HTTP POST request

[0850] Step 4: The server receives the JSON data and prepares it for parsing

[0851] The server receives the HTTP POST request sent from the terminal, extracts the email body from the received JSON data, and prepares it for parsing. At this time, it generates a prompt like this:

[0852] "Check the body of your email for typos and generate suggested corrections."

[0853] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[0854] Input: JSON format data

[0855] Output: Email body, prompt

[0856] Step 5: The server requests the generated AI model and emotion engine for analysis.

[0857] The server requests the generative AI model and emotion engine to analyze the extracted email text. Specifically, it requests the generative AI model to check for typos and suggest corrections, and requests the emotion engine to analyze emotions and suggest corrections to expressions.

[0858] Input: Email body, prompt text

[0859] Output: Analysis requests to generative AI models and emotion engines

[0860] Step 6: Generative AI model and emotion engine perform analysis

[0861] The generative AI model detects typos and omissions in the received email text and generates correction suggestions, while the emotion engine analyzes the email text, recognizes the user's emotions, and generates correction suggestions based on those emotions.

[0862] Input: Email body, prompt text

[0863] Output: Suggested corrections for typos and emotions

[0864] Step 7: The server receives and organizes the analysis results

[0865] The server receives the analysis results returned by the generative AI model and emotion engine. For example, it receives data such as:

[0866] json

[0867] {

[0868] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[0869] "suggestions": [

[0870] {"original": "Conference", "suggestion": "Meeting"},

[0871] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[0872] ]

[0873] }

[0874] The server organizes these results and formats them for transmission to the terminal.

[0875] Input: Suggestions for correcting typos and emote expressions

[0876] Output: Organized analysis results

[0877] Step 8: The server sends the analysis results to the device.

[0878] The server sends the organized analysis results to the terminal as an HTTP response.

[0879] Input: Organized analysis results

[0880] Output: Parsed results as an HTTP response

[0881] Step 9: The device receives the analysis results and displays them on the user interface.

[0882] The terminal analyzes the response received from the server, applies automatic correction suggestions to the email body area, and displays the suggestions in a separate display area to allow the user to confirm the correction suggestions.

[0883] Input: Parsed result as HTTP response

[0884] Output: Suggested fixes displayed in the user interface

[0885] Step 10: User reviews and applies suggested fixes

[0886] The user checks the suggested corrections displayed on the terminal and applies them as necessary. If the user is satisfied, the suggested corrections are applied to the original email body and the email is prepared for sending.

[0887] Input: Suggested corrections displayed in the user interface

[0888] Output: Modified email body

[0889] (Application example 2)

[0890] 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."

[0891] In today's world, it is important to improve the quality of email and message communication. However, emails and messages entered by users often contain typos, which can lead to misunderstandings and discomfort. Furthermore, especially in customer service situations, expressions that reflect appropriate emotions are required. However, correcting these errors on your own is a time-consuming and labor-intensive process, which presents a challenge.

[0892] The identification process by the identification 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 sending the email body to the generative AI model and the emotion engine for analysis, means for receiving typographical error detection results from the generative AI model and correction suggestions from the emotion engine, and means for displaying the received correction suggestions on a user interface. This makes it possible to detect typos in emails and provide users with correction suggestions that take emotions into consideration.

[0893] The "email typo and omission detection system" is a system that automatically detects typos and omissions in emails written by users and suggests corrections.

[0894] The "user interface" refers to a screen or operating means through which a user inputs the body of an email, checks for typos, and confirms the results of sentiment analysis.

[0895] The "means for accepting instructions for checking for typos and omissions and for sentiment analysis" is a function for accepting an operation from the user to request checking for typos and omissions and for sentiment analysis.

[0896] A "generative AI model" is an artificial intelligence model that learns from a variety of text data in advance and detects typos and misspellings in the body of an email.

[0897] The "emotion engine" is an analysis engine that analyzes the user's email text and suggests appropriate emotional expressions.

[0898] The "means of analysis" is a function that sends the email body to a generative AI model and emotion engine to analyze typos and emotions.

[0899] The "means for receiving correction suggestions" is a function for receiving analysis results and correction suggestions from the generative AI model and the emotion engine.

[0900] The "means for displaying the proposed revisions on the user interface" is a function for visually presenting the received analysis results and proposed revisions to the user.

[0901] To implement the present invention, the system includes the following components:

[0902] User Interface Behavior

[0903] A user creates an email using a dedicated application on a smartphone. This application includes an input field for the email body and buttons for checking for typos and sentiment analysis. For example, the user enters "Hello, thank you for your support. I would like to ask you to make a few changes to the information about tomorrow's campaign," and clicks the buttons for checking for typos and sentiment analysis.

[0904] Data transmission and analysis

[0905] The device captures the email body entered by the user, converts it to JSON format, and sends it to the server as an HTTP POST request. The server then passes this email body to the generative AI model and emotion engine, which perform typo analysis and sentiment analysis, respectively. At this time, the generative AI model detects typos and generates correction suggestions based on a large amount of text data it has previously learned. Meanwhile, the emotion engine analyzes the sentiment of the email body and generates correction suggestions that match the user's intention.

[0906] Receive and view suggested revisions

[0907] The server receives the analysis results from the generative AI model and emotion engine and sends data including suggested modifications back to the device. The device receives this data and displays it on the user interface. The user can review the suggested modifications and select and apply them as needed.

[0908] Hardware and software used

[0909] Device: Smartphone, smart glasses, or head-mounted display

[0910] Server: The server can be a cloud server and has the necessary processing power.

[0911] Software: Generative AI models, emotion engines, HTTP and JSON libraries (e.g., requests)

[0912] Data format: Serialize and deserialize data in JSON format

[0913] Adding specific examples

[0914] For example, it operates as follows.

[0915] The user enters a specific email body into the application (e.g., "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign.").

[0916] The application sends this email to the server for analysis.

[0917] The server analyzes the email using a generative AI model and an emotion engine and generates a suggested revision, such as: "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?"

[0918] The server returns the analysis results to the terminal, which displays them on a user interface.

[0919] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0920] Step 1:

[0921] The user launches the dedicated application and enters the email body. For example, the user might enter, "Hello, thank you for your support. I would like to request a small change to the information about tomorrow's campaign." This email body is then displayed in the user interface.

[0922] Type: "Hello, thank you for your help. I'd like to make a small change to the information about tomorrow's campaign."

[0923] Output: The email body is displayed in the user interface.

[0924] Step 2:

[0925] The user clicks the buttons for checking for typos and sentiment analysis, and the device receives the user's instructions and captures the email body.

[0926] Input: Button click for typo checking and sentiment analysis

[0927] Output: The email body is captured in the device's memory.

[0928] Step 3:

[0929] The device converts the captured email body into JSON format and sends it to the server as an HTTP POST request. For example, it generates the following JSON data:

[0930] Input: Captured email body

[0931] Output: The email body in JSON data format is sent to the server as an HTTP POST request.

[0932] {

[0933] "email": "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign."

[0934] }

[0935] Step 4:

[0936] The server receives the JSON data sent from the device, extracts the body of the received email, and passes it to the generative AI model and emotion engine.

[0937] Input: Email body in JSON format

[0938] Output: The email body is passed to the generative AI model and sentiment engine.

[0939] Step 5:

[0940] The generative AI model detects typos and generates correction suggestions in the email body, while the sentiment engine analyzes the email body and generates sentiment-based correction suggestions.

[0941] Input: Email body

[0942] Output: Analysis results with suggested modifications

[0943] {

[0944] "corrected_text": "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?",

[0945] "suggestions": [

[0946] {"original": "I'd like to make some changes.", "suggestion": "Could you please make some changes?"}

[0947] ]

[0948] }

[0949] Step 6:

[0950] The server receives the analysis results from the generative AI model and emotion engine and sends the data back to the device.

[0951] Input: Analysis results with suggested modifications

[0952] Output: The analysis results, including suggested corrections, are sent back to the device as an HTTP response.

[0953] Step 7:

[0954] The device receives the analysis results from the server and displays them on a user interface, which visually indicates suggested modifications and allows the user to confirm and apply them.

[0955] Input: Analysis results with suggested modifications

[0956] Output: The suggested corrections are displayed in the user interface.

[0957] Step 8:

[0958] The user checks the proposed corrections and applies them as necessary. The email body with the corrections applied is then reflected in the user interface.

[0959] Enter: Confirm suggested corrections

[0960] Output: The email body with the applied corrections is reflected in the user interface.

[0961] 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.

[0962] 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.

[0963] 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.

[0964] [Third embodiment]

[0965] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0966] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0967] 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).

[0968] 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.

[0969] 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.

[0970] 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).

[0971] 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.

[0972] 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.

[0973] 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.

[0974] 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.

[0975] 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.

[0976] 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."

[0977] This invention relates to a system that automatically detects typos in emails in business communications and provides correction suggestions. The system consists of four main components: a user interface, a generative AI model, a server, and a terminal.

[0978] Overall system overview

[0979] The user uses a user interface for composing an email and inputs the body of the email. When the user issues an instruction to check for typos, the device captures this instruction and sends the email body to the server. The server passes the received email body to a generative AI model, which analyzes it for typos. The generative AI model detects typos, suggests corrections, and returns the results to the server. The server sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[0980] The operation of each component of the system will be specifically described below.

[0981] User operations

[0982] The user creates a new email on the user interface and inputs the message body. For example, the user might input "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[0983] The user clicks the "Check for typos" button on the email composition screen, which sends a command to check for typos to the terminal.

[0984] Terminal handling

[0985] The terminal receives instructions from the user to check for typos, converts the email body into JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[0986] json

[0987] {

[0988] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[0989] }

[0990] Server Processing

[0991] The server analyzes the request received from the device, extracts the email body data, and then passes this email body to the generative AI model for analysis.

[0992] Based on the data it has learned, the generative AI model detects typos in the email body and generates correction suggestions. For example, it might generate the following correction suggestions:

[0993] "Hello, thank you for your help. Tomorrow's meeting" was changed to "Hello, thank you for your help. Tomorrow's meeting"

[0994] "I want to change the time." remains as "I want to change the time."

[0995] Returning and displaying results

[0996] The server sends the detection results and correction suggestions returned by the generative AI model back to the device. For example, it sends JSON data summarizing the correction suggestions as follows:

[0997] json

[0998] {

[0999] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1000] "suggestions": [

[1001] {"original": "Conference", "suggestion": "Meeting"}

[1002] ]

[1003] }

[1004] The terminal displays the received correction suggestions on a user interface, allowing the user to check the suggested corrections for typos and apply the corrections as necessary.

[1005] Processing of specific examples

[1006] For example, it operates as follows.

[1007] 1. The user clicks the "Check for typos" button.

[1008] 2. The device sends the email body to the server.

[1009] 3. The server requests the generated AI model to perform analysis.

[1010] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[1011] 5. The server sends the suggested revisions back to the device.

[1012] 6. The device displays the suggested corrections in the user interface.

[1013] 7. The user reviews the suggested fixes and applies them as needed.

[1014] This series of operations allows the user to efficiently and accurately create an email without any typos or errors.

[1015] The processing flow will be explained below.

[1016] Step 1:

[1017] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1018] Step 2:

[1019] The user clicks the "Check for typos" button on the email composition screen, which triggers a command to check for typos.

[1020] Step 3:

[1021] The device catches the user's click event, obtains the email body, converts it to JSON format, and sends it to the server as an HTTP POST request.

[1022] Step 4:

[1023] The server receives the request sent from the terminal and extracts the data from the email body. For example, the received data looks like this:

[1024] json

[1025] {

[1026] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1027] }

[1028] Step 5:

[1029] The server passes the extracted email text to a generative AI model and requests it to analyze typos. The generative AI model analyzes the email text based on data it has learned in advance.

[1030] Step 6:

[1031] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It makes no changes to "I would like to change it."

[1032] Step 7:

[1033] The analysis results and suggested corrections from the generative AI model are sent back to the server. For example, the returned data looks like this:

[1034] json

[1035] {

[1036] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1037] "suggestions": [

[1038] {"original": "Conference", "suggestion": "Meeting"}

[1039] ]

[1040] }

[1041] Step 8:

[1042] The server sends the analysis results and correction suggestions received from the generative AI model to the terminal as an HTTP response.

[1043] Step 9:

[1044] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1045] Step 10:

[1046] The user reviews the suggested corrections and applies them if necessary, and finally the error-free email is ready to be sent.

[1047] This series of processing steps allows the system to efficiently and accurately detect typographical errors in emails and provide correction suggestions.

[1048] Example 1

[1049] 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."

[1050] In modern business communication, quickly creating text without typos and errors is an important challenge. However, manual proofreading is time-consuming and labor-intensive, and carries a high risk of errors remaining. To solve this problem, a system is needed that can automatically detect typos and errors in text and provide appropriate correction suggestions.

[1051] 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.

[1052] In this invention, the server includes a means for analyzing text and passing it to a generative model, a means for receiving error detection results and correction suggestions from the generative model, and a means for returning the received correction suggestions to the terminal, thereby enabling the user to accurately and quickly create text without typos or errors.

[1053] "Text" refers to a string of characters written in electronic format.

[1054] An "interface" refers to a software or hardware component that provides a screen or operating means for a user to input information into a system.

[1055] A "generative model" refers to an artificial intelligence model that learns from a variety of data in advance and performs specific analytical or generative tasks.

[1056] "Server" refers to a central computer system that receives, analyzes, and returns data.

[1057] "User" refers to an individual or group that directly operates the system and utilizes the intended functions.

[1058] An "instruction" refers to an operation or command that a user makes to request a specific action from the system.

[1059] "Analysis" refers to the operation of extracting and processing specific information based on given data.

[1060] "Suggested corrections" refers to corrections suggested by the system for detected typos.

[1061] "Visual" refers to displaying information in a way that is easily seen and understood by the user.

[1062] "Application" refers to the operation of reflecting the proposed corrections in the actual data.

[1063] This invention is a system that automatically detects typos in emails and provides correction suggestions. The system consists of four main components: a user interface, a generative model, a server, and a terminal.

[1064] Overall system overview

[1065] The user uses the interface for creating an email and inputs the body of the email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." When the user clicks the "Check for typos" button, the terminal captures this instruction and sends the body of the email to the server.

[1066] The server analyzes the received email text and passes it to a generative model to analyze typos. The generative model detects typos and generates correction suggestions and returns the results to the server. The server then sends the results back to the device, which displays them in the user interface. The user can review them and apply corrections as needed.

[1067] User Interface

[1068] The user interface provides a field for entering text and a "check for typos" button. This interface is often implemented as a browser-based application. The user enters text and clicks the "check for typos" button, which then sends the text to the device.

[1069] Generative Model

[1070] A generative model is an artificial intelligence model that analyzes input text, detects typos, and suggests corrections. This AI model has been trained on a variety of email sentences in advance, so it is able to detect typos with high accuracy.

[1071] server

[1072] The server analyzes the text data received from the device and passes it to the generative model. The server receives the typographical error detection results and correction suggestions sent back from the generative model and sends them back to the device.

[1073] Terminal

[1074] The terminal is responsible for sending user input to the server, receiving replies from the server, and displaying them on a user interface.

[1075] Specific examples

[1076] For example, if a user enters the following text and clicks the "Check for typos" button:

[1077] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1078] The device then generates the following prompt sentence and sends it to the Generative Model via the server:

[1079] "Please detect any typos in the following text and suggest appropriate corrections:

[1080] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1081] The generative model receives this prompt and performs analysis, generating a suggestion to modify, for example, "conference" to "meeting." This suggestion is sent back to the server and ultimately displayed to the user via the device.

[1082] In this way, the user can efficiently and accurately compose emails without any typos or errors.

[1083] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1084] Step 1:

[1085] The user enters the email body on the email creation screen and clicks the "Check for typos" button. This action requests the system to check for typos. The email body is provided as input, and instructions to check for typos are sent to the terminal as output.

[1086] Step 2:

[1087] The terminal receives the user's instructions and converts the entered email body into JSON format. The converted data looks like this:

[1088] json

[1089] {

[1090] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1091] }

[1092] Send an HTTP POST request containing this JSON data to the server, taking the email body as input and sending the JSON data to the server as output.

[1093] Step 3:

[1094] The server parses the HTTP POST request received from the terminal and extracts the email body data. It receives JSON data as input and prepares the email body as output to be passed to the generative model. Next, the server generates and sends the following prompt to the generative model:

[1095] "Please detect any typos in the following text and suggest appropriate corrections:

[1096] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1097] It takes the email body as input and generates a prompt as output.

[1098] Step 4:

[1099] The generative model receives the prompt and analyzes the email body for typos. It takes the prompt as input and generates typo detection results and correction suggestions as output. For example, the following correction suggestions are generated:

[1100] json

[1101] {

[1102] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1103] "suggestions": [

[1104] {"original": "Conference", "suggestion": "Meeting"}

[1105] ]

[1106] }

[1107] The generative model sends the correction suggestions back to the server. It takes the prompt sentence as input and generates the correction suggestions as output.

[1108] Step 5:

[1109] The server receives the correction suggestions returned from the generative model and parses them. It takes the correction suggestions as input and returns JSON data to the device as output. For example, the following data is returned:

[1110] json

[1111] {

[1112] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1113] "suggestions": [

[1114] {"original": "Conference", "suggestion": "Meeting"}

[1115] ]

[1116] }

[1117] It receives correction suggestions as input and produces parsed data as output, which it sends back to the terminal.

[1118] Step 6:

[1119] The terminal parses the correction suggestions received from the server and displays them in the user interface. It receives JSON data from the server as input and displays the correction suggestions in the user interface as output. The user can review the results and choose whether to apply the correction suggestions.

[1120] Step 7:

[1121] The user reviews the suggested corrections displayed in the user interface and applies them as necessary. The suggested corrections displayed in the interface are taken as input, and the final, typographically correct email body is generated as output. The user selects and applies the suggested corrections.

[1122] (Application example 1)

[1123] 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."

[1124] In recent years, the quality of articles, blogs, and newsletters has become increasingly important in content distribution services. However, the presence of typos and misspellings undermines the reliability of the content and leads to a poor user experience. Traditional manual typographical error checking is time-consuming, labor-intensive, and inefficient. Furthermore, while suggestions for improvements in sentence structure and style are also desired, there is a lack of efficient means to achieve this. A system that can solve these issues and improve the quality of content distribution is needed.

[1125] 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.

[1126] In this invention, the server includes means for providing a user interface for inputting content such as articles, blogs, and newsletters, means for accepting instructions from a user to check for typos and omissions, means for transmitting the input content to a generative AI model for analysis, means for receiving typo detection results and correction suggestions from the generative AI model, and means for displaying the received correction suggestions on the user interface, thereby enabling content creators to automatically detect and correct typos and provide efficient, high-quality content.

[1127] An "article" is a piece of written text intended to convey information in the form of news, blogs, newsletters, etc.

[1128] A "blog" is a diary-style piece of writing published on the web that records the opinions, experiences, and thoughts of an individual or organization.

[1129] A "newsletter" is a regularly published informational document used by a company or organization to inform interested parties of the latest information and updates.

[1130] "User interface" refers to an operation screen or input device that allows a user to directly interact with a system.

[1131] "Typo" refers to an error where a character is typed incorrectly or a character is missing from a sentence.

[1132] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes sentences.

[1133] "Analysis" refers to the process of examining data or information in detail to understand its structure and meaning.

[1134] "Correction proposals" are proposals for appropriate corrections to detected errors or areas for improvement.

[1135] A "server" is a computer system that provides services to other computers on a network.

[1136] A specific method for implementing this invention is described below. This system detects typographical errors in articles, blogs, and newsletters in a content distribution service and suggests corrections. The system consists of a user interface, a server, a generative AI model, and a terminal.

[1137] System Overview

[1138] Users input content such as articles, blogs, and newsletters through a user interface that can be used on a variety of devices, including smartphones and PCs.

[1139] The terminal receives instructions from the user to check for typos. The input content is sent to the server as JSON format data via an HTTP POST request. For example, the following data is sent:

[1140] json

[1141] {

[1142] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1143] }

[1144] The server receives this request and requests the generative AI model to analyze it. OpenAI's GPT-4 is used as the generative AI model. This model has been trained on a variety of content texts in advance, enabling highly accurate analysis.

[1145] The generative AI model detects typos in the received content and generates correction suggestions, such as the following prompt:

[1146] "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1147] The server receives the analysis results of the generative AI model and sends them back to the device. The returned data includes the corrected text and suggested corrections:

[1148] json

[1149] {

[1150] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[1151] "suggestions": [

[1152] {"original": "camera technology", "suggestion": "camera technology"}

[1153] ]

[1154] }

[1155] The device displays the suggested corrections in a user interface, allowing the user to review the suggestions and apply or correct them, resulting in high-quality content that is free of typos and errors.

[1156] This system allows users to efficiently and accurately correct typos and distribute content. The hardware uses a high-performance computer system for the server, and the software uses Flask (a Python framework) and the OpenAI API.

[1157] Such a system and method can significantly improve the quality of content delivery.

[1158] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1159] Step 1:

[1160] The user enters a content body (e.g., "A new smartphone has been released. It has the latest camera technology, a larger battery, and a more powerful processor.") into the user interface. The content body is provided as input to the system.

[1161] Step 2:

[1162] The user clicks the "Check for typos" button. This action causes the device to receive an instruction to check for typos. The device converts the entered content body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is in the following format:

[1163] json

[1164] {

[1165] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1166] }

[1167] Step 3:

[1168] The server parses the request received from the device and extracts the content body from the JSON data. This extracted body is then provided to a generative AI model (e.g., OpenAI GPT-4) for analysis. The purpose of the analysis is to detect typos and generate correction suggestions.

[1169] Step 4:

[1170] The generative AI model analyzes the input content body, detects typos, and generates correction suggestions that are sent back to the server, such as:

[1171] json

[1172] {

[1173] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[1174] "suggestions": [

[1175] {"original": "camera technology", "suggestion": "camera technology"}

[1176] ]

[1177] }

[1178] Step 5:

[1179] The server receives the analysis results and correction suggestions from the generative AI model and sends them back to the device. The data sent from the server to the device includes the corrected text and the correction suggestions.

[1180] Step 6:

[1181] The terminal displays the correction suggestions and analysis results received from the server on a user interface. The user checks the displayed results and applies corrections as necessary. For example, the user checks the correction suggestions and selects whether to change "camera technique" to "camera technology."

[1182] Step 7:

[1183] The user then confirms the final corrections and confirms the applied content body, resulting in a high-quality content that is free of typos and errors.

[1184] This allows users to efficiently create and distribute high-quality content, and also clarifies the data generated at each step and its transformation.

[1185] 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.

[1186] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[1187] Overall system overview

[1188] The user uses a user interface for composing an email and inputs the body of the email. When the user issues instructions for checking for typos and sentiment analysis, the device receives these instructions and sends the email body to the server. The server then passes the received email body to a generative AI model and an emotion engine, which analyzes the typos and sentiment. The generative AI model and emotion engine then generate suggestions for correcting typos and sentiment-based expressions, respectively, and return the results to the server. The server then sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[1189] The operation of each component of the system will be specifically described below.

[1190] User operations

[1191] The user creates a new email on the user interface and enters the message body, for example, "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1192] The user clicks the "Check for typos and analyze emotions" button on the email composition screen, which sends instructions to the terminal to check for typos and analyze emotions.

[1193] Terminal handling

[1194] The device catches the user's click event, gets the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[1195] json

[1196] {

[1197] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1198] }

[1199] Server Processing

[1200] The server receives the request sent from the device, extracts the data from the email body, and then passes the extracted email body to the generative AI model and emotion engine for analysis.

[1201] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[1202] Returning and displaying results

[1203] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[1204] json

[1205] {

[1206] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1207] "suggestions": [

[1208] {"original": "Conference", "suggestion": "Meeting"},

[1209] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1210] ]

[1211] }

[1212] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[1213] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1214] Processing of specific examples

[1215] For example, it operates as follows.

[1216] 1. The user clicks the "Check for typos and sentiment analysis" button.

[1217] 2. The device sends the email body to the server.

[1218] 3. The server requests analysis from the generative AI model and emotion engine.

[1219] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[1220] 5. The emotion engine generates a suggestion to change "I'd like to change it" to "Could you please change it?"

[1221] 6. The server sends the suggested revisions back to the device.

[1222] 7. The device displays the suggested corrections in the user interface.

[1223] 8. The user reviews the suggested fixes and applies them as needed.

[1224] This series of operations allows the user to create an email that is free of typos and that is highly accurate and suited to the user's emotions.

[1225] The processing flow will be explained below.

[1226] Step 1:

[1227] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1228] Step 2:

[1229] The user clicks the "Check for typos and analyze sentiment" button on the email composition screen, which triggers the typo check and sentiment analysis.

[1230] Step 3:

[1231] The device catches the user's click event, retrieves the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. The data sent will be in the following format:

[1232] json

[1233] {

[1234] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1235] }

[1236] Step 4:

[1237] The server receives the request sent from the device and extracts the data from the email body, for example, extracting the text from the email field.

[1238] Step 5:

[1239] The server passes the extracted email body data to the generative AI model and emotion engine, requesting them to analyze typos and emotions, respectively.

[1240] Step 6:

[1241] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It also makes no specific suggestion for "I would like to change it."

[1242] Step 7:

[1243] The emotion engine analyzes the email body and recognizes the user's emotions. In this example, it generates a suggestion to change a phrase like "I would like to change it" to the more polite "Could you please change it?"

[1244] Step 8:

[1245] The generative AI model and emotion engine send back analysis results and suggested modifications to the server. For example, the following data may be returned:

[1246] json

[1247] {

[1248] "corrected_text": "Hello, thank you for your help. Regarding our meeting tomorrow, could we please change the time?",

[1249] "suggestions": [

[1250] {"original": "Conference", "suggestion": "Meeting"},

[1251] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1252] ]

[1253] }

[1254] Step 9:

[1255] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[1256] Step 10:

[1257] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1258] Step 11:

[1259] The user reviews the suggested corrections and applies them as necessary, for example, using "meeting" instead of the suggested "conference," and changing "I'd like to change it" to "Could you please change it?". Finally, the email is completed with no typos and in line with the user's sentiment.

[1260] This series of processing steps allows the user to efficiently create an email without any typos or mistakes, and the expressions in the email will be appropriate to the user's feelings.

[1261] Example 2

[1262] 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."

[1263] Conventional email writing support systems are limited to detecting typos and suggesting corrections, and do not provide sufficient support for users when considering the emotional expression in the email body. In particular, business emails often require appropriate emotional expression, and it is necessary to use appropriate expressions to avoid misunderstandings and rudeness. To solve this problem, a system is needed that not only detects typos but also analyzes the emotional content of the email body and suggests corrections based on that analysis.

[1264] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server is a system that detects typos and omissions in emails and performs sentiment analysis, and includes an information input means for inputting the email body, a means for accepting instructions from a user to check for typos and omissions and to analyze sentiment, a means for transmitting the input email body to an analysis device for analysis, a means for receiving the typo detection results and suggestions for correcting expressions from the analysis device, and a means for displaying the received suggestions for correction on an information display means. This enables a user not only to correct typos and omissions in the email body, but also to easily create an email that expresses appropriate sentiment.

[1265] The "mail body" is the text content that the user inputs to send as an email.

[1266] "Information input means" refers to an input device or software interface that allows a user to input the body of an email.

[1267] "User" refers to the person who operates this system and creates and sends emails.

[1268] "Typo checking" is the process of detecting mistyped or missing characters in the body of an email.

[1269] "Emotion analysis" is the process of recognizing the user's emotions and intentions from the content of the email text entered,

[1270] It is a process of proposing appropriate expressions.

[1271] The "analysis device" is part of a system that includes a generative AI model and an emotion engine, and is a device that analyzes text data and generates analysis results for typos and emotions.

[1272] "Suggested corrections" are suggestions for specific changes to improve entered text based on detected typos and analyzed sentiment.

[1273] "Information display means" refers to a device or interface for visually displaying analysis results and suggested modifications to the user.

[1274] A "prompt" is an input sentence that instructs a generative AI model to perform a specific task, such as "Please check the body of the email for typos and generate suggested corrections."

[1275] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[1276] The hardware and software required to implement this system are as follows: The server is hosted using AWS (Amazon Web Services), and NGINX is used for request routing. The generative AI model uses OpenAI GPT-4, combined with IBM Watson for the emotion engine. The user interface was developed using React.js, and the terminal is connected to a personal computer or smartphone.

[1277] User operations

[1278] The user creates a new email using the email creation user interface. For example, the user might type, "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." Next, the user clicks the "Check for typos and analyze sentiment" button on the email creation screen. This sends instructions to the terminal to check for typos and analyze sentiment.

[1279] Terminal handling

[1280] The device catches the user's click event and retrieves the email body. It converts the retrieved email body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is as follows:

[1281] json

[1282] {

[1283] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1284] }

[1285] Server Processing

[1286] The server receives the HTTP POST request sent from the device and extracts the data from the email body. Then, it passes the extracted email body to the generative AI model and emotion engine for analysis. Specifically, it gives the generative AI model the following prompt:

[1287] "Check the body of your email for typos and generate suggested corrections."

[1288] Meanwhile, the emotion engine is given the following prompt:

[1289] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[1290] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[1291] Returning and displaying results

[1292] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[1293] json

[1294] {

[1295] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1296] "suggestions": [

[1297] {"original": "Conference", "suggestion": "Meeting"},

[1298] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1299] ]

[1300] }

[1301] The server sends the analysis results and suggested revisions received from the generative AI model and emotion engine to the device as an HTTP response. The device receives the response from the server and applies the suggested revisions to the email body area. It also displays the suggested revisions in a separate display area, allowing the user to confirm the suggested revisions.

[1302] Specific examples of operation

[1303] For example, a user types "Hello, thank you for your help. We have a meeting tomorrow, but I'd like to change the time," and clicks the "Check for typos and sentiment analysis" button. The device then sends the email body to the server, which then requests analysis from the generative AI model and sentiment engine. The generative AI model generates a suggestion to change "conference" to "meeting," and the sentiment engine generates a suggestion to change "I'd like to change it" to "Could you please change it?" The server collects and organizes the suggested changes and sends them to the device. The device displays the suggested changes on the user interface, and the user can confirm and apply them.

[1304] This system allows users to easily create emails that are free of typos and that are accurate and reflect the user's emotions.

[1305] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1306] Step 1: User enters email content

[1307] A user uses a user interface for creating emails to input a new email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." This input is the base data for subsequent processing.

[1308] Step 2: User clicks on the "Check spelling and sentiment analysis" button

[1309] The user clicks the "Check for typos and sentiment analysis" button on the email composition screen, which triggers a request to check for typos and sentiment analysis for the entered email body.

[1310] Input: The instruction the user clicked

[1311] Output: Request for typo checking and sentiment analysis

[1312] Step 3: The device converts the email body into JSON format and sends it to the server.

[1313] The terminal converts the email body entered in step 1 into JSON format. The converted data has the following format:

[1314] json

[1315] {

[1316] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1317] }

[1318] The converted data is then sent to the server via an HTTP POST request.

[1319] Input: Email body

[1320] Output: JSON formatted data, HTTP POST request

[1321] Step 4: The server receives the JSON data and prepares it for parsing

[1322] The server receives the HTTP POST request sent from the terminal, extracts the email body from the received JSON data, and prepares it for parsing. At this time, it generates a prompt like this:

[1323] "Check the body of your email for typos and generate suggested corrections."

[1324] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[1325] Input: JSON format data

[1326] Output: Email body, prompt

[1327] Step 5: The server requests the generated AI model and emotion engine for analysis.

[1328] The server requests the generative AI model and emotion engine to analyze the extracted email text. Specifically, it requests the generative AI model to check for typos and suggest corrections, and requests the emotion engine to analyze emotions and suggest corrections to expressions.

[1329] Input: Email body, prompt text

[1330] Output: Analysis requests to generative AI models and emotion engines

[1331] Step 6: Generative AI model and emotion engine perform analysis

[1332] The generative AI model detects typos and omissions in the received email text and generates correction suggestions, while the emotion engine analyzes the email text, recognizes the user's emotions, and generates correction suggestions based on those emotions.

[1333] Input: Email body, prompt text

[1334] Output: Suggested corrections for typos and emotions

[1335] Step 7: The server receives and organizes the analysis results

[1336] The server receives the analysis results returned by the generative AI model and emotion engine. For example, it receives data such as:

[1337] json

[1338] {

[1339] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1340] "suggestions": [

[1341] {"original": "Conference", "suggestion": "Meeting"},

[1342] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1343] ]

[1344] }

[1345] The server organizes these results and formats them for transmission to the terminal.

[1346] Input: Suggestions for correcting typos and emote expressions

[1347] Output: Organized analysis results

[1348] Step 8: The server sends the analysis results to the device.

[1349] The server sends the organized analysis results to the terminal as an HTTP response.

[1350] Input: Organized analysis results

[1351] Output: Parsed results as an HTTP response

[1352] Step 9: The device receives the analysis results and displays them on the user interface.

[1353] The terminal analyzes the response received from the server, applies automatic correction suggestions to the email body area, and displays the suggestions in a separate display area to allow the user to confirm the correction suggestions.

[1354] Input: Parsed result as HTTP response

[1355] Output: Suggested fixes displayed in the user interface

[1356] Step 10: User reviews and applies suggested fixes

[1357] The user checks the suggested corrections displayed on the terminal and applies them as necessary. If the user is satisfied, the suggested corrections are applied to the original email body and the email is prepared for sending.

[1358] Input: Suggested corrections displayed in the user interface

[1359] Output: Modified email body

[1360] (Application example 2)

[1361] 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."

[1362] In today's world, it is important to improve the quality of email and message communication. However, emails and messages entered by users often contain typos, which can lead to misunderstandings and discomfort. Furthermore, especially in customer service situations, expressions that reflect appropriate emotions are required. However, correcting these errors on your own is a time-consuming and labor-intensive process, which presents a challenge.

[1363] The identification process by the identification 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 sending the email body to the generative AI model and the emotion engine for analysis, means for receiving typographical error detection results from the generative AI model and correction suggestions from the emotion engine, and means for displaying the received correction suggestions on a user interface. This makes it possible to detect typos in emails and provide users with correction suggestions that take emotions into consideration.

[1364] The "email typo and omission detection system" is a system that automatically detects typos and omissions in emails written by users and suggests corrections.

[1365] The "user interface" refers to a screen or operating means through which a user inputs the body of an email, checks for typos, and confirms the results of sentiment analysis.

[1366] The "means for accepting instructions for checking for typos and omissions and for sentiment analysis" is a function for accepting an operation from the user to request checking for typos and omissions and for sentiment analysis.

[1367] A "generative AI model" is an artificial intelligence model that learns from a variety of text data in advance and detects typos and misspellings in the body of an email.

[1368] The "emotion engine" is an analysis engine that analyzes the user's email text and suggests appropriate emotional expressions.

[1369] The "means of analysis" is a function that sends the email body to a generative AI model and emotion engine to analyze typos and emotions.

[1370] The "means for receiving correction suggestions" is a function for receiving analysis results and correction suggestions from the generative AI model and the emotion engine.

[1371] The "means for displaying the proposed revisions on the user interface" is a function for visually presenting the received analysis results and proposed revisions to the user.

[1372] To implement the present invention, the system includes the following components:

[1373] User Interface Behavior

[1374] A user creates an email using a dedicated application on a smartphone. This application includes an input field for the email body and buttons for checking for typos and sentiment analysis. For example, the user enters "Hello, thank you for your support. I would like to ask you to make a few changes to the information about tomorrow's campaign," and clicks the buttons for checking for typos and sentiment analysis.

[1375] Data transmission and analysis

[1376] The device captures the email body entered by the user, converts it to JSON format, and sends it to the server as an HTTP POST request. The server then passes this email body to the generative AI model and emotion engine, which perform typo analysis and sentiment analysis, respectively. At this time, the generative AI model detects typos and generates correction suggestions based on a large amount of text data it has previously learned. Meanwhile, the emotion engine analyzes the sentiment of the email body and generates correction suggestions that match the user's intention.

[1377] Receive and view suggested revisions

[1378] The server receives the analysis results from the generative AI model and emotion engine and sends data including suggested modifications back to the device. The device receives this data and displays it on the user interface. The user can review the suggested modifications and select and apply them as needed.

[1379] Hardware and software used

[1380] Device: Smartphone, smart glasses, or head-mounted display

[1381] Server: The server can be a cloud server and has the necessary processing power.

[1382] Software: Generative AI models, emotion engines, HTTP and JSON libraries (e.g., requests)

[1383] Data format: Serialize and deserialize data in JSON format

[1384] Adding specific examples

[1385] For example, it operates as follows.

[1386] The user enters a specific email body into the application (e.g., "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign.").

[1387] The application sends this email to the server for analysis.

[1388] The server analyzes the email using a generative AI model and an emotion engine and generates a suggested revision, such as: "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?"

[1389] The server returns the analysis results to the terminal, which displays them on a user interface.

[1390] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1391] Step 1:

[1392] The user launches the dedicated application and enters the email body. For example, the user might enter, "Hello, thank you for your support. I would like to request a small change to the information about tomorrow's campaign." This email body is then displayed in the user interface.

[1393] Type: "Hello, thank you for your help. I'd like to make a small change to the information about tomorrow's campaign."

[1394] Output: The email body is displayed in the user interface.

[1395] Step 2:

[1396] The user clicks the buttons for checking for typos and sentiment analysis, and the device receives the user's instructions and captures the email body.

[1397] Input: Button click for typo checking and sentiment analysis

[1398] Output: The email body is captured in the device's memory.

[1399] Step 3:

[1400] The device converts the captured email body into JSON format and sends it to the server as an HTTP POST request. For example, it generates the following JSON data:

[1401] Input: Captured email body

[1402] Output: The email body in JSON data format is sent to the server as an HTTP POST request.

[1403] {

[1404] "email": "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign."

[1405] }

[1406] Step 4:

[1407] The server receives the JSON data sent from the device, extracts the body of the received email, and passes it to the generative AI model and emotion engine.

[1408] Input: Email body in JSON format

[1409] Output: The email body is passed to the generative AI model and sentiment engine.

[1410] Step 5:

[1411] The generative AI model detects typos and generates correction suggestions in the email body, while the sentiment engine analyzes the email body and generates sentiment-based correction suggestions.

[1412] Input: Email body

[1413] Output: Analysis results with suggested modifications

[1414] {

[1415] "corrected_text": "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?",

[1416] "suggestions": [

[1417] {"original": "I'd like to make some changes.", "suggestion": "Could you please make some changes?"}

[1418] ]

[1419] }

[1420] Step 6:

[1421] The server receives the analysis results from the generative AI model and emotion engine and sends the data back to the device.

[1422] Input: Analysis results with suggested modifications

[1423] Output: The analysis results, including suggested corrections, are sent back to the device as an HTTP response.

[1424] Step 7:

[1425] The device receives the analysis results from the server and displays them on a user interface, which visually indicates suggested modifications and allows the user to confirm and apply them.

[1426] Input: Analysis results with suggested modifications

[1427] Output: The suggested corrections are displayed in the user interface.

[1428] Step 8:

[1429] The user checks the proposed corrections and applies them as necessary. The email body with the corrections applied is then reflected in the user interface.

[1430] Enter: Confirm suggested corrections

[1431] Output: The email body with the applied corrections is reflected in the user interface.

[1432] 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.

[1433] 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.

[1434] 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.

[1435] [Fourth embodiment]

[1436] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1437] 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.

[1438] 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).

[1439] 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.

[1440] 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.

[1441] 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).

[1442] 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.

[1443] 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.

[1444] 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.

[1445] 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.

[1446] 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.

[1447] 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.

[1448] 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."

[1449] This invention relates to a system that automatically detects typos in emails in business communications and provides correction suggestions. The system consists of four main components: a user interface, a generative AI model, a server, and a terminal.

[1450] Overall system overview

[1451] The user uses a user interface for composing an email and inputs the body of the email. When the user issues an instruction to check for typos, the device captures this instruction and sends the email body to the server. The server passes the received email body to a generative AI model, which analyzes it for typos. The generative AI model detects typos, suggests corrections, and returns the results to the server. The server sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[1452] The operation of each component of the system will be specifically described below.

[1453] User operations

[1454] The user creates a new email on the user interface and inputs the message body. For example, the user might input "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1455] The user clicks the "Check for typos" button on the email composition screen, which sends a command to check for typos to the terminal.

[1456] Terminal handling

[1457] The terminal receives instructions from the user to check for typos, converts the email body into JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[1458] json

[1459] {

[1460] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1461] }

[1462] Server Processing

[1463] The server analyzes the request received from the device, extracts the email body data, and then passes this email body to the generative AI model for analysis.

[1464] Based on the data it has learned, the generative AI model detects typos in the email body and generates correction suggestions. For example, it might generate the following correction suggestions:

[1465] "Hello, thank you for your help. Tomorrow's meeting" was changed to "Hello, thank you for your help. Tomorrow's meeting"

[1466] "I want to change the time." remains as "I want to change the time."

[1467] Returning and displaying results

[1468] The server sends the detection results and correction suggestions returned by the generative AI model back to the device. For example, it sends JSON data summarizing the correction suggestions as follows:

[1469] json

[1470] {

[1471] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1472] "suggestions": [

[1473] {"original": "Conference", "suggestion": "Meeting"}

[1474] ]

[1475] }

[1476] The terminal displays the received correction suggestions on a user interface, allowing the user to check the suggested corrections for typos and apply the corrections as necessary.

[1477] Processing of specific examples

[1478] For example, it operates as follows.

[1479] 1. The user clicks the "Check for typos" button.

[1480] 2. The device sends the email body to the server.

[1481] 3. The server requests the generated AI model to perform analysis.

[1482] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[1483] 5. The server sends the suggested revisions back to the device.

[1484] 6. The device displays the suggested corrections in the user interface.

[1485] 7. The user reviews the suggested fixes and applies them as needed.

[1486] This series of operations allows the user to efficiently and accurately create an email without any typos or errors.

[1487] The processing flow will be explained below.

[1488] Step 1:

[1489] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1490] Step 2:

[1491] The user clicks the "Check for typos" button on the email composition screen, which triggers a command to check for typos.

[1492] Step 3:

[1493] The device catches the user's click event, obtains the email body, converts it to JSON format, and sends it to the server as an HTTP POST request.

[1494] Step 4:

[1495] The server receives the request sent from the terminal and extracts the data from the email body. For example, the received data looks like this:

[1496] json

[1497] {

[1498] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1499] }

[1500] Step 5:

[1501] The server passes the extracted email text to a generative AI model and requests it to analyze typos. The generative AI model analyzes the email text based on data it has learned in advance.

[1502] Step 6:

[1503] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It makes no changes to "I would like to change it."

[1504] Step 7:

[1505] The analysis results and suggested corrections from the generative AI model are sent back to the server. For example, the returned data looks like this:

[1506] json

[1507] {

[1508] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1509] "suggestions": [

[1510] {"original": "Conference", "suggestion": "Meeting"}

[1511] ]

[1512] }

[1513] Step 8:

[1514] The server sends the analysis results and correction suggestions received from the generative AI model to the terminal as an HTTP response.

[1515] Step 9:

[1516] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1517] Step 10:

[1518] The user reviews the suggested corrections and applies them if necessary, and finally the error-free email is ready to be sent.

[1519] This series of processing steps allows the system to efficiently and accurately detect typographical errors in emails and provide correction suggestions.

[1520] Example 1

[1521] 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."

[1522] In modern business communication, quickly creating text without typos and errors is an important challenge. However, manual proofreading is time-consuming and labor-intensive, and carries a high risk of errors remaining. To solve this problem, a system is needed that can automatically detect typos and errors in text and provide appropriate correction suggestions.

[1523] 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.

[1524] In this invention, the server includes a means for analyzing text and passing it to a generative model, a means for receiving error detection results and correction suggestions from the generative model, and a means for returning the received correction suggestions to the terminal, thereby enabling the user to accurately and quickly create text without typos or errors.

[1525] "Text" refers to a string of characters written in electronic format.

[1526] An "interface" refers to a software or hardware component that provides a screen or operating means for a user to input information into a system.

[1527] A "generative model" refers to an artificial intelligence model that learns from a variety of data in advance and performs specific analytical or generative tasks.

[1528] "Server" refers to a central computer system that receives, analyzes, and returns data.

[1529] "User" refers to an individual or group that directly operates the system and utilizes the intended functions.

[1530] An "instruction" refers to an operation or command that a user makes to request a specific action from the system.

[1531] "Analysis" refers to the operation of extracting and processing specific information based on given data.

[1532] "Suggested corrections" refers to corrections suggested by the system for detected typos.

[1533] "Visual" refers to displaying information in a way that is easily seen and understood by the user.

[1534] "Application" refers to the operation of reflecting the proposed corrections in the actual data.

[1535] This invention is a system that automatically detects typos in emails and provides correction suggestions. The system consists of four main components: a user interface, a generative model, a server, and a terminal.

[1536] Overall system overview

[1537] The user uses the interface for creating an email and inputs the body of the email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." When the user clicks the "Check for typos" button, the terminal captures this instruction and sends the body of the email to the server.

[1538] The server analyzes the received email text and passes it to a generative model to analyze typos. The generative model detects typos and generates correction suggestions and returns the results to the server. The server then sends the results back to the device, which displays them in the user interface. The user can review them and apply corrections as needed.

[1539] User Interface

[1540] The user interface provides a field for entering text and a "check for typos" button. This interface is often implemented as a browser-based application. The user enters text and clicks the "check for typos" button, which then sends the text to the device.

[1541] Generative Model

[1542] A generative model is an artificial intelligence model that analyzes input text, detects typos, and suggests corrections. This AI model has been trained on a variety of email sentences in advance, so it is able to detect typos with high accuracy.

[1543] server

[1544] The server analyzes the text data received from the device and passes it to the generative model. The server receives the typographical error detection results and correction suggestions sent back from the generative model and sends them back to the device.

[1545] Terminal

[1546] The terminal is responsible for sending user input to the server, receiving replies from the server, and displaying them on a user interface.

[1547] Specific examples

[1548] For example, if a user enters the following text and clicks the "Check for typos" button:

[1549] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1550] The device then generates the following prompt sentence and sends it to the Generative Model via the server:

[1551] "Please detect any typos in the following text and suggest appropriate corrections:

[1552] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1553] The generative model receives this prompt and performs analysis, generating a suggestion to modify, for example, "conference" to "meeting." This suggestion is sent back to the server and ultimately displayed to the user via the device.

[1554] In this way, the user can efficiently and accurately compose emails without any typos or errors.

[1555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1556] Step 1:

[1557] The user enters the email body on the email creation screen and clicks the "Check for typos" button. This action requests the system to check for typos. The email body is provided as input, and instructions to check for typos are sent to the terminal as output.

[1558] Step 2:

[1559] The terminal receives the user's instructions and converts the entered email body into JSON format. The converted data looks like this:

[1560] json

[1561] {

[1562] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1563] }

[1564] Send an HTTP POST request containing this JSON data to the server, taking the email body as input and sending the JSON data to the server as output.

[1565] Step 3:

[1566] The server parses the HTTP POST request received from the terminal and extracts the email body data. It receives JSON data as input and prepares the email body as output to be passed to the generative model. Next, the server generates and sends the following prompt to the generative model:

[1567] "Please detect any typos in the following text and suggest appropriate corrections:

[1568] Hello, thank you for your help. I would like to change the time of tomorrow's meeting.

[1569] It takes the email body as input and generates a prompt as output.

[1570] Step 4:

[1571] The generative model receives the prompt and analyzes the email body for typos. It takes the prompt as input and generates typo detection results and correction suggestions as output. For example, the following correction suggestions are generated:

[1572] json

[1573] {

[1574] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1575] "suggestions": [

[1576] {"original": "Conference", "suggestion": "Meeting"}

[1577] ]

[1578] }

[1579] The generative model sends the correction suggestions back to the server. It takes the prompt sentence as input and generates the correction suggestions as output.

[1580] Step 5:

[1581] The server receives the correction suggestions returned from the generative model and parses them. It takes the correction suggestions as input and returns JSON data to the device as output. For example, the following data is returned:

[1582] json

[1583] {

[1584] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1585] "suggestions": [

[1586] {"original": "Conference", "suggestion": "Meeting"}

[1587] ]

[1588] }

[1589] It receives correction suggestions as input and produces parsed data as output, which it sends back to the terminal.

[1590] Step 6:

[1591] The terminal parses the correction suggestions received from the server and displays them in the user interface. It receives JSON data from the server as input and displays the correction suggestions in the user interface as output. The user can review the results and choose whether to apply the correction suggestions.

[1592] Step 7:

[1593] The user reviews the suggested corrections displayed in the user interface and applies them as necessary. The suggested corrections displayed in the interface are taken as input, and the final, typographically correct email body is generated as output. The user selects and applies the suggested corrections.

[1594] (Application example 1)

[1595] 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."

[1596] In recent years, the quality of articles, blogs, and newsletters has become increasingly important in content distribution services. However, the presence of typos and misspellings undermines the reliability of the content and leads to a poor user experience. Traditional manual typographical error checking is time-consuming, labor-intensive, and inefficient. Furthermore, while suggestions for improvements in sentence structure and style are also desired, there is a lack of efficient means to achieve this. A system that can solve these issues and improve the quality of content distribution is needed.

[1597] 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.

[1598] In this invention, the server includes means for providing a user interface for inputting content such as articles, blogs, and newsletters, means for accepting instructions from a user to check for typos and omissions, means for transmitting the input content to a generative AI model for analysis, means for receiving typo detection results and correction suggestions from the generative AI model, and means for displaying the received correction suggestions on the user interface, thereby enabling content creators to automatically detect and correct typos and provide efficient, high-quality content.

[1599] An "article" is a piece of written text intended to convey information in the form of news, blogs, newsletters, etc.

[1600] A "blog" is a diary-style piece of writing published on the web that records the opinions, experiences, and thoughts of an individual or organization.

[1601] A "newsletter" is a regularly published informational document used by a company or organization to inform interested parties of the latest information and updates.

[1602] "User interface" refers to an operation screen or input device that allows a user to directly interact with a system.

[1603] "Typo" refers to an error where a character is typed incorrectly or a character is missing from a sentence.

[1604] A "generative AI model" is an artificial intelligence model that learns from large amounts of text data and generates and analyzes sentences.

[1605] "Analysis" refers to the process of examining data or information in detail to understand its structure and meaning.

[1606] "Correction proposals" are proposals for appropriate corrections to detected errors or areas for improvement.

[1607] A "server" is a computer system that provides services to other computers on a network.

[1608] A specific method for implementing this invention is described below. This system detects typographical errors in articles, blogs, and newsletters in a content distribution service and suggests corrections. The system consists of a user interface, a server, a generative AI model, and a terminal.

[1609] System Overview

[1610] Users input content such as articles, blogs, and newsletters through a user interface that can be used on a variety of devices, including smartphones and PCs.

[1611] The terminal receives instructions from the user to check for typos. The input content is sent to the server as JSON format data via an HTTP POST request. For example, the following data is sent:

[1612] json

[1613] {

[1614] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1615] }

[1616] The server receives this request and requests the generative AI model to analyze it. OpenAI's GPT-4 is used as the generative AI model. This model has been trained on a variety of content texts in advance, enabling highly accurate analysis.

[1617] The generative AI model detects typos in the received content and generates correction suggestions, such as the following prompt:

[1618] "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1619] The server receives the analysis results of the generative AI model and sends them back to the device. The returned data includes the corrected text and suggested corrections:

[1620] json

[1621] {

[1622] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[1623] "suggestions": [

[1624] {"original": "camera technology", "suggestion": "camera technology"}

[1625] ]

[1626] }

[1627] The device displays the suggested corrections in a user interface, allowing the user to review the suggestions and apply or correct them, resulting in high-quality content that is free of typos and errors.

[1628] This system allows users to efficiently and accurately correct typos and distribute content. The hardware uses a high-performance computer system for the server, and the software uses Flask (a Python framework) and the OpenAI API.

[1629] Such a system and method can significantly improve the quality of content delivery.

[1630] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1631] Step 1:

[1632] The user enters a content body (e.g., "A new smartphone has been released. It has the latest camera technology, a larger battery, and a more powerful processor.") into the user interface. The content body is provided as input to the system.

[1633] Step 2:

[1634] The user clicks the "Check for typos" button. This action causes the device to receive an instruction to check for typos. The device converts the entered content body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is in the following format:

[1635] json

[1636] {

[1637] "content": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor."

[1638] }

[1639] Step 3:

[1640] The server parses the request received from the device and extracts the content body from the JSON data. This extracted body is then provided to a generative AI model (e.g., OpenAI GPT-4) for analysis. The purpose of the analysis is to detect typos and generate correction suggestions.

[1641] Step 4:

[1642] The generative AI model analyzes the input content body, detects typos, and generates correction suggestions that are sent back to the server, such as:

[1643] json

[1644] {

[1645] "corrected_text": "A new smartphone has been released. It comes with the latest camera technology, a large battery, and a powerful processor.",

[1646] "suggestions": [

[1647] {"original": "camera technology", "suggestion": "camera technology"}

[1648] ]

[1649] }

[1650] Step 5:

[1651] The server receives the analysis results and correction suggestions from the generative AI model and sends them back to the device. The data sent from the server to the device includes the corrected text and the correction suggestions.

[1652] Step 6:

[1653] The terminal displays the correction suggestions and analysis results received from the server on a user interface. The user checks the displayed results and applies corrections as necessary. For example, the user checks the correction suggestions and selects whether to change "camera technique" to "camera technology."

[1654] Step 7:

[1655] The user then confirms the final corrections and confirms the applied content body, resulting in a high-quality content that is free of typos and errors.

[1656] This allows users to efficiently create and distribute high-quality content, and also clarifies the data generated at each step and its transformation.

[1657] 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.

[1658] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[1659] Overall system overview

[1660] The user uses a user interface for composing an email and inputs the body of the email. When the user issues instructions for checking for typos and sentiment analysis, the device receives these instructions and sends the email body to the server. The server then passes the received email body to a generative AI model and an emotion engine, which analyzes the typos and sentiment. The generative AI model and emotion engine then generate suggestions for correcting typos and sentiment-based expressions, respectively, and return the results to the server. The server then sends the results back to the device, which displays them on the user interface, allowing the user to review them and apply corrections as necessary.

[1661] The operation of each component of the system will be specifically described below.

[1662] User operations

[1663] The user creates a new email on the user interface and enters the message body, for example, "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1664] The user clicks the "Check for typos and analyze emotions" button on the email composition screen, which sends instructions to the terminal to check for typos and analyze emotions.

[1665] Terminal handling

[1666] The device catches the user's click event, gets the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. For example, the data sent is as follows:

[1667] json

[1668] {

[1669] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1670] }

[1671] Server Processing

[1672] The server receives the request sent from the device, extracts the data from the email body, and then passes the extracted email body to the generative AI model and emotion engine for analysis.

[1673] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[1674] Returning and displaying results

[1675] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[1676] json

[1677] {

[1678] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1679] "suggestions": [

[1680] {"original": "Conference", "suggestion": "Meeting"},

[1681] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1682] ]

[1683] }

[1684] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[1685] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1686] Processing of specific examples

[1687] For example, it operates as follows.

[1688] 1. The user clicks the "Check for typos and sentiment analysis" button.

[1689] 2. The device sends the email body to the server.

[1690] 3. The server requests analysis from the generative AI model and emotion engine.

[1691] 4. The generative AI model generates a suggestion to change "conference" to "meeting."

[1692] 5. The emotion engine generates a suggestion to change "I'd like to change it" to "Could you please change it?"

[1693] 6. The server sends the suggested revisions back to the device.

[1694] 7. The device displays the suggested corrections in the user interface.

[1695] 8. The user reviews the suggested fixes and applies them as needed.

[1696] This series of operations allows the user to create an email that is free of typos and that is highly accurate and suited to the user's emotions.

[1697] The processing flow will be explained below.

[1698] Step 1:

[1699] The user opens the email creation screen and enters the message body to create a new email. For example, the user might enter "Hello, thank you for your help. I would like to change the time of tomorrow's meeting."

[1700] Step 2:

[1701] The user clicks the "Check for typos and analyze sentiment" button on the email composition screen, which triggers the typo check and sentiment analysis.

[1702] Step 3:

[1703] The device catches the user's click event, retrieves the email body, converts it to JSON format, and sends it to the server as an HTTP POST request. The data sent will be in the following format:

[1704] json

[1705] {

[1706] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1707] }

[1708] Step 4:

[1709] The server receives the request sent from the device and extracts the data from the email body, for example, extracting the text from the email field.

[1710] Step 5:

[1711] The server passes the extracted email body data to the generative AI model and emotion engine, requesting them to analyze typos and emotions, respectively.

[1712] Step 6:

[1713] The generative AI model analyzes the email body and detects typos. For example, it generates a suggestion to change "meeting" from "conference" to "meeting." It also makes no specific suggestion for "I would like to change it."

[1714] Step 7:

[1715] The emotion engine analyzes the email body and recognizes the user's emotions. In this example, it generates a suggestion to change a phrase like "I would like to change it" to the more polite "Could you please change it?"

[1716] Step 8:

[1717] The generative AI model and emotion engine send back analysis results and suggested modifications to the server. For example, the following data may be returned:

[1718] json

[1719] {

[1720] "corrected_text": "Hello, thank you for your help. Regarding our meeting tomorrow, could we please change the time?",

[1721] "suggestions": [

[1722] {"original": "Conference", "suggestion": "Meeting"},

[1723] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1724] ]

[1725] }

[1726] Step 9:

[1727] The server sends the analysis results and correction suggestions received from the generative AI model and emotion engine to the terminal as an HTTP response.

[1728] Step 10:

[1729] The terminal receives the response from the server, applies the suggested corrections to the email body area, and displays the suggested corrections in a separate display area to allow the user to confirm the suggested corrections.

[1730] Step 11:

[1731] The user reviews the suggested corrections and applies them as necessary, for example, using "meeting" instead of the suggested "conference," and changing "I'd like to change it" to "Could you please change it?". Finally, the email is completed with no typos and in line with the user's sentiment.

[1732] This series of processing steps allows the user to efficiently create an email without any typos or mistakes, and the expressions in the email will be appropriate to the user's feelings.

[1733] Example 2

[1734] 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."

[1735] Conventional email writing support systems are limited to detecting typos and suggesting corrections, and do not provide sufficient support for users when considering the emotional expression in the email body. In particular, business emails often require appropriate emotional expression, and it is necessary to use appropriate expressions to avoid misunderstandings and rudeness. To solve this problem, a system is needed that not only detects typos but also analyzes the emotional content of the email body and suggests corrections based on that analysis.

[1736] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server is a system that detects typos and omissions in emails and performs sentiment analysis, and includes an information input means for inputting the email body, a means for accepting instructions from a user to check for typos and omissions and to analyze sentiment, a means for transmitting the input email body to an analysis device for analysis, a means for receiving the typo detection results and suggestions for correcting expressions from the analysis device, and a means for displaying the received suggestions for correction on an information display means. This enables a user not only to correct typos and omissions in the email body, but also to easily create an email that expresses appropriate sentiment.

[1737] The "mail body" is the text content that the user inputs to send as an email.

[1738] "Information input means" refers to an input device or software interface that allows a user to input the body of an email.

[1739] "User" refers to the person who operates this system and creates and sends emails.

[1740] "Typo checking" is the process of detecting mistyped or missing characters in the body of an email.

[1741] "Emotion analysis" is the process of recognizing the user's emotions and intentions from the content of the email text entered,

[1742] It is a process of proposing appropriate expressions.

[1743] The "analysis device" is part of a system that includes a generative AI model and an emotion engine, and is a device that analyzes text data and generates analysis results for typos and emotions.

[1744] "Suggested corrections" are suggestions for specific changes to improve entered text based on detected typos and analyzed sentiment.

[1745] "Information display means" refers to a device or interface for visually displaying analysis results and suggested modifications to the user.

[1746] A "prompt" is an input sentence that instructs a generative AI model to perform a specific task, such as "Please check the body of the email for typos and generate suggested corrections."

[1747] This invention relates to a system that detects typos in emails and analyzes the user's emotions to suggest appropriate corrections. The system consists of the following main components: a user interface, a generative AI model, an emotion engine, a server, and a terminal.

[1748] The hardware and software required to implement this system are as follows: The server is hosted using AWS (Amazon Web Services), and NGINX is used for request routing. The generative AI model uses OpenAI GPT-4, combined with IBM Watson for the emotion engine. The user interface was developed using React.js, and the terminal is connected to a personal computer or smartphone.

[1749] User operations

[1750] The user creates a new email using the email creation user interface. For example, the user might type, "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." Next, the user clicks the "Check for typos and analyze sentiment" button on the email creation screen. This sends instructions to the terminal to check for typos and analyze sentiment.

[1751] Terminal handling

[1752] The device catches the user's click event and retrieves the email body. It converts the retrieved email body into JSON format and sends it to the server as an HTTP POST request. For example, the data to be sent is as follows:

[1753] json

[1754] {

[1755] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1756] }

[1757] Server Processing

[1758] The server receives the HTTP POST request sent from the device and extracts the data from the email body. Then, it passes the extracted email body to the generative AI model and emotion engine for analysis. Specifically, it gives the generative AI model the following prompt:

[1759] "Check the body of your email for typos and generate suggested corrections."

[1760] Meanwhile, the emotion engine is given the following prompt:

[1761] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[1762] The generative AI model detects typos and misspellings in the email body based on pre-trained data and generates correction suggestions. The emotion engine analyzes the email body entered by the user and recognizes the user's emotions. Based on the recognized emotions, it generates correction suggestions with appropriate expressions.

[1763] Returning and displaying results

[1764] The analysis results and suggested modifications from the generative AI model and emotion engine are sent back to the server. For example, the returned data might look like this:

[1765] json

[1766] {

[1767] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1768] "suggestions": [

[1769] {"original": "Conference", "suggestion": "Meeting"},

[1770] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1771] ]

[1772] }

[1773] The server sends the analysis results and suggested revisions received from the generative AI model and emotion engine to the device as an HTTP response. The device receives the response from the server and applies the suggested revisions to the email body area. It also displays the suggested revisions in a separate display area, allowing the user to confirm the suggested revisions.

[1774] Specific examples of operation

[1775] For example, a user types "Hello, thank you for your help. We have a meeting tomorrow, but I'd like to change the time," and clicks the "Check for typos and sentiment analysis" button. The device then sends the email body to the server, which then requests analysis from the generative AI model and sentiment engine. The generative AI model generates a suggestion to change "conference" to "meeting," and the sentiment engine generates a suggestion to change "I'd like to change it" to "Could you please change it?" The server collects and organizes the suggested changes and sends them to the device. The device displays the suggested changes on the user interface, and the user can confirm and apply them.

[1776] This system allows users to easily create emails that are free of typos and that are accurate and reflect the user's emotions.

[1777] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1778] Step 1: User enters email content

[1779] A user uses a user interface for creating emails to input a new email. For example, the user might input "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time." This input is the base data for subsequent processing.

[1780] Step 2: User clicks on the "Check spelling and sentiment analysis" button

[1781] The user clicks the "Check for typos and sentiment analysis" button on the email composition screen, which triggers a request to check for typos and sentiment analysis for the entered email body.

[1782] Input: The instruction the user clicked

[1783] Output: Request for typo checking and sentiment analysis

[1784] Step 3: The device converts the email body into JSON format and sends it to the server.

[1785] The terminal converts the email body entered in step 1 into JSON format. The converted data has the following format:

[1786] json

[1787] {

[1788] "email": "Hello, thank you for your help. I have a meeting tomorrow, but I'd like to change the time."

[1789] }

[1790] The converted data is then sent to the server via an HTTP POST request.

[1791] Input: Email body

[1792] Output: JSON formatted data, HTTP POST request

[1793] Step 4: The server receives the JSON data and prepares it for parsing

[1794] The server receives the HTTP POST request sent from the terminal, extracts the email body from the received JSON data, and prepares it for parsing. At this time, it generates a prompt like this:

[1795] "Check the body of your email for typos and generate suggested corrections."

[1796] "Furthermore, analyze the user's sentiment based on the content of the text and suggest appropriate wording corrections."

[1797] Input: JSON format data

[1798] Output: Email body, prompt

[1799] Step 5: The server requests the generated AI model and emotion engine for analysis.

[1800] The server requests the generative AI model and emotion engine to analyze the extracted email text. Specifically, it requests the generative AI model to check for typos and suggest corrections, and requests the emotion engine to analyze emotions and suggest corrections to expressions.

[1801] Input: Email body, prompt text

[1802] Output: Analysis requests to generative AI models and emotion engines

[1803] Step 6: Generative AI model and emotion engine perform analysis

[1804] The generative AI model detects typos and omissions in the received email text and generates correction suggestions, while the emotion engine analyzes the email text, recognizes the user's emotions, and generates correction suggestions based on those emotions.

[1805] Input: Email body, prompt text

[1806] Output: Suggested corrections for typos and emotions

[1807] Step 7: The server receives and organizes the analysis results

[1808] The server receives the analysis results returned by the generative AI model and emotion engine. For example, it receives data such as:

[1809] json

[1810] {

[1811] "corrected_text": "Hello, thank you for your help. I have a meeting tomorrow and I'd like to change the time.",

[1812] "suggestions": [

[1813] {"original": "Conference", "suggestion": "Meeting"},

[1814] {"original": "I'd like to change it.", "suggestion": "Could you please make the change?"}

[1815] ]

[1816] }

[1817] The server organizes these results and formats them for transmission to the terminal.

[1818] Input: Suggestions for correcting typos and emote expressions

[1819] Output: Organized analysis results

[1820] Step 8: The server sends the analysis results to the device.

[1821] The server sends the organized analysis results to the terminal as an HTTP response.

[1822] Input: Organized analysis results

[1823] Output: Parsed results as an HTTP response

[1824] Step 9: The device receives the analysis results and displays them on the user interface.

[1825] The terminal analyzes the response received from the server, applies automatic correction suggestions to the email body area, and displays the suggestions in a separate display area to allow the user to confirm the correction suggestions.

[1826] Input: Parsed result as HTTP response

[1827] Output: Suggested fixes displayed in the user interface

[1828] Step 10: User reviews and applies suggested fixes

[1829] The user checks the suggested corrections displayed on the terminal and applies them as necessary. If the user is satisfied, the suggested corrections are applied to the original email body and the email is prepared for sending.

[1830] Input: Suggested corrections displayed in the user interface

[1831] Output: Modified email body

[1832] (Application example 2)

[1833] 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."

[1834] In today's world, it is important to improve the quality of email and message communication. However, emails and messages entered by users often contain typos, which can lead to misunderstandings and discomfort. Furthermore, especially in customer service situations, expressions that reflect appropriate emotions are required. However, correcting these errors on your own is a time-consuming and labor-intensive process, which presents a challenge.

[1835] The identification process by the identification 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 sending the email body to the generative AI model and the emotion engine for analysis, means for receiving typographical error detection results from the generative AI model and correction suggestions from the emotion engine, and means for displaying the received correction suggestions on a user interface. This makes it possible to detect typos in emails and provide users with correction suggestions that take emotions into consideration.

[1836] The "email typo and omission detection system" is a system that automatically detects typos and omissions in emails written by users and suggests corrections.

[1837] The "user interface" refers to a screen or operating means through which a user inputs the body of an email, checks for typos, and confirms the results of sentiment analysis.

[1838] The "means for accepting instructions for checking for typos and omissions and for sentiment analysis" is a function for accepting an operation from the user to request checking for typos and omissions and for sentiment analysis.

[1839] A "generative AI model" is an artificial intelligence model that learns from a variety of text data in advance and detects typos and misspellings in the body of an email.

[1840] The "emotion engine" is an analysis engine that analyzes the user's email text and suggests appropriate emotional expressions.

[1841] The "means of analysis" is a function that sends the email body to a generative AI model and emotion engine to analyze typos and emotions.

[1842] The "means for receiving correction suggestions" is a function for receiving analysis results and correction suggestions from the generative AI model and the emotion engine.

[1843] The "means for displaying the proposed revisions on the user interface" is a function for visually presenting the received analysis results and proposed revisions to the user.

[1844] To implement the present invention, the system includes the following components:

[1845] User Interface Behavior

[1846] A user creates an email using a dedicated application on a smartphone. This application includes an input field for the email body and buttons for checking for typos and sentiment analysis. For example, the user enters "Hello, thank you for your support. I would like to ask you to make a few changes to the information about tomorrow's campaign," and clicks the buttons for checking for typos and sentiment analysis.

[1847] Data transmission and analysis

[1848] The device captures the email body entered by the user, converts it to JSON format, and sends it to the server as an HTTP POST request. The server then passes this email body to the generative AI model and emotion engine, which perform typo analysis and sentiment analysis, respectively. At this time, the generative AI model detects typos and generates correction suggestions based on a large amount of text data it has previously learned. Meanwhile, the emotion engine analyzes the sentiment of the email body and generates correction suggestions that match the user's intention.

[1849] Receive and view suggested revisions

[1850] The server receives the analysis results from the generative AI model and emotion engine and sends data including suggested modifications back to the device. The device receives this data and displays it on the user interface. The user can review the suggested modifications and select and apply them as needed.

[1851] Hardware and software used

[1852] Device: Smartphone, smart glasses, or head-mounted display

[1853] Server: The server can be a cloud server and has the necessary processing power.

[1854] Software: Generative AI models, emotion engines, HTTP and JSON libraries (e.g., requests)

[1855] Data format: Serialize and deserialize data in JSON format

[1856] Adding specific examples

[1857] For example, it operates as follows.

[1858] The user enters a specific email body into the application (e.g., "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign.").

[1859] The application sends this email to the server for analysis.

[1860] The server analyzes the email using a generative AI model and an emotion engine and generates a suggested revision, such as: "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?"

[1861] The server returns the analysis results to the terminal, which displays them on a user interface.

[1862] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1863] Step 1:

[1864] The user launches the dedicated application and enters the email body. For example, the user might enter, "Hello, thank you for your support. I would like to request a small change to the information about tomorrow's campaign." This email body is then displayed in the user interface.

[1865] Type: "Hello, thank you for your help. I'd like to make a small change to the information about tomorrow's campaign."

[1866] Output: The email body is displayed in the user interface.

[1867] Step 2:

[1868] The user clicks the buttons for checking for typos and sentiment analysis, and the device receives the user's instructions and captures the email body.

[1869] Input: Button click for typo checking and sentiment analysis

[1870] Output: The email body is captured in the device's memory.

[1871] Step 3:

[1872] The device converts the captured email body into JSON format and sends it to the server as an HTTP POST request. For example, it generates the following JSON data:

[1873] Input: Captured email body

[1874] Output: The email body in JSON data format is sent to the server as an HTTP POST request.

[1875] {

[1876] "email": "Hello, thank you for your support. I would like to make a small change to the information about tomorrow's campaign."

[1877] }

[1878] Step 4:

[1879] The server receives the JSON data sent from the device, extracts the body of the received email, and passes it to the generative AI model and emotion engine.

[1880] Input: Email body in JSON format

[1881] Output: The email body is passed to the generative AI model and sentiment engine.

[1882] Step 5:

[1883] The generative AI model detects typos and generates correction suggestions in the email body, while the sentiment engine analyzes the email body and generates sentiment-based correction suggestions.

[1884] Input: Email body

[1885] Output: Analysis results with suggested modifications

[1886] {

[1887] "corrected_text": "Hello, thank you for your support. Regarding tomorrow's campaign information, could you please make a small change?",

[1888] "suggestions": [

[1889] {"original": "I'd like to make some changes.", "suggestion": "Could you please make some changes?"}

[1890] ]

[1891] }

[1892] Step 6:

[1893] The server receives the analysis results from the generative AI model and emotion engine and sends the data back to the device.

[1894] Input: Analysis results with suggested modifications

[1895] Output: The analysis results, including suggested corrections, are sent back to the device as an HTTP response.

[1896] Step 7:

[1897] The device receives the analysis results from the server and displays them on a user interface, which visually indicates suggested modifications and allows the user to confirm and apply them.

[1898] Input: Analysis results with suggested modifications

[1899] Output: The suggested corrections are displayed in the user interface.

[1900] Step 8:

[1901] The user checks the proposed corrections and applies them as necessary. The email body with the corrections applied is then reflected in the user interface.

[1902] Enter: Confirm suggested corrections

[1903] Output: The email body with the applied corrections is reflected in the user interface.

[1904] 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.

[1905] 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.

[1906] 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.

[1907] 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.

[1908] 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.

[1909] 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.

[1910] 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).

[1911] 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.

[1912] 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."

[1913] 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.

[1914] 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).

[1915] 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.

[1916] 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.

[1917] 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.

[1918] 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.

[1919] 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.

[1920] 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.

[1921] 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.

[1922] 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.

[1923] 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.

[1924] 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.

[1925] The following is further disclosed regarding the above embodiment.

[1926] (Claim 1)

[1927] A system for detecting typos in emails, comprising:

[1928] means for providing a user interface for inputting the body of an email;

[1929] means for receiving an instruction from a user to check for typos and omissions;

[1930] A means of sending the input email text to a generative AI model for analysis;

[1931] means for receiving typographical error detection results and correction suggestions from the generative AI model;

[1932] means for displaying the received revision suggestions in a user interface;

[1933] A system including:

[1934] (Claim 2)

[1935] The system of claim 1, wherein the generative AI model includes a means for improving the accuracy of detecting typos and omissions by learning a variety of email sentences in advance.

[1936] (Claim 3)

[1937] 10. The system of claim 1, wherein the user interface includes means for visually indicating the results of checking the email body for typographical errors and for allowing the user to select and apply suggested corrections.

[1938] "Example 1"

[1939] (Claim 1)

[1940] A system for detecting typos in emails, comprising:

[1941] means for providing an interface for inputting text;

[1942] means for receiving an instruction from a user to check for typos and omissions;

[1943] means for converting the input text and transmitting it to a server;

[1944] A means for analyzing input text on the server and passing it to a generative model;

[1945] means for receiving typographical error detection results and correction suggestions from the generative model;

[1946] means for displaying the received revision suggestions in the interface;

[1947] A system including:

[1948] (Claim 2)

[1949] The system of claim 1, wherein the generative model includes means for improving the accuracy of detecting typos and omissions by learning from a variety of text data in advance.

[1950] (Claim 3)

[1951] 10. The system of claim 1, wherein the interface includes means for visually indicating text entry results and for allowing a user to select and apply suggested corrections.

[1952] "Application Example 1"

[1953] (Claim 1)

[1954] A system for detecting typos in emails, comprising:

[1955] a means for providing a user interface for inputting content such as articles, blogs, newsletters, etc.;

[1956] means for receiving an instruction from a user to check for typos and omissions;

[1957] A means for transmitting input content to a generative AI model for analysis;

[1958] means for receiving typographical error detection results and correction suggestions from the generative AI model;

[1959] means for displaying the received revision suggestions in a user interface;

[1960] A system including:

[1961] (Claim 2)

[1962] The system of claim 1, wherein the generative AI model includes a means for improving the accuracy of detecting typos and omissions by learning a variety of content sentences in advance.

[1963] (Claim 3)

[1964] 10. The system of claim 1, wherein the user interface includes means for visually indicating the results of checking the body of content for typographical errors and for allowing a user to select and apply suggested corrections.

[1965] "Example 2: Combining Emotion Engines"

[1966] (Claim 1)

[1967] A system for detecting typos and performing sentiment analysis in emails,

[1968] an information input means for inputting the body of the email;

[1969] A means for receiving instructions from a user for checking for typos and for sentiment analysis;

[1970] means for transmitting the text of the input email to an analysis device for analysis;

[1971] means for receiving the typographical error detection result and the suggested correction of the expression from the analysis device;

[1972] means for displaying the received revision suggestions on the information display means;

[1973] A system including:

[1974] (Claim 2)

[1975] 2. The system of claim 1, wherein the analysis device includes means for improving accuracy of typographical error and sentiment analysis by learning from a variety of documents in advance.

[1976] (Claim 3)

[1977] 2. The system according to claim 1, wherein the information input means includes means for visually displaying the results of a check for typos and omissions in the email body and the results of a sentiment analysis, and allowing the user to select and apply suggested corrections.

[1978] "Application example 2 when combining emotion engines"

[1979] (Claim 1)

[1980] A system for detecting typos in emails, comprising:

[1981] means for providing a user interface for inputting the body of an email;

[1982] A means for receiving instructions from a user for checking for typos and for sentiment analysis;

[1983] A means for sending the input email text to a generative AI model and an emotion engine for analysis;

[1984] means for receiving typographical error detection results from the generative AI model and correction suggestions from the emotion engine;

[1985] means for displaying the received revision suggestions in a user interface;

[1986] A system including:

[1987] (Claim 2)

[1988] The system of claim 1, wherein the generative AI model includes a means for improving the accuracy of detecting typos and omissions by learning from a variety of text data in advance.

[1989] (Claim 3)

[1990] 10. The system of claim 1, wherein the user interface includes means for visually displaying the results of a spelling check and sentiment analysis of the email body, and for allowing the user to select and apply suggested corrections. [Explanation of symbols]

[1991] 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 system for detecting typos in emails, comprising: means for providing a user interface for inputting the body of an email; means for receiving an instruction from a user to check for typos and omissions; A means of sending the input email text to a generative AI model for analysis; means for receiving typographical error detection results and correction suggestions from the generative AI model; means for displaying the received revision suggestions in a user interface; A system including:

2. The system of claim 1, wherein the generative AI model includes a means for improving the accuracy of detecting typos and omissions by learning a variety of email sentences in advance.

3. 2. The system of claim 1, wherein the user interface includes means for visually indicating the results of checking the body of the email for spelling errors and for allowing the user to select and apply suggested corrections.

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