system

Abstract

We provide the system. [Solution] A means of receiving electronic messages via a computer network, A means for linguistically analyzing the content of a received electronic message, A means for detecting a specific tone of voice contained in a message based on the results of language analysis, A conversion means for correcting specific expressions based on the detected tone of voice, A means of sending the revised electronic message, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In communication in the workplace or within an organization, a harsh tone or inappropriate language used unconsciously often causes problems in human relationships and stress. These problems often occur when the speaker does not understand the impact of their expression, and it is a situation that is difficult to improve. Therefore, it is required to appropriately improve language usage in natural communication and promote better human relationships.

Means for Solving the Problems

[0005] This invention provides a system that receives electronic messages via a computer network and uses means to analyze the content of those messages to detect specific tones of voice. Furthermore, it uses conversion means to modify the message's expression based on the detected tones, thereby automatically correcting the language to be more appropriate. This system includes means for transmitting the corrected electronic message, and also includes means for recording the results of the language analysis and generating periodic reports, thereby improving communication and reducing interpersonal problems throughout the organization.

[0006] A "computer network" refers to a communication system that connects multiple computers to each other, enabling them to share data and resources.

[0007] "Electronic messaging" refers to a form of communication that is sent and received digitally through email or instant messaging.

[0008] "Linguistic analysis" is the process of analyzing text data using natural language processing techniques to understand its structure and meaning.

[0009] "Tone of voice" refers to the distinctive characteristics of one's speaking style and expression in written and spoken language, and can influence interpersonal relationships.

[0010] "Conversion means" refers to a function or device that changes input data into another format according to certain rules based on specific conditions.

[0011] "Record keeping" is the process of retaining data and information and storing it so that it can be accessed or analyzed later.

[0012] A "report" refers to a document or presentation that compiles specific data or analytical results and provides them to others. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a labeled communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] To implement this system, users must first create an electronic message using a terminal within a computer network environment and send it to a designated recipient. The message created by the user is first sent from the terminal to the server.

[0035] The server provides message data to a language analysis engine that uses natural language processing to analyze the received messages. This language analysis engine analyzes the vocabulary within the messages and detects specific tones and strong expressions.

[0036] The server automatically performs a conversion process to change the detected tone to an appropriate one. For example, if an imperative sentence is included, it can be converted into a request.

[0037] The revised message is then formatted again by the server and delivered to the final recipient. This ensures that the recipient receives a message that includes more polite and considerate language.

[0038] Furthermore, the server records the entire analysis and correction process and periodically generates reports for users and organizational administrators. These reports include information such as the types of corrections made most frequently and the tone tendencies of individual users, providing valuable data for improving communication.

[0039] As a concrete example, suppose a user creates a message saying, "Complete this report immediately." The server detects this strong, imperative tone and automatically converts it into a more polite request such as, "I apologize for the inconvenience, but could you please complete this report as soon as possible?" This creates a situation where the recipient feels they are communicating more smoothly.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user composes an electronic message on their device and presses the send button. This action prepares the message data for rapid transmission to the server.

[0043] Step 2:

[0044] Messages sent from the terminal are received by the server. The server then passes these messages to the analysis engine, which begins the analysis process.

[0045] Step 3:

[0046] The server uses an analysis engine to scan the linguistic content of messages and evaluates the tone of each sentence using natural language processing techniques. Specifically, it identifies certain words and phrases and measures their intensity.

[0047] Step 4:

[0048] If the server detects harsh language in a message, it will generate suggested corrections based on that detection and automatically transform the expression according to a pre-configured language template.

[0049] Step 5:

[0050] Once the conversion is complete, the message is reformatted on the server and ready to be sent.

[0051] Step 6:

[0052] The server sends the corrected message to the designated recipient's device. The recipient's device displays this message, and the user confirms the corrected content.

[0053] Step 7:

[0054] The server records logs containing details of analysis and corrections, and stores this data for analysis. This record is reflected in periodic reports and provided to users and administrators.

[0055] (Example 1)

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

[0057] In digital communication, direct and strong-tongued messages can lead to misunderstandings and friction. Especially in cross-cultural and business settings, overly commanding language can cause discomfort and pressure on the recipient. Therefore, softer communication is desirable, but doing so manually is time-consuming and laborious. Furthermore, advanced language analysis and conversion technologies are needed to transform softer language into more natural expressions.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes means for receiving electronic messages created by users via a network, means for analyzing the received messages using natural language processing technology and evaluating the vocabulary and grammatical structure within the text, and means for converting the detected expressions to conform to set standards and modifying them into more polite expressions such as request forms. This makes it possible to automatically adjust the tone of the message, reduce misunderstandings, and ensure smooth communication.

[0060] A "user" refers to an individual or organization that uses this system to create and send electronic messages.

[0061] A "network" refers to an interconnected digital environment that provides means of communication that enable the sending and receiving of data.

[0062] An "electronic message" refers to communication content created in digital format and transmitted via a computer network.

[0063] "Natural language processing technology" refers to the field of technology that uses computers to understand, analyze, and generate human language.

[0064] "Linguistic analysis" refers to the process of structurally analyzing the content of electronic messages and evaluating their grammar, vocabulary, and context.

[0065] "Imperative forms and strong expressions" refer to words or sentences that have a tone that makes the recipient feel coerced or pressured.

[0066] "Conversion means" refers to processes and techniques for modifying detected expressions into other forms to generate more appropriate expressions.

[0067] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate natural language based on training data.

[0068] The following describes embodiments for carrying out the invention.

[0069] To implement this invention, the process begins with a user creating an electronic message using a terminal in a network environment and sending it to a server over the network. The message created by the user on the terminal is entered using standard word processing software or messaging applications. For example, an office suite or a standard email client can be used as the terminal.

[0070] When a message reaches the server, it analyzes it using specialized software for natural language processing. Specifically, it uses Python natural language processing libraries (such as NLTK and spaCy) to analyze the vocabulary and grammar within the message, identifying imperative forms and strong language. This analysis helps determine which parts should be changed to more polite expressions.

[0071] Based on the analyzed data, the server uses a conversion mechanism to convert it into a response format as needed. A generative AI model can be used for this conversion. For example, a GPT model can be used to convert imperative forms into appropriate request forms. This process is performed automatically within the server and is processed in real time.

[0072] The converted message is then formatted again and sent to the designated recipient. This ensures the recipient receives a message with more polite language, facilitating smoother communication.

[0073] For example, if the input is "Complete this report immediately," the generating AI model will use the prompt "Translate the following message into a more polite expression: Complete this report immediately," which will then be translated as "We apologize for the inconvenience, but could you please complete this report immediately?"

[0074] This invention enables more appropriate and smoother communication by modifying the tone of messages through an automated process.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user creates an electronic message using a terminal. The input is the user's intended content, using standard document creation software or messaging applications on the terminal. The output is saved digitally as an electronic message. For example, the user might input, "Complete this report immediately."

[0078] Step 2:

[0079] The user creates a message and sends it from the terminal to the server via the network. The input at this stage is the electronic message previously created on the terminal. The output is the message safely reaching the server.

[0080] Step 3:

[0081] The server analyzes the messages it receives using natural language processing software. The input is the message that reached the server, and the output is the analyzed language elements. Specifically, the server uses a Python natural language processing library (such as NLTK or spaCy) to scan the vocabulary and grammatical structure within the message and extract imperative forms and strong expressions.

[0082] Step 4:

[0083] The server uses a generative AI model to transform the message based on the analysis results. The input is the analyzed linguistic information, and the output is the corrected, polite version of the message. For example, the GPT model is used to perform a transformation using the prompt "Transform the following message into a more polite expression: Complete this report immediately," and the result is "We apologize for the inconvenience, but could you please complete this report immediately?"

[0084] Step 5:

[0085] The server reformats the corrected message and sends it to the designated recipient. The input is the converted, politely formatted message, and the output is the recipient receiving the message in the appropriate format. After formatting the message, the server delivers it over the network.

[0086] Step 6:

[0087] The server records all processing and accumulates data for periodic reporting. Input is all the data from analysis and transformation, and output is report information for users and administrators to refer to later. The server stores the message revision history in a database and generates periodic reports to help improve communication.

[0088] (Application Example 1)

[0089] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0090] In electronic communications, inappropriate language or disrespectful tone between users and support organizations can hinder smooth communication. Therefore, it is necessary to maintain a positive relationship between the two parties and improve the user experience. Furthermore, it is also required to utilize the improved quality of communication in performance evaluations.

[0091] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0092] In this invention, the server includes means for receiving electronic communications via a computer network, means for analyzing the information in the received electronic communications, means for detecting specific expressions contained in the communications based on the results of the analysis, means for converting them to appropriate language, means for distributing the corrected electronic communications, and means for automatically converting electronic communications between the user and the support organization into a polite format. This enables smooth communication.

[0093] A "computer network" is a communication infrastructure that connects multiple computers to exchange information with each other.

[0094] "Electronic communication" refers to texts and messages transmitted through electronic means.

[0095] "Means of analyzing information" refers to a system that performs a process to examine received data in detail and understand its content and context.

[0096] "Means for detecting specific expressions" refers to methods for identifying blunt expressions or inappropriate tone within a message.

[0097] A "conversion means" is a function that automatically changes the detected expression into a more favorable form.

[0098] "Means of delivering corrected electronic communications" refers to the method of managing the process of delivering the converted message to the recipient.

[0099] "Methods for automatically converting to a polite format" refer to automatic functions that transform the original expression into a polite and refined style.

[0100] This invention begins with a user creating an electronic communication using a computer terminal and transmitting that communication through a computer network. The server holds the received communication and provides the data to a language processing engine for initial information analysis. This process uses natural language processing (NLP) software to analyze the vocabulary and context within the communication and detect specific expressions and tones.

[0101] Based on the detected expressions, the server uses a translation mechanism to modify the tone of the communication. In this process, if there are imperative sentences, they are converted into more polite and considerate expressions. For example, in an inquiry to customer support for an electronic payment service, the message "Please complete this procedure immediately!" is changed to "We apologize for the inconvenience, but could you please complete this procedure as quickly as possible?"

[0102] The server also plays a role in distributing corrected communications, ensuring recipients receive revised and acceptable messages. Furthermore, it records the correction process and results, generating periodic reports based on this data. This facilitates smoother communication between users and support organizations, improving the overall customer service quality of the organization. This data may also be used for performance evaluations.

[0103] As a concrete example, a prompt using a generative AI model can be used in the form of, "Please convert the following imperative message into a more polite request: (message content)." Servers and related systems require hardware with high-performance processors and sufficient storage capacity to process data at an appropriate speed, even when used by multiple users simultaneously.

[0104] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0105] Step 1:

[0106] A user creates an electronic message using a terminal. The user enters a message on the electronic payment service application and presses the send button. The text data entered by the user is retrieved as input. The output is the sent message data.

[0107] Step 2:

[0108] The server records the electronic communications it receives and provides the data to the language processing engine to begin analysis. Here, natural language processing software is used to analyze the text data and check for specific vocabulary and context. The input is message data sent by the user, and the output is information such as the type of expression and tone as a result of the analysis.

[0109] Step 3:

[0110] The server detects portions containing specific expressions based on the analysis results and uses a conversion mechanism to correct them to appropriate wording. This step involves processing imperative sentences to change them into more polite request forms. The input is the analysis result, and the output is the corrected message data.

[0111] Step 4:

[0112] The server delivers the corrected electronic communication to the final recipient. The user sends the corrected message over the network so that the converted message is delivered. The input is the corrected message data, and the output is the message delivered to the recipient.

[0113] Step 5:

[0114] The server records all conversion processes and results and generates periodic reports. This allows users and support organizations to obtain statistical data on what corrections have been made. The input is the conversion history data for each message, and the output is the generated report.

[0115] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0116] This invention relates to a system that recognizes user emotions and optimizes message content accordingly, primarily in message communication environments utilizing computer networks. The system aims to better express the user's true intentions by combining message linguistic analysis and an emotion engine.

[0117] When a user composes an electronic message on their device, the message is first sent from the device to the server. The server then passes the received message to a language analysis engine, which begins analyzing the text. This scans the message's syntax and keywords, and evaluates the tone used.

[0118] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions from the wording used in the message. The emotion engine determines the overall tone and underlying emotions of the message based on an emotion model, and then uses that result to determine what kind of wording is appropriate.

[0119] For example, if a user tries to send a message expressing frustration, such as "Why isn't it finished yet?", the emotion engine detects the frustration contained in that message. The server uses this information to soften the message, such as "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0120] The revised message is delivered from the server to the final recipient, who receives the message with the adjusted tone. Furthermore, the server records a history of sentiment recognition and revisions, enabling long-term analysis and reporting. This information is used to improve communication within the organization and may even be reflected in performance evaluations.

[0121] The following describes the processing flow.

[0122] Step 1:

[0123] The user types an electronic message on their device and presses the send button. This action prepares the message data for transmission to the server.

[0124] Step 2:

[0125] A message arrives from the terminal to the server. The server receives this message and queues it for further processing.

[0126] Step 3:

[0127] The server passes the received message to the language analysis engine. The engine analyzes the message text, performing syntax checks and evaluating tone and bias.

[0128] Step 4:

[0129] The server inputs the analysis results into the sentiment engine. The sentiment engine analyzes the keywords and context contained in the message to determine the sender's emotions. If an emotion is determined, that information is used in the correction process.

[0130] Step 5:

[0131] The server automatically applies necessary corrections based on the emotion engine's output. In doing so, it selects appropriate wording and tone according to the emotion, adjusting the message's expression.

[0132] Step 6:

[0133] The server reformats the modified message to match the user's intent and then sends the message to the final recipient's terminal based on that format.

[0134] Step 7:

[0135] The adjusted message will be displayed on the recipient's device. The recipient can then review this adjusted message.

[0136] Step 8:

[0137] The server meticulously records the analysis and correction process. This recorded data is used for subsequent analysis, periodic reporting, and performance evaluations.

[0138] (Example 2)

[0139] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0140] In modern society, communication via electronic messages is commonplace, but a challenge remains: misunderstandings and friction can easily arise due to misinterpretations of message tone and emotion. Such situations can hinder smooth communication and potentially lead to decreased work efficiency and worsening interpersonal relationships. Therefore, there is a need for technology that can interpret and optimize the content of electronic messages to achieve accurate and effective communication.

[0141] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0142] In this invention, the server includes means for receiving information and analyzing it using natural language processing technology, means for detecting the style of communication, and means for performing sentiment analysis and converting it into the most appropriate expression. This makes it possible to accurately read the tone and emotions of electronic messages and transmit them to the recipient in an unambiguous manner.

[0143] A "computer network" is a system in which multiple computer devices are connected in a way that allows them to communicate data with one another.

[0144] "Information" refers to data and knowledge expressed in digital format, including electronic messages.

[0145] "Natural language processing technology" refers to the technology of using computers to understand, analyze, and generate human language, and includes methods for extracting meaning and structure from text.

[0146] "Communication style" refers to the wording, expressions, and tone used within a message.

[0147] "Emotional analysis" is a method of extracting and analyzing emotional and sentimental information from text.

[0148] "Transforming into the most appropriate expression" means adjusting or modifying the content of a received message according to its purpose, so that it can be conveyed in a more suitable way.

[0149] "Tone" refers to the emotional or attitudinal color conveyed by a message or information.

[0150] "Communicating without misunderstanding" means adjusting the message so that its intent and emotions are accurately conveyed to the recipient.

[0151] This invention is a system for analyzing electronic information messages and optimizing their content. It primarily achieves efficient message transmission and reception, as well as sentiment analysis, through the mutual cooperation of a server, terminal, and user.

[0152] Users create electronic messages using a terminal. The terminal is a general communication device, such as a smartphone or personal computer. When a user creates a message and presses the send button, the message is sent from the terminal to the server.

[0153] The server uses natural language processing (NLP) techniques to analyze received messages. Specifically, it utilizes NLTK and spaCy, for example, natural language processing libraries. The syntax and keywords of the message are analyzed using these NLP techniques.

[0154] Next, the server performs sentiment analysis. At this stage, generative AI models such as BERT and GPT-3 (registered trademark) are used. This allows the server to grasp the tone of the message and the user's underlying emotions. Based on this, the server adjusts the wording and expression of the message as needed.

[0155] Specifically, if a user tries to send a message such as, "Why isn't it finished yet?", the server can read their emotions and translate it into a softer expression like, "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0156] The optimized message is sent from the server to the recipient's terminal. By receiving the adjusted message, the recipient can understand the message's intent without misunderstanding.

[0157] As a concrete example of this system, the prompt message to the generative AI model could be written as follows: "Identify the possible emotions a user might feel upon receiving the following message, and then revise the message to alleviate those emotions. Message: 'Why isn't it over yet?'"

[0158] This invention aims to facilitate communication between users and reduce misunderstandings and friction by efficiently optimizing the content of electronic messages using appropriate algorithms.

[0159] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0160] Step 1:

[0161] Subject: User

[0162] The user creates an electronic message using the terminal. Input includes the user typing text into the terminal's message input field. For example, the user might type "Why isn't it finished yet?". The created message is prepared as string data. The output is this string data.

[0163] Step 2:

[0164] Subject: terminal

[0165] The terminal sends a message created by the user to the server over the network. The input is the string data described above. Specifically, the terminal sends that message to the server's address. The output is the message data that reaches the server.

[0166] Step 3:

[0167] Subject: Server

[0168] The server passes the received message to the natural language processing engine, which then begins the analysis. The input is the message data sent from the terminal. Specifically, the natural language processing engine performs syntactic analysis of the message and extracts information about tone and intonation. The output is the data points (syntax and keywords) resulting from the analysis.

[0169] Step 4:

[0170] Subject: Server

[0171] The server then uses an emotion engine to recognize the user's emotions from the message. The input is analyzed data obtained through natural language processing. At this stage, an emotion model is used, and emotion scoring is performed. As a concrete example, a generative AI model identifies an emotion such as "frustration." The output is the emotion score.

[0172] Step 5:

[0173] Subject: Server

[0174] The server optimizes the message based on the sentiment analysis results. The input is the sentiment score. Specifically, a generative AI model processes the content to soften the expression. For example, it might be transformed into something like, "I apologize for bothering you while you're busy, but could you tell me about the progress of your work?" The output is the optimized message data.

[0175] Step 6:

[0176] Subject: Server

[0177] The server delivers data over the network to send an optimized message to the recipient. The input is the optimized message data. Specifically, the message is transferred to the recipient's terminal using the network protocol. The output is the message displayed on the recipient's terminal.

[0178] Step 7:

[0179] Subject: Server

[0180] The server records the history of all analysis processes and message transformations. Input consists of the data generated at each step. Specifically, this data is saved to log files and databases. Output is historical data usable for analysis and report creation.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] Electronic messaging often fails to accurately reflect the sender's emotions and intentions, leading to misunderstandings. Such communication discrepancies frequently impact customer satisfaction and trust, particularly in the area of ​​customer support. Therefore, a system is needed that appropriately captures user emotions and intentions and optimizes electronic messaging.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for receiving electronic messages via a computer network, means for linguistically analyzing the content of the electronic messages, and means for sentiment analysis to identify the user's emotions and optimize the message content. This enables communication that takes into account the emotions the user is experiencing, thus avoiding misunderstandings.

[0186] A "computer network" is a connection infrastructure that allows multiple computers or terminals to communicate with each other.

[0187] An "electronic message" is a message sent in digital format, primarily referring to emails and chat messages.

[0188] "Linguistic analysis" is the process of analyzing the grammar, syntax, and keywords of electronic messages to understand their content.

[0189] "Tone" refers to elements that describe the language and attitude used in a message, and is an indicator that influences the sender's intentions and emotions.

[0190] "Sentiment analysis" is the process of identifying a user's emotions from the content of a message, and it is a technology that performs analysis based on sentiment models.

[0191] "Conversion means" refers to methods or techniques for modifying or optimizing message representation based on analysis results.

[0192] "Application methods" refer to methods or techniques for utilizing the functions of a system in a specific environment or situation.

[0193] The system that realizes this invention includes a procedure for receiving electronic messages via a computer network and analyzing the content of those messages. The server analyzes the received messages using a language analysis engine and extracts keywords and grammatical structures. This analysis uses natural language processing software such as Google® Natural Language API and Microsoft® Azure® Text Analytics.

[0194] Subsequently, using the analyzed data, the server identifies the user's emotions through an emotion analysis engine. Based on the emotion model, a process is executed to evaluate the tone of the message and the underlying emotions. This generates a message that takes the user's emotions into account, and the message is optimized using a transformation mechanism.

[0195] For example, suppose a user expresses dissatisfaction in customer support, such as "the amount on the invoice is incorrect." In this case, the sentiment analysis engine identifies the dissatisfaction and translates the message into a gentler tone, such as, "We apologize for the inconvenience. We will investigate the details and address the issue immediately."

[0196] The final corrected message is sent to the user via the server. In this way, misunderstandings in communication are reduced, and smooth exchanges are possible.

[0197] An example of a prompt could be: "Use the generative AI model to recognize the emotions in the message sent by the customer and reply with appropriate, polite language." This prompt provides concrete assistance to the generative AI model in actually transforming the message into appropriate language.

[0198] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0199] Step 1:

[0200] The terminal receives electronic messages created by the user and sends them to the server. The input is a raw text message written by the user, and the output is the electronic data of that message.

[0201] Step 2:

[0202] The server analyzes the received electronic message using a language analysis engine. The input is the electronic message data received in step 1, and the output is the analysis data, such as the grammatical structure and keywords of the message. Specifically, it performs syntactic analysis of the text using the Google Natural Language API.

[0203] Step 3:

[0204] The server uses an emotion analysis engine to identify the user's emotions from the analyzed data. The input is the linguistic analysis data from step 2, and the output is evaluation data indicating the user's emotions. For example, an emotion model is applied to determine, based on the model, whether the user has emotions such as irritation or dissatisfaction.

[0205] Step 4:

[0206] The server executes transformation mechanisms to optimize message expression based on the results of sentiment analysis. The input is evaluation data obtained through sentiment analysis, and the output is the optimized electronic message. Specific operations include transforming irritation into softer expressions.

[0207] Step 5:

[0208] The server sends an optimized electronic message to the terminal, which is ultimately received by the user. The input is the optimized message obtained as a result of step 4, and the output is the electronic data configured to be sent to the user.

[0209] This series of steps generates optimized messages that respond to the user's emotions, resulting in effective and unambiguous communication.

[0210] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0211] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0212] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0213] [Second Embodiment]

[0214] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0215] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0216] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0217] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0218] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0219] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0220] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0221] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0222] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0223] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0224] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0225] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0226] To implement this system, users must first create an electronic message using a terminal within a computer network environment and send it to a designated recipient. The message created by the user is first sent from the terminal to the server.

[0227] The server provides message data to a language analysis engine that uses natural language processing to analyze the received messages. This language analysis engine analyzes the vocabulary within the messages and detects specific tones and strong expressions.

[0228] The server automatically performs a conversion process to change the detected tone to an appropriate one. For example, if an imperative sentence is included, it can be converted into a request.

[0229] The revised message is then formatted again by the server and delivered to the final recipient. This ensures that the recipient receives a message that includes more polite and considerate language.

[0230] Furthermore, the server records the entire analysis and correction process and periodically generates reports for users and organizational administrators. These reports include information such as the types of corrections made most frequently and the tone tendencies of individual users, providing valuable data for improving communication.

[0231] As a concrete example, suppose a user creates a message saying, "Complete this report immediately." The server detects this strong, imperative tone and automatically converts it into a more polite request such as, "I apologize for the inconvenience, but could you please complete this report as soon as possible?" This creates a situation where the recipient feels they are communicating more smoothly.

[0232] The following describes the processing flow.

[0233] Step 1:

[0234] The user composes an electronic message on their device and presses the send button. This action prepares the message data for rapid transmission to the server.

[0235] Step 2:

[0236] Messages sent from the terminal are received by the server. The server then passes these messages to the analysis engine, which begins the analysis process.

[0237] Step 3:

[0238] The server uses an analysis engine to scan the linguistic content of messages and evaluates the tone of each sentence using natural language processing techniques. Specifically, it identifies certain words and phrases and measures their intensity.

[0239] Step 4:

[0240] If the server detects harsh language in a message, it will generate suggested corrections based on that detection and automatically transform the expression according to a pre-configured language template.

[0241] Step 5:

[0242] Once the conversion is complete, the message is reformatted on the server and ready to be sent.

[0243] Step 6:

[0244] The server sends the corrected message to the designated recipient's device. The recipient's device displays this message, and the user confirms the corrected content.

[0245] Step 7:

[0246] The server records logs containing details of analysis and corrections, and stores this data for analysis. This record is reflected in periodic reports and provided to users and administrators.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] In digital communication, direct and strong-tongued messages can lead to misunderstandings and friction. Especially in cross-cultural and business settings, overly commanding language can cause discomfort and pressure on the recipient. Therefore, softer communication is desirable, but doing so manually is time-consuming and laborious. Furthermore, advanced language analysis and conversion technologies are needed to transform softer language into more natural expressions.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for receiving electronic messages created by users via a network, means for analyzing the received messages using natural language processing technology and evaluating the vocabulary and grammatical structure within the text, and means for converting the detected expressions to conform to set standards and modifying them into more polite expressions such as request forms. This makes it possible to automatically adjust the tone of the message, reduce misunderstandings, and ensure smooth communication.

[0252] A "user" refers to an individual or organization that uses this system to create and send electronic messages.

[0253] A "network" refers to an interconnected digital environment that provides means of communication that enable the sending and receiving of data.

[0254] An "electronic message" refers to communication content created in digital format and transmitted via a computer network.

[0255] "Natural language processing technology" refers to the field of technology that uses computers to understand, analyze, and generate human language.

[0256] "Linguistic analysis" refers to the process of structurally analyzing the content of electronic messages and evaluating their grammar, vocabulary, and context.

[0257] "Imperative forms and strong expressions" refer to words or sentences that have a tone that makes the recipient feel coerced or pressured.

[0258] "Conversion means" refers to processes and techniques for modifying detected expressions into other forms to generate more appropriate expressions.

[0259] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate natural language based on training data.

[0260] The following describes embodiments for carrying out the invention.

[0261] To implement this invention, the process begins with a user creating an electronic message using a terminal in a network environment and sending it to a server over the network. The message created by the user on the terminal is entered using standard word processing software or messaging applications. For example, an office suite or a standard email client can be used as the terminal.

[0262] When a message reaches the server, it analyzes it using specialized software for natural language processing. Specifically, it uses Python natural language processing libraries (such as NLTK and spaCy) to analyze the vocabulary and grammar within the message, identifying imperative forms and strong language. This analysis helps determine which parts should be changed to more polite expressions.

[0263] Based on the analyzed data, the server uses a conversion mechanism to convert it into a response format as needed. A generative AI model can be used for this conversion. For example, a GPT model can be used to convert imperative forms into appropriate request forms. This process is performed automatically within the server and is processed in real time.

[0264] The converted message is then formatted again and sent to the designated recipient. This ensures the recipient receives a message with more polite language, facilitating smoother communication.

[0265] For example, if the input is "Complete this report immediately," the generating AI model will use the prompt "Translate the following message into a more polite expression: Complete this report immediately," which will then be translated as "We apologize for the inconvenience, but could you please complete this report immediately?"

[0266] This invention enables more appropriate and smoother communication by modifying the tone of messages through an automated process.

[0267] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0268] Step 1:

[0269] The user creates an electronic message using a terminal. The input is the user's intended content, using standard document creation software or messaging applications on the terminal. The output is saved digitally as an electronic message. For example, the user might input, "Complete this report immediately."

[0270] Step 2:

[0271] The user creates a message and sends it from the terminal to the server via the network. The input at this stage is the electronic message previously created on the terminal. The output is the message safely reaching the server.

[0272] Step 3:

[0273] The server analyzes the messages it receives using natural language processing software. The input is the message that reached the server, and the output is the analyzed language elements. Specifically, the server uses a Python natural language processing library (such as NLTK or spaCy) to scan the vocabulary and grammatical structure within the message and extract imperative forms and strong expressions.

[0274] Step 4:

[0275] The server uses a generative AI model to transform the message based on the analysis results. The input is the analyzed linguistic information, and the output is the corrected, polite version of the message. For example, the GPT model is used to perform a transformation using the prompt "Transform the following message into a more polite expression: Complete this report immediately," and the result is "We apologize for the inconvenience, but could you please complete this report immediately?"

[0276] Step 5:

[0277] The server reformats the corrected message and sends it to the designated recipient. The input is the converted, politely formatted message, and the output is the recipient receiving the message in the appropriate format. After formatting the message, the server delivers it over the network.

[0278] Step 6:

[0279] The server records all processing and accumulates data for periodic reporting. Input is all the data from analysis and transformation, and output is report information for users and administrators to refer to later. The server stores the message revision history in a database and generates periodic reports to help improve communication.

[0280] (Application Example 1)

[0281] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal".

[0282] In electronic communication, the inappropriateness of expressions or discourteous tones that may occur between a user and a support organization may impede smooth communication between the two parties. Therefore, it is necessary to maintain a good relationship between the two and improve the user experience. Furthermore, it is also required to utilize the improved communication quality in performance evaluation.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0284] In this invention, the server includes means for receiving electronic communication via a computer network, means for analyzing the information of the received electronic communication, means for detecting a specific expression included in the communication based on the analysis result, conversion means for modifying it into appropriate diction, means for distributing the modified electronic communication, and means for automatically converting the electronic communication between the user and the support organization into a polite form. Thereby, smooth communication becomes possible.

[0285] A "computer network" is a communication infrastructure that connects multiple computers to exchange information with each other.

[0286] "Electronic communication" is a text or message transmitted through electronic means.

[0287] "Means for analyzing information" is a mechanism that executes a process for examining the received data in detail and understanding its content and context.

[0288] "Means for detecting a specific expression" is a method for identifying direct expressions or inappropriate tones included in a message.

[0289] A "conversion means" is a function that automatically changes the detected expression into a more favorable form.

[0290] "Means of delivering corrected electronic communications" refers to the method of managing the process of delivering the converted message to the recipient.

[0291] "Methods for automatically converting to a polite format" refer to automatic functions that transform the original expression into a polite and refined style.

[0292] This invention begins with a user creating an electronic communication using a computer terminal and transmitting that communication through a computer network. The server holds the received communication and provides the data to a language processing engine for initial information analysis. This process uses natural language processing (NLP) software to analyze the vocabulary and context within the communication and detect specific expressions and tones.

[0293] Based on the detected expressions, the server uses a translation mechanism to modify the tone of the communication. In this process, if there are imperative sentences, they are converted into more polite and considerate expressions. For example, in an inquiry to customer support for an electronic payment service, the message "Please complete this procedure immediately!" is changed to "We apologize for the inconvenience, but could you please complete this procedure as quickly as possible?"

[0294] The server also plays a role in distributing corrected communications, ensuring recipients receive revised and acceptable messages. Furthermore, it records the correction process and results, generating periodic reports based on this data. This facilitates smoother communication between users and support organizations, improving the overall customer service quality of the organization. This data may also be used for performance evaluations.

[0295] As a concrete example, a prompt using a generative AI model can be used in the form of, "Please convert the following imperative message into a more polite request: (message content)." Servers and related systems require hardware with high-performance processors and sufficient storage capacity to process data at an appropriate speed, even when used by multiple users simultaneously.

[0296] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0297] Step 1:

[0298] A user creates an electronic message using a terminal. The user enters a message on the electronic payment service application and presses the send button. The text data entered by the user is retrieved as input. The output is the sent message data.

[0299] Step 2:

[0300] The server records the electronic communications it receives and provides the data to the language processing engine to begin analysis. Here, natural language processing software is used to analyze the text data and check for specific vocabulary and context. The input is message data sent by the user, and the output is information such as the type of expression and tone as a result of the analysis.

[0301] Step 3:

[0302] The server detects portions containing specific expressions based on the analysis results and uses a conversion mechanism to correct them to appropriate wording. This step involves processing imperative sentences to change them into more polite request forms. The input is the analysis result, and the output is the corrected message data.

[0303] Step 4:

[0304] The server distributes the corrected electronic communication to the final recipient. The corrected message is sent via the network so that the user can receive the converted message. The input is the corrected message data, and the output is the message distributed to the recipient.

[0305] Step 5:

[0306] The server records all conversion processes and results and generates regular reports. This enables users and support institutions to obtain statistical data on what corrections have been made. The input is the conversion history data of each message, and the output is the generated report.

[0307] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion recognition model 59 and perform specific processing using the user's emotions.

[0308] The present invention mainly relates to a system for recognizing the user's emotions and optimizing the message content according to the emotions in a message communication environment using a computer network. This system aims to better express the true intention of the user by combining language analysis of the message and an emotion engine.

[0309] When the user creates an electronic message on the terminal, the message is first sent from the terminal to the server. The server passes the received message to the language analysis engine and starts analyzing the text. As a result, the syntax and keywords of the message are scanned, and the tone used is evaluated.

[0310] Furthermore, an emotion engine is introduced in this system, and the user's emotions can be recognized from the diction in the message. The emotion engine determines the overall tone and potential emotions of the message based on the emotion model, and based on the result, determines what diction is appropriate.

[0311] For example, if a user tries to send a message expressing frustration, such as "Why isn't it finished yet?", the emotion engine detects the frustration contained in that message. The server uses this information to soften the message, such as "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0312] The revised message is delivered from the server to the final recipient, who receives the message with the adjusted tone. Furthermore, the server records a history of sentiment recognition and revisions, enabling long-term analysis and reporting. This information is used to improve communication within the organization and may even be reflected in performance evaluations.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] The user types an electronic message on their device and presses the send button. This action prepares the message data for transmission to the server.

[0316] Step 2:

[0317] A message arrives from the terminal to the server. The server receives this message and queues it for further processing.

[0318] Step 3:

[0319] The server passes the received message to the language analysis engine. The engine analyzes the message text, performing syntax checks and evaluating tone and bias.

[0320] Step 4:

[0321] The server inputs the analysis results into the sentiment engine. The sentiment engine analyzes the keywords and context contained in the message to determine the sender's emotions. If an emotion is determined, that information is used in the correction process.

[0322] Step 5:

[0323] The server automatically applies necessary corrections based on the emotion engine's output. In doing so, it selects appropriate wording and tone according to the emotion, adjusting the message's expression.

[0324] Step 6:

[0325] The server reformats the modified message to match the user's intent and then sends the message to the final recipient's terminal based on that format.

[0326] Step 7:

[0327] The adjusted message will be displayed on the recipient's device. The recipient can then review this adjusted message.

[0328] Step 8:

[0329] The server meticulously records the analysis and correction process. This recorded data is used for subsequent analysis, periodic reporting, and performance evaluations.

[0330] (Example 2)

[0331] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0332] In modern society, communication via electronic messages is commonplace, but a challenge remains: misunderstandings and friction can easily arise due to misinterpretations of message tone and emotion. Such situations can hinder smooth communication and potentially lead to decreased work efficiency and worsening interpersonal relationships. Therefore, there is a need for technology that can interpret and optimize the content of electronic messages to achieve accurate and effective communication.

[0333] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0334] In this invention, the server includes means for receiving information and analyzing it using natural language processing technology, means for detecting the style of communication, and means for performing sentiment analysis and converting it into the most appropriate expression. This makes it possible to accurately read the tone and emotions of electronic messages and transmit them to the recipient in an unambiguous manner.

[0335] A "computer network" is a system in which multiple computer devices are connected in a way that allows them to communicate data with one another.

[0336] "Information" refers to data and knowledge expressed in digital format, including electronic messages.

[0337] "Natural language processing technology" refers to the technology of using computers to understand, analyze, and generate human language, and includes methods for extracting meaning and structure from text.

[0338] "Communication style" refers to the wording, expressions, and tone used within a message.

[0339] "Emotional analysis" is a method of extracting and analyzing emotional and sentimental information from text.

[0340] "Transforming into the most appropriate expression" means adjusting or modifying the content of a received message according to its purpose, so that it can be conveyed in a more suitable way.

[0341] "Tone" refers to the emotional or attitudinal color conveyed by a message or information.

[0342] "Communicating without misunderstanding" means adjusting the message so that its intent and emotions are accurately conveyed to the recipient.

[0343] This invention is a system for analyzing electronic information messages and optimizing their content. It primarily achieves efficient message transmission and reception, as well as sentiment analysis, through the mutual cooperation of a server, terminal, and user.

[0344] Users create electronic messages using a terminal. The terminal is a general communication device, such as a smartphone or personal computer. When a user creates a message and presses the send button, the message is sent from the terminal to the server.

[0345] The server uses natural language processing (NLP) techniques to analyze received messages. Specifically, it utilizes NLTK and spaCy, for example, natural language processing libraries. The syntax and keywords of the message are analyzed using these NLP techniques.

[0346] Next, the server performs sentiment analysis. At this stage, generative AI models such as BERT and GPT-3 are used. This allows the server to grasp the tone of the message and the user's underlying emotions. Based on this, the server adjusts the wording and expression of the message as needed.

[0347] Specifically, if a user tries to send a message such as, "Why isn't it finished yet?", the server can read their emotions and translate it into a softer expression like, "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0348] The optimized message is sent from the server to the recipient's terminal. By receiving the adjusted message, the recipient can understand the message's intent without misunderstanding.

[0349] As a concrete example of this system, the prompt message to the generative AI model could be written as follows: "Identify the possible emotions a user might feel upon receiving the following message, and then revise the message to alleviate those emotions. Message: 'Why isn't it over yet?'"

[0350] This invention aims to facilitate communication between users and reduce misunderstandings and friction by efficiently optimizing the content of electronic messages using appropriate algorithms.

[0351] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0352] Step 1:

[0353] Subject: User

[0354] The user creates an electronic message using the terminal. Input includes the user typing text into the terminal's message input field. For example, the user might type "Why isn't it finished yet?". The created message is prepared as string data. The output is this string data.

[0355] Step 2:

[0356] Subject: terminal

[0357] The terminal sends a message created by the user to the server over the network. The input is the string data described above. Specifically, the terminal sends that message to the server's address. The output is the message data that reaches the server.

[0358] Step 3:

[0359] Subject: Server

[0360] The server passes the received message to the natural language processing engine, which then begins the analysis. The input is the message data sent from the terminal. Specifically, the natural language processing engine performs syntactic analysis of the message and extracts information about tone and intonation. The output is the data points (syntax and keywords) resulting from the analysis.

[0361] Step 4:

[0362] Subject: Server

[0363] The server then uses an emotion engine to recognize the user's emotions from the message. The input is analyzed data obtained through natural language processing. At this stage, an emotion model is used, and emotion scoring is performed. As a concrete example, a generative AI model identifies an emotion such as "frustration." The output is the emotion score.

[0364] Step 5:

[0365] Subject: Server

[0366] The server optimizes the message based on the sentiment analysis results. The input is the sentiment score. Specifically, a generative AI model processes the content to soften the expression. For example, it might be transformed into something like, "I apologize for bothering you while you're busy, but could you tell me about the progress of your work?" The output is the optimized message data.

[0367] Step 6:

[0368] Subject: Server

[0369] The server delivers data over the network to send an optimized message to the recipient. The input is the optimized message data. Specifically, the message is transferred to the recipient's terminal using the network protocol. The output is the message displayed on the recipient's terminal.

[0370] Step 7:

[0371] Subject: Server

[0372] The server records the history of all analysis processes and message transformations. Input consists of the data generated at each step. Specifically, this data is saved to log files and databases. Output is historical data usable for analysis and report creation.

[0373] (Application Example 2)

[0374] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0375] Electronic messaging often fails to accurately reflect the sender's emotions and intentions, leading to misunderstandings. Such communication discrepancies frequently impact customer satisfaction and trust, particularly in the area of ​​customer support. Therefore, a system is needed that appropriately captures user emotions and intentions and optimizes electronic messaging.

[0376] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0377] In this invention, the server includes means for receiving electronic messages via a computer network, means for linguistically analyzing the content of the electronic messages, and means for sentiment analysis to identify the user's emotions and optimize the message content. This enables communication that takes into account the emotions the user is experiencing, thus avoiding misunderstandings.

[0378] A "computer network" is a connection infrastructure that allows multiple computers or terminals to communicate with each other.

[0379] An "electronic message" is a message sent in digital format, primarily referring to emails and chat messages.

[0380] "Linguistic analysis" is the process of analyzing the grammar, syntax, and keywords of electronic messages to understand their content.

[0381] "Tone" refers to elements that describe the language and attitude used in a message, and is an indicator that influences the sender's intentions and emotions.

[0382] "Sentiment analysis" is the process of identifying a user's emotions from the content of a message, and it is a technology that performs analysis based on sentiment models.

[0383] "Conversion means" refers to methods or techniques for modifying or optimizing message representation based on analysis results.

[0384] "Application methods" refer to methods or techniques for utilizing the functions of a system in a specific environment or situation.

[0385] The system that realizes this invention includes a procedure for receiving electronic messages via a computer network and analyzing the content of those messages. The server analyzes the received messages using a language analysis engine and extracts keywords and grammatical structures. This analysis uses natural language processing software such as Google Natural Language API and Microsoft Azure Text Analytics.

[0386] Subsequently, using the analyzed data, the server identifies the user's emotions through an emotion analysis engine. Based on the emotion model, a process is executed to evaluate the tone of the message and the underlying emotions. This generates a message that takes the user's emotions into account, and the message is optimized using a transformation mechanism.

[0387] For example, suppose a user expresses dissatisfaction in customer support, such as "the amount on the invoice is incorrect." In this case, the sentiment analysis engine identifies the dissatisfaction and translates the message into a gentler tone, such as, "We apologize for the inconvenience. We will investigate the details and address the issue immediately."

[0388] The final corrected message is sent to the user via the server. In this way, misunderstandings in communication are reduced, and smooth exchanges are possible.

[0389] An example of a prompt could be: "Use the generative AI model to recognize the emotions in the message sent by the customer and reply with appropriate, polite language." This prompt provides concrete assistance to the generative AI model in actually transforming the message into appropriate language.

[0390] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0391] Step 1:

[0392] The terminal receives electronic messages created by the user and sends them to the server. The input is a raw text message written by the user, and the output is the electronic data of that message.

[0393] Step 2:

[0394] The server analyzes the received electronic message using a language analysis engine. The input is the electronic message data received in step 1, and the output is the analysis data, such as the grammatical structure and keywords of the message. Specifically, it performs syntactic analysis of the text using the Google Natural Language API.

[0395] Step 3:

[0396] The server uses an emotion analysis engine to identify the user's emotions from the analyzed data. The input is the linguistic analysis data from step 2, and the output is evaluation data indicating the user's emotions. For example, an emotion model is applied to determine, based on the model, whether the user has emotions such as irritation or dissatisfaction.

[0397] Step 4:

[0398] The server executes transformation mechanisms to optimize message expression based on the results of sentiment analysis. The input is evaluation data obtained through sentiment analysis, and the output is the optimized electronic message. Specific operations include transforming irritation into softer expressions.

[0399] Step 5:

[0400] The server sends an optimized electronic message to the terminal, which is ultimately received by the user. The input is the optimized message obtained as a result of step 4, and the output is the electronic data configured to be sent to the user.

[0401] This series of steps generates optimized messages that respond to the user's emotions, resulting in effective and unambiguous communication.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0403] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0405] [Third Embodiment]

[0406] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0407] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0408] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0409] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0410] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0411] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0412] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0413] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0414] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0415] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0416] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0417] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0418] To implement this system, users must first create an electronic message using a terminal within a computer network environment and send it to a designated recipient. The message created by the user is first sent from the terminal to the server.

[0419] The server provides message data to a language analysis engine that uses natural language processing to analyze the received messages. This language analysis engine analyzes the vocabulary within the messages and detects specific tones and strong expressions.

[0420] The server automatically performs a conversion process to change the detected tone to an appropriate one. For example, if an imperative sentence is included, it can be converted into a request.

[0421] The revised message is then formatted again by the server and delivered to the final recipient. This ensures that the recipient receives a message that includes more polite and considerate language.

[0422] Furthermore, the server records the entire analysis and correction process and periodically generates reports for users and organizational administrators. These reports include information such as the types of corrections made most frequently and the tone tendencies of individual users, providing valuable data for improving communication.

[0423] As a concrete example, suppose a user creates a message saying, "Complete this report immediately." The server detects this strong, imperative tone and automatically converts it into a more polite request such as, "I apologize for the inconvenience, but could you please complete this report as soon as possible?" This creates a situation where the recipient feels they are communicating more smoothly.

[0424] The following describes the processing flow.

[0425] Step 1:

[0426] The user composes an electronic message on their device and presses the send button. This action prepares the message data for rapid transmission to the server.

[0427] Step 2:

[0428] Messages sent from the terminal are received by the server. The server then passes these messages to the analysis engine, which begins the analysis process.

[0429] Step 3:

[0430] The server uses an analysis engine to scan the linguistic content of messages and evaluates the tone of each sentence using natural language processing techniques. Specifically, it identifies certain words and phrases and measures their intensity.

[0431] Step 4:

[0432] If the server detects harsh language in a message, it will generate suggested corrections based on that detection and automatically transform the expression according to a pre-configured language template.

[0433] Step 5:

[0434] Once the conversion is complete, the message is reformatted on the server and ready to be sent.

[0435] Step 6:

[0436] The server sends the corrected message to the designated recipient's device. The recipient's device displays this message, and the user confirms the corrected content.

[0437] Step 7:

[0438] The server records logs containing details of analysis and corrections, and stores this data for analysis. This record is reflected in periodic reports and provided to users and administrators.

[0439] (Example 1)

[0440] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0441] In digital communication, direct and strong-tongued messages can lead to misunderstandings and friction. Especially in cross-cultural and business settings, overly commanding language can cause discomfort and pressure on the recipient. Therefore, softer communication is desirable, but doing so manually is time-consuming and laborious. Furthermore, advanced language analysis and conversion technologies are needed to transform softer language into more natural expressions.

[0442] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0443] In this invention, the server includes means for receiving electronic messages created by users via a network, means for analyzing the received messages using natural language processing technology and evaluating the vocabulary and grammatical structure within the text, and means for converting the detected expressions to conform to set standards and modifying them into more polite expressions such as request forms. This makes it possible to automatically adjust the tone of the message, reduce misunderstandings, and ensure smooth communication.

[0444] A "user" refers to an individual or organization that uses this system to create and send electronic messages.

[0445] A "network" refers to an interconnected digital environment that provides means of communication that enable the sending and receiving of data.

[0446] An "electronic message" refers to communication content created in digital format and transmitted via a computer network.

[0447] "Natural language processing technology" refers to the field of technology that uses computers to understand, analyze, and generate human language.

[0448] "Linguistic analysis" refers to the process of structurally analyzing the content of electronic messages and evaluating their grammar, vocabulary, and context.

[0449] "Imperative forms and strong expressions" refer to words or sentences that have a tone that makes the recipient feel coerced or pressured.

[0450] "Conversion means" refers to processes and techniques for modifying detected expressions into other forms to generate more appropriate expressions.

[0451] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate natural language based on training data.

[0452] The following describes embodiments for carrying out the invention.

[0453] To implement this invention, the process begins with a user creating an electronic message using a terminal in a network environment and sending it to a server over the network. The message created by the user on the terminal is entered using standard word processing software or messaging applications. For example, an office suite or a standard email client can be used as the terminal.

[0454] When a message reaches the server, it analyzes it using specialized software for natural language processing. Specifically, it uses Python natural language processing libraries (such as NLTK and spaCy) to analyze the vocabulary and grammar within the message, identifying imperative forms and strong language. This analysis helps determine which parts should be changed to more polite expressions.

[0455] Based on the analyzed data, the server uses a conversion mechanism to convert it into a response format as needed. A generative AI model can be used for this conversion. For example, a GPT model can be used to convert imperative forms into appropriate request forms. This process is performed automatically within the server and is processed in real time.

[0456] The converted message is then formatted again and sent to the designated recipient. This ensures the recipient receives a message with more polite language, facilitating smoother communication.

[0457] For example, if the input is "Complete this report immediately," the generating AI model will use the prompt "Translate the following message into a more polite expression: Complete this report immediately," which will then be translated as "We apologize for the inconvenience, but could you please complete this report immediately?"

[0458] This invention enables more appropriate and smoother communication by modifying the tone of messages through an automated process.

[0459] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0460] Step 1:

[0461] The user creates an electronic message using a terminal. The input is the user's intended content, using standard document creation software or messaging applications on the terminal. The output is saved digitally as an electronic message. For example, the user might input, "Complete this report immediately."

[0462] Step 2:

[0463] The user creates a message and sends it from the terminal to the server via the network. The input at this stage is the electronic message previously created on the terminal. The output is the message safely reaching the server.

[0464] Step 3:

[0465] The server analyzes the messages it receives using natural language processing software. The input is the message that reached the server, and the output is the analyzed language elements. Specifically, the server uses a Python natural language processing library (such as NLTK or spaCy) to scan the vocabulary and grammatical structure within the message and extract imperative forms and strong expressions.

[0466] Step 4:

[0467] The server uses a generative AI model to transform the message based on the analysis results. The input is the analyzed linguistic information, and the output is the corrected, polite version of the message. For example, the GPT model is used to perform a transformation using the prompt "Transform the following message into a more polite expression: Complete this report immediately," and the result is "We apologize for the inconvenience, but could you please complete this report immediately?"

[0468] Step 5:

[0469] The server reformats the corrected message and sends it to the designated recipient. The input is the converted, politely formatted message, and the output is the recipient receiving the message in the appropriate format. After formatting the message, the server delivers it over the network.

[0470] Step 6:

[0471] The server records all processing and accumulates data for periodic reporting. Input is all the data from analysis and transformation, and output is report information for users and administrators to refer to later. The server stores the message revision history in a database and generates periodic reports to help improve communication.

[0472] (Application Example 1)

[0473] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0474] In electronic communications, inappropriate language or disrespectful tone between users and support organizations can hinder smooth communication. Therefore, it is necessary to maintain a positive relationship between the two parties and improve the user experience. Furthermore, it is also required to utilize the improved quality of communication in performance evaluations.

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

[0476] In this invention, the server includes means for receiving electronic communications via a computer network, means for analyzing the information in the received electronic communications, means for detecting specific expressions contained in the communications based on the results of the analysis, means for converting them to appropriate language, means for distributing the corrected electronic communications, and means for automatically converting electronic communications between the user and the support organization into a polite format. This enables smooth communication.

[0477] A "computer network" is a communication infrastructure that connects multiple computers to exchange information with each other.

[0478] "Electronic communication" refers to texts and messages transmitted through electronic means.

[0479] "Means of analyzing information" refers to a system that performs a process to examine received data in detail and understand its content and context.

[0480] "Means for detecting specific expressions" refers to methods for identifying blunt expressions or inappropriate tone within a message.

[0481] A "conversion means" is a function that automatically changes the detected expression into a more favorable form.

[0482] "Means of delivering corrected electronic communications" refers to the method of managing the process of delivering the converted message to the recipient.

[0483] "Methods for automatically converting to a polite format" refer to automatic functions that transform the original expression into a polite and refined style.

[0484] This invention begins with a user creating an electronic communication using a computer terminal and transmitting that communication through a computer network. The server holds the received communication and provides the data to a language processing engine for initial information analysis. This process uses natural language processing (NLP) software to analyze the vocabulary and context within the communication and detect specific expressions and tones.

[0485] Based on the detected expressions, the server uses a translation mechanism to modify the tone of the communication. In this process, if there are imperative sentences, they are converted into more polite and considerate expressions. For example, in an inquiry to customer support for an electronic payment service, the message "Please complete this procedure immediately!" is changed to "We apologize for the inconvenience, but could you please complete this procedure as quickly as possible?"

[0486] The server also plays a role in distributing corrected communications, ensuring recipients receive revised and acceptable messages. Furthermore, it records the correction process and results, generating periodic reports based on this data. This facilitates smoother communication between users and support organizations, improving the overall customer service quality of the organization. This data may also be used for performance evaluations.

[0487] As a concrete example, a prompt using a generative AI model can be used in the form of, "Please convert the following imperative message into a more polite request: (message content)." Servers and related systems require hardware with high-performance processors and sufficient storage capacity to process data at an appropriate speed, even when used by multiple users simultaneously.

[0488] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0489] Step 1:

[0490] A user creates an electronic message using a terminal. The user enters a message on the electronic payment service application and presses the send button. The text data entered by the user is retrieved as input. The output is the sent message data.

[0491] Step 2:

[0492] The server records the electronic communications it receives and provides the data to the language processing engine to begin analysis. Here, natural language processing software is used to analyze the text data and check for specific vocabulary and context. The input is message data sent by the user, and the output is information such as the type of expression and tone as a result of the analysis.

[0493] Step 3:

[0494] The server detects portions containing specific expressions based on the analysis results and uses a conversion mechanism to correct them to appropriate wording. This step involves processing imperative sentences to change them into more polite request forms. The input is the analysis result, and the output is the corrected message data.

[0495] Step 4:

[0496] The server delivers the corrected electronic communication to the final recipient. The user sends the corrected message over the network so that the converted message is delivered. The input is the corrected message data, and the output is the message delivered to the recipient.

[0497] Step 5:

[0498] The server records all conversion processes and results and generates periodic reports. This allows users and support organizations to obtain statistical data on what corrections have been made. The input is the conversion history data for each message, and the output is the generated report.

[0499] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0500] This invention relates to a system that recognizes user emotions and optimizes message content accordingly, primarily in message communication environments utilizing computer networks. The system aims to better express the user's true intentions by combining message linguistic analysis and an emotion engine.

[0501] When a user composes an electronic message on their device, the message is first sent from the device to the server. The server then passes the received message to a language analysis engine, which begins analyzing the text. This scans the message's syntax and keywords, and evaluates the tone used.

[0502] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions from the wording used in the message. The emotion engine determines the overall tone and underlying emotions of the message based on an emotion model, and then uses that result to determine what kind of wording is appropriate.

[0503] For example, if a user tries to send a message expressing frustration, such as "Why isn't it finished yet?", the emotion engine detects the frustration contained in that message. The server uses this information to soften the message, such as "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0504] The revised message is delivered from the server to the final recipient, who receives the message with the adjusted tone. Furthermore, the server records a history of sentiment recognition and revisions, enabling long-term analysis and reporting. This information is used to improve communication within the organization and may even be reflected in performance evaluations.

[0505] The following describes the processing flow.

[0506] Step 1:

[0507] The user types an electronic message on their device and presses the send button. This action prepares the message data for transmission to the server.

[0508] Step 2:

[0509] A message arrives from the terminal to the server. The server receives this message and queues it for further processing.

[0510] Step 3:

[0511] The server passes the received message to the language analysis engine. The engine analyzes the message text, performing syntax checks and evaluating tone and bias.

[0512] Step 4:

[0513] The server inputs the analysis results into the sentiment engine. The sentiment engine analyzes the keywords and context contained in the message to determine the sender's emotions. If an emotion is determined, that information is used in the correction process.

[0514] Step 5:

[0515] The server automatically applies necessary corrections based on the emotion engine's output. In doing so, it selects appropriate wording and tone according to the emotion, adjusting the message's expression.

[0516] Step 6:

[0517] The server reformats the modified message to match the user's intent and then sends the message to the final recipient's terminal based on that format.

[0518] Step 7:

[0519] The adjusted message will be displayed on the recipient's device. The recipient can then review this adjusted message.

[0520] Step 8:

[0521] The server meticulously records the analysis and correction process. This recorded data is used for subsequent analysis, periodic reporting, and performance evaluations.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] In modern society, communication via electronic messages is commonplace, but a challenge remains: misunderstandings and friction can easily arise due to misinterpretations of message tone and emotion. Such situations can hinder smooth communication and potentially lead to decreased work efficiency and worsening interpersonal relationships. Therefore, there is a need for technology that can interpret and optimize the content of electronic messages to achieve accurate and effective communication.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes means for receiving information and analyzing it using natural language processing technology, means for detecting the style of communication, and means for performing sentiment analysis and converting it into the most appropriate expression. This makes it possible to accurately read the tone and emotions of electronic messages and transmit them to the recipient in an unambiguous manner.

[0527] A "computer network" is a system in which multiple computer devices are connected in a way that allows them to communicate data with one another.

[0528] "Information" refers to data and knowledge expressed in digital format, including electronic messages.

[0529] "Natural language processing technology" refers to the technology of using computers to understand, analyze, and generate human language, and includes methods for extracting meaning and structure from text.

[0530] "Communication style" refers to the wording, expressions, and tone used within a message.

[0531] "Emotional analysis" is a method of extracting and analyzing emotional and sentimental information from text.

[0532] "Transforming into the most appropriate expression" means adjusting or modifying the content of a received message according to its purpose, so that it can be conveyed in a more suitable way.

[0533] "Tone" refers to the emotional or attitudinal color conveyed by a message or information.

[0534] "Communicating without misunderstanding" means adjusting the message so that its intent and emotions are accurately conveyed to the recipient.

[0535] This invention is a system for analyzing electronic information messages and optimizing their content. It primarily achieves efficient message transmission and reception, as well as sentiment analysis, through the mutual cooperation of a server, terminal, and user.

[0536] Users create electronic messages using a terminal. The terminal is a general communication device, such as a smartphone or personal computer. When a user creates a message and presses the send button, the message is sent from the terminal to the server.

[0537] The server uses natural language processing (NLP) techniques to analyze received messages. Specifically, it utilizes NLTK and spaCy, for example, natural language processing libraries. The syntax and keywords of the message are analyzed using these NLP techniques.

[0538] Next, the server performs sentiment analysis. At this stage, generative AI models such as BERT and GPT-3 are used. This allows the server to grasp the tone of the message and the user's underlying emotions. Based on this, the server adjusts the wording and expression of the message as needed.

[0539] Specifically, if a user tries to send a message such as, "Why isn't it finished yet?", the server can read their emotions and translate it into a softer expression like, "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0540] The optimized message is sent from the server to the recipient's terminal. By receiving the adjusted message, the recipient can understand the message's intent without misunderstanding.

[0541] As a concrete example of this system, the prompt message to the generative AI model could be written as follows: "Identify the possible emotions a user might feel upon receiving the following message, and then revise the message to alleviate those emotions. Message: 'Why isn't it over yet?'"

[0542] This invention aims to facilitate communication between users and reduce misunderstandings and friction by efficiently optimizing the content of electronic messages using appropriate algorithms.

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] Subject: User

[0546] The user creates an electronic message using the terminal. Input includes the user typing text into the terminal's message input field. For example, the user might type "Why isn't it finished yet?". The created message is prepared as string data. The output is this string data.

[0547] Step 2:

[0548] Subject: terminal

[0549] The terminal sends a message created by the user to the server over the network. The input is the string data described above. Specifically, the terminal sends that message to the server's address. The output is the message data that reaches the server.

[0550] Step 3:

[0551] Subject: Server

[0552] The server passes the received message to the natural language processing engine, which then begins the analysis. The input is the message data sent from the terminal. Specifically, the natural language processing engine performs syntactic analysis of the message and extracts information about tone and intonation. The output is the data points (syntax and keywords) resulting from the analysis.

[0553] Step 4:

[0554] Subject: Server

[0555] The server then uses an emotion engine to recognize the user's emotions from the message. The input is analyzed data obtained through natural language processing. At this stage, an emotion model is used, and emotion scoring is performed. As a concrete example, a generative AI model identifies an emotion such as "frustration." The output is the emotion score.

[0556] Step 5:

[0557] Subject: Server

[0558] The server optimizes the message based on the sentiment analysis results. The input is the sentiment score. Specifically, a generative AI model processes the content to soften the expression. For example, it might be transformed into something like, "I apologize for bothering you while you're busy, but could you tell me about the progress of your work?" The output is the optimized message data.

[0559] Step 6:

[0560] Subject: Server

[0561] The server delivers data over the network to send an optimized message to the recipient. The input is the optimized message data. Specifically, the message is transferred to the recipient's terminal using the network protocol. The output is the message displayed on the recipient's terminal.

[0562] Step 7:

[0563] Subject: Server

[0564] The server records the history of all analysis processes and message transformations. Input consists of the data generated at each step. Specifically, this data is saved to log files and databases. Output is historical data usable for analysis and report creation.

[0565] (Application Example 2)

[0566] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0567] Electronic messaging often fails to accurately reflect the sender's emotions and intentions, leading to misunderstandings. Such communication discrepancies frequently impact customer satisfaction and trust, particularly in the area of ​​customer support. Therefore, a system is needed that appropriately captures user emotions and intentions and optimizes electronic messaging.

[0568] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0569] In this invention, the server includes means for receiving electronic messages via a computer network, means for linguistically analyzing the content of the electronic messages, and means for sentiment analysis to identify the user's emotions and optimize the message content. This enables communication that takes into account the emotions the user is experiencing, thus avoiding misunderstandings.

[0570] A "computer network" is a connection infrastructure that allows multiple computers or terminals to communicate with each other.

[0571] An "electronic message" is a message sent in digital format, primarily referring to emails and chat messages.

[0572] "Linguistic analysis" is the process of analyzing the grammar, syntax, and keywords of electronic messages to understand their content.

[0573] "Tone" refers to elements that describe the language and attitude used in a message, and is an indicator that influences the sender's intentions and emotions.

[0574] "Sentiment analysis" is the process of identifying a user's emotions from the content of a message, and it is a technology that performs analysis based on sentiment models.

[0575] "Conversion means" refers to methods or techniques for modifying or optimizing message representation based on analysis results.

[0576] "Application methods" refer to methods or techniques for utilizing the functions of a system in a specific environment or situation.

[0577] The system that realizes this invention includes a procedure for receiving electronic messages via a computer network and analyzing the content of those messages. The server analyzes the received messages using a language analysis engine and extracts keywords and grammatical structures. This analysis uses natural language processing software such as Google Natural Language API and Microsoft Azure Text Analytics.

[0578] Subsequently, using the analyzed data, the server identifies the user's emotions through an emotion analysis engine. Based on the emotion model, a process is executed to evaluate the tone of the message and the underlying emotions. This generates a message that takes the user's emotions into account, and the message is optimized using a transformation mechanism.

[0579] For example, suppose a user expresses dissatisfaction in customer support, such as "the amount on the invoice is incorrect." In this case, the sentiment analysis engine identifies the dissatisfaction and translates the message into a gentler tone, such as, "We apologize for the inconvenience. We will investigate the details and address the issue immediately."

[0580] The final corrected message is sent to the user via the server. In this way, misunderstandings in communication are reduced, and smooth exchanges are possible.

[0581] An example of a prompt could be: "Use the generative AI model to recognize the emotions in the message sent by the customer and reply with appropriate, polite language." This prompt provides concrete assistance to the generative AI model in actually transforming the message into appropriate language.

[0582] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0583] Step 1:

[0584] The terminal receives electronic messages created by the user and sends them to the server. The input is a raw text message written by the user, and the output is the electronic data of that message.

[0585] Step 2:

[0586] The server analyzes the received electronic message using a language analysis engine. The input is the electronic message data received in step 1, and the output is the analysis data, such as the grammatical structure and keywords of the message. Specifically, it performs syntactic analysis of the text using the Google Natural Language API.

[0587] Step 3:

[0588] The server uses an emotion analysis engine to identify the user's emotions from the analyzed data. The input is the linguistic analysis data from step 2, and the output is evaluation data indicating the user's emotions. For example, an emotion model is applied to determine, based on the model, whether the user has emotions such as irritation or dissatisfaction.

[0589] Step 4:

[0590] The server executes transformation mechanisms to optimize message expression based on the results of sentiment analysis. The input is evaluation data obtained through sentiment analysis, and the output is the optimized electronic message. Specific operations include transforming irritation into softer expressions.

[0591] Step 5:

[0592] The server sends an optimized electronic message to the terminal, which is ultimately received by the user. The input is the optimized message obtained as a result of step 4, and the output is the electronic data configured to be sent to the user.

[0593] This series of steps generates optimized messages that respond to the user's emotions, resulting in effective and unambiguous communication.

[0594] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0595] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0596] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0597] [Fourth Embodiment]

[0598] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0599] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0600] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0601] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0602] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0603] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0604] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0605] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0606] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0607] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0608] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0609] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0610] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0611] To implement this system, users must first create an electronic message using a terminal within a computer network environment and send it to a designated recipient. The message created by the user is first sent from the terminal to the server.

[0612] The server provides message data to a language analysis engine that uses natural language processing to analyze the received messages. This language analysis engine analyzes the vocabulary within the messages and detects specific tones and strong expressions.

[0613] The server automatically performs a conversion process to change the detected tone to an appropriate one. For example, if an imperative sentence is included, it can be converted into a request.

[0614] The revised message is then formatted again by the server and delivered to the final recipient. This ensures that the recipient receives a message that includes more polite and considerate language.

[0615] Furthermore, the server records the entire analysis and correction process and periodically generates reports for users and organizational administrators. These reports include information such as the types of corrections made most frequently and the tone tendencies of individual users, providing valuable data for improving communication.

[0616] As a concrete example, suppose a user creates a message saying, "Complete this report immediately." The server detects this strong, imperative tone and automatically converts it into a more polite request such as, "I apologize for the inconvenience, but could you please complete this report as soon as possible?" This creates a situation where the recipient feels they are communicating more smoothly.

[0617] The following describes the processing flow.

[0618] Step 1:

[0619] The user composes an electronic message on their device and presses the send button. This action prepares the message data for rapid transmission to the server.

[0620] Step 2:

[0621] Messages sent from the terminal are received by the server. The server then passes these messages to the analysis engine, which begins the analysis process.

[0622] Step 3:

[0623] The server uses an analysis engine to scan the linguistic content of messages and evaluates the tone of each sentence using natural language processing techniques. Specifically, it identifies certain words and phrases and measures their intensity.

[0624] Step 4:

[0625] If the server detects harsh language in a message, it will generate suggested corrections based on that detection and automatically transform the expression according to a pre-configured language template.

[0626] Step 5:

[0627] Once the conversion is complete, the message is reformatted on the server and ready to be sent.

[0628] Step 6:

[0629] The server sends the corrected message to the designated recipient's device. The recipient's device displays this message, and the user confirms the corrected content.

[0630] Step 7:

[0631] The server records logs containing details of analysis and corrections, and stores this data for analysis. This record is reflected in periodic reports and provided to users and administrators.

[0632] (Example 1)

[0633] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0634] In digital communication, direct and strong-tongued messages can lead to misunderstandings and friction. Especially in cross-cultural and business settings, overly commanding language can cause discomfort and pressure on the recipient. Therefore, softer communication is desirable, but doing so manually is time-consuming and laborious. Furthermore, advanced language analysis and conversion technologies are needed to transform softer language into more natural expressions.

[0635] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0636] In this invention, the server includes means for receiving electronic messages created by users via a network, means for analyzing the received messages using natural language processing technology and evaluating the vocabulary and grammatical structure within the text, and means for converting the detected expressions to conform to set standards and modifying them into more polite expressions such as request forms. This makes it possible to automatically adjust the tone of the message, reduce misunderstandings, and ensure smooth communication.

[0637] A "user" refers to an individual or organization that uses this system to create and send electronic messages.

[0638] A "network" refers to an interconnected digital environment that provides means of communication that enable the sending and receiving of data.

[0639] An "electronic message" refers to communication content created in digital format and transmitted via a computer network.

[0640] "Natural language processing technology" refers to the field of technology that uses computers to understand, analyze, and generate human language.

[0641] "Linguistic analysis" refers to the process of structurally analyzing the content of electronic messages and evaluating their grammar, vocabulary, and context.

[0642] "Imperative forms and strong expressions" refer to words or sentences that have a tone that makes the recipient feel coerced or pressured.

[0643] "Conversion means" refers to processes and techniques for modifying detected expressions into other forms to generate more appropriate expressions.

[0644] A "generative AI model" refers to an algorithm or program that uses artificial intelligence to generate natural language based on training data.

[0645] The following describes embodiments for carrying out the invention.

[0646] To implement this invention, the process begins with a user creating an electronic message using a terminal in a network environment and sending it to a server over the network. The message created by the user on the terminal is entered using standard word processing software or messaging applications. For example, an office suite or a standard email client can be used as the terminal.

[0647] When a message reaches the server, it analyzes it using specialized software for natural language processing. Specifically, it uses Python natural language processing libraries (such as NLTK and spaCy) to analyze the vocabulary and grammar within the message, identifying imperative forms and strong language. This analysis helps determine which parts should be changed to more polite expressions.

[0648] Based on the analyzed data, the server uses a conversion mechanism to convert it into a response format as needed. A generative AI model can be used for this conversion. For example, a GPT model can be used to convert imperative forms into appropriate request forms. This process is performed automatically within the server and is processed in real time.

[0649] The converted message is then formatted again and sent to the designated recipient. This ensures the recipient receives a message with more polite language, facilitating smoother communication.

[0650] For example, if the input is "Complete this report immediately," the generating AI model will use the prompt "Translate the following message into a more polite expression: Complete this report immediately," which will then be translated as "We apologize for the inconvenience, but could you please complete this report immediately?"

[0651] This invention enables more appropriate and smoother communication by modifying the tone of messages through an automated process.

[0652] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0653] Step 1:

[0654] The user creates an electronic message using a terminal. The input is the user's intended content, using standard document creation software or messaging applications on the terminal. The output is saved digitally as an electronic message. For example, the user might input, "Complete this report immediately."

[0655] Step 2:

[0656] The user creates a message and sends it from the terminal to the server via the network. The input at this stage is the electronic message previously created on the terminal. The output is the message safely reaching the server.

[0657] Step 3:

[0658] The server analyzes the messages it receives using natural language processing software. The input is the message that reached the server, and the output is the analyzed language elements. Specifically, the server uses a Python natural language processing library (such as NLTK or spaCy) to scan the vocabulary and grammatical structure within the message and extract imperative forms and strong expressions.

[0659] Step 4:

[0660] The server uses a generative AI model to transform the message based on the analysis results. The input is the analyzed linguistic information, and the output is the corrected, polite version of the message. For example, the GPT model is used to perform a transformation using the prompt "Transform the following message into a more polite expression: Complete this report immediately," and the result is "We apologize for the inconvenience, but could you please complete this report immediately?"

[0661] Step 5:

[0662] The server reformats the corrected message and sends it to the designated recipient. The input is the converted, politely formatted message, and the output is the recipient receiving the message in the appropriate format. After formatting the message, the server delivers it over the network.

[0663] Step 6:

[0664] The server records all processing and accumulates data for periodic reporting. Input is all the data from analysis and transformation, and output is report information for users and administrators to refer to later. The server stores the message revision history in a database and generates periodic reports to help improve communication.

[0665] (Application Example 1)

[0666] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0667] In electronic communications, inappropriate language or disrespectful tone between users and support organizations can hinder smooth communication. Therefore, it is necessary to maintain a positive relationship between the two parties and improve the user experience. Furthermore, it is also required to utilize the improved quality of communication in performance evaluations.

[0668] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0669] In this invention, the server includes means for receiving electronic communications via a computer network, means for analyzing the information in the received electronic communications, means for detecting specific expressions contained in the communications based on the results of the analysis, means for converting them to appropriate language, means for distributing the corrected electronic communications, and means for automatically converting electronic communications between the user and the support organization into a polite format. This enables smooth communication.

[0670] A "computer network" is a communication infrastructure that connects multiple computers to exchange information with each other.

[0671] "Electronic communication" refers to texts and messages transmitted through electronic means.

[0672] "Means of analyzing information" refers to a system that performs a process to examine received data in detail and understand its content and context.

[0673] "Means for detecting specific expressions" refers to methods for identifying blunt expressions or inappropriate tone within a message.

[0674] A "conversion means" is a function that automatically changes the detected expression into a more favorable form.

[0675] "Means of delivering corrected electronic communications" refers to the method of managing the process of delivering the converted message to the recipient.

[0676] "Methods for automatically converting to a polite format" refer to automatic functions that transform the original expression into a polite and refined style.

[0677] This invention begins with a user creating an electronic communication using a computer terminal and transmitting that communication through a computer network. The server holds the received communication and provides the data to a language processing engine for initial information analysis. This process uses natural language processing (NLP) software to analyze the vocabulary and context within the communication and detect specific expressions and tones.

[0678] Based on the detected expressions, the server uses a translation mechanism to modify the tone of the communication. In this process, if there are imperative sentences, they are converted into more polite and considerate expressions. For example, in an inquiry to customer support for an electronic payment service, the message "Please complete this procedure immediately!" is changed to "We apologize for the inconvenience, but could you please complete this procedure as quickly as possible?"

[0679] The server also plays a role in distributing corrected communications, ensuring recipients receive revised and acceptable messages. Furthermore, it records the correction process and results, generating periodic reports based on this data. This facilitates smoother communication between users and support organizations, improving the overall customer service quality of the organization. This data may also be used for performance evaluations.

[0680] As a concrete example, a prompt using a generative AI model can be used in the form of, "Please convert the following imperative message into a more polite request: (message content)." Servers and related systems require hardware with high-performance processors and sufficient storage capacity to process data at an appropriate speed, even when used by multiple users simultaneously.

[0681] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0682] Step 1:

[0683] A user creates an electronic message using a terminal. The user enters a message on the electronic payment service application and presses the send button. The text data entered by the user is retrieved as input. The output is the sent message data.

[0684] Step 2:

[0685] The server records the electronic communications it receives and provides the data to the language processing engine to begin analysis. Here, natural language processing software is used to analyze the text data and check for specific vocabulary and context. The input is message data sent by the user, and the output is information such as the type of expression and tone as a result of the analysis.

[0686] Step 3:

[0687] The server detects portions containing specific expressions based on the analysis results and uses a conversion mechanism to correct them to appropriate wording. This step involves processing imperative sentences to change them into more polite request forms. The input is the analysis result, and the output is the corrected message data.

[0688] Step 4:

[0689] The server delivers the corrected electronic communication to the final recipient. The user sends the corrected message over the network so that the converted message is delivered. The input is the corrected message data, and the output is the message delivered to the recipient.

[0690] Step 5:

[0691] The server records all conversion processes and results and generates periodic reports. This allows users and support organizations to obtain statistical data on what corrections have been made. The input is the conversion history data for each message, and the output is the generated report.

[0692] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0693] This invention relates to a system that recognizes user emotions and optimizes message content accordingly, primarily in message communication environments utilizing computer networks. The system aims to better express the user's true intentions by combining message linguistic analysis and an emotion engine.

[0694] When a user composes an electronic message on their device, the message is first sent from the device to the server. The server then passes the received message to a language analysis engine, which begins analyzing the text. This scans the message's syntax and keywords, and evaluates the tone used.

[0695] Furthermore, this system incorporates an emotion engine that can recognize the user's emotions from the wording used in the message. The emotion engine determines the overall tone and underlying emotions of the message based on an emotion model, and then uses that result to determine what kind of wording is appropriate.

[0696] For example, if a user tries to send a message expressing frustration, such as "Why isn't it finished yet?", the emotion engine detects the frustration contained in that message. The server uses this information to soften the message, such as "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0697] The revised message is delivered from the server to the final recipient, who receives the message with the adjusted tone. Furthermore, the server records a history of sentiment recognition and revisions, enabling long-term analysis and reporting. This information is used to improve communication within the organization and may even be reflected in performance evaluations.

[0698] The following describes the processing flow.

[0699] Step 1:

[0700] The user types an electronic message on their device and presses the send button. This action prepares the message data for transmission to the server.

[0701] Step 2:

[0702] A message arrives from the terminal to the server. The server receives this message and queues it for further processing.

[0703] Step 3:

[0704] The server passes the received message to the language analysis engine. The engine analyzes the message text, performing syntax checks and evaluating tone and bias.

[0705] Step 4:

[0706] The server inputs the analysis results into the sentiment engine. The sentiment engine analyzes the keywords and context contained in the message to determine the sender's emotions. If an emotion is determined, that information is used in the correction process.

[0707] Step 5:

[0708] The server automatically applies necessary corrections based on the emotion engine's output. In doing so, it selects appropriate wording and tone according to the emotion, adjusting the message's expression.

[0709] Step 6:

[0710] The server reformats the modified message to match the user's intent and then sends the message to the final recipient's terminal based on that format.

[0711] Step 7:

[0712] The adjusted message will be displayed on the recipient's device. The recipient can then review this adjusted message.

[0713] Step 8:

[0714] The server meticulously records the analysis and correction process. This recorded data is used for subsequent analysis, periodic reporting, and performance evaluations.

[0715] (Example 2)

[0716] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] In modern society, communication via electronic messages is commonplace, but a challenge remains: misunderstandings and friction can easily arise due to misinterpretations of message tone and emotion. Such situations can hinder smooth communication and potentially lead to decreased work efficiency and worsening interpersonal relationships. Therefore, there is a need for technology that can interpret and optimize the content of electronic messages to achieve accurate and effective communication.

[0718] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0719] In this invention, the server includes means for receiving information and analyzing it using natural language processing technology, means for detecting the style of communication, and means for performing sentiment analysis and converting it into the most appropriate expression. This makes it possible to accurately read the tone and emotions of electronic messages and transmit them to the recipient in an unambiguous manner.

[0720] A "computer network" is a system in which multiple computer devices are connected in a way that allows them to communicate data with one another.

[0721] "Information" refers to data and knowledge expressed in digital format, including electronic messages.

[0722] "Natural language processing technology" refers to the technology of using computers to understand, analyze, and generate human language, and includes methods for extracting meaning and structure from text.

[0723] "Communication style" refers to the wording, expressions, and tone used within a message.

[0724] "Emotional analysis" is a method of extracting and analyzing emotional and sentimental information from text.

[0725] "Transforming into the most appropriate expression" means adjusting or modifying the content of a received message according to its purpose, so that it can be conveyed in a more suitable way.

[0726] "Tone" refers to the emotional or attitudinal color conveyed by a message or information.

[0727] "Communicating without misunderstanding" means adjusting the message so that its intent and emotions are accurately conveyed to the recipient.

[0728] This invention is a system for analyzing electronic information messages and optimizing their content. It primarily achieves efficient message transmission and reception, as well as sentiment analysis, through the mutual cooperation of a server, terminal, and user.

[0729] Users create electronic messages using a terminal. The terminal is a general communication device, such as a smartphone or personal computer. When a user creates a message and presses the send button, the message is sent from the terminal to the server.

[0730] The server uses natural language processing (NLP) techniques to analyze received messages. Specifically, it utilizes NLTK and spaCy, for example, natural language processing libraries. The syntax and keywords of the message are analyzed using these NLP techniques.

[0731] Next, the server performs sentiment analysis. At this stage, generative AI models such as BERT and GPT-3 are used. This allows the server to grasp the tone of the message and the user's underlying emotions. Based on this, the server adjusts the wording and expression of the message as needed.

[0732] Specifically, if a user tries to send a message such as, "Why isn't it finished yet?", the server can read their emotions and translate it into a softer expression like, "I apologize for bothering you while you're busy, but could you please let me know the progress of the work?"

[0733] The optimized message is sent from the server to the recipient's terminal. By receiving the adjusted message, the recipient can understand the message's intent without misunderstanding.

[0734] As a concrete example of this system, the prompt message to the generative AI model could be written as follows: "Identify the possible emotions a user might feel upon receiving the following message, and then revise the message to alleviate those emotions. Message: 'Why isn't it over yet?'"

[0735] This invention aims to facilitate communication between users and reduce misunderstandings and friction by efficiently optimizing the content of electronic messages using appropriate algorithms.

[0736] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0737] Step 1:

[0738] Subject: User

[0739] The user creates an electronic message using the terminal. Input includes the user typing text into the terminal's message input field. For example, the user might type "Why isn't it finished yet?". The created message is prepared as string data. The output is this string data.

[0740] Step 2:

[0741] Subject: terminal

[0742] The terminal sends a message created by the user to the server over the network. The input is the string data described above. Specifically, the terminal sends that message to the server's address. The output is the message data that reaches the server.

[0743] Step 3:

[0744] Subject: Server

[0745] The server passes the received message to the natural language processing engine, which then begins the analysis. The input is the message data sent from the terminal. Specifically, the natural language processing engine performs syntactic analysis of the message and extracts information about tone and intonation. The output is the data points (syntax and keywords) resulting from the analysis.

[0746] Step 4:

[0747] Subject: Server

[0748] The server then uses an emotion engine to recognize the user's emotions from the message. The input is analyzed data obtained through natural language processing. At this stage, an emotion model is used, and emotion scoring is performed. As a concrete example, a generative AI model identifies an emotion such as "frustration." The output is the emotion score.

[0749] Step 5:

[0750] Subject: Server

[0751] The server optimizes the message based on the sentiment analysis results. The input is the sentiment score. Specifically, a generative AI model processes the content to soften the expression. For example, it might be transformed into something like, "I apologize for bothering you while you're busy, but could you tell me about the progress of your work?" The output is the optimized message data.

[0752] Step 6:

[0753] Subject: Server

[0754] The server delivers data over the network to send an optimized message to the recipient. The input is the optimized message data. Specifically, the message is transferred to the recipient's terminal using the network protocol. The output is the message displayed on the recipient's terminal.

[0755] Step 7:

[0756] Subject: Server

[0757] The server records the history of all analysis processes and message transformations. Input consists of the data generated at each step. Specifically, this data is saved to log files and databases. Output is historical data usable for analysis and report creation.

[0758] (Application Example 2)

[0759] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0760] Electronic messaging often fails to accurately reflect the sender's emotions and intentions, leading to misunderstandings. Such communication discrepancies frequently impact customer satisfaction and trust, particularly in the area of ​​customer support. Therefore, a system is needed that appropriately captures user emotions and intentions and optimizes electronic messaging.

[0761] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0762] In this invention, the server includes means for receiving electronic messages via a computer network, means for linguistically analyzing the content of the electronic messages, and means for sentiment analysis to identify the user's emotions and optimize the message content. This enables communication that takes into account the emotions the user is experiencing, thus avoiding misunderstandings.

[0763] A "computer network" is a connection infrastructure that allows multiple computers or terminals to communicate with each other.

[0764] An "electronic message" is a message sent in digital format, primarily referring to emails and chat messages.

[0765] "Linguistic analysis" is the process of analyzing the grammar, syntax, and keywords of electronic messages to understand their content.

[0766] "Tone" refers to elements that describe the language and attitude used in a message, and is an indicator that influences the sender's intentions and emotions.

[0767] "Sentiment analysis" is the process of identifying a user's emotions from the content of a message, and it is a technology that performs analysis based on sentiment models.

[0768] "Conversion means" refers to methods or techniques for modifying or optimizing message representation based on analysis results.

[0769] "Application methods" refer to methods or techniques for utilizing the functions of a system in a specific environment or situation.

[0770] The system that realizes this invention includes a procedure for receiving electronic messages via a computer network and analyzing the content of those messages. The server analyzes the received messages using a language analysis engine and extracts keywords and grammatical structures. This analysis uses natural language processing software such as Google Natural Language API and Microsoft Azure Text Analytics.

[0771] Subsequently, using the analyzed data, the server identifies the user's emotions through an emotion analysis engine. Based on the emotion model, a process is executed to evaluate the tone of the message and the underlying emotions. This generates a message that takes the user's emotions into account, and the message is optimized using a transformation mechanism.

[0772] For example, suppose a user expresses dissatisfaction in customer support, such as "the amount on the invoice is incorrect." In this case, the sentiment analysis engine identifies the dissatisfaction and translates the message into a gentler tone, such as, "We apologize for the inconvenience. We will investigate the details and address the issue immediately."

[0773] The final corrected message is sent to the user via the server. In this way, misunderstandings in communication are reduced, and smooth exchanges are possible.

[0774] An example of a prompt could be: "Use the generative AI model to recognize the emotions in the message sent by the customer and reply with appropriate, polite language." This prompt provides concrete assistance to the generative AI model in actually transforming the message into appropriate language.

[0775] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0776] Step 1:

[0777] The terminal receives electronic messages created by the user and sends them to the server. The input is a raw text message written by the user, and the output is the electronic data of that message.

[0778] Step 2:

[0779] The server analyzes the received electronic message using a language analysis engine. The input is the electronic message data received in step 1, and the output is the analysis data, such as the grammatical structure and keywords of the message. Specifically, it performs syntactic analysis of the text using the Google Natural Language API.

[0780] Step 3:

[0781] The server uses an emotion analysis engine to identify the user's emotions from the analyzed data. The input is the linguistic analysis data from step 2, and the output is evaluation data indicating the user's emotions. For example, an emotion model is applied to determine, based on the model, whether the user has emotions such as irritation or dissatisfaction.

[0782] Step 4:

[0783] The server executes transformation mechanisms to optimize message expression based on the results of sentiment analysis. The input is evaluation data obtained through sentiment analysis, and the output is the optimized electronic message. Specific operations include transforming irritation into softer expressions.

[0784] Step 5:

[0785] The server sends an optimized electronic message to the terminal, which is ultimately received by the user. The input is the optimized message obtained as a result of step 4, and the output is the electronic data configured to be sent to the user.

[0786] This series of steps generates optimized messages that respond to the user's emotions, resulting in effective and unambiguous communication.

[0787] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0788] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0789] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0790] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0791] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0792] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0793] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0794] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0795] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0796] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0797] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0798] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0799] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0800] 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.

[0801] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0802] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0803] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0804] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0805] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0806] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0807] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0808] The following is further disclosed regarding the embodiments described above.

[0809] (Claim 1)

[0810] A means of receiving electronic messages via a computer network,

[0811] A means for linguistically analyzing the content of a received electronic message,

[0812] A means for detecting a specific tone of voice contained in a message based on the results of language analysis,

[0813] A conversion means for correcting specific expressions based on the detected tone of voice,

[0814] A means of sending the revised electronic message,

[0815] A system that includes this.

[0816] (Claim 2)

[0817] The system according to claim 1, comprising means for recording the results of language analysis and generating periodic reports.

[0818] (Claim 3)

[0819] The system according to claim 1, which includes means for utilizing the improved tone of voice achieved through the revision process in personnel evaluations.

[0820] "Example 1"

[0821] (Claim 1)

[0822] A means of receiving user-created electronic messages via a network,

[0823] A means for analyzing received messages using natural language processing technology and evaluating the vocabulary and grammatical structure within the text,

[0824] A means for identifying and detecting imperative forms and strong expressions contained in a message based on the results of language analysis,

[0825] A means to convert the detected expressions to conform to set standards and correct them to more polite expressions such as request formats,

[0826] A means of sending the revised message to the recipient,

[0827] A means of recording the process of message analysis and transformation and saving it as data,

[0828] A means of generating reports aimed at improving communication based on regularly recorded data,

[0829] A system that includes this.

[0830] (Claim 2)

[0831] The system according to claim 1, comprising means for receiving and utilizing a generative AI model for use in analysis and transformation.

[0832] (Claim 3)

[0833] The system according to claim 1, which includes means for using statistics of modified messages and reflecting them in the organization's talent development and evaluation processes.

[0834] "Application Example 1"

[0835] (Claim 1)

[0836] A means of receiving electronic communications via a computer network,

[0837] A means of analyzing the information from received electronic communications,

[0838] Based on the results of the analysis, means for detecting specific expressions contained in the communication,

[0839] A conversion means for correcting the detected expression to appropriate wording,

[0840] A means of distributing the revised electronic communications,

[0841] A means of automatically converting electronic communications between users and support organizations into a more formal format,

[0842] A system that includes this.

[0843] (Claim 2)

[0844] The system according to claim 1, comprising means for storing the results of the analysis and generating periodic data.

[0845] (Claim 3)

[0846] The system according to claim 1, comprising means for which the modified communication quality can be used for performance evaluation.

[0847] "Example 2 of combining an emotion engine"

[0848] (Claim 1)

[0849] Means for receiving information via a computer network,

[0850] A means of analyzing the content of received information using natural language processing technology,

[0851] Based on the results of the analysis, a means for detecting a specific communication style contained in the information,

[0852] A transformation means for adjusting specific expressions based on the detected style,

[0853] A means of transmitting the adjusted information,

[0854] A means of performing sentiment analysis and optimizing the content of information based on the results,

[0855] A means of recording the history of sentiment analysis and transformation of information to help improve organizational communication,

[0856] A system that includes this.

[0857] (Claim 2)

[0858] The system according to claim 1, comprising means for recording analysis results and conversion history, generating periodic analysis reports, and utilizing the acquired data for evaluation or improvement.

[0859] (Claim 3)

[0860] The system according to claim 1, which provides means for applying the transformed communication style to the organization's evaluation process.

[0861] "Application example 2 when combining with an emotional engine"

[0862] (Claim 1)

[0863] A means of receiving electronic messages via a computer network,

[0864] A means for linguistically analyzing the content of a received electronic message,

[0865] A means for detecting a specific tone of voice contained in a message based on the results of language analysis,

[0866] A conversion means for correcting specific expressions based on the detected tone of voice,

[0867] A sentiment analysis method for identifying user emotions and optimizing message content,

[0868] A means of sending the revised electronic message,

[0869] Application methods implemented in information devices used by users,

[0870] A system that includes this.

[0871] (Claim 2)

[0872] The system according to claim 1, comprising means for recording the results of language analysis and sentiment analysis, and for generating periodic reports.

[0873] (Claim 3)

[0874] The system according to claim 1, which includes means for utilizing the improved tone of voice achieved through the revision process in performance evaluation. [Explanation of Symbols]

[0875] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving electronic messages via a computer network, A means for linguistically analyzing the content of a received electronic message, A means for detecting a specific tone of voice contained in a message based on the results of language analysis, A conversion means for correcting specific expressions based on the detected tone of voice, A means of sending the revised electronic message, A system that includes this.

2. The system according to claim 1, comprising means for recording the results of language analysis and generating periodic reports.

3. The system according to claim 1, which includes means for utilizing the improved tone of voice achieved through the revision process in personnel evaluations.

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