system

Abstract

We provide the system. [Solution] Information processing means for receiving a communication message and analyzing the tone and content of the communication message, A generation means that proposes an appropriate expression method and word choice for the communication message based on the analysis results, A display means that presents proposed expressions and words to the user and provides an interface for modifying the content of the communication message in accordance with the proposed expressions, 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, the method including 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] Engineers and technical professionals have advanced expertise, but their communication is often direct and may lead to misunderstandings. This causes problems such as impaired cooperation between teams and project progress, ultimately affecting the success of the project. Improving the communication ability of such technical experts is important in order to make the most of the knowledge they possess and achieve smooth project execution.

Means for Solving the Problems

[0005] This invention solves the above problems by providing an information processing means for receiving communication messages and analyzing their tone and content. Furthermore, it promotes more effective communication by providing a generation means that suggests appropriate expressions and word choices for the communication message based on the analysis results. In addition, it provides an interface for presenting the suggested expressions and words to the user and modifying the message content according to the suggested suggestions. This makes it possible to improve communication skills and increase the success rate of projects.

[0006] "Communication messages" refer to linguistic information sent or received by users via email or chat applications.

[0007] "Information processing means" refers to a computer system or program for receiving communication messages and analyzing their tone and content.

[0008] "Generation means" refers to a technology or algorithm for proposing appropriate expression methods and word choices for a communication message based on the analysis results.

[0009] "Display means" refers to a device or technology that presents proposed expressions and words to the user and provides an interface for modifying the content of the message.

[0010] A "grammatical error" is a part of a language that violates rules or structure, and is usually an inappropriate expression that requires proofreading.

[0011] "Tone" refers to the emotional or expressive nuances in a communication message and is a factor that determines the impression it gives to the recipient.

[0012] "Feedback" refers to information that includes suggestions and comments regarding communication messages, and serves as support material for improving users' communication skills.

[0013] A "report" is a document that summarizes the analysis results and the history of proposals, and is provided to the user as material for evaluation and improvement.

[0014] A "reporting mechanism" is a process or system for generating and regularly providing feedback and reports to users. [Brief explanation of the drawing]

[0015] [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]It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered 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.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered 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, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] 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."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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".

[0036] The present invention provides a system for analyzing communication messages used daily by technical professionals, including engineers, to enable more effective communication. In a specific embodiment, the process begins with the user entering a message via email or chat application on a terminal used for daily work.

[0037] In this system, when a user enters a message, it is sent to the server. The server analyzes the received message using information processing means and evaluates its tone and content. If the evaluated tone is deemed negative or the content is deemed potentially misleading, a generation means generates improvement suggestions based on the analysis results.

[0038] The generated suggestions are displayed in real time on the user's device via email or chat interfaces. This allows the user to review the suggestions and revise their message based on them. For example, if a user enters the message "My report is late, but I will submit it soon," the server, if it determines the tone is neutral or negative, will suggest revising it to "My report is in the final stages and will be submitted shortly."

[0039] Furthermore, this system simultaneously performs grammatical checks on the text and generates appropriate feedback on the analyzed communication messages, helping users efficiently improve their communication. In addition, the server records the user's communication patterns and generates detailed feedback and reports for long-term improvement. This enables users to continuously improve their communication skills.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] The user types a message in an email or chat application and sends it. The message is temporarily stored on the user's device.

[0043] Step 2:

[0044] The user's terminal sends the entered communication message to the server. The message data is transmitted securely and received on the server.

[0045] Step 3:

[0046] The server feeds the received communication message into a parsing process. This involves using natural language processing (NLP) algorithms to evaluate the message's tone, grammar, and context.

[0047] Step 4:

[0048] Based on the analysis results, the server generates improvement suggestions using a generation mechanism. These suggestions include alternative ways of expressing the message to soften its tone or make it clearer.

[0049] Step 5:

[0050] The server sends the generated suggestions to the user's terminal. The suggestions are displayed in real time and provided to the user as feedback that can be applied.

[0051] Step 6:

[0052] The user reviews the suggested improvements and modifies the communication messages as needed. This allows for more effective communication.

[0053] Step 7:

[0054] The server records the history of previous messages and suggestions, accumulating data on the user's communication patterns. This data is used to generate feedback and reports later on.

[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] For modern technology professionals, communicating with the appropriate tone and avoiding misunderstandings is crucial. However, it is difficult to make such adjustments every time in a busy daily life. Furthermore, there is a lack of concrete means to consistently improve the quality of communication. Therefore, the present invention aims to achieve efficient and smooth communication by analyzing the emotions and content of communication data and automatically making improvement suggestions as needed.

[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 data processing means for receiving communication data and analyzing the sentiment and information of the communication data; generation means for proposing appropriate expressions and vocabulary for the communication data based on the analysis results; and operation means for presenting the proposed expressions and vocabulary to the user and modifying the content of the communication data according to the presented suggestions. This enables the user to plan effective communication in real time.

[0060] "Communication data" refers to units of information that are transmitted and received electronically, and includes text, audio, or image data.

[0061] "Emotions" refer to the sender's intentions and psychological state, which are analyzed from the tone and expression of communication data.

[0062] "Information" refers to the content and facts contained in communication data, and is an element of data transmitted through a user's message.

[0063] "Data processing means" refers to a device or software that performs a technical process to analyze received communication data and evaluate its emotions and information.

[0064] "Generation means" refers to a device or program that has the function of automatically creating and suggesting appropriate expressions and vocabulary based on analyzed data.

[0065] "Operating means" refers to a device or software that provides a user interface for the user to review proposed expressions and vocabulary and modify or accept them as necessary.

[0066] A "generative AI model" is a learning model that uses artificial intelligence technology to analyze data and generate new information.

[0067] A "revised text" is a new expression or wording generated to improve the original communication data based on analysis and suggestions.

[0068] A "user terminal" is an electronic device used by a user to create, transmit, and receive communication data.

[0069] This invention begins with a terminal used by a user in their daily work. The user inputs a message via email or chat application. The terminal sends the input message to a server. The server receives the communication data and analyzes the sentiment and information of the data using data processing means. This analysis utilizes natural language processing techniques and generative AI models. For example, general-purpose natural language processing models and generative AI models are used to perform sentiment analysis and grammar checks on messages.

[0070] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary for the message. This generation process utilizes a generation AI model to generate appropriate revised sentences through prompts. The generated revised sentences are sent to the user's terminal as suggestions. The terminal displays the suggested revised sentences to the user, allowing the user to review the message and either revise or approve it. This enables smoother and more effective communication for the user.

[0071] For example, if a user enters the message, "My report is late, but I will submit it soon," the server can use a generation tool to determine whether the tone of this message is neutral or negative and suggest a revised version, such as, "My report is in the final stages and will be submitted shortly." Through this process, users can receive continuous feedback to improve the quality of their communication.

[0072] Examples of prompt statements are as follows:

[0073] Please change the tone of the following message to a softer one: "I apologize for the delay in reporting, but I will submit it as soon as possible."

[0074] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0075] Step 1:

[0076] The user launches an email or chat application on their terminal used for daily work and enters a message. The input data includes the text message entered by the user. The terminal sends this message to the server for the next processing step. In this step, the user's intent and content are preserved as input data.

[0077] Step 2:

[0078] The server passes the received message to a data processing system. This data includes the message text. The server uses natural language processing techniques to analyze the sentiment and information of the message. Here, a generative AI model is used to analyze the tone of the text and group the content. As a result of the analysis, the tone of the message (e.g., positive, negative, neutral) is evaluated.

[0079] Step 3:

[0080] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary. In this step, a generative AI model is used to generate improvement suggestions from the analyzed data. The input includes the analysis results, and the output is the text of the revised suggestions. This revised text is obtained by sending a generation request to the model via a prompt message.

[0081] Step 4:

[0082] The server sends the generated revised version to the terminal. Here, it outputs a revised message for the user to see. The terminal displays this revised version in real time on the interface of an email or chat application. The user can review the suggested revised text and, if necessary, modify the message or send it as is.

[0083] Step 5:

[0084] The server records user behavior patterns and generates long-term feedback and reports. This feedback is generated based on analyzed past communication data and its improvement history. Through this feedback, users can continuously improve their communication skills. Input includes the user's correction history, and output is a feedback report.

[0085] (Application Example 1)

[0086] 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."

[0087] Modern engineers and professionals are required to communicate efficiently and accurately in many situations. However, misunderstandings and inefficiencies can occur if the tone or expression used in the information transmission process is inappropriate. This problem can negatively impact productivity, especially in work environments where rapid responses are required. Therefore, there is a need for means to solve these communication challenges and support efficient communication.

[0088] 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.

[0089] In this invention, the server includes a processing unit that receives communication information and analyzes the tone and content of the communication information; a generating unit that proposes appropriate expression methods and word selections for the communication information based on the analysis results; and a presentation unit that displays the proposals in real time on the user's visual aid. This enables the user to communicate quickly and accurately.

[0090] "Communication information" refers to messages transmitted to the recipient in the process of sending and receiving information, either in linguistic or digital data format.

[0091] "Tone" refers to elements that indicate the emotional nuances and attitude of a message, and is classified as positive, negative, neutral, etc.

[0092] A "processing device" refers to a hardware or software component that receives information input and performs analysis and evaluation.

[0093] A "generation device" refers to a device that automatically generates improvement suggestions and optimized expressions based on analyzed information.

[0094] A "visual assistance device" refers to a digital display device designed to support a user's field of vision, specifically a device that directly displays information.

[0095] A "presentation device" refers to a device that provides an interface for displaying generated improvement suggestions and feedback to users in an easily viewable format.

[0096] The embodiment of the invention is a system for effectively analyzing and improving communication information. This system consists of a processing unit, a generation unit, and a presentation unit. When a user inputs information, it is processed by a server. Specifically, the information processing unit receives the communication information and analyzes its tone and content. Natural language processing libraries such as SpaCy and Transformers may be used for the analysis. This determines whether the tone is positive, negative, or neutral.

[0097] Based on the analysis results, the server automatically generates improvement suggestions using a generation device. This generation process utilizes a generation AI model, optimizing the suggested expressions and word choices. The generated improvement suggestions are then displayed in real time through a presentation device to the user's visual aids, specifically smart glasses.

[0098] For example, suppose a technician working in a factory reports a malfunction in a piece of equipment and enters the message, "There is a problem with this equipment." The server receives this message and generates a suggested improvement, such as, "We have observed a minor issue with this equipment, but we will address it promptly," which is then displayed on a visual aid, facilitating smoother communication on-site.

[0099] An example of a prompt to be input into the generation AI model is text such as, "Based on the current status report regarding the factory's parts supply, please generate an appropriate follow-up message for your supervisor."

[0100] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0101] Step 1:

[0102] The server receives communication information entered by the user. The user enters a message as voice or text, which is sent to the server via the terminal. The received data is then sent directly to the next processing step.

[0103] Step 2:

[0104] The server analyzes received communication information using natural language processing libraries. Here, SpaCy and Transformers are used to analyze the tone (positive, negative, neutral, etc.) and content of messages. The input data is the communication information, and the output data is the analysis results. Specific operations include sentence structure analysis and sentiment analysis.

[0105] Step 3:

[0106] The server generates improvement suggestions using a generative AI model based on the analysis results. The generative AI model, such as GPT-3 (registered trademark), suggests appropriate expressions and word choices. The input is the analysis results, and the output is the generated improvement suggestions. This process involves suggestions based on known expression patterns.

[0107] Step 4:

[0108] The server transmits the generated improvement suggestions to the user's visual aids in real time via a display device. Specifically, the information is displayed on a digital display such as smart glasses. The input is the generated improvement suggestions, and the output is what is displayed on the visual aids. In this process, data format conversion is performed to ensure that the information is displayed in a visually understandable format.

[0109] Step 5:

[0110] The user reviews the proposed improvements and modifies the original communication information as needed. The user's input is the modified message, and the output is the final modified communication information that is sent. At this step, the user decides whether or not to accept the suggestions.

[0111] 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.

[0112] This invention is a technology that analyzes communication messages used daily by engineers and technical professionals and promotes effective communication based on the information contained in those messages. This system is used in combination with a program installed on the terminal that the user uses for their daily work and a server.

[0113] When a user enters a message via email or chat application and sends it from their device to the server, the server uses information processing tools to analyze the tone and context of the message. Furthermore, by using an emotion engine, the server identifies the user's emotional state from the entered message and tracks changes in that emotional state.

[0114] Based on the analyzed results, the server uses a generation mechanism to construct suggestions for improving the message. These suggestions include expressions and word choices that take the user's emotions into consideration, and are presented to the user's device in real time. For example, if a user types "I'm worried because the project is behind schedule," the emotion engine identifies the emotion "worry," and the generation mechanism suggests something like "We are seeing progress on the project, and we will do our best with the team."

[0115] Users can modify their communication messages as needed by referring to the suggestions provided. Furthermore, the server records the user's communication patterns and periodically generates reports based on this data, including feedback and long-term emotional trends. This allows users to continuously improve their emotional expression and communication skills.

[0116] The following describes the processing flow.

[0117] Step 1:

[0118] The user types a message in the email or chat application on their device and presses the send button. This prepares the message data, which is then immediately sent to the server.

[0119] Step 2:

[0120] The server passes the received communication message to an information processing module for analysis. Here, an initial evaluation of the tone and content is performed using Natural Language Processing (NLP) techniques.

[0121] Step 3:

[0122] The server uses an emotion engine to identify the emotions contained in the user's message. For example, it can identify the user's emotional state, such as "anxiety" or "relief."

[0123] Step 4:

[0124] Based on these analysis results and emotional states, the server generates improvement suggestions using a generation mechanism. These suggestions include specific expressions and word choices to soften the tone of the message while taking the user's emotions into consideration.

[0125] Step 5:

[0126] The server generates improvement suggestions and sends them to the user's terminal, displaying them to the user in real time. The user can receive this feedback and modify the actual communication messages as needed.

[0127] Step 6:

[0128] Users review the suggested message content, edit it as needed, and send it. This allows for more effective and emotionally sensitive communication.

[0129] Step 7:

[0130] The server records all message history and emotional state data, and generates reports that include long-term communication improvements and emotional trends. These reports are provided to users as needed to help improve their communication skills and emotional expression.

[0131] (Example 2)

[0132] 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".

[0133] In today's communication environment, users compose messages with a wide range of emotions, but effectively conveying these emotions is often difficult. Furthermore, choosing appropriate expressions and words that are sensitive to emotions is particularly challenging, sometimes leading to misunderstandings. Moreover, there is a lack of feedback to help improve one's emotional expression and communication skills in the long term.

[0134] 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.

[0135] In this invention, the server includes information processing means for receiving communication messages and analyzing the sentiment and context of the communication messages; generation means for proposing appropriate expressions and word choices that take the sentiment of the communication message into consideration, based on the analyzed sentiment information; and analysis means for analyzing sentiment trends based on the user's communication history and providing feedback based on the analysis results. This enables the user to communicate more effectively and to continuously improve their emotional expression and communication skills.

[0136] "Communication messages" are information sent and received in text format, exchanged between users through email or chat applications.

[0137] "Information processing means" is a general term for software and hardware used to analyze communication messages, and has the function of understanding the content of messages by performing language processing and sentiment analysis.

[0138] "Emotions" refer to the psychological states that users include in their messages, encompassing internal human feelings such as anxiety, joy, and sadness.

[0139] A "generation method" is a mechanism that selects appropriate expressions and words based on analyzed information and makes suggestions to the user.

[0140] "Method of expression" refers to the choice of words and phrases that a user uses when sending a message, and is the method for appropriately conveying emotions and intentions.

[0141] "Analysis methods" refer to technologies that analyze patterns and trends based on a user's communication history, and the process of generating feedback from this analysis and providing it to the user.

[0142] "Display means" refers to methods or interfaces for visually presenting generated proposals or information to the user.

[0143] "Emotional trends" refer to the tendency of emotional changes based on a user's past messages, and show a history of how a user has expressed emotions.

[0144] This invention is a system that combines a program installed on a terminal used by a user for their daily work with a server. The user inputs communication messages via email or chat applications and sends those messages from the terminal to the server.

[0145] The server uses a natural language processing engine to analyze the tone and context of messages. Specifically, it uses natural language processing libraries such as NLTK and spaCy to analyze the structure and content of messages and extract emotions. Additionally, an emotion engine is installed, and an emotion AI model is running to identify psychological states. This allows the server to identify emotional states from user-input messages and track changes in those states.

[0146] Based on the analyzed data, the server uses a generation mechanism to construct improvement suggestions. This generation mechanism employs a generative AI model, which prioritizes expressions and word choices that take the user's emotions into consideration. For example, if a user inputs a message such as, "I feel that we won't meet the deadline at this rate," the emotional AI model will analyze the emotion of "anxiety" and generate a suggestion such as, "Let's re-examine the project progress and strengthen the support system as needed."

[0147] The generated suggestions are presented to the user's device in real time, allowing the user to modify their message based on the suggestions. Furthermore, the server analyzes emotional trends by recording the user's past messages and communication history, and provides regular feedback to the user. This helps users continuously improve their emotional expression and communication skills.

[0148] A concrete example of a prompt message would be something like, "Please provide suggestions for improving this message. Please be mindful of the user's feelings when giving advice." This can improve the quality of user communication.

[0149] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0150] Step 1:

[0151] Users type communication messages into email or chat applications and send them from their devices. The data entered is textual information, typically including purpose, requests, and emotions.

[0152] Step 2:

[0153] The terminal sends the input communication message to the server. During this process, data is securely transferred using a communication protocol. The input is the text entered by the user, and the output is that text being sent to the server.

[0154] Step 3:

[0155] The server analyzes received messages using a natural language processing (NLP) engine. Specifically, it utilizes NLP libraries (e.g., NLTK, spaCy) to extract keywords and emotional tone from the messages. The input is the received text, and the output is the analyzed tone and emotional information.

[0156] Step 4:

[0157] The server uses an emotion engine to further process the analyzed data and identify the user's emotional state. This process uses an emotion AI model to identify various emotions (anxiety, joy, sadness, etc.). The input is the analyzed information obtained by the NLP engine, and the output is data on emotion labels and their intensity.

[0158] Step 5:

[0159] Based on the generation method, the server generates message improvement suggestions that take into account emotional information, considering expression methods and word choices. An AI generation model is used to construct appropriate suggestions. The input is emotional state data, and the output is the text of the improvement suggestion.

[0160] Step 6:

[0161] The server sends the generated improvement suggestions to the user's terminal. The user's terminal displays the suggestions to the user in real time, and the user reviews them to help with decision-making. The input is the suggestion data from the server, and the output is a visual presentation to the user.

[0162] Step 7:

[0163] The server records the user's communication history and the results of improvement suggestions, and analyzes long-term sentiment trends. Based on this, it periodically generates and provides feedback to the user. The input is past messages and suggestion history, and the output is a feedback report as a result of the analysis.

[0164] (Application Example 2)

[0165] 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".

[0166] In technical work environments, engineers and professionals routinely receive and send numerous communication messages. This often leads to communication friction due to misunderstandings of message tone and context. Furthermore, a decline in communication quality caused by emotional fluctuations and grammatical errors hinders improvements in work efficiency. A system is needed to address these challenges and achieve effective and emotionally sensitive communication.

[0167] 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.

[0168] In this invention, the server includes information processing means for receiving a communication message and analyzing the tone and context of the communication message; generation means for proposing appropriate expressions and word choices for the communication message using the analysis results and sentiment evaluation means; display means for presenting the proposed expressions and words to the user and providing an interactive interface for modifying the content of the communication message according to the presented suggestions; and report generation means for recording the user's communication patterns and providing feedback including long-term sentiment trends. This enables effective communication that takes emotions into consideration.

[0169] "Communication messages" refer to messages such as emails and chats that users send and receive in their daily work.

[0170] "Tone" refers to the emotional or psychological nuances conveyed by the words and expressions within a message.

[0171] "Context" refers to the meaning derived from considering the surrounding circumstances in which words and phrases within a message are placed.

[0172] "Information processing means" refers to methods and techniques for analyzing the content of communication messages and understanding their tone and context.

[0173] "Emotion evaluation means" refers to technologies and devices for identifying a user's emotional state from communication messages and tracking its fluctuations.

[0174] "Generation method" refers to technology that automatically generates appropriate expressions and word choices to suggest to the user based on the analyzed information.

[0175] An "interactive interface" refers to a user interface that allows users to select and modify suggested expressions and words.

[0176] "Report generation means" refers to a method or system for recording user communication patterns and providing feedback and long-term sentiment trends.

[0177] "Suggestions" refer to appropriate expressions and word choices presented to improve the user's message.

[0178] A "user" refers to a technical professional who sends and receives communication messages and uses this system to improve communication.

[0179] The system for implementing this invention operates in conjunction with a cloud server and a user's terminal. When the server receives a communication message sent from the user's terminal, it analyzes the tone and context of the message using spaCy, a Natural Language Processing (NLP) library. Furthermore, it performs sentiment evaluation by analyzing the sentiment of the message using the Google® Cloud NLP API.

[0180] Based on the analysis results, the server automatically generates expressions and word choices that take into account the user's emotional state. This generation utilizes a generation AI model to provide optimal suggestions for the user. The generated suggestions are displayed on the user's device in real time. The user can review the suggestions through this display interface and modify their communication messages as needed.

[0181] The server also records user communication patterns and analyzes long-term emotional trends. Based on this, it regularly generates feedback and reports to improve user communication. This allows users to continuously improve their emotional expression and communication skills.

[0182] As a concrete example, a user's message, "This needs fixing immediately," is transformed in real time into a suggestion such as, "This seems urgent, but would you like to specify a time frame?" An example of a prompt for the generative AI model used on the server side would be, "Identify the user's sentiment from this message and make a more helpful suggestion: 'This needs fixing immediately'."

[0183] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0184] Step 1:

[0185] The terminal inputs the communication message. The user enters the communication message into the messaging application on the terminal and presses the send button. The entered message data is sent to the server.

[0186] Step 2:

[0187] The server receives the communication message. The server receives the message data sent by the user and stores this data in a message queue for preparation for analysis.

[0188] Step 3:

[0189] The server analyzes the tone and context of the message. The server uses spaCy to perform NLP analysis on the message data. It uses the message data as input and outputs contextual structural information and tone analysis results.

[0190] Step 4:

[0191] The server analyzes the sentiment of the message. The server calls the Google Cloud NLP API to perform sentiment analysis on the input message. It uses the tone analysis results as input and outputs the sentiment identification result.

[0192] Step 5:

[0193] The server generates suggestions using a generative AI model. Based on the analysis results and sentiment identification results, the generative AI model on the server generates suggestions for the optimal expression and word choice. This process uses prompts. The generated suggestion message is obtained as output.

[0194] Step 6:

[0195] The server sends a suggestion message to the user's terminal. The generated suggestion message is sent from the server to the user's terminal. The user reviews the suggestion on their terminal and uses it to modify the communication message.

[0196] Step 7:

[0197] The server records communication patterns. This interaction, along with the user's modified messages, is recorded in a database for long-term analysis.

[0198] Step 8:

[0199] The server generates feedback and reports. Using the recorded data, the server periodically generates and provides users with feedback and reports on long-term sentiment trends.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] [Second Embodiment]

[0204] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0205] 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.

[0206] 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).

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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".

[0216] The present invention provides a system for analyzing communication messages used daily by technical professionals, including engineers, to enable more effective communication. In a specific embodiment, the process begins with the user entering a message via email or chat application on a terminal used for daily work.

[0217] In this system, when a user enters a message, it is sent to the server. The server analyzes the received message using information processing means and evaluates its tone and content. If the evaluated tone is deemed negative or the content is deemed potentially misleading, a generation means generates improvement suggestions based on the analysis results.

[0218] The generated suggestions are displayed in real time on the user's device via email or chat interfaces. This allows the user to review the suggestions and revise their message based on them. For example, if a user enters the message "My report is late, but I will submit it soon," the server, if it determines the tone is neutral or negative, will suggest revising it to "My report is in the final stages and will be submitted shortly."

[0219] Furthermore, this system simultaneously performs grammatical checks on the text and generates appropriate feedback on the analyzed communication messages, helping users efficiently improve their communication. In addition, the server records the user's communication patterns and generates detailed feedback and reports for long-term improvement. This enables users to continuously improve their communication skills.

[0220] The following describes the processing flow.

[0221] Step 1:

[0222] The user types a message in an email or chat application and sends it. The message is temporarily stored on the user's device.

[0223] Step 2:

[0224] The user's terminal sends the entered communication message to the server. The message data is transmitted securely and received on the server.

[0225] Step 3:

[0226] The server feeds the received communication message into a parsing process. This involves using natural language processing (NLP) algorithms to evaluate the message's tone, grammar, and context.

[0227] Step 4:

[0228] Based on the analysis results, the server generates improvement suggestions using a generation mechanism. These suggestions include alternative ways of expressing the message to soften its tone or make it clearer.

[0229] Step 5:

[0230] The server sends the generated suggestions to the user's terminal. The suggestions are displayed in real time and provided to the user as feedback that can be applied.

[0231] Step 6:

[0232] The user reviews the suggested improvements and modifies the communication messages as needed. This allows for more effective communication.

[0233] Step 7:

[0234] The server records the history of previous messages and suggestions, accumulating data on the user's communication patterns. This data is used to generate feedback and reports later on.

[0235] (Example 1)

[0236] 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".

[0237] For modern technology professionals, communicating with the appropriate tone and avoiding misunderstandings is crucial. However, it is difficult to make such adjustments every time in a busy daily life. Furthermore, there is a lack of concrete means to consistently improve the quality of communication. Therefore, the present invention aims to achieve efficient and smooth communication by analyzing the emotions and content of communication data and automatically making improvement suggestions as needed.

[0238] 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.

[0239] In this invention, the server includes data processing means for receiving communication data and analyzing the sentiment and information of the communication data; generation means for proposing appropriate expressions and vocabulary for the communication data based on the analysis results; and operation means for presenting the proposed expressions and vocabulary to the user and modifying the content of the communication data according to the presented suggestions. This enables the user to plan effective communication in real time.

[0240] "Communication data" refers to units of information that are transmitted and received electronically, and includes text, audio, or image data.

[0241] "Emotions" refer to the sender's intentions and psychological state, which are analyzed from the tone and expression of communication data.

[0242] "Information" refers to the content and facts contained in communication data, and is an element of data transmitted through a user's message.

[0243] "Data processing means" refers to a device or software that performs a technical process to analyze received communication data and evaluate its emotions and information.

[0244] "Generation means" refers to a device or program that has the function of automatically creating and suggesting appropriate expressions and vocabulary based on analyzed data.

[0245] "Operating means" refers to a device or software that provides a user interface for the user to review proposed expressions and vocabulary and modify or accept them as necessary.

[0246] A "generative AI model" is a learning model that uses artificial intelligence technology to analyze data and generate new information.

[0247] A "revised text" is a new expression or wording generated to improve the original communication data based on analysis and suggestions.

[0248] A "user terminal" is an electronic device used by a user to create, transmit, and receive communication data.

[0249] This invention begins with a terminal used by a user in their daily work. The user inputs a message via email or chat application. The terminal sends the input message to a server. The server receives the communication data and analyzes the sentiment and information of the data using data processing means. This analysis utilizes natural language processing techniques and generative AI models. For example, general-purpose natural language processing models and generative AI models are used to perform sentiment analysis and grammar checks on messages.

[0250] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary for the message. This generation process utilizes a generation AI model to generate appropriate revised sentences through prompts. The generated revised sentences are sent to the user's terminal as suggestions. The terminal displays the suggested revised sentences to the user, allowing the user to review the message and either revise or approve it. This enables smoother and more effective communication for the user.

[0251] For example, if a user enters the message, "My report is late, but I will submit it soon," the server can use a generation tool to determine whether the tone of this message is neutral or negative and suggest a revised version, such as, "My report is in the final stages and will be submitted shortly." Through this process, users can receive continuous feedback to improve the quality of their communication.

[0252] Examples of prompt statements are as follows:

[0253] Please change the tone of the following message to a softer one: "I apologize for the delay in reporting, but I will submit it as soon as possible."

[0254] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0255] Step 1:

[0256] The user launches an email or chat application on their terminal used for daily work and enters a message. The input data includes the text message entered by the user. The terminal sends this message to the server for the next processing step. In this step, the user's intent and content are preserved as input data.

[0257] Step 2:

[0258] The server passes the received message to a data processing system. This data includes the message text. The server uses natural language processing techniques to analyze the sentiment and information of the message. Here, a generative AI model is used to analyze the tone of the text and group the content. As a result of the analysis, the tone of the message (e.g., positive, negative, neutral) is evaluated.

[0259] Step 3:

[0260] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary. In this step, a generative AI model is used to generate improvement suggestions from the analyzed data. The input includes the analysis results, and the output is the text of the revised suggestions. This revised text is obtained by sending a generation request to the model via a prompt message.

[0261] Step 4:

[0262] The server sends the generated revised version to the terminal. Here, it outputs a revised message for the user to see. The terminal displays this revised version in real time on the interface of an email or chat application. The user can review the suggested revised text and, if necessary, modify the message or send it as is.

[0263] Step 5:

[0264] The server records user behavior patterns and generates long-term feedback and reports. This feedback is generated based on analyzed past communication data and its improvement history. Through this feedback, users can continuously improve their communication skills. Input includes the user's correction history, and output is a feedback report.

[0265] (Application Example 1)

[0266] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0267] Modern engineers and professionals are required to communicate efficiently and accurately in many situations. However, misunderstandings and inefficiencies can occur if the tone or expression used in the information transmission process is inappropriate. This problem can negatively impact productivity, especially in work environments where rapid responses are required. Therefore, there is a need for means to solve these communication challenges and support efficient communication.

[0268] 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.

[0269] In this invention, the server includes a processing unit that receives communication information and analyzes the tone and content of the communication information; a generating unit that proposes appropriate expression methods and word selections for the communication information based on the analysis results; and a presentation unit that displays the proposals in real time on the user's visual aid. This enables the user to communicate quickly and accurately.

[0270] "Communication information" refers to messages transmitted to the recipient in the process of sending and receiving information, either in linguistic or digital data format.

[0271] "Tone" refers to elements that indicate the emotional nuances and attitude of a message, and is classified as positive, negative, neutral, etc.

[0272] A "processing device" refers to a hardware or software component that receives information input and performs analysis and evaluation.

[0273] A "generation device" refers to a device that automatically generates improvement suggestions and optimized expressions based on analyzed information.

[0274] A "visual assistance device" refers to a digital display device designed to support a user's field of vision, specifically a device that directly displays information.

[0275] A "presentation device" refers to a device that provides an interface for displaying generated improvement suggestions and feedback to users in an easily viewable format.

[0276] The embodiment of the invention is a system for effectively analyzing and improving communication information. This system consists of a processing unit, a generation unit, and a presentation unit. When a user inputs information, it is processed by a server. Specifically, the information processing unit receives the communication information and analyzes its tone and content. Natural language processing libraries such as SpaCy and Transformers may be used for the analysis. This determines whether the tone is positive, negative, or neutral.

[0277] Based on the analysis results, the server automatically generates improvement suggestions using a generation device. This generation process utilizes a generation AI model, optimizing the suggested expressions and word choices. The generated improvement suggestions are then displayed in real time through a presentation device to the user's visual aids, specifically smart glasses.

[0278] For example, suppose a technician working in a factory reports a malfunction in a piece of equipment and enters the message, "There is a problem with this equipment." The server receives this message and generates a suggested improvement, such as, "We have observed a minor issue with this equipment, but we will address it promptly," which is then displayed on a visual aid, facilitating smoother communication on-site.

[0279] An example of a prompt to be input into the generation AI model is text such as, "Based on the current status report regarding the factory's parts supply, please generate an appropriate follow-up message for your supervisor."

[0280] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0281] Step 1:

[0282] The server receives the communication information input by the user. The user inputs a message as voice or text, which is sent to the server through the terminal. The received data is directly sent to the next processing step.

[0283] Step 2:

[0284] The server analyzes the received communication information using a natural language processing library. Here, SpaCy or Transformers is used to analyze the tone (positive, negative, neutral, etc.) and content of the message. The input data is the communication information, and the output data is the analysis result. Specific operations include syntactic analysis and sentiment analysis of sentences.

[0285] Step 3:

[0286] The server generates improvement suggestions using a generative AI model based on the analysis result. For example, GPT-3 or the like is used as the generative AI model to propose appropriate expression methods and word selections. The input is the analysis result, and the output is the generated improvement suggestions. In this process, suggestions are made based on known expression patterns.

[0287] Step 4:

[0288] The server sends the generated improvement suggestions to the user's visual assistance device in real time through a presentation device. Specifically, information is displayed on a digital display such as smart glasses. The input is the generated improvement suggestions, and the output is the content displayed on the visual assistance device. In this process, data format conversion is performed to display in a visually understandable form.

[0289] Step 5:

[0290] The user checks the presented improvement suggestions and modifies the original communication information if necessary. The user's input is the modified message, and the output is the finally transmitted revised communication information. In this step, a judgment is made on whether the user accepts the suggestion.

[0291] 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.

[0292] This invention is a technology that analyzes communication messages used daily by engineers and technical professionals and promotes effective communication based on the information contained in those messages. This system is used in combination with a program installed on the terminal that the user uses for their daily work and a server.

[0293] When a user enters a message via email or chat application and sends it from their device to the server, the server uses information processing tools to analyze the tone and context of the message. Furthermore, by using an emotion engine, the server identifies the user's emotional state from the entered message and tracks changes in that emotional state.

[0294] Based on the analyzed results, the server uses a generation mechanism to construct suggestions for improving the message. These suggestions include expressions and word choices that take the user's emotions into consideration, and are presented to the user's device in real time. For example, if a user types "I'm worried because the project is behind schedule," the emotion engine identifies the emotion "worry," and the generation mechanism suggests something like "We are seeing progress on the project, and we will do our best with the team."

[0295] Users can modify their communication messages as needed by referring to the suggestions provided. Furthermore, the server records the user's communication patterns and periodically generates reports based on this data, including feedback and long-term emotional trends. This allows users to continuously improve their emotional expression and communication skills.

[0296] The following describes the processing flow.

[0297] Step 1:

[0298] The user inputs a communication message in the mail or chat application on the terminal and presses the send button. Thereby, the message data is prepared and immediately sent to the server.

[0299] Step 2:

[0300] The server delivers the received communication message to the information processing module for analysis. Here, an initial evaluation is performed using natural language processing (NLP) technology for the tone and content.

[0301] Step 3:

[0302] The server uses an emotion engine to identify the emotion contained in the user's message. For example, it is possible to identify the user's emotional state such as "anxiety" or "relief".

[0303] Step 4:

[0304] Based on these analysis results and emotional state, the server creates an improvement proposal by the generation means. This proposal includes specific expressions and word choices for softening the tone of the message while considering the user's emotion.

[0305] Step 5:

[0306] The server sends the improvement proposal generated to the user's terminal and displays it to the user in real time. The user can receive this feedback and appropriately modify the actual communication message.

[0307] Step 6:

[0308] Users review the suggested message content, edit it as needed, and send it. This allows for more effective and emotionally sensitive communication.

[0309] Step 7:

[0310] The server records all message history and emotional state data, and generates reports that include long-term communication improvements and emotional trends. These reports are provided to users as needed to help improve their communication skills and emotional expression.

[0311] (Example 2)

[0312] 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".

[0313] In today's communication environment, users compose messages with a wide range of emotions, but effectively conveying these emotions is often difficult. Furthermore, choosing appropriate expressions and words that are sensitive to emotions is particularly challenging, sometimes leading to misunderstandings. Moreover, there is a lack of feedback to help improve one's emotional expression and communication skills in the long term.

[0314] 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.

[0315] In this invention, the server includes information processing means for receiving communication messages and analyzing the sentiment and context of the communication messages; generation means for proposing appropriate expressions and word choices that take the sentiment of the communication message into consideration, based on the analyzed sentiment information; and analysis means for analyzing sentiment trends based on the user's communication history and providing feedback based on the analysis results. This enables the user to communicate more effectively and to continuously improve their emotional expression and communication skills.

[0316] "Communication messages" are information sent and received in text format, exchanged between users through email or chat applications.

[0317] "Information processing means" is a general term for software and hardware used to analyze communication messages, and has the function of understanding the content of messages by performing language processing and sentiment analysis.

[0318] "Emotions" refer to the psychological states that users include in their messages, encompassing internal human feelings such as anxiety, joy, and sadness.

[0319] A "generation method" is a mechanism that selects appropriate expressions and words based on analyzed information and makes suggestions to the user.

[0320] "Method of expression" refers to the choice of words and phrases that a user uses when sending a message, and is the method for appropriately conveying emotions and intentions.

[0321] "Analysis methods" refer to technologies that analyze patterns and trends based on a user's communication history, and the process of generating feedback from this analysis and providing it to the user.

[0322] "Display means" refers to methods or interfaces for visually presenting generated proposals or information to the user.

[0323] "Emotional trends" refer to the tendency of emotional changes based on a user's past messages, and show a history of how a user has expressed emotions.

[0324] This invention is a system that combines a program installed on a terminal used by a user for their daily work with a server. The user inputs communication messages via email or chat applications and sends those messages from the terminal to the server.

[0325] The server uses a natural language processing engine to analyze the tone and context of messages. Specifically, it uses natural language processing libraries such as NLTK and spaCy to analyze the structure and content of messages and extract emotions. Additionally, an emotion engine is installed, and an emotion AI model is running to identify psychological states. This allows the server to identify emotional states from user-input messages and track changes in those states.

[0326] Based on the analyzed data, the server uses a generation mechanism to construct improvement suggestions. This generation mechanism employs a generative AI model, which prioritizes expressions and word choices that take the user's emotions into consideration. For example, if a user inputs a message such as, "I feel that we won't meet the deadline at this rate," the emotional AI model will analyze the emotion of "anxiety" and generate a suggestion such as, "Let's re-examine the project progress and strengthen the support system as needed."

[0327] The generated suggestions are presented to the user's device in real time, allowing the user to modify their message based on the suggestions. Furthermore, the server analyzes emotional trends by recording the user's past messages and communication history, and provides regular feedback to the user. This helps users continuously improve their emotional expression and communication skills.

[0328] A concrete example of a prompt message would be something like, "Please provide suggestions for improving this message. Please be mindful of the user's feelings when giving advice." This can improve the quality of user communication.

[0329] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0330] Step 1:

[0331] Users type communication messages into email or chat applications and send them from their devices. The data entered is textual information, typically including purpose, requests, and emotions.

[0332] Step 2:

[0333] The terminal sends the input communication message to the server. During this process, data is securely transferred using a communication protocol. The input is the text entered by the user, and the output is that text being sent to the server.

[0334] Step 3:

[0335] The server analyzes received messages using a natural language processing (NLP) engine. Specifically, it utilizes NLP libraries (e.g., NLTK, spaCy) to extract keywords and emotional tone from the messages. The input is the received text, and the output is the analyzed tone and emotional information.

[0336] Step 4:

[0337] The server uses an emotion engine to further process the analyzed data and identify the user's emotional state. This process uses an emotion AI model to identify various emotions (anxiety, joy, sadness, etc.). The input is the analyzed information obtained by the NLP engine, and the output is data on emotion labels and their intensity.

[0338] Step 5:

[0339] Based on the generation method, the server generates message improvement suggestions that take into account emotional information, considering expression methods and word choices. An AI generation model is used to construct appropriate suggestions. The input is emotional state data, and the output is the text of the improvement suggestion.

[0340] Step 6:

[0341] The server sends the generated improvement suggestions to the user's terminal. The user's terminal displays the suggestions to the user in real time, and the user reviews them to help with decision-making. The input is the suggestion data from the server, and the output is a visual presentation to the user.

[0342] Step 7:

[0343] The server records the user's communication history and the results of improvement suggestions, and analyzes long-term sentiment trends. Based on this, it periodically generates and provides feedback to the user. The input is past messages and suggestion history, and the output is a feedback report as a result of the analysis.

[0344] (Application Example 2)

[0345] 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."

[0346] In technical work environments, engineers and professionals routinely receive and send numerous communication messages. This often leads to communication friction due to misunderstandings of message tone and context. Furthermore, a decline in communication quality caused by emotional fluctuations and grammatical errors hinders improvements in work efficiency. A system is needed to address these challenges and achieve effective and emotionally sensitive communication.

[0347] 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.

[0348] In this invention, the server includes information processing means for receiving a communication message and analyzing the tone and context of the communication message; generation means for proposing appropriate expressions and word choices for the communication message using the analysis results and sentiment evaluation means; display means for presenting the proposed expressions and words to the user and providing an interactive interface for modifying the content of the communication message according to the presented suggestions; and report generation means for recording the user's communication patterns and providing feedback including long-term sentiment trends. This enables effective communication that takes emotions into consideration.

[0349] "Communication messages" refer to messages such as emails and chats that users send and receive in their daily work.

[0350] "Tone" refers to the emotional or psychological nuances conveyed by the words and expressions within a message.

[0351] "Context" refers to the meaning derived from considering the surrounding circumstances in which words and phrases within a message are placed.

[0352] "Information processing means" refers to methods and techniques for analyzing the content of communication messages and understanding their tone and context.

[0353] "Emotion evaluation means" refers to technologies and devices for identifying a user's emotional state from communication messages and tracking its fluctuations.

[0354] "Generation method" refers to technology that automatically generates appropriate expressions and word choices to suggest to the user based on the analyzed information.

[0355] An "interactive interface" refers to a user interface that allows users to select and modify suggested expressions and words.

[0356] "Report generation means" refers to a method or system for recording user communication patterns and providing feedback and long-term sentiment trends.

[0357] "Suggestions" refer to appropriate expressions and word choices presented to improve the user's message.

[0358] A "user" refers to a technical professional who sends and receives communication messages and uses this system to improve communication.

[0359] The system for implementing this invention operates in conjunction with a cloud server and a user's terminal. When the server receives a communication message sent from the user's terminal, it analyzes the tone and context of the message using spaCy, a Natural Language Processing (NLP) library. Furthermore, it performs sentiment evaluation by analyzing the sentiment of the message using the Google Cloud NLP API.

[0360] Based on the analysis results, the server automatically generates expressions and word choices that take into account the user's emotional state. This generation utilizes a generation AI model to provide optimal suggestions for the user. The generated suggestions are displayed on the user's device in real time. The user can review the suggestions through this display interface and modify their communication messages as needed.

[0361] The server also records user communication patterns and analyzes long-term emotional trends. Based on this, it regularly generates feedback and reports to improve user communication. This allows users to continuously improve their emotional expression and communication skills.

[0362] As a concrete example, a user's message, "This needs fixing immediately," is transformed in real time into a suggestion such as, "This seems urgent, but would you like to specify a time frame?" An example of a prompt for the generative AI model used on the server side would be, "Identify the user's sentiment from this message and make a more helpful suggestion: 'This needs fixing immediately'."

[0363] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0364] Step 1:

[0365] The terminal inputs the communication message. The user enters the communication message into the messaging application on the terminal and presses the send button. The entered message data is sent to the server.

[0366] Step 2:

[0367] The server receives the communication message. The server receives the message data sent by the user and stores this data in a message queue for preparation for analysis.

[0368] Step 3:

[0369] The server analyzes the tone and context of the message. The server uses spaCy to perform NLP analysis on the message data. It uses the message data as input and outputs contextual structural information and tone analysis results.

[0370] Step 4:

[0371] The server analyzes the sentiment of the message. The server calls the Google Cloud NLP API to perform sentiment analysis on the input message. It uses the tone analysis results as input and outputs the sentiment identification result.

[0372] Step 5:

[0373] The server generates suggestions using a generative AI model. Based on the analysis results and sentiment identification results, the generative AI model on the server generates suggestions for the optimal expression and word choice. This process uses prompts. The generated suggestion message is obtained as output.

[0374] Step 6:

[0375] The server sends a suggestion message to the user's terminal. The generated suggestion message is sent from the server to the user's terminal. The user reviews the suggestion on their terminal and uses it to modify the communication message.

[0376] Step 7:

[0377] The server records communication patterns. This interaction, along with the user's modified messages, is recorded in a database for long-term analysis.

[0378] Step 8:

[0379] The server generates feedback and reports. Using the recorded data, the server periodically generates and provides users with feedback and reports on long-term sentiment trends.

[0380] 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.

[0381] 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.

[0382] 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.

[0383] [Third Embodiment]

[0384] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0385] 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.

[0386] 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).

[0387] 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.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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.

[0393] 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.

[0394] 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.

[0395] 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".

[0396] The present invention provides a system for analyzing communication messages used daily by technical professionals, including engineers, to enable more effective communication. In a specific embodiment, the process begins with the user entering a message via email or chat application on a terminal used for daily work.

[0397] In this system, when a user enters a message, it is sent to the server. The server analyzes the received message using information processing means and evaluates its tone and content. If the evaluated tone is deemed negative or the content is deemed potentially misleading, a generation means generates improvement suggestions based on the analysis results.

[0398] The generated suggestions are displayed in real time on the user's device via email or chat interfaces. This allows the user to review the suggestions and revise their message based on them. For example, if a user enters the message "My report is late, but I will submit it soon," the server, if it determines the tone is neutral or negative, will suggest revising it to "My report is in the final stages and will be submitted shortly."

[0399] Furthermore, this system simultaneously performs grammatical checks on the text and generates appropriate feedback on the analyzed communication messages, helping users efficiently improve their communication. In addition, the server records the user's communication patterns and generates detailed feedback and reports for long-term improvement. This enables users to continuously improve their communication skills.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The user types a message in an email or chat application and sends it. The message is temporarily stored on the user's device.

[0403] Step 2:

[0404] The user's terminal sends the entered communication message to the server. The message data is transmitted securely and received on the server.

[0405] Step 3:

[0406] The server feeds the received communication message into a parsing process. This involves using natural language processing (NLP) algorithms to evaluate the message's tone, grammar, and context.

[0407] Step 4:

[0408] Based on the analysis results, the server generates improvement suggestions using a generation mechanism. These suggestions include alternative ways of expressing the message to soften its tone or make it clearer.

[0409] Step 5:

[0410] The server sends the generated suggestions to the user's terminal. The suggestions are displayed in real time and provided to the user as feedback that can be applied.

[0411] Step 6:

[0412] The user reviews the suggested improvements and modifies the communication messages as needed. This allows for more effective communication.

[0413] Step 7:

[0414] The server records the history of previous messages and suggestions, accumulating data on the user's communication patterns. This data is used to generate feedback and reports later on.

[0415] (Example 1)

[0416] 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."

[0417] For modern technology professionals, communicating with the appropriate tone and avoiding misunderstandings is crucial. However, it is difficult to make such adjustments every time in a busy daily life. Furthermore, there is a lack of concrete means to consistently improve the quality of communication. Therefore, the present invention aims to achieve efficient and smooth communication by analyzing the emotions and content of communication data and automatically making improvement suggestions as needed.

[0418] 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.

[0419] In this invention, the server includes data processing means for receiving communication data and analyzing the sentiment and information of the communication data; generation means for proposing appropriate expressions and vocabulary for the communication data based on the analysis results; and operation means for presenting the proposed expressions and vocabulary to the user and modifying the content of the communication data according to the presented suggestions. This enables the user to plan effective communication in real time.

[0420] "Communication data" refers to units of information that are transmitted and received electronically, and includes text, audio, or image data.

[0421] "Emotions" refer to the sender's intentions and psychological state, which are analyzed from the tone and expression of communication data.

[0422] "Information" refers to the content and facts contained in communication data, and is an element of data transmitted through a user's message.

[0423] "Data processing means" refers to a device or software that performs a technical process to analyze received communication data and evaluate its emotions and information.

[0424] "Generation means" refers to a device or program that has the function of automatically creating and suggesting appropriate expressions and vocabulary based on analyzed data.

[0425] "Operating means" refers to a device or software that provides a user interface for the user to review proposed expressions and vocabulary and modify or accept them as necessary.

[0426] A "generative AI model" is a learning model that uses artificial intelligence technology to analyze data and generate new information.

[0427] A "revised text" is a new expression or wording generated to improve the original communication data based on analysis and suggestions.

[0428] A "user terminal" is an electronic device used by a user to create, transmit, and receive communication data.

[0429] This invention begins with a terminal used by a user in their daily work. The user inputs a message via email or chat application. The terminal sends the input message to a server. The server receives the communication data and analyzes the sentiment and information of the data using data processing means. This analysis utilizes natural language processing techniques and generative AI models. For example, general-purpose natural language processing models and generative AI models are used to perform sentiment analysis and grammar checks on messages.

[0430] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary for the message. This generation process utilizes a generation AI model to generate appropriate revised sentences through prompts. The generated revised sentences are sent to the user's terminal as suggestions. The terminal displays the suggested revised sentences to the user, allowing the user to review the message and either revise or approve it. This enables smoother and more effective communication for the user.

[0431] For example, if a user enters the message, "My report is late, but I will submit it soon," the server can use a generation tool to determine whether the tone of this message is neutral or negative and suggest a revised version, such as, "My report is in the final stages and will be submitted shortly." Through this process, users can receive continuous feedback to improve the quality of their communication.

[0432] Examples of prompt statements are as follows:

[0433] Please change the tone of the following message to a softer one: "I apologize for the delay in reporting, but I will submit it as soon as possible."

[0434] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0435] Step 1:

[0436] The user launches an email or chat application on their terminal used for daily work and enters a message. The input data includes the text message entered by the user. The terminal sends this message to the server for the next processing step. In this step, the user's intent and content are preserved as input data.

[0437] Step 2:

[0438] The server passes the received message to a data processing system. This data includes the message text. The server uses natural language processing techniques to analyze the sentiment and information of the message. Here, a generative AI model is used to analyze the tone of the text and group the content. As a result of the analysis, the tone of the message (e.g., positive, negative, neutral) is evaluated.

[0439] Step 3:

[0440] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary. In this step, a generative AI model is used to generate improvement suggestions from the analyzed data. The input includes the analysis results, and the output is the text of the revised suggestions. This revised text is obtained by sending a generation request to the model via a prompt message.

[0441] Step 4:

[0442] The server sends the generated revised version to the terminal. Here, it outputs a revised message for the user to see. The terminal displays this revised version in real time on the interface of an email or chat application. The user can review the suggested revised text and, if necessary, modify the message or send it as is.

[0443] Step 5:

[0444] The server records user behavior patterns and generates long-term feedback and reports. This feedback is generated based on analyzed past communication data and its improvement history. Through this feedback, users can continuously improve their communication skills. Input includes the user's correction history, and output is a feedback report.

[0445] (Application Example 1)

[0446] 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."

[0447] Modern engineers and professionals are required to communicate efficiently and accurately in many situations. However, misunderstandings and inefficiencies can occur if the tone or expression used in the information transmission process is inappropriate. This problem can negatively impact productivity, especially in work environments where rapid responses are required. Therefore, there is a need for means to solve these communication challenges and support efficient communication.

[0448] 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.

[0449] In this invention, the server includes a processing unit that receives communication information and analyzes the tone and content of the communication information; a generating unit that proposes appropriate expression methods and word selections for the communication information based on the analysis results; and a presentation unit that displays the proposals in real time on the user's visual aid. This enables the user to communicate quickly and accurately.

[0450] "Communication information" refers to messages transmitted to the recipient in the process of sending and receiving information, either in linguistic or digital data format.

[0451] "Tone" refers to elements that indicate the emotional nuances and attitude of a message, and is classified as positive, negative, neutral, etc.

[0452] A "processing device" refers to a hardware or software component that receives information input and performs analysis and evaluation.

[0453] A "generation device" refers to a device that automatically generates improvement suggestions and optimized expressions based on analyzed information.

[0454] A "visual assistance device" refers to a digital display device designed to support a user's field of vision, specifically a device that directly displays information.

[0455] A "presentation device" refers to a device that provides an interface for displaying generated improvement suggestions and feedback to users in an easily viewable format.

[0456] The embodiment of the invention is a system for effectively analyzing and improving communication information. This system consists of a processing unit, a generation unit, and a presentation unit. When a user inputs information, it is processed by a server. Specifically, the information processing unit receives the communication information and analyzes its tone and content. Natural language processing libraries such as SpaCy and Transformers may be used for the analysis. This determines whether the tone is positive, negative, or neutral.

[0457] Based on the analysis results, the server automatically generates improvement suggestions using a generation device. This generation process utilizes a generation AI model, optimizing the suggested expressions and word choices. The generated improvement suggestions are then displayed in real time through a presentation device to the user's visual aids, specifically smart glasses.

[0458] For example, suppose a technician working in a factory reports a malfunction in a piece of equipment and enters the message, "There is a problem with this equipment." The server receives this message and generates a suggested improvement, such as, "We have observed a minor issue with this equipment, but we will address it promptly," which is then displayed on a visual aid, facilitating smoother communication on-site.

[0459] An example of a prompt to be input into the generation AI model is text such as, "Based on the current status report regarding the factory's parts supply, please generate an appropriate follow-up message for your supervisor."

[0460] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0461] Step 1:

[0462] The server receives communication information entered by the user. The user enters a message as voice or text, which is sent to the server via the terminal. The received data is then sent directly to the next processing step.

[0463] Step 2:

[0464] The server analyzes received communication information using natural language processing libraries. Here, SpaCy and Transformers are used to analyze the tone (positive, negative, neutral, etc.) and content of messages. The input data is the communication information, and the output data is the analysis results. Specific operations include sentence structure analysis and sentiment analysis.

[0465] Step 3:

[0466] The server generates improvement suggestions using a generative AI model based on the analysis results. The generative AI model, such as GPT-3, suggests appropriate expressions and word choices. The input is the analysis results, and the output is the generated improvement suggestions. This process involves suggestions based on known expression patterns.

[0467] Step 4:

[0468] The server transmits the generated improvement suggestions to the user's visual aids in real time via a display device. Specifically, the information is displayed on a digital display such as smart glasses. The input is the generated improvement suggestions, and the output is what is displayed on the visual aids. In this process, data format conversion is performed to ensure that the information is displayed in a visually understandable format.

[0469] Step 5:

[0470] The user reviews the proposed improvements and modifies the original communication information as needed. The user's input is the modified message, and the output is the final modified communication information that is sent. At this step, the user decides whether or not to accept the suggestions.

[0471] 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.

[0472] This invention is a technology that analyzes communication messages used daily by engineers and technical professionals and promotes effective communication based on the information contained in those messages. This system is used in combination with a program installed on the terminal that the user uses for their daily work and a server.

[0473] When a user enters a message via email or chat application and sends it from their device to the server, the server uses information processing tools to analyze the tone and context of the message. Furthermore, by using an emotion engine, the server identifies the user's emotional state from the entered message and tracks changes in that emotional state.

[0474] Based on the analyzed results, the server uses a generation mechanism to construct suggestions for improving the message. These suggestions include expressions and word choices that take the user's emotions into consideration, and are presented to the user's device in real time. For example, if a user types "I'm worried because the project is behind schedule," the emotion engine identifies the emotion "worry," and the generation mechanism suggests something like "We are seeing progress on the project, and we will do our best with the team."

[0475] Users can modify their communication messages as needed by referring to the suggestions provided. Furthermore, the server records the user's communication patterns and periodically generates reports based on this data, including feedback and long-term emotional trends. This allows users to continuously improve their emotional expression and communication skills.

[0476] The following describes the processing flow.

[0477] Step 1:

[0478] The user types a message in the email or chat application on their device and presses the send button. This prepares the message data, which is then immediately sent to the server.

[0479] Step 2:

[0480] The server passes the received communication message to an information processing module for analysis. Here, an initial evaluation of the tone and content is performed using Natural Language Processing (NLP) techniques.

[0481] Step 3:

[0482] The server uses an emotion engine to identify the emotions contained in the user's message. For example, it can identify the user's emotional state, such as "anxiety" or "relief."

[0483] Step 4:

[0484] Based on these analysis results and emotional states, the server generates improvement suggestions using a generation mechanism. These suggestions include specific expressions and word choices to soften the tone of the message while taking the user's emotions into consideration.

[0485] Step 5:

[0486] The server generates improvement suggestions and sends them to the user's terminal, displaying them to the user in real time. The user can receive this feedback and modify the actual communication messages as needed.

[0487] Step 6:

[0488] Users review the suggested message content, edit it as needed, and send it. This allows for more effective and emotionally sensitive communication.

[0489] Step 7:

[0490] The server records all message history and emotional state data, and generates reports that include long-term communication improvements and emotional trends. These reports are provided to users as needed to help improve their communication skills and emotional expression.

[0491] (Example 2)

[0492] 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."

[0493] In today's communication environment, users compose messages with a wide range of emotions, but effectively conveying these emotions is often difficult. Furthermore, choosing appropriate expressions and words that are sensitive to emotions is particularly challenging, sometimes leading to misunderstandings. Moreover, there is a lack of feedback to help improve one's emotional expression and communication skills in the long term.

[0494] 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.

[0495] In this invention, the server includes information processing means for receiving communication messages and analyzing the sentiment and context of the communication messages; generation means for proposing appropriate expressions and word choices that take the sentiment of the communication message into consideration, based on the analyzed sentiment information; and analysis means for analyzing sentiment trends based on the user's communication history and providing feedback based on the analysis results. This enables the user to communicate more effectively and to continuously improve their emotional expression and communication skills.

[0496] "Communication messages" are information sent and received in text format, exchanged between users through email or chat applications.

[0497] "Information processing means" is a general term for software and hardware used to analyze communication messages, and has the function of understanding the content of messages by performing language processing and sentiment analysis.

[0498] "Emotions" refer to the psychological states that users include in their messages, encompassing internal human feelings such as anxiety, joy, and sadness.

[0499] A "generation method" is a mechanism that selects appropriate expressions and words based on analyzed information and makes suggestions to the user.

[0500] "Method of expression" refers to the choice of words and phrases that a user uses when sending a message, and is the method for appropriately conveying emotions and intentions.

[0501] "Analysis methods" refer to technologies that analyze patterns and trends based on a user's communication history, and the process of generating feedback from this analysis and providing it to the user.

[0502] "Display means" refers to methods or interfaces for visually presenting generated proposals or information to the user.

[0503] "Emotional trends" refer to the tendency of emotional changes based on a user's past messages, and show a history of how a user has expressed emotions.

[0504] This invention is a system that combines a program installed on a terminal used by a user for their daily work with a server. The user inputs communication messages via email or chat applications and sends those messages from the terminal to the server.

[0505] The server uses a natural language processing engine to analyze the tone and context of messages. Specifically, it uses natural language processing libraries such as NLTK and spaCy to analyze the structure and content of messages and extract emotions. Additionally, an emotion engine is installed, and an emotion AI model is running to identify psychological states. This allows the server to identify emotional states from user-input messages and track changes in those states.

[0506] Based on the analyzed data, the server uses a generation mechanism to construct improvement suggestions. This generation mechanism employs a generative AI model, which prioritizes expressions and word choices that take the user's emotions into consideration. For example, if a user inputs a message such as, "I feel that we won't meet the deadline at this rate," the emotional AI model will analyze the emotion of "anxiety" and generate a suggestion such as, "Let's re-examine the project progress and strengthen the support system as needed."

[0507] The generated suggestions are presented to the user's device in real time, allowing the user to modify their message based on the suggestions. Furthermore, the server analyzes emotional trends by recording the user's past messages and communication history, and provides regular feedback to the user. This helps users continuously improve their emotional expression and communication skills.

[0508] A concrete example of a prompt message would be something like, "Please provide suggestions for improving this message. Please be mindful of the user's feelings when giving advice." This can improve the quality of user communication.

[0509] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0510] Step 1:

[0511] Users type communication messages into email or chat applications and send them from their devices. The data entered is textual information, typically including purpose, requests, and emotions.

[0512] Step 2:

[0513] The terminal sends the input communication message to the server. During this process, data is securely transferred using a communication protocol. The input is the text entered by the user, and the output is that text being sent to the server.

[0514] Step 3:

[0515] The server analyzes received messages using a natural language processing (NLP) engine. Specifically, it utilizes NLP libraries (e.g., NLTK, spaCy) to extract keywords and emotional tone from the messages. The input is the received text, and the output is the analyzed tone and emotional information.

[0516] Step 4:

[0517] The server uses an emotion engine to further process the analyzed data and identify the user's emotional state. This process uses an emotion AI model to identify various emotions (anxiety, joy, sadness, etc.). The input is the analyzed information obtained by the NLP engine, and the output is data on emotion labels and their intensity.

[0518] Step 5:

[0519] Based on the generation method, the server generates message improvement suggestions that take into account emotional information, considering expression methods and word choices. An AI generation model is used to construct appropriate suggestions. The input is emotional state data, and the output is the text of the improvement suggestion.

[0520] Step 6:

[0521] The server sends the generated improvement suggestions to the user's terminal. The user's terminal displays the suggestions to the user in real time, and the user reviews them to help with decision-making. The input is the suggestion data from the server, and the output is a visual presentation to the user.

[0522] Step 7:

[0523] The server records the user's communication history and the results of improvement suggestions, and analyzes long-term sentiment trends. Based on this, it periodically generates and provides feedback to the user. The input is past messages and suggestion history, and the output is a feedback report as a result of the analysis.

[0524] (Application Example 2)

[0525] 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."

[0526] In technical work environments, engineers and professionals routinely receive and send numerous communication messages. This often leads to communication friction due to misunderstandings of message tone and context. Furthermore, a decline in communication quality caused by emotional fluctuations and grammatical errors hinders improvements in work efficiency. A system is needed to address these challenges and achieve effective and emotionally sensitive communication.

[0527] 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.

[0528] In this invention, the server includes information processing means for receiving a communication message and analyzing the tone and context of the communication message; generation means for proposing appropriate expressions and word choices for the communication message using the analysis results and sentiment evaluation means; display means for presenting the proposed expressions and words to the user and providing an interactive interface for modifying the content of the communication message according to the presented suggestions; and report generation means for recording the user's communication patterns and providing feedback including long-term sentiment trends. This enables effective communication that takes emotions into consideration.

[0529] "Communication messages" refer to messages such as emails and chats that users send and receive in their daily work.

[0530] "Tone" refers to the emotional or psychological nuances conveyed by the words and expressions within a message.

[0531] "Context" refers to the meaning derived from considering the surrounding circumstances in which words and phrases within a message are placed.

[0532] "Information processing means" refers to methods and techniques for analyzing the content of communication messages and understanding their tone and context.

[0533] "Emotion evaluation means" refers to technologies and devices for identifying a user's emotional state from communication messages and tracking its fluctuations.

[0534] "Generation method" refers to technology that automatically generates appropriate expressions and word choices to suggest to the user based on the analyzed information.

[0535] An "interactive interface" refers to a user interface that allows users to select and modify suggested expressions and words.

[0536] "Report generation means" refers to a method or system for recording user communication patterns and providing feedback and long-term sentiment trends.

[0537] "Suggestions" refer to appropriate expressions and word choices presented to improve the user's message.

[0538] A "user" refers to a technical professional who sends and receives communication messages and uses this system to improve communication.

[0539] The system for implementing this invention operates in conjunction with a cloud server and a user's terminal. When the server receives a communication message sent from the user's terminal, it analyzes the tone and context of the message using spaCy, a Natural Language Processing (NLP) library. Furthermore, it performs sentiment evaluation by analyzing the sentiment of the message using the Google Cloud NLP API.

[0540] Based on the analysis results, the server automatically generates expressions and word choices that take into account the user's emotional state. This generation utilizes a generation AI model to provide optimal suggestions for the user. The generated suggestions are displayed on the user's device in real time. The user can review the suggestions through this display interface and modify their communication messages as needed.

[0541] The server also records user communication patterns and analyzes long-term emotional trends. Based on this, it regularly generates feedback and reports to improve user communication. This allows users to continuously improve their emotional expression and communication skills.

[0542] As a concrete example, a user's message, "This needs fixing immediately," is transformed in real time into a suggestion such as, "This seems urgent, but would you like to specify a time frame?" An example of a prompt for the generative AI model used on the server side would be, "Identify the user's sentiment from this message and make a more helpful suggestion: 'This needs fixing immediately'."

[0543] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0544] Step 1:

[0545] The terminal inputs the communication message. The user enters the communication message into the messaging application on the terminal and presses the send button. The entered message data is sent to the server.

[0546] Step 2:

[0547] The server receives the communication message. The server receives the message data sent by the user and stores this data in a message queue for preparation for analysis.

[0548] Step 3:

[0549] The server analyzes the tone and context of the message. The server uses spaCy to perform NLP analysis on the message data. It uses the message data as input and outputs contextual structural information and tone analysis results.

[0550] Step 4:

[0551] The server analyzes the sentiment of the message. The server calls the Google Cloud NLP API to perform sentiment analysis on the input message. It uses the tone analysis results as input and outputs the sentiment identification result.

[0552] Step 5:

[0553] The server generates suggestions using a generative AI model. Based on the analysis results and sentiment identification results, the generative AI model on the server generates suggestions for the optimal expression and word choice. This process uses prompts. The generated suggestion message is obtained as output.

[0554] Step 6:

[0555] The server sends a suggestion message to the user's terminal. The generated suggestion message is sent from the server to the user's terminal. The user reviews the suggestion on their terminal and uses it to modify the communication message.

[0556] Step 7:

[0557] The server records communication patterns. This interaction, along with the user's modified messages, is recorded in a database for long-term analysis.

[0558] Step 8:

[0559] The server generates feedback and reports. Using the recorded data, the server periodically generates and provides users with feedback and reports on long-term sentiment trends.

[0560] 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.

[0561] 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.

[0562] 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.

[0563] [Fourth Embodiment]

[0564] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0565] 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.

[0566] 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).

[0567] 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.

[0568] 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.

[0569] 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).

[0570] 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.

[0571] 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.

[0572] 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.

[0573] 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.

[0574] 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.

[0575] 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.

[0576] 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".

[0577] The present invention provides a system for analyzing communication messages used daily by technical professionals, including engineers, to enable more effective communication. In a specific embodiment, the process begins with the user entering a message via email or chat application on a terminal used for daily work.

[0578] In this system, when a user enters a message, it is sent to the server. The server analyzes the received message using information processing means and evaluates its tone and content. If the evaluated tone is deemed negative or the content is deemed potentially misleading, a generation means generates improvement suggestions based on the analysis results.

[0579] The generated suggestions are displayed in real time on the user's device via email or chat interfaces. This allows the user to review the suggestions and revise their message based on them. For example, if a user enters the message "My report is late, but I will submit it soon," the server, if it determines the tone is neutral or negative, will suggest revising it to "My report is in the final stages and will be submitted shortly."

[0580] Furthermore, this system simultaneously performs grammatical checks on the text and generates appropriate feedback on the analyzed communication messages, helping users efficiently improve their communication. In addition, the server records the user's communication patterns and generates detailed feedback and reports for long-term improvement. This enables users to continuously improve their communication skills.

[0581] The following describes the processing flow.

[0582] Step 1:

[0583] The user types a message in an email or chat application and sends it. The message is temporarily stored on the user's device.

[0584] Step 2:

[0585] The user's terminal sends the entered communication message to the server. The message data is transmitted securely and received on the server.

[0586] Step 3:

[0587] The server feeds the received communication message into a parsing process. This involves using natural language processing (NLP) algorithms to evaluate the message's tone, grammar, and context.

[0588] Step 4:

[0589] Based on the analysis results, the server generates improvement suggestions using a generation mechanism. These suggestions include alternative ways of expressing the message to soften its tone or make it clearer.

[0590] Step 5:

[0591] The server sends the generated suggestions to the user's terminal. The suggestions are displayed in real time and provided to the user as feedback that can be applied.

[0592] Step 6:

[0593] The user reviews the suggested improvements and modifies the communication messages as needed. This allows for more effective communication.

[0594] Step 7:

[0595] The server records the history of previous messages and suggestions, accumulating data on the user's communication patterns. This data is used to generate feedback and reports later on.

[0596] (Example 1)

[0597] 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".

[0598] For modern technology professionals, communicating with the appropriate tone and avoiding misunderstandings is crucial. However, it is difficult to make such adjustments every time in a busy daily life. Furthermore, there is a lack of concrete means to consistently improve the quality of communication. Therefore, the present invention aims to achieve efficient and smooth communication by analyzing the emotions and content of communication data and automatically making improvement suggestions as needed.

[0599] 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.

[0600] In this invention, the server includes data processing means for receiving communication data and analyzing the sentiment and information of the communication data; generation means for proposing appropriate expressions and vocabulary for the communication data based on the analysis results; and operation means for presenting the proposed expressions and vocabulary to the user and modifying the content of the communication data according to the presented suggestions. This enables the user to plan effective communication in real time.

[0601] "Communication data" refers to units of information that are transmitted and received electronically, and includes text, audio, or image data.

[0602] "Emotions" refer to the sender's intentions and psychological state, which are analyzed from the tone and expression of communication data.

[0603] "Information" refers to the content and facts contained in communication data, and is an element of data transmitted through a user's message.

[0604] "Data processing means" refers to a device or software that performs a technical process to analyze received communication data and evaluate its emotions and information.

[0605] "Generation means" refers to a device or program that has the function of automatically creating and suggesting appropriate expressions and vocabulary based on analyzed data.

[0606] "Operating means" refers to a device or software that provides a user interface for the user to review proposed expressions and vocabulary and modify or accept them as necessary.

[0607] A "generative AI model" is a learning model that uses artificial intelligence technology to analyze data and generate new information.

[0608] A "revised text" is a new expression or wording generated to improve the original communication data based on analysis and suggestions.

[0609] A "user terminal" is an electronic device used by a user to create, transmit, and receive communication data.

[0610] This invention begins with a terminal used by a user in their daily work. The user inputs a message via email or chat application. The terminal sends the input message to a server. The server receives the communication data and analyzes the sentiment and information of the data using data processing means. This analysis utilizes natural language processing techniques and generative AI models. For example, general-purpose natural language processing models and generative AI models are used to perform sentiment analysis and grammar checks on messages.

[0611] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary for the message. This generation process utilizes a generation AI model to generate appropriate revised sentences through prompts. The generated revised sentences are sent to the user's terminal as suggestions. The terminal displays the suggested revised sentences to the user, allowing the user to review the message and either revise or approve it. This enables smoother and more effective communication for the user.

[0612] For example, if a user enters the message, "My report is late, but I will submit it soon," the server can use a generation tool to determine whether the tone of this message is neutral or negative and suggest a revised version, such as, "My report is in the final stages and will be submitted shortly." Through this process, users can receive continuous feedback to improve the quality of their communication.

[0613] Examples of prompt statements are as follows:

[0614] Please change the tone of the following message to a softer one: "I apologize for the delay in reporting, but I will submit it as soon as possible."

[0615] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0616] Step 1:

[0617] The user launches an email or chat application on their terminal used for daily work and enters a message. The input data includes the text message entered by the user. The terminal sends this message to the server for the next processing step. In this step, the user's intent and content are preserved as input data.

[0618] Step 2:

[0619] The server passes the received message to a data processing system. This data includes the message text. The server uses natural language processing techniques to analyze the sentiment and information of the message. Here, a generative AI model is used to analyze the tone of the text and group the content. As a result of the analysis, the tone of the message (e.g., positive, negative, neutral) is evaluated.

[0620] Step 3:

[0621] Based on the analysis results, the server uses a generation mechanism to suggest appropriate expressions and vocabulary. In this step, a generative AI model is used to generate improvement suggestions from the analyzed data. The input includes the analysis results, and the output is the text of the revised suggestions. This revised text is obtained by sending a generation request to the model via a prompt message.

[0622] Step 4:

[0623] The server sends the generated revised version to the terminal. Here, it outputs a revised message for the user to see. The terminal displays this revised version in real time on the interface of an email or chat application. The user can review the suggested revised text and, if necessary, modify the message or send it as is.

[0624] Step 5:

[0625] The server records user behavior patterns and generates long-term feedback and reports. This feedback is generated based on analyzed past communication data and its improvement history. Through this feedback, users can continuously improve their communication skills. Input includes the user's correction history, and output is a feedback report.

[0626] (Application Example 1)

[0627] 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".

[0628] Modern engineers and professionals are required to communicate efficiently and accurately in many situations. However, misunderstandings and inefficiencies can occur if the tone or expression used in the information transmission process is inappropriate. This problem can negatively impact productivity, especially in work environments where rapid responses are required. Therefore, there is a need for means to solve these communication challenges and support efficient communication.

[0629] 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.

[0630] In this invention, the server includes a processing unit that receives communication information and analyzes the tone and content of the communication information; a generating unit that proposes appropriate expression methods and word selections for the communication information based on the analysis results; and a presentation unit that displays the proposals in real time on the user's visual aid. This enables the user to communicate quickly and accurately.

[0631] "Communication information" refers to messages transmitted to the recipient in the process of sending and receiving information, either in linguistic or digital data format.

[0632] "Tone" refers to elements that indicate the emotional nuances and attitude of a message, and is classified as positive, negative, neutral, etc.

[0633] A "processing device" refers to a hardware or software component that receives information input and performs analysis and evaluation.

[0634] A "generation device" refers to a device that automatically generates improvement suggestions and optimized expressions based on analyzed information.

[0635] A "visual assistance device" refers to a digital display device designed to support a user's field of vision, specifically a device that directly displays information.

[0636] A "presentation device" refers to a device that provides an interface for displaying generated improvement suggestions and feedback to users in an easily viewable format.

[0637] The embodiment of the invention is a system for effectively analyzing and improving communication information. This system consists of a processing unit, a generation unit, and a presentation unit. When a user inputs information, it is processed by a server. Specifically, the information processing unit receives the communication information and analyzes its tone and content. Natural language processing libraries such as SpaCy and Transformers may be used for the analysis. This determines whether the tone is positive, negative, or neutral.

[0638] Based on the analysis results, the server automatically generates improvement suggestions using a generation device. This generation process utilizes a generation AI model, optimizing the suggested expressions and word choices. The generated improvement suggestions are then displayed in real time through a presentation device to the user's visual aids, specifically smart glasses.

[0639] For example, suppose a technician working in a factory reports a malfunction in a piece of equipment and enters the message, "There is a problem with this equipment." The server receives this message and generates a suggested improvement, such as, "We have observed a minor issue with this equipment, but we will address it promptly," which is then displayed on a visual aid, facilitating smoother communication on-site.

[0640] An example of a prompt to be input into the generation AI model is text such as, "Based on the current status report regarding the factory's parts supply, please generate an appropriate follow-up message for your supervisor."

[0641] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0642] Step 1:

[0643] The server receives communication information entered by the user. The user enters a message as voice or text, which is sent to the server via the terminal. The received data is then sent directly to the next processing step.

[0644] Step 2:

[0645] The server analyzes received communication information using natural language processing libraries. Here, SpaCy and Transformers are used to analyze the tone (positive, negative, neutral, etc.) and content of messages. The input data is the communication information, and the output data is the analysis results. Specific operations include sentence structure analysis and sentiment analysis.

[0646] Step 3:

[0647] The server generates improvement suggestions using a generative AI model based on the analysis results. The generative AI model, such as GPT-3, suggests appropriate expressions and word choices. The input is the analysis results, and the output is the generated improvement suggestions. This process involves suggestions based on known expression patterns.

[0648] Step 4:

[0649] The server transmits the generated improvement suggestions to the user's visual aids in real time via a display device. Specifically, the information is displayed on a digital display such as smart glasses. The input is the generated improvement suggestions, and the output is what is displayed on the visual aids. In this process, data format conversion is performed to ensure that the information is displayed in a visually understandable format.

[0650] Step 5:

[0651] The user reviews the proposed improvements and modifies the original communication information as needed. The user's input is the modified message, and the output is the final modified communication information that is sent. At this step, the user decides whether or not to accept the suggestions.

[0652] 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.

[0653] This invention is a technology that analyzes communication messages used daily by engineers and technical professionals and promotes effective communication based on the information contained in those messages. This system is used in combination with a program installed on the terminal that the user uses for their daily work and a server.

[0654] When a user enters a message via email or chat application and sends it from their device to the server, the server uses information processing tools to analyze the tone and context of the message. Furthermore, by using an emotion engine, the server identifies the user's emotional state from the entered message and tracks changes in that emotional state.

[0655] Based on the analyzed results, the server uses a generation mechanism to construct suggestions for improving the message. These suggestions include expressions and word choices that take the user's emotions into consideration, and are presented to the user's device in real time. For example, if a user types "I'm worried because the project is behind schedule," the emotion engine identifies the emotion "worry," and the generation mechanism suggests something like "We are seeing progress on the project, and we will do our best with the team."

[0656] Users can modify their communication messages as needed by referring to the suggestions provided. Furthermore, the server records the user's communication patterns and periodically generates reports based on this data, including feedback and long-term emotional trends. This allows users to continuously improve their emotional expression and communication skills.

[0657] The following describes the processing flow.

[0658] Step 1:

[0659] The user types a message in the email or chat application on their device and presses the send button. This prepares the message data, which is then immediately sent to the server.

[0660] Step 2:

[0661] The server passes the received communication message to an information processing module for analysis. Here, an initial evaluation of the tone and content is performed using Natural Language Processing (NLP) techniques.

[0662] Step 3:

[0663] The server uses an emotion engine to identify the emotions contained in the user's message. For example, it can identify the user's emotional state, such as "anxiety" or "relief."

[0664] Step 4:

[0665] Based on these analysis results and emotional states, the server generates improvement suggestions using a generation mechanism. These suggestions include specific expressions and word choices to soften the tone of the message while taking the user's emotions into consideration.

[0666] Step 5:

[0667] The server generates improvement suggestions and sends them to the user's terminal, displaying them to the user in real time. The user can receive this feedback and modify the actual communication messages as needed.

[0668] Step 6:

[0669] Users review the suggested message content, edit it as needed, and send it. This allows for more effective and emotionally sensitive communication.

[0670] Step 7:

[0671] The server records all message history and emotional state data, and generates reports that include long-term communication improvements and emotional trends. These reports are provided to users as needed to help improve their communication skills and emotional expression.

[0672] (Example 2)

[0673] 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".

[0674] In today's communication environment, users compose messages with a wide range of emotions, but effectively conveying these emotions is often difficult. Furthermore, choosing appropriate expressions and words that are sensitive to emotions is particularly challenging, sometimes leading to misunderstandings. Moreover, there is a lack of feedback to help improve one's emotional expression and communication skills in the long term.

[0675] 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.

[0676] In this invention, the server includes information processing means for receiving communication messages and analyzing the sentiment and context of the communication messages; generation means for proposing appropriate expressions and word choices that take the sentiment of the communication message into consideration, based on the analyzed sentiment information; and analysis means for analyzing sentiment trends based on the user's communication history and providing feedback based on the analysis results. This enables the user to communicate more effectively and to continuously improve their emotional expression and communication skills.

[0677] "Communication messages" are information sent and received in text format, exchanged between users through email or chat applications.

[0678] "Information processing means" is a general term for software and hardware used to analyze communication messages, and has the function of understanding the content of messages by performing language processing and sentiment analysis.

[0679] "Emotions" refer to the psychological states that users include in their messages, encompassing internal human feelings such as anxiety, joy, and sadness.

[0680] A "generation method" is a mechanism that selects appropriate expressions and words based on analyzed information and makes suggestions to the user.

[0681] "Method of expression" refers to the choice of words and phrases that a user uses when sending a message, and is the method for appropriately conveying emotions and intentions.

[0682] "Analysis methods" refer to technologies that analyze patterns and trends based on a user's communication history, and the process of generating feedback from this analysis and providing it to the user.

[0683] "Display means" refers to methods or interfaces for visually presenting generated proposals or information to the user.

[0684] "Emotional trends" refer to the tendency of emotional changes based on a user's past messages, and show a history of how a user has expressed emotions.

[0685] This invention is a system that combines a program installed on a terminal used by a user for their daily work with a server. The user inputs communication messages via email or chat applications and sends those messages from the terminal to the server.

[0686] The server uses a natural language processing engine to analyze the tone and context of messages. Specifically, it uses natural language processing libraries such as NLTK and spaCy to analyze the structure and content of messages and extract emotions. Additionally, an emotion engine is installed, and an emotion AI model is running to identify psychological states. This allows the server to identify emotional states from user-input messages and track changes in those states.

[0687] Based on the analyzed data, the server uses a generation mechanism to construct improvement suggestions. This generation mechanism employs a generative AI model, which prioritizes expressions and word choices that take the user's emotions into consideration. For example, if a user inputs a message such as, "I feel that we won't meet the deadline at this rate," the emotional AI model will analyze the emotion of "anxiety" and generate a suggestion such as, "Let's re-examine the project progress and strengthen the support system as needed."

[0688] The generated suggestions are presented to the user's device in real time, allowing the user to modify their message based on the suggestions. Furthermore, the server analyzes emotional trends by recording the user's past messages and communication history, and provides regular feedback to the user. This helps users continuously improve their emotional expression and communication skills.

[0689] A concrete example of a prompt message would be something like, "Please provide suggestions for improving this message. Please be mindful of the user's feelings when giving advice." This can improve the quality of user communication.

[0690] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0691] Step 1:

[0692] Users type communication messages into email or chat applications and send them from their devices. The data entered is textual information, typically including purpose, requests, and emotions.

[0693] Step 2:

[0694] The terminal sends the input communication message to the server. During this process, data is securely transferred using a communication protocol. The input is the text entered by the user, and the output is that text being sent to the server.

[0695] Step 3:

[0696] The server analyzes received messages using a natural language processing (NLP) engine. Specifically, it utilizes NLP libraries (e.g., NLTK, spaCy) to extract keywords and emotional tone from the messages. The input is the received text, and the output is the analyzed tone and emotional information.

[0697] Step 4:

[0698] The server uses an emotion engine to further process the analyzed data and identify the user's emotional state. This process uses an emotion AI model to identify various emotions (anxiety, joy, sadness, etc.). The input is the analyzed information obtained by the NLP engine, and the output is data on emotion labels and their intensity.

[0699] Step 5:

[0700] Based on the generation method, the server generates message improvement suggestions that take into account emotional information, considering expression methods and word choices. An AI generation model is used to construct appropriate suggestions. The input is emotional state data, and the output is the text of the improvement suggestion.

[0701] Step 6:

[0702] The server sends the generated improvement suggestions to the user's terminal. The user's terminal displays the suggestions to the user in real time, and the user reviews them to help with decision-making. The input is the suggestion data from the server, and the output is a visual presentation to the user.

[0703] Step 7:

[0704] The server records the user's communication history and the results of improvement suggestions, and analyzes long-term sentiment trends. Based on this, it periodically generates and provides feedback to the user. The input is past messages and suggestion history, and the output is a feedback report as a result of the analysis.

[0705] (Application Example 2)

[0706] 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".

[0707] In technical work environments, engineers and professionals routinely receive and send numerous communication messages. This often leads to communication friction due to misunderstandings of message tone and context. Furthermore, a decline in communication quality caused by emotional fluctuations and grammatical errors hinders improvements in work efficiency. A system is needed to address these challenges and achieve effective and emotionally sensitive communication.

[0708] 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.

[0709] In this invention, the server includes information processing means for receiving a communication message and analyzing the tone and context of the communication message; generation means for proposing appropriate expressions and word choices for the communication message using the analysis results and sentiment evaluation means; display means for presenting the proposed expressions and words to the user and providing an interactive interface for modifying the content of the communication message according to the presented suggestions; and report generation means for recording the user's communication patterns and providing feedback including long-term sentiment trends. This enables effective communication that takes emotions into consideration.

[0710] "Communication messages" refer to messages such as emails and chats that users send and receive in their daily work.

[0711] "Tone" refers to the emotional or psychological nuances conveyed by the words and expressions within a message.

[0712] "Context" refers to the meaning derived from considering the surrounding circumstances in which words and phrases within a message are placed.

[0713] "Information processing means" refers to methods and techniques for analyzing the content of communication messages and understanding their tone and context.

[0714] "Emotion evaluation means" refers to technologies and devices for identifying a user's emotional state from communication messages and tracking its fluctuations.

[0715] "Generation method" refers to technology that automatically generates appropriate expressions and word choices to suggest to the user based on the analyzed information.

[0716] An "interactive interface" refers to a user interface that allows users to select and modify suggested expressions and words.

[0717] "Report generation means" refers to a method or system for recording user communication patterns and providing feedback and long-term sentiment trends.

[0718] "Suggestions" refer to appropriate expressions and word choices presented to improve the user's message.

[0719] A "user" refers to a technical professional who sends and receives communication messages and uses this system to improve communication.

[0720] The system for implementing this invention operates in conjunction with a cloud server and a user's terminal. When the server receives a communication message sent from the user's terminal, it analyzes the tone and context of the message using spaCy, a Natural Language Processing (NLP) library. Furthermore, it performs sentiment evaluation by analyzing the sentiment of the message using the Google Cloud NLP API.

[0721] Based on the analysis results, the server automatically generates expressions and word choices that take into account the user's emotional state. This generation utilizes a generation AI model to provide optimal suggestions for the user. The generated suggestions are displayed on the user's device in real time. The user can review the suggestions through this display interface and modify their communication messages as needed.

[0722] The server also records user communication patterns and analyzes long-term emotional trends. Based on this, it regularly generates feedback and reports to improve user communication. This allows users to continuously improve their emotional expression and communication skills.

[0723] As a concrete example, a user's message, "This needs fixing immediately," is transformed in real time into a suggestion such as, "This seems urgent, but would you like to specify a time frame?" An example of a prompt for the generative AI model used on the server side would be, "Identify the user's sentiment from this message and make a more helpful suggestion: 'This needs fixing immediately'."

[0724] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0725] Step 1:

[0726] The terminal inputs the communication message. The user enters the communication message into the messaging application on the terminal and presses the send button. The entered message data is sent to the server.

[0727] Step 2:

[0728] The server receives the communication message. The server receives the message data sent by the user and stores this data in a message queue for preparation for analysis.

[0729] Step 3:

[0730] The server analyzes the tone and context of the message. The server uses spaCy to perform NLP analysis on the message data. It uses the message data as input and outputs contextual structural information and tone analysis results.

[0731] Step 4:

[0732] The server analyzes the sentiment of the message. The server calls the Google Cloud NLP API to perform sentiment analysis on the input message. It uses the tone analysis results as input and outputs the sentiment identification result.

[0733] Step 5:

[0734] The server generates suggestions using a generative AI model. Based on the analysis results and sentiment identification results, the generative AI model on the server generates suggestions for the optimal expression and word choice. This process uses prompts. The generated suggestion message is obtained as output.

[0735] Step 6:

[0736] The server sends a suggestion message to the user's terminal. The generated suggestion message is sent from the server to the user's terminal. The user reviews the suggestion on their terminal and uses it to modify the communication message.

[0737] Step 7:

[0738] The server records communication patterns. This interaction, along with the user's modified messages, is recorded in a database for long-term analysis.

[0739] Step 8:

[0740] The server generates feedback and reports. Using the recorded data, the server periodically generates and provides users with feedback and reports on long-term sentiment trends.

[0741] 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.

[0742] 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.

[0743] 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.

[0744] 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.

[0745] 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.

[0746] 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.

[0747] 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.

[0748] 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.

[0749] 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."

[0750] 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.

[0751] 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.

[0752] 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.

[0753] 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.

[0754] 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.

[0755] 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.

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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 as being incorporated by reference.

[0762] The following is further disclosed regarding the embodiments described above.

[0763] (Claim 1)

[0764] Information processing means for receiving a communication message and analyzing the tone and content of the communication message,

[0765] A generation means that proposes an appropriate expression method and word choice for the communication message based on the analysis results,

[0766] A display means that presents proposed expressions and words to the user and provides an interface for modifying the content of the communication message in accordance with the proposed expressions,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, further comprising a proofreading function that identifies grammatical errors and tone contained in the communication message and suggests areas for improvement based on the identification results.

[0770] (Claim 3)

[0771] The system according to claim 1, further comprising reporting means for generating and periodically providing feedback and reports for improvement of analyzed communication messages.

[0772] "Example 1"

[0773] (Claim 1)

[0774] A data processing means that receives communication data and analyzes the emotions and information of said communication data,

[0775] A generation means that proposes an appropriate representation method and vocabulary for the communication data based on the analysis results,

[0776] A means for presenting proposed expressions and vocabulary to the user and for modifying the content of the communication data in accordance with the proposed suggestions,

[0777] A generation AI means that uses a generation AI model to generate appropriate corrected sentences when improvements are needed based on analyzed communication data,

[0778] A transmission means for sending the generated revised text to the user's terminal in real time,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising a correction function that identifies grammatical errors and sentiments contained in the communication data and suggests areas for improvement based on the identification results.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising a reporting function that generates feedback and reports for improvement of analyzed communication data and provides such feedback and reports on an ongoing basis.

[0784] "Application Example 1"

[0785] (Claim 1)

[0786] A processing device that receives communication information and analyzes the tone and content of said communication information,

[0787] A generation device that proposes an appropriate representation method and word selection for the communication information based on the analysis results,

[0788] A display device that presents proposed expressions and words to the user and provides an interface for modifying the content of the communication information in accordance with the proposed expressions,

[0789] A presentation device that displays the proposal in real time on the user's visual aid,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, comprising a proofreading function that identifies grammatical errors and tone contained in the communication information and suggests areas for improvement based on the identification results.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising a reporting device that generates and periodically provides feedback and reports for improvement of analyzed communication information.

[0795] "Example 2 of combining an emotion engine"

[0796] (Claim 1)

[0797] Information processing means for receiving a communication message and analyzing the sentiment and context of the communication message,

[0798] A generation means that proposes appropriate expression methods and word choices that take into account the emotions of the communication message, based on the analyzed emotional information.

[0799] An analytical means that analyzes emotional trends based on the user's communication history and provides feedback based on the results of the analysis,

[0800] A display means that presents proposed expressions and words to the user and provides an interface for modifying the content of the communication message in accordance with the proposed expressions,

[0801] A system that includes this.

[0802] (Claim 2)

[0803] The system according to claim 1, further comprising a proofreading function that identifies grammatical errors and emotional tone contained in the communication message and suggests areas for improvement based on the identification results.

[0804] (Claim 3)

[0805] The system according to claim 1, further comprising reporting means for generating feedback and periodic sentiment trend reports for improvement of analyzed communication messages and providing said feedback and reports to the user.

[0806] "Application example 2 when combining with an emotional engine"

[0807] (Claim 1)

[0808] Information processing means for receiving a communication message and analyzing the tone and context of the communication message,

[0809] A generation means that uses the analysis results and emotion evaluation means to propose appropriate expression methods and word choices for the communication message,

[0810] A display means that presents proposed expressions and words to the user and provides an interactive interface for modifying the content of the communication message in accordance with the proposed expressions,

[0811] A means for generating reports that record user communication patterns and provide feedback including long-term emotional trends,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, further comprising a proofreading function that identifies grammatical errors and tone contained in the communication message and suggests areas for improvement based on the identification results and emotional changes.

[0815] (Claim 3)

[0816] The system according to claim 1, further comprising reporting means for generating feedback for improvement and continuous sentiment trend reports for analyzed communication messages, and for periodically providing said feedback and reports. [Explanation of Symbols]

[0817] 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. Information processing means for receiving a communication message and analyzing the tone and content of the communication message, A generation means that proposes an appropriate expression method and word choice for the communication message based on the analysis results, A display means that presents proposed expressions and words to the user and provides an interface for modifying the content of the communication message in accordance with the proposed expressions, A system that includes this.

2. The system according to claim 1, further comprising a proofreading function that identifies grammatical errors and tone contained in the communication message and suggests areas for improvement based on the identification results.

3. The system according to claim 1, further comprising reporting means for generating and periodically providing feedback and reports for improvement of analyzed communication messages.

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