System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a message input by a user; means for analyzing an emotion of the received message; means for converting an offensive or rude expression into a constructive and kind expression according to an analysis result; and means for returning the converted message to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Text-based communication is the norm on modern social media platforms, but one issue is that it is difficult to convey emotions and tone of voice through text. This can easily lead to misunderstandings between users, and in some cases, unnecessary arguments can escalate, causing users to leave social media. Furthermore, aggressive and rude language is rampant on social media, often creating a stressful environment for users. To address this issue, a means is needed to improve the quality of communication between users and make dialogue more constructive. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for receiving messages entered by users, a means for analyzing the sentiment of the received messages, a means for converting aggressive or rude expressions into constructive and kind expressions based on the analysis results, and a means for returning the converted messages to the users. This system uses an algorithm that classifies the sentiment of messages entered by users into positive, negative, or neutral, and converts the expressions into more gentle and kind expressions while preserving the meaning of the original message. This facilitates communication on social networking sites, reduces misunderstandings and conflicts, and provides a more comfortable environment for users.

[0006] "User" refers to a person who posts and receives messages using the SNS Platform.

[0007] A "message" refers to a sentence or comment that a user types and posts on a social media platform.

[0008] "Server" refers to a computer system that receives, analyzes, and transforms messages from users.

[0009] "Sentiment analysis" refers to the process of analyzing the text of received messages and classifying their content as positive, negative, or neutral.

[0010] "Transformers" refer to algorithms or processes that modify messages based on sentiment analysis, turning offensive or disrespectful language into constructive and kind language.

[0011] "Return means" refers to the process or system that transmits the converted message to the user terminal.

[0012] "Constructive language" refers to language that has a positive impact on other users, fosters dialogue, and helps build friendly relationships.

[0013] "Kind expressions" refer to expressions that are considerate of other users, use polite language, and facilitate smooth dialogue.

[0014] A "text analysis algorithm" refers to a computational method or model for analyzing input messages and understanding their meaning and sentiment.

[0015] An "algorithm" refers to a sequence of steps or computational techniques used to solve a particular problem. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert aggressive or rude expressions into more gentle and kind expressions.

[0038] System configuration and operation

[0039] The system consists of the following main components:

[0040] User Device

[0041] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0042] server

[0043] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0044] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0045] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0046] 3. Message transformation: Transforming messages judged to be negative or offensive into kinder, more constructive language without losing their original meaning.

[0047] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0048] What the program does

[0049] The program of this system operates as follows.

[0050] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0051] 2. Sending the message: The user terminal sends the entered message to the server, along with the user's identification information.

[0052] 3. Sentiment analysis: The server analyzes the received message. The generating AI analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0053] 4. Message Regeneration: Based on the results of sentiment analysis, messages that are judged to be negative are transformed into more helpful ones. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0054] 5. Returning the converted message: The server returns the converted message to the user's device and displays it to the user. Instead of seeing "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0055] Specific examples

[0056] The following is an example of this system:

[0057] 1. Example 1

[0058] User input message: I'm not interested in this project at all.

[0059] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0060] 2. Example 2

[0061] User input: I think your idea is stupid.

[0062] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0063] These specific examples have the effect of facilitating communication between users and reducing conflicts and misunderstandings on social media.

[0064] The processing flow will be explained below.

[0065] Step 1:

[0066] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[0067] Step 2:

[0068] The terminal receives the message entered by the user and sends it to the server, along with the user's identification information.

[0069] Step 3:

[0070] The server receives the message and the user's identification information from the terminal and prepares it for emotion analysis.

[0071] Step 4:

[0072] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[0073] Step 5:

[0074] Based on the analysis, the generative AI begins the process of transforming the message. If it's classified as negative, it preserves the meaning of the message while transforming it into a kinder, more constructive expression. In this case, "You're completely wrong. You should reconsider" becomes "I understand your point of view, but there may be other ways of thinking about it."

[0075] Step 6:

[0076] The server receives the converted message from the generating AI and prepares it for sending back to the user terminal.

[0077] Step 7:

[0078] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[0079] Step 8:

[0080] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0081] Specific examples

[0082] Example 1

[0083] Step 1: User types "I'm not interested in this project at all."

[0084] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0085] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0086] Steps 6-8: The server sends the converted message "I'm not really interested in this project, but I'd be happy to discuss other ideas" to the user's device, which displays it.

[0087] Example 2

[0088] Step 1: User types, "I think your idea is stupid."

[0089] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0090] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0091] Steps 6-8: The server sends the converted message "I feel a little uncomfortable with your idea, but I'd like to consider it from another perspective" to the user's terminal, which displays it.

[0092] This is expected to facilitate smoother communication between users and reduce conflicts and misunderstandings on social media.

[0093] Example 1

[0094] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0095] Traditional social media platforms have a problem in that communication between users can sometimes lead to conflicts and misunderstandings due to aggressive or rude language. This problem is particularly pronounced in online environments where emotions tend to run high, causing a decline in the quality of the user experience. Furthermore, aggressive messages can undermine the health of the entire platform. To solve these issues, a system is needed that can analyze the emotions in messages posted by users and, if necessary, convert those expressions into kinder and more constructive ones.

[0096] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0097] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for presenting the converted messages on the user terminals, and means for performing emotion analysis and message conversion using a generative AI model, thereby facilitating communication between users and reducing conflicts and misunderstandings on SNS.

[0098] "User" refers to an individual user who accesses the SNS Platform and posts messages.

[0099] "Terminal" refers to the device (e.g., smartphone, tablet, PC, etc.) used by a User to access the SNS Platform.

[0100] "Server" refers to a central computer system that receives messages sent by users and performs analysis and transformation processing on them.

[0101] "Message" refers to any opinion or comment in text form that a User expresses on a social media platform.

[0102] "Means for receiving" refers to the technical mechanism by which the server receives the message entered by the user.

[0103] "Sentiment analysis means" refers to the technical mechanisms used to evaluate the content of a message and classify its sentiment as positive, negative, or neutral.

[0104] "Transformation means" refers to a technical mechanism that, based on the analysis results, transforms offensive or disrespectful language into kind and constructive language.

[0105] "Means for returning" refers to the technical mechanism for returning the converted message back to the user terminal.

[0106] "Presenting means" refers to the technical mechanism for displaying the returned converted message on the user terminal.

[0107] A "generative AI model" refers to an artificial intelligence algorithm that automatically analyzes context and emotions and converts them into appropriate expressions.

[0108] The present invention is a system for making text-based communication on social networking platforms kind and constructive. The system analyzes the sentiment of messages posted by users and provides a means to convert aggressive or rude expressions into gentle and kind expressions as needed.

[0109] System configuration and operation

[0110] User terminal

[0111] The user terminal is a device for accessing the SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0112] server

[0113] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following main functions:

[0114] How to receive messages

[0115] A means of analyzing emotions

[0116] A means of transforming messages

[0117] A means of returning a converted message

[0118] Means for presenting the converted message on a user terminal

[0119] Hardware and software used

[0120] Hardware:

[0121] User device: A device such as a smartphone, tablet, or PC.

[0122] Server: A data center server with a powerful processor.

[0123] software:

[0124] SNS platform: A representative social media application.

[0125] Generative AI model: An artificial intelligence algorithm for performing contextual analysis and representation transformation (e.g., OpenAI's GPT-4).

[0126] Sentiment analysis software: Natural Language Processing (NLP) tools.

[0127] Specific examples

[0128] As a concrete example of this system, the following message transformations are performed:

[0129] Example 1:

[0130] User input message: "I'm not interested in this project at all."

[0131] Resulting message: "I'm not particularly interested in this project, but I'd be happy to discuss other ideas."

[0132] Example 2:

[0133] User input message: "I think your idea is stupid."

[0134] Transformed message: "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[0135] Prompt Sentence Examples

[0136] Here are some example prompts for a generative AI model:

[0137] Sentiment analysis prompt: "Analyze the context of the user-entered message "[input message]" and determine whether the sentiment is classified as positive, negative, or neutral."

[0138] Message Regeneration Prompt: "The message you entered, "[Input Message]", has been identified as negative or offensive. Please rewrite this message to something more kind and constructive."

[0139] This system will prevent users from unintentionally sending offensive messages and make communication on social media more kind and constructive.

[0140] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0141] Step 1:

[0142] A user uses a device to access a social networking platform and type a message, for example, "Your opinion is completely wrong. You should reconsider." The input at this point is the text message typed by the user. The output is the message that is displayed on the device.

[0143] Step 2:

[0144] The terminal sends the input message to the server. During this process, a request is generated that includes the message content and the user's identification information (such as the user ID). The input is data that includes the input message and the user ID, and the output is that data being sent to the server.

[0145] Step 3:

[0146] The server receives messages from the terminal. During this receiving process, the message content and user ID are stored in the server. The input is the sent message data, and the output is the received message data being saved in the server.

[0147] Step 4:

[0148] The server uses a generative AI model to analyze the sentiment of the received message. During this stage, the context and wording of the message are evaluated and its sentiment is classified as positive, negative, or neutral. For example, a message that says, "You're completely wrong. You should reconsider," would be classified as negative. The input is the received text message, and the output is the result of the sentiment analysis (positive, negative, or neutral).

[0149] Step 5:

[0150] The server uses a generative AI model to transform messages that are deemed negative or offensive based on the results of sentiment analysis into more kind and constructive expressions. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking." The input is a message that is deemed negative, and the output is a kinder message.

[0151] Step 6:

[0152] The server returns the converted message to the user terminal. The terminal presents the received converted message to the user for confirmation. The input is the converted message, and the output is that the message is displayed on the user terminal.

[0153] Step 7:

[0154] The user checks the proposed converted message and, if there is no problem with the content, finally posts it to the SNS platform. The input is the proposed converted message, and the output is that the message is posted on the SNS platform. This shows other users a friendly message saying, "I understand your point of view, but there may be other ways of thinking."

[0155] (Application example 1)

[0156] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0157] On modern social media platforms, offensive or rude messages are frequently posted, often sparking conflicts between users. Such comments undermine the health of online communities and degrade the user experience. Furthermore, methods for preventing and addressing these issues are immature, and there is a need for effective methods, particularly those that can respond automatically and in real time. The present invention aims to address this issue.

[0158] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0159] In this invention, the server includes means for receiving messages entered by users, means for analyzing the sentiment of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for comparing the pre-conversion message with the converted message using a generative AI model to improve the quality of the reply while preserving the meaning of the original message, and means for linking the converted message with other applications as a prompt sentence. This makes it possible to facilitate smoother communication between users on SNS platforms and maintain the health of online communities.

[0160] "Means for receiving messages entered by users" refers to devices or software that have the function of receiving messages entered by users on the SNS platform.

[0161] "Means for analyzing the sentiment of received messages" refers to algorithms or tools that analyze received messages and classify their sentiment as positive, negative, or neutral.

[0162] A "means for transforming offensive or rude language into constructive and kind language" is an algorithm or method for changing negative messages into more gentle and kind language while preserving the original meaning.

[0163] "Means for returning the converted message to the user" refers to a device or software that has the function of sending the converted message to the user terminal and displaying it to the user.

[0164] A "generative AI model" is an artificial intelligence model used to generate or convert text, primarily for message conversion.

[0165] A "prompt sentence" is an initial input sentence given to a generative AI model to instruct it to perform a specific task.

[0166] The present invention is a system for converting offensive or rude expressions into constructive and kind expressions when users communicate on social media platforms. The system mainly consists of the following components: a user device, a server, a generative AI model, and a sentiment analysis algorithm.

[0167] System configuration and operation

[0168] User Device

[0169] A user terminal is a device used to access a social networking platform, providing a means for users to type and post messages, and an interface for users to view the converted messages. A smartphone is typically used as a user terminal.

[0170] server

[0171] The server is the main component that processes messages received from user devices, performs sentiment analysis, and converts messages. Specifically, it has the following functions:

[0172] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0173] 2. Sentiment Analysis: Received messages are classified as positive, negative, or neutral using a sentiment analysis algorithm such as TextBlob.

[0174] 3. Message transformation: Messages judged as negative or offensive are transformed into friendly representations using the GPT-2 model from the transformers library.

[0175] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0176] Example

[0177] A specific example of the system is shown below:

[0178] User input message

[0179] The user uses the device to access a social media platform and type something like, "Your opinion is completely wrong. You should reconsider."

[0180] Message sentiment analysis

[0181] The server receives this message and performs sentiment analysis using TextBlob, in this case "You're completely wrong. You should reconsider" is determined to be negative.

[0182] Message Transformation

[0183] Use the GPT-2 model from the transformers library to transform "You're completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking about it."

[0184] Returning a conversion message

[0185] The converted message is then sent back to the user's device, where the user is told, "We understand your point of view, but there may be other ways of thinking."

[0186] Prompt Sentence Examples

[0187] An example of an input prompt for a generative AI model is, "Please translate the following sentence into a more helpful expression: Your idea is completely useless."

[0188] This system will facilitate smoother communication between users on social media platforms and help maintain the health of online communities.

[0189] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0190] Step 1:

[0191] The user uses the device to log into the social media platform and type a message, such as "You're completely wrong. You should reconsider."

[0192] Step 2:

[0193] The terminal transmits the input message to the server, and the transmitted data includes the user's identification information and the message.

[0194] Step 3:

[0195] The server processes the received messages and performs sentiment analysis. Using the TextBlob library, it parses the messages and classifies the sentiment as positive, negative or neutral. In this case, the message "You're completely wrong. You should reconsider" is considered negative.

[0196] Step 4:

[0197] The server transforms messages that are deemed negative or offensive based on the results of sentiment analysis, using a GPT-2 model from the transformers library to convert "You're completely wrong. You should reconsider" to "I understand your point of view, but there may be other ways of thinking."

[0198] Step 5:

[0199] The server compares the original and converted messages using a generative AI model and checks to improve the quality of the reply while preserving the meaning of the original message. This process verifies that the meaning of the original message is preserved.

[0200] Step 6:

[0201] The converted message is prepared as a prompt sentence and prepared for integration with other applications. Specifically, the converted message is included in the prompt sentence to convert it into a format that can be transferred to other generative AI models and related software.

[0202] Step 7:

[0203] Finally, the server sends the converted message back to the user's device and displays it to the user, who now sees a message that reads "I understand your point of view, but there may be other ways of thinking about it," instead of the original message, "Your opinion is completely wrong. You should reconsider."

[0204] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0205] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert offensive or rude expressions into more gentle and kind expressions, while combining an emotion engine that recognizes users' emotions in real time.

[0206] System configuration and operation

[0207] The system consists of the following main components:

[0208] User Device

[0209] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0210] server

[0211] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0212] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0213] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0214] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[0215] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[0216] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0217] What the program does

[0218] The program of this system operates as follows.

[0219] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0220] 2. Message transmission: The user device sends the input message to the server, along with the user's identification information and real-time emotional data.

[0221] 3. Sentiment analysis: The server analyzes the received message. The generator analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0222] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[0223] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. If the message is classified as negative and the user is angry, the message is transformed into a more kind and constructive expression while retaining its meaning. In this case, "You're completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0224] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[0225] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. Instead of "Your opinion is completely wrong. You should reconsider," the user will see "I understand your point of view, but there may be other ways of thinking."

[0226] Specific examples

[0227] The following is an example of this system:

[0228] 1. Example 1

[0229] User input message: I'm not interested in this project at all.

[0230] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0231] 2. Example 2

[0232] User input: I think your idea is stupid.

[0233] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0234] These specific examples will help facilitate communication between users and reduce conflicts and misunderstandings on social media. In addition, by combining this with real-time emotion recognition of users, more appropriate and effective message translation will be realized.

[0235] The processing flow will be explained below.

[0236] Step 1:

[0237] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[0238] Step 2:

[0239] The terminal receives the message entered by the user and transmits it to the server, along with the user's identification information and emotion data acquired in real time.

[0240] Step 3:

[0241] The server receives the message, identification information, and emotion data received from the terminal and prepares it for emotion analysis.

[0242] Step 4:

[0243] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[0244] Step 5:

[0245] The server uses an emotion engine to recognize the user's current emotion. The server processes the received emotion data to determine whether the user is feeling angry or annoyed, for example.

[0246] Step 6:

[0247] The server converts messages based on the analysis results of the generation AI and the recognition results of the emotion engine. If a message is judged to be negative, it is converted into a kinder, more constructive expression while retaining its original meaning, especially if the user is feeling angry. In this case, "Your opinion is completely wrong. You should reconsider" is converted to "I understand your point of view, but there may be other ways of thinking."

[0248] Step 7:

[0249] The server receives the converted message from the generation AI and prepares it for sending back to the user terminal.

[0250] Step 8:

[0251] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[0252] Step 9:

[0253] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0254] Specific examples

[0255] Example 1

[0256] Step 1: User types "I'm not interested in this project at all."

[0257] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[0258] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as anger.

[0259] Step 6: The server translates the message to "I'm not really interested in this project, but I'd be happy to discuss other ideas."

[0260] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[0261] Example 2

[0262] Step 1: User types, "I think your idea is stupid."

[0263] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[0264] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as irritation.

[0265] Step 6: The server translates the message into "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[0266] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[0267] This is expected to facilitate smooth communication between users and reduce conflicts and misunderstandings on social media. Furthermore, by combining this with real-time emotion recognition of users, more appropriate and effective message conversion can be achieved.

[0268] Example 2

[0269] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0270] The increase in offensive or rude messages on social media platforms is leading to poor communication between users and resulting in trouble and conflict. This problem is particularly pronounced in online environments where users' feelings and intentions are easily misunderstood. Therefore, a method is needed to transform the messages users send into more kind and constructive ones.

[0271] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a message entered by a user, a means for analyzing the emotion of the received message, a means for recognizing the analysis result and the user's real-time emotion, and converting aggressive or rude expressions into constructive and kind expressions, and a means for returning the converted message to the user. This facilitates communication on the SNS platform and makes it possible to prevent trouble and conflicts between users.

[0272] "Receiving" refers to the server taking in message data entered by the user.

[0273] "Sentiment analysis" is the process of analyzing the context and wording of a received message and classifying its emotional nature.

[0274] "Real-time recognition" refers to analyzing and understanding the user's emotions at the moment they type a message.

[0275] "Transformation" is the process of rewriting a received message into a different representation without moving it, while preserving its original meaning.

[0276] "Return" refers to sending the converted message back to the user's terminal.

[0277] "Aggression" refers to expressions that show hostility or negative feelings toward others.

[0278] "Rudeness" is an expression that lacks courtesy and language that shows a lack of respect for others.

[0279] "Kindness" refers to showing friendliness and consideration towards others.

[0280] "Constructive" is an expression that aims to improve a situation or bring about a positive outcome.

[0281] This invention is a system for converting text-based communication on social media platforms into kinder, more constructive ones. The system analyzes the sentiment of messages posted by users and, if necessary, converts offensive or rude expressions into more gentle and kinder ones. It also incorporates an emotion engine that recognizes users' sentiment in real time.

[0282] System configuration

[0283] User Device

[0284] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0285] server

[0286] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0287] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0288] 2. Sentiment analysis: Generative AI models are used to analyze the context and wording of incoming messages and classify them as positive, negative, or neutral.

[0289] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[0290] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[0291] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0292] What the program does

[0293] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0294] 2. Message transmission: The user terminal transmits the input message, the user's identification information, and the emotion data acquired in real time to the server.

[0295] 3. Sentiment analysis: The server runs the received message through a generative AI model, analyzes the message's context and wording, and classifies the sentiment as positive, negative, or neutral. In this case, a message like "You're completely wrong. You should reconsider" would be classified as negative.

[0296] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[0297] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. For example, transforming "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[0298] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[0299] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. For example, instead of "Your opinion is completely wrong. You should reconsider," it will display "I understand your point of view, but there may be other ways of thinking."

[0300] Specific examples

[0301] Example 1

[0302] User input message: I'm not interested in this project at all.

[0303] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0304] Example 2

[0305] User input: I think your idea is stupid.

[0306] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0307] Examples of prompt statements

[0308] Below are some example prompts to input to a generative AI model:

[0309] Prompt statement (input 1)

[0310] Offensive message: "Your opinion is completely wrong. You should reconsider."

[0311] User Emotion: Anger

[0312] Convert it into a kind and constructive message.

[0313] Prompt statement (input 2)

[0314] Offensive message: "I think your idea is stupid."

[0315] User Emotion: Discomfort

[0316] Convert it into a kind and constructive message.

[0317] By implementing this invention, it is expected that conflicts and misunderstandings on SNS will be reduced and communication between users will become smoother.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1:

[0320] A user accesses a social networking platform using a device and types a message into the message input field, such as "Your opinion is completely wrong. You should reconsider."

[0321] Input: Offensive message typed by the user

[0322] Output: The entered message displayed on the terminal

[0323] Step 2:

[0324] When the user clicks the "Send" button, the device sends the entered message, the user's identification information, and the emotion data acquired in real time to the server, typically using HTTP or HTTPS.

[0325] Input: Typed message, user identification information, real-time emotion data

[0326] Output: Messages and data sent to the server

[0327] Step 3:

[0328] The server receives the received message and the user's emotional data. It uses a generative AI model to analyze the message's context and wording and classify the emotion as positive, negative, or neutral. For example, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0329] Input: Received message, user emotion data

[0330] Output: Message sentiment classification result

[0331] Step 4:

[0332] The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives that data. For example, it may determine that the user is feeling angry or annoyed.

[0333] Input: Real-time user emotion data

[0334] Output: Parsed user's emotional state

[0335] Step 5:

[0336] The server starts the process of converting the message based on the results of the emotion analysis and the user's emotion recognition. It sends the prompt sentence to the generative AI model and obtains a new message. For example, it converts "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[0337] Input: Sentiment analysis results and user emotion recognition results

[0338] Output: The converted kind and constructive message

[0339] Step 6:

[0340] The server receives the converted message from the generating AI and prepares it to be sent back to the user's device. It creates a response containing the converted message and user identification information. The sending protocol is HTTP or HTTPS.

[0341] Input: Translated message, user identification information

[0342] Output: The transformed message that is sent back as a response

[0343] Step 7:

[0344] The device analyzes the response received from the server and displays a converted message to the user, for example, "I understand your point of view, but there may be other ways of thinking."

[0345] Input: The converted message returned by the server

[0346] Output: A friendly, constructive message that is displayed on the user's terminal.

[0347] (Application example 2)

[0348] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0349] Conventional social media platforms and advertising systems lack the means to recognize users' emotions in real time and promote kind and constructive communication accordingly. This has led to the problem of offensive or rude language being conveyed as is, reducing the quality of communication. Furthermore, advertising messages are not displayed in a way that reflects users' emotions, reducing the effectiveness of advertising.

[0350] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0351] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for recognizing the users' real-time emotions, means for optimizing the messages based on the users' real-time emotional data, and means for converting the messages based on prompt sentences using a generative AI model. This facilitates communication between users, reduces conflicts and misunderstandings on SNS, and enables the display of effective advertising messages tailored to the users' emotions.

[0352] "User-entered messages" refers to information entered as text by users of social media platforms or advertising systems.

[0353] The "means for receiving" is a mechanism for obtaining a message input by a user and processing it within the system.

[0354] "Means for analyzing emotions" refers to algorithms or technologies that analyze the text data of received messages and classify the emotions as positive, negative, or neutral.

[0355] "Transformation tools" are techniques that implement the process of transforming offensive or disrespectful speech into constructive and kind speech.

[0356] A "means for sending back" is a mechanism for sending the converted message back to the user's terminal for display.

[0357] "Means for recognizing a user's real-time emotions" refers to technology that detects the emotions of a user when they enter a message in real time and acquires them as data.

[0358] The "means for optimizing a message based on real-time emotional data" is a process for appropriately and effectively adjusting the expression of a message based on emotional data obtained in real time.

[0359] A "generative AI model" is an artificial intelligence system that uses deep learning and natural language processing techniques to understand input text data and generate appropriate output.

[0360] "Means for transforming messages based on prompt sentences" refers to a technology in which a generative AI model executes a process to transform messages based on specific input conditions or settings.

[0361] This invention is a system for making text-based communication on social networking platforms and advertising systems friendly and constructive. The main configuration and operation of the system will be specifically described below.

[0362] System configuration

[0363] This system mainly consists of the following components:

[0364] 1. User Device

[0365] A user terminal is a device used to access a social networking platform or advertising system. It provides the means for users to type and post messages, and also provides the interface for users to view the converted messages. Typically, this is a smartphone, tablet, or PC.

[0366] 2. Server

[0367] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The specific processing is as follows:

[0368] 3. Emotion Engine

[0369] The emotion engine is a software module that recognizes the emotions of users when they input messages in real time and acquires that data. This emotional data is an important element in message conversion.

[0370] 4. Generative AI Models

[0371] A generative AI model is an artificial intelligence system that performs message transformations based on received message and sentiment data. This model uses deep learning and natural language processing techniques.

[0372] What the program does

[0373] The server first receives the message entered by the user. This is text data sent from the user's device and is based on a specific communication protocol. The server then passes the received message to the emotion engine for emotion analysis. This analysis determines whether the message is positive, negative, or neutral.

[0374] In addition, to recognize users' real-time emotions, the emotion engine acquires user emotion data, which is also sent to the server and used as a reference for message conversion.

[0375] Next, a generative AI model is activated and converts offensive or rude expressions into constructive and kind expressions based on the received emotion data and message content. Examples of prompts used in this process include "User emotion data: Negative," "Original advertising message: Why not try this product? It's 30% off now!", and "Generate the converted advertising message."

[0376] The message converted by the generative AI model is finally sent back from the server to the user's device and displayed to the user. This series of processes enables appropriate message conversion based on the user's emotional data, making communication in social media and advertising systems more constructive.

[0377] Adding specific examples

[0378] As a concrete example, consider the case where a user types, "I've been feeling very tired lately." The emotion engine judges this as negative, and the generative AI model generates a converted message: "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!" This message is sent back to the user's device and displayed. This enables kind and constructive communication that is in line with the user's emotions.

[0379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0380] Step 1:

[0381] The user enters a message.

[0382] A user enters a text message into an input field on a social media platform or advertising system and presses the "send" button. The input message is, "I've been feeling very tired lately."

[0383] Step 2:

[0384] The user terminal transmits the input message to the server.

[0385] The input message data and the user's identification information are sent to the server, along with the user's real-time emotion data.

[0386] Step 3:

[0387] The server receives the message and performs sentiment analysis.

[0388] The server passes the received message data to the emotion engine, which performs emotion analysis on the text data. The emotion engine classifies the message as positive, negative, or neutral. For example, "I've been very tired lately" is judged to be negative.

[0389] Step 4:

[0390] The server obtains the user's real-time emotions using an emotion engine.

[0391] To obtain real-time emotion data, the server uses an emotion engine, for example, to determine the emotion a user is feeling when inputting, such as "fatigue" or "stress."

[0392] Step 5:

[0393] The server uses a generative AI model to transform the message based on the analysis and emotional data.

[0394] The server launches the generative AI model and sends a prompt message. Based on the following: "User emotion data: Negative" "Original advertising message: Why not try this product? It's 30% off now!" "Generate the converted advertising message.", the generative AI model generates an appropriate converted message. In this case, it generates "If you're feeling tired, why not try it? It's your chance to relax with 30% off now!"

[0395] Step 6:

[0396] The server returns the converted message to the user terminal.

[0397] The generated message data is sent back to the user's device, and the server makes the converted message available to the user via a social networking platform or advertising system.

[0398] Step 7:

[0399] The user terminal displays the translated message.

[0400] The user terminal receives the returned converted message and displays it. The message displayed to the user is, "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!"

[0401] Through the above steps, appropriate message conversion based on the user's emotional data is realized.

[0402] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0403] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0404] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0405] [Second embodiment]

[0406] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0407] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

[0409] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0410] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

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

[0412] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0413] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0416] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0417] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0418] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert aggressive or rude expressions into more gentle and kind expressions.

[0419] System configuration and operation

[0420] The system consists of the following main components:

[0421] User Device

[0422] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0423] server

[0424] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0425] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0426] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0427] 3. Message transformation: Transforming messages judged to be negative or offensive into kinder, more constructive language without losing their original meaning.

[0428] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0429] What the program does

[0430] The program of this system operates as follows.

[0431] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0432] 2. Sending the message: The user terminal sends the entered message to the server, along with the user's identification information.

[0433] 3. Sentiment analysis: The server analyzes the received message. The generating AI analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0434] 4. Message Regeneration: Based on the results of sentiment analysis, messages that are judged to be negative are transformed into more helpful ones. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0435] 5. Returning the converted message: The server returns the converted message to the user's device and displays it to the user. Instead of seeing "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0436] Specific examples

[0437] The following is an example of this system:

[0438] 1. Example 1

[0439] User input message: I'm not interested in this project at all.

[0440] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0441] 2. Example 2

[0442] User input: I think your idea is stupid.

[0443] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0444] These specific examples have the effect of facilitating communication between users and reducing conflicts and misunderstandings on social media.

[0445] The processing flow will be explained below.

[0446] Step 1:

[0447] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[0448] Step 2:

[0449] The terminal receives the message entered by the user and sends it to the server, along with the user's identification information.

[0450] Step 3:

[0451] The server receives the message and the user's identification information from the terminal and prepares it for emotion analysis.

[0452] Step 4:

[0453] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[0454] Step 5:

[0455] Based on the analysis, the generative AI begins the process of transforming the message. If it's classified as negative, it preserves the meaning of the message while transforming it into a kinder, more constructive expression. In this case, "You're completely wrong. You should reconsider" becomes "I understand your point of view, but there may be other ways of thinking about it."

[0456] Step 6:

[0457] The server receives the converted message from the generating AI and prepares it for sending back to the user terminal.

[0458] Step 7:

[0459] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[0460] Step 8:

[0461] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0462] Specific examples

[0463] Example 1

[0464] Step 1: User types "I'm not interested in this project at all."

[0465] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0466] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0467] Steps 6-8: The server sends the converted message "I'm not really interested in this project, but I'd be happy to discuss other ideas" to the user's device, which displays it.

[0468] Example 2

[0469] Step 1: User types, "I think your idea is stupid."

[0470] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0471] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0472] Steps 6-8: The server sends the converted message "I feel a little uncomfortable with your idea, but I'd like to consider it from another perspective" to the user's terminal, which displays it.

[0473] This is expected to facilitate smoother communication between users and reduce conflicts and misunderstandings on social media.

[0474] Example 1

[0475] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0476] Traditional social media platforms have a problem in that communication between users can sometimes lead to conflicts and misunderstandings due to aggressive or rude language. This problem is particularly pronounced in online environments where emotions tend to run high, causing a decline in the quality of the user experience. Furthermore, aggressive messages can undermine the health of the entire platform. To solve these issues, a system is needed that can analyze the emotions in messages posted by users and, if necessary, convert those expressions into kinder and more constructive ones.

[0477] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0478] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for presenting the converted messages on the user terminals, and means for performing emotion analysis and message conversion using a generative AI model, thereby facilitating communication between users and reducing conflicts and misunderstandings on SNS.

[0479] "User" refers to an individual user who accesses the SNS Platform and posts messages.

[0480] "Terminal" refers to the device (e.g., smartphone, tablet, PC, etc.) used by a User to access the SNS Platform.

[0481] "Server" refers to a central computer system that receives messages sent by users and performs analysis and transformation processing on them.

[0482] "Message" refers to any opinion or comment in text form that a User expresses on a social media platform.

[0483] "Means for receiving" refers to the technical mechanism by which the server receives the message entered by the user.

[0484] "Sentiment analysis means" refers to the technical mechanisms used to evaluate the content of a message and classify its sentiment as positive, negative, or neutral.

[0485] "Transformation means" refers to a technical mechanism that, based on the analysis results, transforms offensive or disrespectful language into kind and constructive language.

[0486] "Means for returning" refers to the technical mechanism for returning the converted message back to the user terminal.

[0487] "Presenting means" refers to the technical mechanism for displaying the returned converted message on the user terminal.

[0488] A "generative AI model" refers to an artificial intelligence algorithm that automatically analyzes context and emotions and converts them into appropriate expressions.

[0489] The present invention is a system for making text-based communication on social networking platforms kind and constructive. The system analyzes the sentiment of messages posted by users and provides a means to convert aggressive or rude expressions into gentle and kind expressions as needed.

[0490] System configuration and operation

[0491] User terminal

[0492] The user terminal is a device for accessing the SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0493] server

[0494] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following main functions:

[0495] How to receive messages

[0496] A means of analyzing emotions

[0497] A means of transforming messages

[0498] A means of returning a converted message

[0499] Means for presenting the converted message on a user terminal

[0500] Hardware and software used

[0501] Hardware:

[0502] User device: A device such as a smartphone, tablet, or PC.

[0503] Server: A data center server with a powerful processor.

[0504] software:

[0505] SNS platform: A representative social media application.

[0506] Generative AI model: An artificial intelligence algorithm for performing contextual analysis and representation transformation (e.g., OpenAI's GPT-4).

[0507] Sentiment analysis software: Natural Language Processing (NLP) tools.

[0508] Specific examples

[0509] As a concrete example of this system, the following message transformations are performed:

[0510] Example 1:

[0511] User input message: "I'm not interested in this project at all."

[0512] Resulting message: "I'm not particularly interested in this project, but I'd be happy to discuss other ideas."

[0513] Example 2:

[0514] User input message: "I think your idea is stupid."

[0515] Transformed message: "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[0516] Prompt Sentence Examples

[0517] Here are some example prompts for a generative AI model:

[0518] Sentiment analysis prompt: "Analyze the context of the user-entered message "[input message]" and determine whether the sentiment is classified as positive, negative, or neutral."

[0519] Message Regeneration Prompt: "The message you entered, "[Input Message]", has been identified as negative or offensive. Please rewrite this message to something more kind and constructive."

[0520] This system will prevent users from unintentionally sending offensive messages and make communication on social media more kind and constructive.

[0521] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0522] Step 1:

[0523] A user uses a device to access a social networking platform and type a message, for example, "Your opinion is completely wrong. You should reconsider." The input at this point is the text message typed by the user. The output is the message that is displayed on the device.

[0524] Step 2:

[0525] The terminal sends the input message to the server. During this process, a request is generated that includes the message content and the user's identification information (such as the user ID). The input is data that includes the input message and the user ID, and the output is that data being sent to the server.

[0526] Step 3:

[0527] The server receives messages from the terminal. During this receiving process, the message content and user ID are stored in the server. The input is the sent message data, and the output is the received message data being saved in the server.

[0528] Step 4:

[0529] The server uses a generative AI model to analyze the sentiment of the received message. During this stage, the context and wording of the message are evaluated and its sentiment is classified as positive, negative, or neutral. For example, a message that says, "You're completely wrong. You should reconsider," would be classified as negative. The input is the received text message, and the output is the result of the sentiment analysis (positive, negative, or neutral).

[0530] Step 5:

[0531] The server uses a generative AI model to transform messages that are deemed negative or offensive based on the results of sentiment analysis into more kind and constructive expressions. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking." The input is a message that is deemed negative, and the output is a kinder message.

[0532] Step 6:

[0533] The server returns the converted message to the user terminal. The terminal presents the received converted message to the user for confirmation. The input is the converted message, and the output is that the message is displayed on the user terminal.

[0534] Step 7:

[0535] The user checks the proposed converted message and, if there is no problem with the content, finally posts it to the SNS platform. The input is the proposed converted message, and the output is that the message is posted on the SNS platform. This shows other users a friendly message saying, "I understand your point of view, but there may be other ways of thinking."

[0536] (Application example 1)

[0537] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0538] On modern social media platforms, offensive or rude messages are frequently posted, often sparking conflicts between users. Such comments undermine the health of online communities and degrade the user experience. Furthermore, methods for preventing and addressing these issues are immature, and there is a need for effective methods, particularly those that can respond automatically and in real time. The present invention aims to address this issue.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0540] In this invention, the server includes means for receiving messages entered by users, means for analyzing the sentiment of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for comparing the pre-conversion message with the converted message using a generative AI model to improve the quality of the reply while preserving the meaning of the original message, and means for linking the converted message with other applications as a prompt sentence. This makes it possible to facilitate smoother communication between users on SNS platforms and maintain the health of online communities.

[0541] "Means for receiving messages entered by users" refers to devices or software that have the function of receiving messages entered by users on the SNS platform.

[0542] "Means for analyzing the sentiment of received messages" refers to algorithms or tools that analyze received messages and classify their sentiment as positive, negative, or neutral.

[0543] A "means for transforming offensive or rude language into constructive and kind language" is an algorithm or method for changing negative messages into more gentle and kind language while preserving the original meaning.

[0544] "Means for returning the converted message to the user" refers to a device or software that has the function of sending the converted message to the user terminal and displaying it to the user.

[0545] A "generative AI model" is an artificial intelligence model used to generate or convert text, primarily for message conversion.

[0546] A "prompt sentence" is an initial input sentence given to a generative AI model to instruct it to perform a specific task.

[0547] The present invention is a system for converting offensive or rude expressions into constructive and kind expressions when users communicate on social media platforms. The system mainly consists of the following components: a user device, a server, a generative AI model, and a sentiment analysis algorithm.

[0548] System configuration and operation

[0549] User Device

[0550] A user terminal is a device used to access a social networking platform, providing a means for users to type and post messages, and an interface for users to view the converted messages. A smartphone is typically used as a user terminal.

[0551] server

[0552] The server is the main component that processes messages received from user devices, performs sentiment analysis, and converts messages. Specifically, it has the following functions:

[0553] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0554] 2. Sentiment Analysis: Received messages are classified as positive, negative, or neutral using a sentiment analysis algorithm such as TextBlob.

[0555] 3. Message transformation: Messages judged as negative or offensive are transformed into friendly representations using the GPT-2 model from the transformers library.

[0556] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0557] Example

[0558] A specific example of the system is shown below:

[0559] User input message

[0560] The user uses the device to access a social media platform and type something like, "Your opinion is completely wrong. You should reconsider."

[0561] Message sentiment analysis

[0562] The server receives this message and performs sentiment analysis using TextBlob, in this case "You're completely wrong. You should reconsider" is determined to be negative.

[0563] Message Transformation

[0564] Use the GPT-2 model from the transformers library to transform "You're completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking about it."

[0565] Returning a conversion message

[0566] The converted message is then sent back to the user's device, where the user is told, "We understand your point of view, but there may be other ways of thinking."

[0567] Prompt Sentence Examples

[0568] An example of an input prompt for a generative AI model is, "Please translate the following sentence into a more helpful expression: Your idea is completely useless."

[0569] This system will facilitate smoother communication between users on social media platforms and help maintain the health of online communities.

[0570] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0571] Step 1:

[0572] The user uses the device to log into the social media platform and type a message, such as "You're completely wrong. You should reconsider."

[0573] Step 2:

[0574] The terminal transmits the input message to the server, and the transmitted data includes the user's identification information and the message.

[0575] Step 3:

[0576] The server processes the received messages and performs sentiment analysis. Using the TextBlob library, it parses the messages and classifies the sentiment as positive, negative or neutral. In this case, the message "You're completely wrong. You should reconsider" is considered negative.

[0577] Step 4:

[0578] The server transforms messages that are deemed negative or offensive based on the results of sentiment analysis, using a GPT-2 model from the transformers library to convert "You're completely wrong. You should reconsider" to "I understand your point of view, but there may be other ways of thinking."

[0579] Step 5:

[0580] The server compares the original and converted messages using a generative AI model and checks to improve the quality of the reply while preserving the meaning of the original message. This process verifies that the meaning of the original message is preserved.

[0581] Step 6:

[0582] The converted message is prepared as a prompt sentence and prepared for integration with other applications. Specifically, the converted message is included in the prompt sentence to convert it into a format that can be transferred to other generative AI models and related software.

[0583] Step 7:

[0584] Finally, the server sends the converted message back to the user's device and displays it to the user, who now sees a message that reads "I understand your point of view, but there may be other ways of thinking about it," instead of the original message, "Your opinion is completely wrong. You should reconsider."

[0585] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0586] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert offensive or rude expressions into more gentle and kind expressions, while combining an emotion engine that recognizes users' emotions in real time.

[0587] System configuration and operation

[0588] The system consists of the following main components:

[0589] User Device

[0590] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0591] server

[0592] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0593] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0594] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0595] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[0596] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[0597] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0598] What the program does

[0599] The program of this system operates as follows.

[0600] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0601] 2. Message transmission: The user device sends the input message to the server, along with the user's identification information and real-time emotional data.

[0602] 3. Sentiment analysis: The server analyzes the received message. The generator analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0603] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[0604] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. If the message is classified as negative and the user is angry, the message is transformed into a more kind and constructive expression while retaining its meaning. In this case, "You're completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0605] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[0606] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. Instead of "Your opinion is completely wrong. You should reconsider," the user will see "I understand your point of view, but there may be other ways of thinking."

[0607] Specific examples

[0608] The following is an example of this system:

[0609] 1. Example 1

[0610] User input message: I'm not interested in this project at all.

[0611] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0612] 2. Example 2

[0613] User input: I think your idea is stupid.

[0614] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0615] These specific examples will help facilitate communication between users and reduce conflicts and misunderstandings on social media. In addition, by combining this with real-time emotion recognition of users, more appropriate and effective message translation will be realized.

[0616] The processing flow will be explained below.

[0617] Step 1:

[0618] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[0619] Step 2:

[0620] The terminal receives the message entered by the user and transmits it to the server, along with the user's identification information and emotion data acquired in real time.

[0621] Step 3:

[0622] The server receives the message, identification information, and emotion data received from the terminal and prepares it for emotion analysis.

[0623] Step 4:

[0624] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[0625] Step 5:

[0626] The server uses an emotion engine to recognize the user's current emotion. The server processes the received emotion data to determine whether the user is feeling angry or annoyed, for example.

[0627] Step 6:

[0628] The server converts messages based on the analysis results of the generation AI and the recognition results of the emotion engine. If a message is judged to be negative, it is converted into a kinder, more constructive expression while retaining its original meaning, especially if the user is feeling angry. In this case, "Your opinion is completely wrong. You should reconsider" is converted to "I understand your point of view, but there may be other ways of thinking."

[0629] Step 7:

[0630] The server receives the converted message from the generation AI and prepares it for sending back to the user terminal.

[0631] Step 8:

[0632] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[0633] Step 9:

[0634] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0635] Specific examples

[0636] Example 1

[0637] Step 1: User types "I'm not interested in this project at all."

[0638] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[0639] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as anger.

[0640] Step 6: The server translates the message to "I'm not really interested in this project, but I'd be happy to discuss other ideas."

[0641] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[0642] Example 2

[0643] Step 1: User types, "I think your idea is stupid."

[0644] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[0645] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as irritation.

[0646] Step 6: The server translates the message into "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[0647] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[0648] This is expected to facilitate smooth communication between users and reduce conflicts and misunderstandings on social media. Furthermore, by combining this with real-time emotion recognition of users, more appropriate and effective message conversion can be achieved.

[0649] Example 2

[0650] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0651] The increase in offensive or rude messages on social media platforms is leading to poor communication between users and resulting in trouble and conflict. This problem is particularly pronounced in online environments where users' feelings and intentions are easily misunderstood. Therefore, a method is needed to transform the messages users send into more kind and constructive ones.

[0652] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a message entered by a user, a means for analyzing the emotion of the received message, a means for recognizing the analysis result and the user's real-time emotion, and converting aggressive or rude expressions into constructive and kind expressions, and a means for returning the converted message to the user. This facilitates communication on the SNS platform and makes it possible to prevent trouble and conflicts between users.

[0653] "Receiving" refers to the server taking in message data entered by the user.

[0654] "Sentiment analysis" is the process of analyzing the context and wording of a received message and classifying its emotional nature.

[0655] "Real-time recognition" refers to analyzing and understanding the user's emotions at the moment they type a message.

[0656] "Transformation" is the process of rewriting a received message into a different representation without moving it, while preserving its original meaning.

[0657] "Return" refers to sending the converted message back to the user's terminal.

[0658] "Aggression" refers to expressions that show hostility or negative feelings toward others.

[0659] "Rudeness" is an expression that lacks courtesy and language that shows a lack of respect for others.

[0660] "Kindness" refers to showing friendliness and consideration towards others.

[0661] "Constructive" is an expression that aims to improve a situation or bring about a positive outcome.

[0662] This invention is a system for converting text-based communication on social media platforms into kinder, more constructive ones. The system analyzes the sentiment of messages posted by users and, if necessary, converts offensive or rude expressions into more gentle and kinder ones. It also incorporates an emotion engine that recognizes users' sentiment in real time.

[0663] System configuration

[0664] User Device

[0665] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0666] server

[0667] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0668] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0669] 2. Sentiment analysis: Generative AI models are used to analyze the context and wording of incoming messages and classify them as positive, negative, or neutral.

[0670] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[0671] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[0672] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0673] What the program does

[0674] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0675] 2. Message transmission: The user terminal transmits the input message, the user's identification information, and the emotion data acquired in real time to the server.

[0676] 3. Sentiment analysis: The server runs the received message through a generative AI model, analyzes the message's context and wording, and classifies the sentiment as positive, negative, or neutral. In this case, a message like "You're completely wrong. You should reconsider" would be classified as negative.

[0677] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[0678] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. For example, transforming "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[0679] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[0680] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. For example, instead of "Your opinion is completely wrong. You should reconsider," it will display "I understand your point of view, but there may be other ways of thinking."

[0681] Specific examples

[0682] Example 1

[0683] User input message: I'm not interested in this project at all.

[0684] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0685] Example 2

[0686] User input: I think your idea is stupid.

[0687] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0688] Examples of prompt statements

[0689] Below are some example prompts to input to a generative AI model:

[0690] Prompt statement (input 1)

[0691] Offensive message: "Your opinion is completely wrong. You should reconsider."

[0692] User Emotion: Anger

[0693] Convert it into a kind and constructive message.

[0694] Prompt statement (input 2)

[0695] Offensive message: "I think your idea is stupid."

[0696] User Emotion: Discomfort

[0697] Convert it into a kind and constructive message.

[0698] By implementing this invention, it is expected that conflicts and misunderstandings on SNS will be reduced and communication between users will become smoother.

[0699] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0700] Step 1:

[0701] A user accesses a social networking platform using a device and types a message into the message input field, such as "Your opinion is completely wrong. You should reconsider."

[0702] Input: Offensive message typed by the user

[0703] Output: The entered message displayed on the terminal

[0704] Step 2:

[0705] When the user clicks the "Send" button, the device sends the entered message, the user's identification information, and the emotion data acquired in real time to the server, typically using HTTP or HTTPS.

[0706] Input: Typed message, user identification information, real-time emotion data

[0707] Output: Messages and data sent to the server

[0708] Step 3:

[0709] The server receives the received message and the user's emotional data. It uses a generative AI model to analyze the message's context and wording and classify the emotion as positive, negative, or neutral. For example, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0710] Input: Received message, user emotion data

[0711] Output: Message sentiment classification result

[0712] Step 4:

[0713] The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives that data. For example, it may determine that the user is feeling angry or annoyed.

[0714] Input: Real-time user emotion data

[0715] Output: Parsed user's emotional state

[0716] Step 5:

[0717] The server starts the process of converting the message based on the results of the emotion analysis and the user's emotion recognition. It sends the prompt sentence to the generative AI model and obtains a new message. For example, it converts "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[0718] Input: Sentiment analysis results and user emotion recognition results

[0719] Output: The converted kind and constructive message

[0720] Step 6:

[0721] The server receives the converted message from the generating AI and prepares it to be sent back to the user's device. It creates a response containing the converted message and user identification information. The sending protocol is HTTP or HTTPS.

[0722] Input: Translated message, user identification information

[0723] Output: The transformed message that is sent back as a response

[0724] Step 7:

[0725] The device analyzes the response received from the server and displays a converted message to the user, for example, "I understand your point of view, but there may be other ways of thinking."

[0726] Input: The converted message returned by the server

[0727] Output: A friendly, constructive message that is displayed on the user's terminal.

[0728] (Application example 2)

[0729] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0730] Conventional social media platforms and advertising systems lack the means to recognize users' emotions in real time and promote kind and constructive communication accordingly. This has led to the problem of offensive or rude language being conveyed as is, reducing the quality of communication. Furthermore, advertising messages are not displayed in a way that reflects users' emotions, reducing the effectiveness of advertising.

[0731] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0732] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for recognizing the users' real-time emotions, means for optimizing the messages based on the users' real-time emotional data, and means for converting the messages based on prompt sentences using a generative AI model. This facilitates communication between users, reduces conflicts and misunderstandings on SNS, and enables the display of effective advertising messages tailored to the users' emotions.

[0733] "User-entered messages" refers to information entered as text by users of social media platforms or advertising systems.

[0734] The "means for receiving" is a mechanism for obtaining a message input by a user and processing it within the system.

[0735] "Means for analyzing emotions" refers to algorithms or technologies that analyze the text data of received messages and classify the emotions as positive, negative, or neutral.

[0736] "Transformation tools" are techniques that implement the process of transforming offensive or disrespectful speech into constructive and kind speech.

[0737] A "means for sending back" is a mechanism for sending the converted message back to the user's terminal for display.

[0738] "Means for recognizing a user's real-time emotions" refers to technology that detects the emotions of a user when they enter a message in real time and acquires them as data.

[0739] The "means for optimizing a message based on real-time emotional data" is a process for appropriately and effectively adjusting the expression of a message based on emotional data obtained in real time.

[0740] A "generative AI model" is an artificial intelligence system that uses deep learning and natural language processing techniques to understand input text data and generate appropriate output.

[0741] "Means for transforming messages based on prompt sentences" refers to a technology in which a generative AI model executes a process to transform messages based on specific input conditions or settings.

[0742] This invention is a system for making text-based communication on social networking platforms and advertising systems friendly and constructive. The main configuration and operation of the system will be specifically described below.

[0743] System configuration

[0744] This system mainly consists of the following components:

[0745] 1. User Device

[0746] A user terminal is a device used to access a social networking platform or advertising system. It provides the means for users to type and post messages, and also provides the interface for users to view the converted messages. Typically, this is a smartphone, tablet, or PC.

[0747] 2. Server

[0748] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The specific processing is as follows:

[0749] 3. Emotion Engine

[0750] The emotion engine is a software module that recognizes the emotions of users when they input messages in real time and acquires that data. This emotional data is an important element in message conversion.

[0751] 4. Generative AI Models

[0752] A generative AI model is an artificial intelligence system that performs message transformations based on received message and sentiment data. This model uses deep learning and natural language processing techniques.

[0753] What the program does

[0754] The server first receives the message entered by the user. This is text data sent from the user's device and is based on a specific communication protocol. The server then passes the received message to the emotion engine for emotion analysis. This analysis determines whether the message is positive, negative, or neutral.

[0755] In addition, to recognize users' real-time emotions, the emotion engine acquires user emotion data, which is also sent to the server and used as a reference for message conversion.

[0756] Next, a generative AI model is activated and converts offensive or rude expressions into constructive and kind expressions based on the received emotion data and message content. Examples of prompts used in this process include "User emotion data: Negative," "Original advertising message: Why not try this product? It's 30% off now!", and "Generate the converted advertising message."

[0757] The message converted by the generative AI model is finally sent back from the server to the user's device and displayed to the user. This series of processes enables appropriate message conversion based on the user's emotional data, making communication in social media and advertising systems more constructive.

[0758] Adding specific examples

[0759] As a concrete example, consider the case where a user types, "I've been feeling very tired lately." The emotion engine judges this as negative, and the generative AI model generates a converted message: "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!" This message is sent back to the user's device and displayed. This enables kind and constructive communication that is in line with the user's emotions.

[0760] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0761] Step 1:

[0762] The user enters a message.

[0763] A user enters a text message into an input field on a social media platform or advertising system and presses the "send" button. The input message is, "I've been feeling very tired lately."

[0764] Step 2:

[0765] The user terminal transmits the input message to the server.

[0766] The input message data and the user's identification information are sent to the server, along with the user's real-time emotion data.

[0767] Step 3:

[0768] The server receives the message and performs sentiment analysis.

[0769] The server passes the received message data to the emotion engine, which performs emotion analysis on the text data. The emotion engine classifies the message as positive, negative, or neutral. For example, "I've been very tired lately" is judged to be negative.

[0770] Step 4:

[0771] The server obtains the user's real-time emotions using an emotion engine.

[0772] To obtain real-time emotion data, the server uses an emotion engine, for example, to determine the emotion a user is feeling when inputting, such as "fatigue" or "stress."

[0773] Step 5:

[0774] The server uses a generative AI model to transform the message based on the analysis and emotional data.

[0775] The server launches the generative AI model and sends a prompt message. Based on the following: "User emotion data: Negative" "Original advertising message: Why not try this product? It's 30% off now!" "Generate the converted advertising message.", the generative AI model generates an appropriate converted message. In this case, it generates "If you're feeling tired, why not try it? It's your chance to relax with 30% off now!"

[0776] Step 6:

[0777] The server returns the converted message to the user terminal.

[0778] The generated message data is sent back to the user's device, and the server makes the converted message available to the user via a social networking platform or advertising system.

[0779] Step 7:

[0780] The user terminal displays the translated message.

[0781] The user terminal receives the returned converted message and displays it. The message displayed to the user is, "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!"

[0782] Through the above steps, appropriate message conversion based on the user's emotional data is realized.

[0783] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0784] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0785] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0786] [Third embodiment]

[0787] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0788] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0789] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0790] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0791] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0792] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0793] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0794] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0795] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0796] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0797] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0798] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0799] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert aggressive or rude expressions into more gentle and kind expressions.

[0800] System configuration and operation

[0801] The system consists of the following main components:

[0802] User Device

[0803] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0804] server

[0805] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0806] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0807] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0808] 3. Message transformation: Transforming messages judged to be negative or offensive into kinder, more constructive language without losing their original meaning.

[0809] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0810] What the program does

[0811] The program of this system operates as follows.

[0812] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0813] 2. Sending the message: The user terminal sends the entered message to the server, along with the user's identification information.

[0814] 3. Sentiment analysis: The server analyzes the received message. The generating AI analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0815] 4. Message Regeneration: Based on the results of sentiment analysis, messages that are judged to be negative are transformed into more helpful ones. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0816] 5. Returning the converted message: The server returns the converted message to the user's device and displays it to the user. Instead of seeing "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0817] Specific examples

[0818] The following is an example of this system:

[0819] 1. Example 1

[0820] User input message: I'm not interested in this project at all.

[0821] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0822] 2. Example 2

[0823] User input: I think your idea is stupid.

[0824] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0825] These specific examples have the effect of facilitating communication between users and reducing conflicts and misunderstandings on social media.

[0826] The processing flow will be explained below.

[0827] Step 1:

[0828] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[0829] Step 2:

[0830] The terminal receives the message entered by the user and sends it to the server, along with the user's identification information.

[0831] Step 3:

[0832] The server receives the message and the user's identification information from the terminal and prepares it for emotion analysis.

[0833] Step 4:

[0834] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[0835] Step 5:

[0836] Based on the analysis, the generative AI begins the process of transforming the message. If it's classified as negative, it preserves the meaning of the message while transforming it into a kinder, more constructive expression. In this case, "You're completely wrong. You should reconsider" becomes "I understand your point of view, but there may be other ways of thinking about it."

[0837] Step 6:

[0838] The server receives the converted message from the generating AI and prepares it for sending back to the user terminal.

[0839] Step 7:

[0840] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[0841] Step 8:

[0842] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[0843] Specific examples

[0844] Example 1

[0845] Step 1: User types "I'm not interested in this project at all."

[0846] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0847] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0848] Steps 6-8: The server sends the converted message "I'm not really interested in this project, but I'd be happy to discuss other ideas" to the user's device, which displays it.

[0849] Example 2

[0850] Step 1: User types, "I think your idea is stupid."

[0851] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[0852] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[0853] Steps 6-8: The server sends the converted message "I feel a little uncomfortable with your idea, but I'd like to consider it from another perspective" to the user's terminal, which displays it.

[0854] This is expected to facilitate smoother communication between users and reduce conflicts and misunderstandings on social media.

[0855] Example 1

[0856] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0857] Traditional social media platforms have a problem in that communication between users can sometimes lead to conflicts and misunderstandings due to aggressive or rude language. This problem is particularly pronounced in online environments where emotions tend to run high, causing a decline in the quality of the user experience. Furthermore, aggressive messages can undermine the health of the entire platform. To solve these issues, a system is needed that can analyze the emotions in messages posted by users and, if necessary, convert those expressions into kinder and more constructive ones.

[0858] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0859] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for presenting the converted messages on the user terminals, and means for performing emotion analysis and message conversion using a generative AI model, thereby facilitating communication between users and reducing conflicts and misunderstandings on SNS.

[0860] "User" refers to an individual user who accesses the SNS Platform and posts messages.

[0861] "Terminal" refers to the device (e.g., smartphone, tablet, PC, etc.) used by a User to access the SNS Platform.

[0862] "Server" refers to a central computer system that receives messages sent by users and performs analysis and transformation processing on them.

[0863] "Message" refers to any opinion or comment in text form that a User expresses on a social media platform.

[0864] "Means for receiving" refers to the technical mechanism by which the server receives the message entered by the user.

[0865] "Sentiment analysis means" refers to the technical mechanisms used to evaluate the content of a message and classify its sentiment as positive, negative, or neutral.

[0866] "Transformation means" refers to a technical mechanism that, based on the analysis results, transforms offensive or disrespectful language into kind and constructive language.

[0867] "Means for returning" refers to the technical mechanism for returning the converted message back to the user terminal.

[0868] "Presenting means" refers to the technical mechanism for displaying the returned converted message on the user terminal.

[0869] A "generative AI model" refers to an artificial intelligence algorithm that automatically analyzes context and emotions and converts them into appropriate expressions.

[0870] The present invention is a system for making text-based communication on social networking platforms kind and constructive. The system analyzes the sentiment of messages posted by users and provides a means to convert aggressive or rude expressions into gentle and kind expressions as needed.

[0871] System configuration and operation

[0872] User terminal

[0873] The user terminal is a device for accessing the SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0874] server

[0875] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following main functions:

[0876] How to receive messages

[0877] A means of analyzing emotions

[0878] A means of transforming messages

[0879] A means of returning a converted message

[0880] Means for presenting the converted message on a user terminal

[0881] Hardware and software used

[0882] Hardware:

[0883] User device: A device such as a smartphone, tablet, or PC.

[0884] Server: A data center server with a powerful processor.

[0885] software:

[0886] SNS platform: A representative social media application.

[0887] Generative AI model: An artificial intelligence algorithm for performing contextual analysis and representation transformation (e.g., OpenAI's GPT-4).

[0888] Sentiment analysis software: Natural Language Processing (NLP) tools.

[0889] Specific examples

[0890] As a concrete example of this system, the following message transformations are performed:

[0891] Example 1:

[0892] User input message: "I'm not interested in this project at all."

[0893] Resulting message: "I'm not particularly interested in this project, but I'd be happy to discuss other ideas."

[0894] Example 2:

[0895] User input message: "I think your idea is stupid."

[0896] Transformed message: "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[0897] Prompt Sentence Examples

[0898] Here are some example prompts for a generative AI model:

[0899] Sentiment analysis prompt: "Analyze the context of the user-entered message "[input message]" and determine whether the sentiment is classified as positive, negative, or neutral."

[0900] Message Regeneration Prompt: "The message you entered, "[Input Message]", has been identified as negative or offensive. Please rewrite this message to something more kind and constructive."

[0901] This system will prevent users from unintentionally sending offensive messages and make communication on social media more kind and constructive.

[0902] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0903] Step 1:

[0904] A user uses a device to access a social networking platform and type a message, for example, "Your opinion is completely wrong. You should reconsider." The input at this point is the text message typed by the user. The output is the message that is displayed on the device.

[0905] Step 2:

[0906] The terminal sends the input message to the server. During this process, a request is generated that includes the message content and the user's identification information (such as the user ID). The input is data that includes the input message and the user ID, and the output is that data being sent to the server.

[0907] Step 3:

[0908] The server receives messages from the terminal. During this receiving process, the message content and user ID are stored in the server. The input is the sent message data, and the output is the received message data being saved in the server.

[0909] Step 4:

[0910] The server uses a generative AI model to analyze the sentiment of the received message. During this stage, the context and wording of the message are evaluated and its sentiment is classified as positive, negative, or neutral. For example, a message that says, "You're completely wrong. You should reconsider," would be classified as negative. The input is the received text message, and the output is the result of the sentiment analysis (positive, negative, or neutral).

[0911] Step 5:

[0912] The server uses a generative AI model to transform messages that are deemed negative or offensive based on the results of sentiment analysis into more kind and constructive expressions. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking." The input is a message that is deemed negative, and the output is a kinder message.

[0913] Step 6:

[0914] The server returns the converted message to the user terminal. The terminal presents the received converted message to the user for confirmation. The input is the converted message, and the output is that the message is displayed on the user terminal.

[0915] Step 7:

[0916] The user checks the proposed converted message and, if there is no problem with the content, finally posts it to the SNS platform. The input is the proposed converted message, and the output is that the message is posted on the SNS platform. This shows other users a friendly message saying, "I understand your point of view, but there may be other ways of thinking."

[0917] (Application example 1)

[0918] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] On modern social media platforms, offensive or rude messages are frequently posted, often sparking conflicts between users. Such comments undermine the health of online communities and degrade the user experience. Furthermore, methods for preventing and addressing these issues are immature, and there is a need for effective methods, particularly those that can respond automatically and in real time. The present invention aims to address this issue.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0921] In this invention, the server includes means for receiving messages entered by users, means for analyzing the sentiment of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for comparing the pre-conversion message with the converted message using a generative AI model to improve the quality of the reply while preserving the meaning of the original message, and means for linking the converted message with other applications as a prompt sentence. This makes it possible to facilitate smoother communication between users on SNS platforms and maintain the health of online communities.

[0922] "Means for receiving messages entered by users" refers to devices or software that have the function of receiving messages entered by users on the SNS platform.

[0923] "Means for analyzing the sentiment of received messages" refers to algorithms or tools that analyze received messages and classify their sentiment as positive, negative, or neutral.

[0924] A "means for transforming offensive or rude language into constructive and kind language" is an algorithm or method for changing negative messages into more gentle and kind language while preserving the original meaning.

[0925] "Means for returning the converted message to the user" refers to a device or software that has the function of sending the converted message to the user terminal and displaying it to the user.

[0926] A "generative AI model" is an artificial intelligence model used to generate or convert text, primarily for message conversion.

[0927] A "prompt sentence" is an initial input sentence given to a generative AI model to instruct it to perform a specific task.

[0928] The present invention is a system for converting offensive or rude expressions into constructive and kind expressions when users communicate on social media platforms. The system mainly consists of the following components: a user device, a server, a generative AI model, and a sentiment analysis algorithm.

[0929] System configuration and operation

[0930] User Device

[0931] A user terminal is a device used to access a social networking platform, providing a means for users to type and post messages, and an interface for users to view the converted messages. A smartphone is typically used as a user terminal.

[0932] server

[0933] The server is the main component that processes messages received from user devices, performs sentiment analysis, and converts messages. Specifically, it has the following functions:

[0934] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0935] 2. Sentiment Analysis: Received messages are classified as positive, negative, or neutral using a sentiment analysis algorithm such as TextBlob.

[0936] 3. Message transformation: Messages judged as negative or offensive are transformed into friendly representations using the GPT-2 model from the transformers library.

[0937] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0938] Example

[0939] A specific example of the system is shown below:

[0940] User input message

[0941] The user uses the device to access a social media platform and type something like, "Your opinion is completely wrong. You should reconsider."

[0942] Message sentiment analysis

[0943] The server receives this message and performs sentiment analysis using TextBlob, in this case "You're completely wrong. You should reconsider" is determined to be negative.

[0944] Message Transformation

[0945] Use the GPT-2 model from the transformers library to transform "You're completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking about it."

[0946] Returning a conversion message

[0947] The converted message is then sent back to the user's device, where the user is told, "We understand your point of view, but there may be other ways of thinking."

[0948] Prompt Sentence Examples

[0949] An example of an input prompt for a generative AI model is, "Please translate the following sentence into a more helpful expression: Your idea is completely useless."

[0950] This system will facilitate smoother communication between users on social media platforms and help maintain the health of online communities.

[0951] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0952] Step 1:

[0953] The user uses the device to log into the social media platform and type a message, such as "You're completely wrong. You should reconsider."

[0954] Step 2:

[0955] The terminal transmits the input message to the server, and the transmitted data includes the user's identification information and the message.

[0956] Step 3:

[0957] The server processes the received messages and performs sentiment analysis. Using the TextBlob library, it parses the messages and classifies the sentiment as positive, negative or neutral. In this case, the message "You're completely wrong. You should reconsider" is considered negative.

[0958] Step 4:

[0959] The server transforms messages that are deemed negative or offensive based on the results of sentiment analysis, using a GPT-2 model from the transformers library to convert "You're completely wrong. You should reconsider" to "I understand your point of view, but there may be other ways of thinking."

[0960] Step 5:

[0961] The server compares the original and converted messages using a generative AI model and checks to improve the quality of the reply while preserving the meaning of the original message. This process verifies that the meaning of the original message is preserved.

[0962] Step 6:

[0963] The converted message is prepared as a prompt sentence and prepared for integration with other applications. Specifically, the converted message is included in the prompt sentence to convert it into a format that can be transferred to other generative AI models and related software.

[0964] Step 7:

[0965] Finally, the server sends the converted message back to the user's device and displays it to the user, who now sees a message that reads "I understand your point of view, but there may be other ways of thinking about it," instead of the original message, "Your opinion is completely wrong. You should reconsider."

[0966] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0967] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert offensive or rude expressions into more gentle and kind expressions, while combining an emotion engine that recognizes users' emotions in real time.

[0968] System configuration and operation

[0969] The system consists of the following main components:

[0970] User Device

[0971] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[0972] server

[0973] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[0974] 1. Message reception: Receives a message from the user terminal and processes its contents.

[0975] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[0976] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[0977] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[0978] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[0979] What the program does

[0980] The program of this system operates as follows.

[0981] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[0982] 2. Message transmission: The user device sends the input message to the server, along with the user's identification information and real-time emotional data.

[0983] 3. Sentiment analysis: The server analyzes the received message. The generator analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[0984] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[0985] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. If the message is classified as negative and the user is angry, the message is transformed into a more kind and constructive expression while retaining its meaning. In this case, "You're completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[0986] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[0987] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. Instead of "Your opinion is completely wrong. You should reconsider," the user will see "I understand your point of view, but there may be other ways of thinking."

[0988] Specific examples

[0989] The following is an example of this system:

[0990] 1. Example 1

[0991] User input message: I'm not interested in this project at all.

[0992] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[0993] 2. Example 2

[0994] User input: I think your idea is stupid.

[0995] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[0996] These specific examples will help facilitate communication between users and reduce conflicts and misunderstandings on social media. In addition, by combining this with real-time emotion recognition of users, more appropriate and effective message translation will be realized.

[0997] The processing flow will be explained below.

[0998] Step 1:

[0999] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[1000] Step 2:

[1001] The terminal receives the message entered by the user and transmits it to the server, along with the user's identification information and emotion data acquired in real time.

[1002] Step 3:

[1003] The server receives the message, identification information, and emotion data received from the terminal and prepares it for emotion analysis.

[1004] Step 4:

[1005] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[1006] Step 5:

[1007] The server uses an emotion engine to recognize the user's current emotion. The server processes the received emotion data to determine whether the user is feeling angry or annoyed, for example.

[1008] Step 6:

[1009] The server converts messages based on the analysis results of the generation AI and the recognition results of the emotion engine. If a message is judged to be negative, it is converted into a kinder, more constructive expression while retaining its original meaning, especially if the user is feeling angry. In this case, "Your opinion is completely wrong. You should reconsider" is converted to "I understand your point of view, but there may be other ways of thinking."

[1010] Step 7:

[1011] The server receives the converted message from the generation AI and prepares it for sending back to the user terminal.

[1012] Step 8:

[1013] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[1014] Step 9:

[1015] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[1016] Specific examples

[1017] Example 1

[1018] Step 1: User types "I'm not interested in this project at all."

[1019] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[1020] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as anger.

[1021] Step 6: The server translates the message to "I'm not really interested in this project, but I'd be happy to discuss other ideas."

[1022] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[1023] Example 2

[1024] Step 1: User types, "I think your idea is stupid."

[1025] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[1026] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as irritation.

[1027] Step 6: The server translates the message into "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[1028] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[1029] This is expected to facilitate smooth communication between users and reduce conflicts and misunderstandings on social media. Furthermore, by combining this with real-time emotion recognition of users, more appropriate and effective message conversion can be achieved.

[1030] Example 2

[1031] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1032] The increase in offensive or rude messages on social media platforms is leading to poor communication between users and resulting in trouble and conflict. This problem is particularly pronounced in online environments where users' feelings and intentions are easily misunderstood. Therefore, a method is needed to transform the messages users send into more kind and constructive ones.

[1033] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a message entered by a user, a means for analyzing the emotion of the received message, a means for recognizing the analysis result and the user's real-time emotion, and converting aggressive or rude expressions into constructive and kind expressions, and a means for returning the converted message to the user. This facilitates communication on the SNS platform and makes it possible to prevent trouble and conflicts between users.

[1034] "Receiving" refers to the server taking in message data entered by the user.

[1035] "Sentiment analysis" is the process of analyzing the context and wording of a received message and classifying its emotional nature.

[1036] "Real-time recognition" refers to analyzing and understanding the user's emotions at the moment they type a message.

[1037] "Transformation" is the process of rewriting a received message into a different representation without moving it, while preserving its original meaning.

[1038] "Return" refers to sending the converted message back to the user's terminal.

[1039] "Aggression" refers to expressions that show hostility or negative feelings toward others.

[1040] "Rudeness" is an expression that lacks courtesy and language that shows a lack of respect for others.

[1041] "Kindness" refers to showing friendliness and consideration towards others.

[1042] "Constructive" is an expression that aims to improve a situation or bring about a positive outcome.

[1043] This invention is a system for converting text-based communication on social media platforms into kinder, more constructive ones. The system analyzes the sentiment of messages posted by users and, if necessary, converts offensive or rude expressions into more gentle and kinder ones. It also incorporates an emotion engine that recognizes users' sentiment in real time.

[1044] System configuration

[1045] User Device

[1046] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[1047] server

[1048] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[1049] 1. Message reception: Receives a message from the user terminal and processes its contents.

[1050] 2. Sentiment analysis: Generative AI models are used to analyze the context and wording of incoming messages and classify them as positive, negative, or neutral.

[1051] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[1052] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[1053] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[1054] What the program does

[1055] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[1056] 2. Message transmission: The user terminal transmits the input message, the user's identification information, and the emotion data acquired in real time to the server.

[1057] 3. Sentiment analysis: The server runs the received message through a generative AI model, analyzes the message's context and wording, and classifies the sentiment as positive, negative, or neutral. In this case, a message like "You're completely wrong. You should reconsider" would be classified as negative.

[1058] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[1059] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. For example, transforming "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[1060] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[1061] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. For example, instead of "Your opinion is completely wrong. You should reconsider," it will display "I understand your point of view, but there may be other ways of thinking."

[1062] Specific examples

[1063] Example 1

[1064] User input message: I'm not interested in this project at all.

[1065] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[1066] Example 2

[1067] User input: I think your idea is stupid.

[1068] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[1069] Examples of prompt statements

[1070] Below are some example prompts to input to a generative AI model:

[1071] Prompt statement (input 1)

[1072] Offensive message: "Your opinion is completely wrong. You should reconsider."

[1073] User Emotion: Anger

[1074] Convert it into a kind and constructive message.

[1075] Prompt statement (input 2)

[1076] Offensive message: "I think your idea is stupid."

[1077] User Emotion: Discomfort

[1078] Convert it into a kind and constructive message.

[1079] By implementing this invention, it is expected that conflicts and misunderstandings on SNS will be reduced and communication between users will become smoother.

[1080] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1081] Step 1:

[1082] A user accesses a social networking platform using a device and types a message into the message input field, such as "Your opinion is completely wrong. You should reconsider."

[1083] Input: Offensive message typed by the user

[1084] Output: The entered message displayed on the terminal

[1085] Step 2:

[1086] When the user clicks the "Send" button, the device sends the entered message, the user's identification information, and the emotion data acquired in real time to the server, typically using HTTP or HTTPS.

[1087] Input: Typed message, user identification information, real-time emotion data

[1088] Output: Messages and data sent to the server

[1089] Step 3:

[1090] The server receives the received message and the user's emotional data. It uses a generative AI model to analyze the message's context and wording and classify the emotion as positive, negative, or neutral. For example, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[1091] Input: Received message, user emotion data

[1092] Output: Message sentiment classification result

[1093] Step 4:

[1094] The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives that data. For example, it may determine that the user is feeling angry or annoyed.

[1095] Input: Real-time user emotion data

[1096] Output: Parsed user's emotional state

[1097] Step 5:

[1098] The server starts the process of converting the message based on the results of the emotion analysis and the user's emotion recognition. It sends the prompt sentence to the generative AI model and obtains a new message. For example, it converts "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[1099] Input: Sentiment analysis results and user emotion recognition results

[1100] Output: The converted kind and constructive message

[1101] Step 6:

[1102] The server receives the converted message from the generating AI and prepares it to be sent back to the user's device. It creates a response containing the converted message and user identification information. The sending protocol is HTTP or HTTPS.

[1103] Input: Translated message, user identification information

[1104] Output: The transformed message that is sent back as a response

[1105] Step 7:

[1106] The device analyzes the response received from the server and displays a converted message to the user, for example, "I understand your point of view, but there may be other ways of thinking."

[1107] Input: The converted message returned by the server

[1108] Output: A friendly, constructive message that is displayed on the user's terminal.

[1109] (Application example 2)

[1110] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1111] Conventional social media platforms and advertising systems lack the means to recognize users' emotions in real time and promote kind and constructive communication accordingly. This has led to the problem of offensive or rude language being conveyed as is, reducing the quality of communication. Furthermore, advertising messages are not displayed in a way that reflects users' emotions, reducing the effectiveness of advertising.

[1112] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1113] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for recognizing the users' real-time emotions, means for optimizing the messages based on the users' real-time emotional data, and means for converting the messages based on prompt sentences using a generative AI model. This facilitates communication between users, reduces conflicts and misunderstandings on SNS, and enables the display of effective advertising messages tailored to the users' emotions.

[1114] "User-entered messages" refers to information entered as text by users of social media platforms or advertising systems.

[1115] The "means for receiving" is a mechanism for obtaining a message input by a user and processing it within the system.

[1116] "Means for analyzing emotions" refers to algorithms or technologies that analyze the text data of received messages and classify the emotions as positive, negative, or neutral.

[1117] "Transformation tools" are techniques that implement the process of transforming offensive or disrespectful speech into constructive and kind speech.

[1118] A "means for sending back" is a mechanism for sending the converted message back to the user's terminal for display.

[1119] "Means for recognizing a user's real-time emotions" refers to technology that detects the emotions of a user when they enter a message in real time and acquires them as data.

[1120] The "means for optimizing a message based on real-time emotional data" is a process for appropriately and effectively adjusting the expression of a message based on emotional data obtained in real time.

[1121] A "generative AI model" is an artificial intelligence system that uses deep learning and natural language processing techniques to understand input text data and generate appropriate output.

[1122] "Means for transforming messages based on prompt sentences" refers to a technology in which a generative AI model executes a process to transform messages based on specific input conditions or settings.

[1123] This invention is a system for making text-based communication on social networking platforms and advertising systems friendly and constructive. The main configuration and operation of the system will be specifically described below.

[1124] System configuration

[1125] This system mainly consists of the following components:

[1126] 1. User Device

[1127] A user terminal is a device used to access a social networking platform or advertising system. It provides the means for users to type and post messages, and also provides the interface for users to view the converted messages. Typically, this is a smartphone, tablet, or PC.

[1128] 2. Server

[1129] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The specific processing is as follows:

[1130] 3. Emotion Engine

[1131] The emotion engine is a software module that recognizes the emotions of users when they input messages in real time and acquires that data. This emotional data is an important element in message conversion.

[1132] 4. Generative AI Models

[1133] A generative AI model is an artificial intelligence system that performs message transformations based on received message and sentiment data. This model uses deep learning and natural language processing techniques.

[1134] What the program does

[1135] The server first receives the message entered by the user. This is text data sent from the user's device and is based on a specific communication protocol. The server then passes the received message to the emotion engine for emotion analysis. This analysis determines whether the message is positive, negative, or neutral.

[1136] In addition, to recognize users' real-time emotions, the emotion engine acquires user emotion data, which is also sent to the server and used as a reference for message conversion.

[1137] Next, a generative AI model is activated and converts offensive or rude expressions into constructive and kind expressions based on the received emotion data and message content. Examples of prompts used in this process include "User emotion data: Negative," "Original advertising message: Why not try this product? It's 30% off now!", and "Generate the converted advertising message."

[1138] The message converted by the generative AI model is finally sent back from the server to the user's device and displayed to the user. This series of processes enables appropriate message conversion based on the user's emotional data, making communication in social media and advertising systems more constructive.

[1139] Adding specific examples

[1140] As a concrete example, consider the case where a user types, "I've been feeling very tired lately." The emotion engine judges this as negative, and the generative AI model generates a converted message: "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!" This message is sent back to the user's device and displayed. This enables kind and constructive communication that is in line with the user's emotions.

[1141] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1142] Step 1:

[1143] The user enters a message.

[1144] A user enters a text message into an input field on a social media platform or advertising system and presses the "send" button. The input message is, "I've been feeling very tired lately."

[1145] Step 2:

[1146] The user terminal transmits the input message to the server.

[1147] The input message data and the user's identification information are sent to the server, along with the user's real-time emotion data.

[1148] Step 3:

[1149] The server receives the message and performs sentiment analysis.

[1150] The server passes the received message data to the emotion engine, which performs emotion analysis on the text data. The emotion engine classifies the message as positive, negative, or neutral. For example, "I've been very tired lately" is judged to be negative.

[1151] Step 4:

[1152] The server obtains the user's real-time emotions using an emotion engine.

[1153] To obtain real-time emotion data, the server uses an emotion engine, for example, to determine the emotion a user is feeling when inputting, such as "fatigue" or "stress."

[1154] Step 5:

[1155] The server uses a generative AI model to transform the message based on the analysis and emotional data.

[1156] The server launches the generative AI model and sends a prompt message. Based on the following: "User emotion data: Negative" "Original advertising message: Why not try this product? It's 30% off now!" "Generate the converted advertising message.", the generative AI model generates an appropriate converted message. In this case, it generates "If you're feeling tired, why not try it? It's your chance to relax with 30% off now!"

[1157] Step 6:

[1158] The server returns the converted message to the user terminal.

[1159] The generated message data is sent back to the user's device, and the server makes the converted message available to the user via a social networking platform or advertising system.

[1160] Step 7:

[1161] The user terminal displays the translated message.

[1162] The user terminal receives the returned converted message and displays it. The message displayed to the user is, "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!"

[1163] Through the above steps, appropriate message conversion based on the user's emotional data is realized.

[1164] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1165] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1166] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1167] [Fourth embodiment]

[1168] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1169] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1170] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1172] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1174] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1175] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1176] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1177] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1178] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1179] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1180] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1181] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert aggressive or rude expressions into more gentle and kind expressions.

[1182] System configuration and operation

[1183] The system consists of the following main components:

[1184] User Device

[1185] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[1186] server

[1187] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[1188] 1. Message reception: Receives a message from the user terminal and processes its contents.

[1189] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[1190] 3. Message transformation: Transforming messages judged to be negative or offensive into kinder, more constructive language without losing their original meaning.

[1191] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[1192] What the program does

[1193] The program of this system operates as follows.

[1194] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[1195] 2. Sending the message: The user terminal sends the entered message to the server, along with the user's identification information.

[1196] 3. Sentiment analysis: The server analyzes the received message. The generating AI analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[1197] 4. Message Regeneration: Based on the results of sentiment analysis, messages that are judged to be negative are transformed into more helpful ones. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[1198] 5. Returning the converted message: The server returns the converted message to the user's device and displays it to the user. Instead of seeing "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[1199] Specific examples

[1200] The following is an example of this system:

[1201] 1. Example 1

[1202] User input message: I'm not interested in this project at all.

[1203] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[1204] 2. Example 2

[1205] User input: I think your idea is stupid.

[1206] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[1207] These specific examples have the effect of facilitating communication between users and reducing conflicts and misunderstandings on social media.

[1208] The processing flow will be explained below.

[1209] Step 1:

[1210] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[1211] Step 2:

[1212] The terminal receives the message entered by the user and sends it to the server, along with the user's identification information.

[1213] Step 3:

[1214] The server receives the message and the user's identification information from the terminal and prepares it for emotion analysis.

[1215] Step 4:

[1216] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[1217] Step 5:

[1218] Based on the analysis, the generative AI begins the process of transforming the message. If it's classified as negative, it preserves the meaning of the message while transforming it into a kinder, more constructive expression. In this case, "You're completely wrong. You should reconsider" becomes "I understand your point of view, but there may be other ways of thinking about it."

[1219] Step 6:

[1220] The server receives the converted message from the generating AI and prepares it for sending back to the user terminal.

[1221] Step 7:

[1222] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[1223] Step 8:

[1224] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[1225] Specific examples

[1226] Example 1

[1227] Step 1: User types "I'm not interested in this project at all."

[1228] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[1229] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[1230] Steps 6-8: The server sends the converted message "I'm not really interested in this project, but I'd be happy to discuss other ideas" to the user's device, which displays it.

[1231] Example 2

[1232] Step 1: User types, "I think your idea is stupid."

[1233] Steps 2-3: The terminal sends a message to the server, and the server receives it.

[1234] Steps 4-5: The generation AI determines the message as negative and converts it into a kind expression.

[1235] Steps 6-8: The server sends the converted message "I feel a little uncomfortable with your idea, but I'd like to consider it from another perspective" to the user's terminal, which displays it.

[1236] This is expected to facilitate smoother communication between users and reduce conflicts and misunderstandings on social media.

[1237] Example 1

[1238] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1239] Traditional social media platforms have a problem in that communication between users can sometimes lead to conflicts and misunderstandings due to aggressive or rude language. This problem is particularly pronounced in online environments where emotions tend to run high, causing a decline in the quality of the user experience. Furthermore, aggressive messages can undermine the health of the entire platform. To solve these issues, a system is needed that can analyze the emotions in messages posted by users and, if necessary, convert those expressions into kinder and more constructive ones.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1241] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for presenting the converted messages on the user terminals, and means for performing emotion analysis and message conversion using a generative AI model, thereby facilitating communication between users and reducing conflicts and misunderstandings on SNS.

[1242] "User" refers to an individual user who accesses the SNS Platform and posts messages.

[1243] "Terminal" refers to the device (e.g., smartphone, tablet, PC, etc.) used by a User to access the SNS Platform.

[1244] "Server" refers to a central computer system that receives messages sent by users and performs analysis and transformation processing on them.

[1245] "Message" refers to any opinion or comment in text form that a User expresses on a social media platform.

[1246] "Means for receiving" refers to the technical mechanism by which the server receives the message entered by the user.

[1247] "Sentiment analysis means" refers to the technical mechanisms used to evaluate the content of a message and classify its sentiment as positive, negative, or neutral.

[1248] "Transformation means" refers to a technical mechanism that, based on the analysis results, transforms offensive or disrespectful language into kind and constructive language.

[1249] "Means for returning" refers to the technical mechanism for returning the converted message back to the user terminal.

[1250] "Presenting means" refers to the technical mechanism for displaying the returned converted message on the user terminal.

[1251] A "generative AI model" refers to an artificial intelligence algorithm that automatically analyzes context and emotions and converts them into appropriate expressions.

[1252] The present invention is a system for making text-based communication on social networking platforms kind and constructive. The system analyzes the sentiment of messages posted by users and provides a means to convert aggressive or rude expressions into gentle and kind expressions as needed.

[1253] System configuration and operation

[1254] User terminal

[1255] The user terminal is a device for accessing the SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[1256] server

[1257] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following main functions:

[1258] How to receive messages

[1259] A means of analyzing emotions

[1260] A means of transforming messages

[1261] A means of returning a converted message

[1262] Means for presenting the converted message on a user terminal

[1263] Hardware and software used

[1264] Hardware:

[1265] User device: A device such as a smartphone, tablet, or PC.

[1266] Server: A data center server with a powerful processor.

[1267] software:

[1268] SNS platform: A representative social media application.

[1269] Generative AI model: An artificial intelligence algorithm for performing contextual analysis and representation transformation (e.g., OpenAI's GPT-4).

[1270] Sentiment analysis software: Natural Language Processing (NLP) tools.

[1271] Specific examples

[1272] As a concrete example of this system, the following message transformations are performed:

[1273] Example 1:

[1274] User input message: "I'm not interested in this project at all."

[1275] Resulting message: "I'm not particularly interested in this project, but I'd be happy to discuss other ideas."

[1276] Example 2:

[1277] User input message: "I think your idea is stupid."

[1278] Transformed message: "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[1279] Prompt Sentence Examples

[1280] Here are some example prompts for a generative AI model:

[1281] Sentiment analysis prompt: "Analyze the context of the user-entered message "[input message]" and determine whether the sentiment is classified as positive, negative, or neutral."

[1282] Message Regeneration Prompt: "The message you entered, "[Input Message]", has been identified as negative or offensive. Please rewrite this message to something more kind and constructive."

[1283] This system will prevent users from unintentionally sending offensive messages and make communication on social media more kind and constructive.

[1284] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1285] Step 1:

[1286] A user uses a device to access a social networking platform and type a message, for example, "Your opinion is completely wrong. You should reconsider." The input at this point is the text message typed by the user. The output is the message that is displayed on the device.

[1287] Step 2:

[1288] The terminal sends the input message to the server. During this process, a request is generated that includes the message content and the user's identification information (such as the user ID). The input is data that includes the input message and the user ID, and the output is that data being sent to the server.

[1289] Step 3:

[1290] The server receives messages from the terminal. During this receiving process, the message content and user ID are stored in the server. The input is the sent message data, and the output is the received message data being saved in the server.

[1291] Step 4:

[1292] The server uses a generative AI model to analyze the sentiment of the received message. During this stage, the context and wording of the message are evaluated and its sentiment is classified as positive, negative, or neutral. For example, a message that says, "You're completely wrong. You should reconsider," would be classified as negative. The input is the received text message, and the output is the result of the sentiment analysis (positive, negative, or neutral).

[1293] Step 5:

[1294] The server uses a generative AI model to transform messages that are deemed negative or offensive based on the results of sentiment analysis into more kind and constructive expressions. For example, "Your opinion is completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking." The input is a message that is deemed negative, and the output is a kinder message.

[1295] Step 6:

[1296] The server returns the converted message to the user terminal. The terminal presents the received converted message to the user for confirmation. The input is the converted message, and the output is that the message is displayed on the user terminal.

[1297] Step 7:

[1298] The user checks the proposed converted message and, if there is no problem with the content, finally posts it to the SNS platform. The input is the proposed converted message, and the output is that the message is posted on the SNS platform. This shows other users a friendly message saying, "I understand your point of view, but there may be other ways of thinking."

[1299] (Application example 1)

[1300] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1301] On modern social media platforms, offensive or rude messages are frequently posted, often sparking conflicts between users. Such comments undermine the health of online communities and degrade the user experience. Furthermore, methods for preventing and addressing these issues are immature, and there is a need for effective methods, particularly those that can respond automatically and in real time. The present invention aims to address this issue.

[1302] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1303] In this invention, the server includes means for receiving messages entered by users, means for analyzing the sentiment of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for comparing the pre-conversion message with the converted message using a generative AI model to improve the quality of the reply while preserving the meaning of the original message, and means for linking the converted message with other applications as a prompt sentence. This makes it possible to facilitate smoother communication between users on SNS platforms and maintain the health of online communities.

[1304] "Means for receiving messages entered by users" refers to devices or software that have the function of receiving messages entered by users on the SNS platform.

[1305] "Means for analyzing the sentiment of received messages" refers to algorithms or tools that analyze received messages and classify their sentiment as positive, negative, or neutral.

[1306] A "means for transforming offensive or rude language into constructive and kind language" is an algorithm or method for changing negative messages into more gentle and kind language while preserving the original meaning.

[1307] "Means for returning the converted message to the user" refers to a device or software that has the function of sending the converted message to the user terminal and displaying it to the user.

[1308] A "generative AI model" is an artificial intelligence model used to generate or convert text, primarily for message conversion.

[1309] A "prompt sentence" is an initial input sentence given to a generative AI model to instruct it to perform a specific task.

[1310] The present invention is a system for converting offensive or rude expressions into constructive and kind expressions when users communicate on social media platforms. The system mainly consists of the following components: a user device, a server, a generative AI model, and a sentiment analysis algorithm.

[1311] System configuration and operation

[1312] User Device

[1313] A user terminal is a device used to access a social networking platform, providing a means for users to type and post messages, and an interface for users to view the converted messages. A smartphone is typically used as a user terminal.

[1314] server

[1315] The server is the main component that processes messages received from user devices, performs sentiment analysis, and converts messages. Specifically, it has the following functions:

[1316] 1. Message reception: Receives a message from the user terminal and processes its contents.

[1317] 2. Sentiment Analysis: Received messages are classified as positive, negative, or neutral using a sentiment analysis algorithm such as TextBlob.

[1318] 3. Message transformation: Messages judged as negative or offensive are transformed into friendly representations using the GPT-2 model from the transformers library.

[1319] 4. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[1320] Example

[1321] A specific example of the system is shown below:

[1322] User input message

[1323] The user uses the device to access a social media platform and type something like, "Your opinion is completely wrong. You should reconsider."

[1324] Message sentiment analysis

[1325] The server receives this message and performs sentiment analysis using TextBlob, in this case "You're completely wrong. You should reconsider" is determined to be negative.

[1326] Message Transformation

[1327] Use the GPT-2 model from the transformers library to transform "You're completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking about it."

[1328] Returning a conversion message

[1329] The converted message is then sent back to the user's device, where the user is told, "We understand your point of view, but there may be other ways of thinking."

[1330] Prompt Sentence Examples

[1331] An example of an input prompt for a generative AI model is, "Please translate the following sentence into a more helpful expression: Your idea is completely useless."

[1332] This system will facilitate smoother communication between users on social media platforms and help maintain the health of online communities.

[1333] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1334] Step 1:

[1335] The user uses the device to log into the social media platform and type a message, such as "You're completely wrong. You should reconsider."

[1336] Step 2:

[1337] The terminal transmits the input message to the server, and the transmitted data includes the user's identification information and the message.

[1338] Step 3:

[1339] The server processes the received messages and performs sentiment analysis. Using the TextBlob library, it parses the messages and classifies the sentiment as positive, negative or neutral. In this case, the message "You're completely wrong. You should reconsider" is considered negative.

[1340] Step 4:

[1341] The server transforms messages that are deemed negative or offensive based on the results of sentiment analysis, using a GPT-2 model from the transformers library to convert "You're completely wrong. You should reconsider" to "I understand your point of view, but there may be other ways of thinking."

[1342] Step 5:

[1343] The server compares the original and converted messages using a generative AI model and checks to improve the quality of the reply while preserving the meaning of the original message. This process verifies that the meaning of the original message is preserved.

[1344] Step 6:

[1345] The converted message is prepared as a prompt sentence and prepared for integration with other applications. Specifically, the converted message is included in the prompt sentence to convert it into a format that can be transferred to other generative AI models and related software.

[1346] Step 7:

[1347] Finally, the server sends the converted message back to the user's device and displays it to the user, who now sees a message that reads "I understand your point of view, but there may be other ways of thinking about it," instead of the original message, "Your opinion is completely wrong. You should reconsider."

[1348] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1349] The present invention is a system for making text-based communication on social media platforms kinder and more constructive. The system analyzes the sentiment of messages posted by users and, if necessary, provides a means to convert offensive or rude expressions into more gentle and kind expressions, while combining an emotion engine that recognizes users' emotions in real time.

[1350] System configuration and operation

[1351] The system consists of the following main components:

[1352] User Device

[1353] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[1354] server

[1355] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[1356] 1. Message reception: Receives a message from the user terminal and processes its contents.

[1357] 2. Sentiment analysis: Analyzes the context and wording of received messages and classifies them as positive, negative, or neutral.

[1358] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[1359] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[1360] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[1361] What the program does

[1362] The program of this system operates as follows.

[1363] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[1364] 2. Message transmission: The user device sends the input message to the server, along with the user's identification information and real-time emotional data.

[1365] 3. Sentiment analysis: The server analyzes the received message. The generator analyzes the message's context and wording and classifies the sentiment as positive, negative, or neutral. In this case, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[1366] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[1367] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. If the message is classified as negative and the user is angry, the message is transformed into a more kind and constructive expression while retaining its meaning. In this case, "You're completely wrong. You should reconsider" is transformed into "I understand your point of view, but there may be other ways of thinking."

[1368] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[1369] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. Instead of "Your opinion is completely wrong. You should reconsider," the user will see "I understand your point of view, but there may be other ways of thinking."

[1370] Specific examples

[1371] The following is an example of this system:

[1372] 1. Example 1

[1373] User input message: I'm not interested in this project at all.

[1374] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[1375] 2. Example 2

[1376] User input: I think your idea is stupid.

[1377] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[1378] These specific examples will help facilitate communication between users and reduce conflicts and misunderstandings on social media. In addition, by combining this with real-time emotion recognition of users, more appropriate and effective message translation will be realized.

[1379] The processing flow will be explained below.

[1380] Step 1:

[1381] The user uses the device to access a social media platform and type a message to post, such as "Your opinion is completely wrong. You should reconsider."

[1382] Step 2:

[1383] The terminal receives the message entered by the user and transmits it to the server, along with the user's identification information and emotion data acquired in real time.

[1384] Step 3:

[1385] The server receives the message, identification information, and emotion data received from the terminal and prepares it for emotion analysis.

[1386] Step 4:

[1387] The server calls the generation AI and sends the received message to the generation AI. The generation AI analyzes the context and wording of the message and classifies the sentiment as positive, negative, or neutral. In this example, the message "Your opinion is completely wrong. You should reconsider" is judged to be negative.

[1388] Step 5:

[1389] The server uses an emotion engine to recognize the user's current emotion. The server processes the received emotion data to determine whether the user is feeling angry or annoyed, for example.

[1390] Step 6:

[1391] The server converts messages based on the analysis results of the generation AI and the recognition results of the emotion engine. If a message is judged to be negative, it is converted into a kinder, more constructive expression while retaining its original meaning, especially if the user is feeling angry. In this case, "Your opinion is completely wrong. You should reconsider" is converted to "I understand your point of view, but there may be other ways of thinking."

[1392] Step 7:

[1393] The server receives the converted message from the generation AI and prepares it for sending back to the user terminal.

[1394] Step 8:

[1395] The server then sends the converted message along with the user's identification information to the user's device, allowing the user to see how their post has been converted.

[1396] Step 9:

[1397] The user device displays the converted message received from the server and notifies the user. Instead of "Your opinion is completely wrong. You should reconsider," the user sees "I understand your point of view, but there may be other ways of thinking."

[1398] Specific examples

[1399] Example 1

[1400] Step 1: User types "I'm not interested in this project at all."

[1401] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[1402] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as anger.

[1403] Step 6: The server translates the message to "I'm not really interested in this project, but I'd be happy to discuss other ideas."

[1404] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[1405] Example 2

[1406] Step 1: User types, "I think your idea is stupid."

[1407] Steps 2-3: The device sends a message and emotion data to the server, which receives it.

[1408] Steps 4-5: The generation AI determines the message to be negative, and the emotion engine recognizes the user's emotion as irritation.

[1409] Step 6: The server translates the message into "I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective."

[1410] Steps 7-9: The server sends the converted message to the user terminal, which displays it.

[1411] This is expected to facilitate smooth communication between users and reduce conflicts and misunderstandings on social media. Furthermore, by combining this with real-time emotion recognition of users, more appropriate and effective message conversion can be achieved.

[1412] Example 2

[1413] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1414] The increase in offensive or rude messages on social media platforms is leading to poor communication between users and resulting in trouble and conflict. This problem is particularly pronounced in online environments where users' feelings and intentions are easily misunderstood. Therefore, a method is needed to transform the messages users send into more kind and constructive ones.

[1415] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for receiving a message entered by a user, a means for analyzing the emotion of the received message, a means for recognizing the analysis result and the user's real-time emotion, and converting aggressive or rude expressions into constructive and kind expressions, and a means for returning the converted message to the user. This facilitates communication on the SNS platform and makes it possible to prevent trouble and conflicts between users.

[1416] "Receiving" refers to the server taking in message data entered by the user.

[1417] "Sentiment analysis" is the process of analyzing the context and wording of a received message and classifying its emotional nature.

[1418] "Real-time recognition" refers to analyzing and understanding the user's emotions at the moment they type a message.

[1419] "Transformation" is the process of rewriting a received message into a different representation without moving it, while preserving its original meaning.

[1420] "Return" refers to sending the converted message back to the user's terminal.

[1421] "Aggression" refers to expressions that show hostility or negative feelings toward others.

[1422] "Rudeness" is an expression that lacks courtesy and language that shows a lack of respect for others.

[1423] "Kindness" refers to showing friendliness and consideration towards others.

[1424] "Constructive" is an expression that aims to improve a situation or bring about a positive outcome.

[1425] This invention is a system for converting text-based communication on social media platforms into kinder, more constructive ones. The system analyzes the sentiment of messages posted by users and, if necessary, converts offensive or rude expressions into more gentle and kinder ones. It also incorporates an emotion engine that recognizes users' sentiment in real time.

[1426] System configuration

[1427] User Device

[1428] A user terminal is a device for accessing an SNS platform, providing a means for users to input and post messages, and an interface for users to view converted messages.

[1429] server

[1430] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The server has the following functions:

[1431] 1. Message reception: Receives a message from the user terminal and processes its contents.

[1432] 2. Sentiment analysis: Generative AI models are used to analyze the context and wording of incoming messages and classify them as positive, negative, or neutral.

[1433] 3. User Emotion Recognition: Uses an emotion engine to recognize the emotions of users as they type their messages in real time.

[1434] 4. Message Transformation: Messages judged to be negative or offensive are transformed into kinder and more constructive expressions without losing their original meaning. This process also takes into account the results of user emotion recognition.

[1435] 5. Returning the converted message: The converted message is returned to the user's terminal and displayed to the user.

[1436] What the program does

[1437] 1. Posting a message: A user uses a device to access a social media platform and type a message, for example, "Your opinion is completely wrong. You should reconsider."

[1438] 2. Message transmission: The user terminal transmits the input message, the user's identification information, and the emotion data acquired in real time to the server.

[1439] 3. Sentiment analysis: The server runs the received message through a generative AI model, analyzes the message's context and wording, and classifies the sentiment as positive, negative, or neutral. In this case, a message like "You're completely wrong. You should reconsider" would be classified as negative.

[1440] 4. User Emotion Recognition: The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives the data. For example, it determines that the user is feeling angry or annoyed.

[1441] 5. Message Regeneration: Based on the results of sentiment analysis and user emotion recognition, the process of transforming the message begins. For example, transforming "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[1442] 6. Returning the converted message: The server receives the converted message from the generating AI and prepares it to be sent back to the user terminal.

[1443] 7. Displaying the conversion message: The user device will display the conversion message received from the server and notify the user. For example, instead of "Your opinion is completely wrong. You should reconsider," it will display "I understand your point of view, but there may be other ways of thinking."

[1444] Specific examples

[1445] Example 1

[1446] User input message: I'm not interested in this project at all.

[1447] Resulting message: I'm not particularly interested in this project, but I'd be happy to discuss other ideas.

[1448] Example 2

[1449] User input: I think your idea is stupid.

[1450] Translated message: I'm a little uncomfortable with your idea, but I'd like to consider it from another perspective.

[1451] Examples of prompt statements

[1452] Below are some example prompts to input to a generative AI model:

[1453] Prompt statement (input 1)

[1454] Offensive message: "Your opinion is completely wrong. You should reconsider."

[1455] User Emotion: Anger

[1456] Convert it into a kind and constructive message.

[1457] Prompt statement (input 2)

[1458] Offensive message: "I think your idea is stupid."

[1459] User Emotion: Discomfort

[1460] Convert it into a kind and constructive message.

[1461] By implementing this invention, it is expected that conflicts and misunderstandings on SNS will be reduced and communication between users will become smoother.

[1462] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1463] Step 1:

[1464] A user accesses a social networking platform using a device and types a message into the message input field, such as "Your opinion is completely wrong. You should reconsider."

[1465] Input: Offensive message typed by the user

[1466] Output: The entered message displayed on the terminal

[1467] Step 2:

[1468] When the user clicks the "Send" button, the device sends the entered message, the user's identification information, and the emotion data acquired in real time to the server, typically using HTTP or HTTPS.

[1469] Input: Typed message, user identification information, real-time emotion data

[1470] Output: Messages and data sent to the server

[1471] Step 3:

[1472] The server receives the received message and the user's emotional data. It uses a generative AI model to analyze the message's context and wording and classify the emotion as positive, negative, or neutral. For example, a message like "Your opinion is completely wrong. You should reconsider" would be classified as negative.

[1473] Input: Received message, user emotion data

[1474] Output: Message sentiment classification result

[1475] Step 4:

[1476] The server uses an emotion engine to recognize the emotion of the user when they type a message in real time and receives that data. For example, it may determine that the user is feeling angry or annoyed.

[1477] Input: Real-time user emotion data

[1478] Output: Parsed user's emotional state

[1479] Step 5:

[1480] The server starts the process of converting the message based on the results of the emotion analysis and the user's emotion recognition. It sends the prompt sentence to the generative AI model and obtains a new message. For example, it converts "Your opinion is completely wrong. You should reconsider" into "I understand your point of view, but there may be other ways of thinking."

[1481] Input: Sentiment analysis results and user emotion recognition results

[1482] Output: The converted kind and constructive message

[1483] Step 6:

[1484] The server receives the converted message from the generating AI and prepares it to be sent back to the user's device. It creates a response containing the converted message and user identification information. The sending protocol is HTTP or HTTPS.

[1485] Input: Translated message, user identification information

[1486] Output: The transformed message that is sent back as a response

[1487] Step 7:

[1488] The device analyzes the response received from the server and displays a converted message to the user, for example, "I understand your point of view, but there may be other ways of thinking."

[1489] Input: The converted message returned by the server

[1490] Output: A friendly, constructive message that is displayed on the user's terminal.

[1491] (Application example 2)

[1492] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1493] Conventional social media platforms and advertising systems lack the means to recognize users' emotions in real time and promote kind and constructive communication accordingly. This has led to the problem of offensive or rude language being conveyed as is, reducing the quality of communication. Furthermore, advertising messages are not displayed in a way that reflects users' emotions, reducing the effectiveness of advertising.

[1494] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1495] In this invention, the server includes means for receiving messages entered by users, means for analyzing the emotions of the received messages, means for converting offensive or rude expressions into constructive and kind expressions according to the analysis results, means for returning the converted messages to the users, means for recognizing the users' real-time emotions, means for optimizing the messages based on the users' real-time emotional data, and means for converting the messages based on prompt sentences using a generative AI model. This facilitates communication between users, reduces conflicts and misunderstandings on SNS, and enables the display of effective advertising messages tailored to the users' emotions.

[1496] "User-entered messages" refers to information entered as text by users of social media platforms or advertising systems.

[1497] The "means for receiving" is a mechanism for obtaining a message input by a user and processing it within the system.

[1498] "Means for analyzing emotions" refers to algorithms or technologies that analyze the text data of received messages and classify the emotions as positive, negative, or neutral.

[1499] "Transformation tools" are techniques that implement the process of transforming offensive or disrespectful speech into constructive and kind speech.

[1500] A "means for sending back" is a mechanism for sending the converted message back to the user's terminal for display.

[1501] "Means for recognizing a user's real-time emotions" refers to technology that detects the emotions of a user when they enter a message in real time and acquires them as data.

[1502] The "means for optimizing a message based on real-time emotional data" is a process for appropriately and effectively adjusting the expression of a message based on emotional data obtained in real time.

[1503] A "generative AI model" is an artificial intelligence system that uses deep learning and natural language processing techniques to understand input text data and generate appropriate output.

[1504] "Means for transforming messages based on prompt sentences" refers to a technology in which a generative AI model executes a process to transform messages based on specific input conditions or settings.

[1505] This invention is a system for making text-based communication on social networking platforms and advertising systems friendly and constructive. The main configuration and operation of the system will be specifically described below.

[1506] System configuration

[1507] This system mainly consists of the following components:

[1508] 1. User Device

[1509] A user terminal is a device used to access a social networking platform or advertising system. It provides the means for users to type and post messages, and also provides the interface for users to view the converted messages. Typically, this is a smartphone, tablet, or PC.

[1510] 2. Server

[1511] The server is the central component that processes messages received from user devices, performs sentiment analysis, and converts messages. The specific processing is as follows:

[1512] 3. Emotion Engine

[1513] The emotion engine is a software module that recognizes the emotions of users when they input messages in real time and acquires that data. This emotional data is an important element in message conversion.

[1514] 4. Generative AI Models

[1515] A generative AI model is an artificial intelligence system that performs message transformations based on received message and sentiment data. This model uses deep learning and natural language processing techniques.

[1516] What the program does

[1517] The server first receives the message entered by the user. This is text data sent from the user's device and is based on a specific communication protocol. The server then passes the received message to the emotion engine for emotion analysis. This analysis determines whether the message is positive, negative, or neutral.

[1518] In addition, to recognize users' real-time emotions, the emotion engine acquires user emotion data, which is also sent to the server and used as a reference for message conversion.

[1519] Next, a generative AI model is activated and converts offensive or rude expressions into constructive and kind expressions based on the received emotion data and message content. Examples of prompts used in this process include "User emotion data: Negative," "Original advertising message: Why not try this product? It's 30% off now!", and "Generate the converted advertising message."

[1520] The message converted by the generative AI model is finally sent back from the server to the user's device and displayed to the user. This series of processes enables appropriate message conversion based on the user's emotional data, making communication in social media and advertising systems more constructive.

[1521] Adding specific examples

[1522] As a concrete example, consider the case where a user types, "I've been feeling very tired lately." The emotion engine judges this as negative, and the generative AI model generates a converted message: "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!" This message is sent back to the user's device and displayed. This enables kind and constructive communication that is in line with the user's emotions.

[1523] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1524] Step 1:

[1525] The user enters a message.

[1526] A user enters a text message into an input field on a social media platform or advertising system and presses the "send" button. The input message is, "I've been feeling very tired lately."

[1527] Step 2:

[1528] The user terminal transmits the input message to the server.

[1529] The input message data and the user's identification information are sent to the server, along with the user's real-time emotion data.

[1530] Step 3:

[1531] The server receives the message and performs sentiment analysis.

[1532] The server passes the received message data to the emotion engine, which performs emotion analysis on the text data. The emotion engine classifies the message as positive, negative, or neutral. For example, "I've been very tired lately" is judged to be negative.

[1533] Step 4:

[1534] The server obtains the user's real-time emotions using an emotion engine.

[1535] To obtain real-time emotion data, the server uses an emotion engine, for example, to determine the emotion a user is feeling when inputting, such as "fatigue" or "stress."

[1536] Step 5:

[1537] The server uses a generative AI model to transform the message based on the analysis and emotional data.

[1538] The server launches the generative AI model and sends a prompt message. Based on the following: "User emotion data: Negative" "Original advertising message: Why not try this product? It's 30% off now!" "Generate the converted advertising message.", the generative AI model generates an appropriate converted message. In this case, it generates "If you're feeling tired, why not try it? It's your chance to relax with 30% off now!"

[1539] Step 6:

[1540] The server returns the converted message to the user terminal.

[1541] The generated message data is sent back to the user's device, and the server makes the converted message available to the user via a social networking platform or advertising system.

[1542] Step 7:

[1543] The user terminal displays the translated message.

[1544] The user terminal receives the returned converted message and displays it. The message displayed to the user is, "If you're feeling tired, why not give it a try? Now's your chance to relax with 30% off!"

[1545] Through the above steps, appropriate message conversion based on the user's emotional data is realized.

[1546] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1547] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1548] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1549] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1550] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1551] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1552] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1553] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1554] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1555] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1556] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1557] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1558] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1559] 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.

[1560] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1561] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1562] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1563] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1564] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1565] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1566] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1567] The following is further disclosed regarding the above embodiment.

[1568] (Claim 1)

[1569] means for receiving a message input by a user;

[1570] means for analyzing the sentiment of received messages;

[1571] Depending on the analysis results, a means of transforming offensive or rude language into constructive and kind language;

[1572] means for returning the converted message to the user;

[1573] A system including:

[1574] (Claim 2)

[1575] 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

[1576] (Claim 3)

[1577] The system of claim 1 uses an algorithm that converts a message into a more gentle and friendly expression while preserving the meaning of the original message.

[1578] "Example 1"

[1579] (Claim 1)

[1580] means for receiving a message input by a user;

[1581] means for analyzing the sentiment of received messages;

[1582] Depending on the analysis results, a means of transforming offensive or rude language into constructive and kind language;

[1583] means for returning the converted message to the user;

[1584] means for presenting the converted message on a user terminal;

[1585] A means for using generative AI models to perform sentiment analysis and message transformation; and

[1586] A system including:

[1587] (Claim 2)

[1588] 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

[1589] (Claim 3)

[1590] The system of claim 1 uses an algorithm that converts a message into a more gentle and friendly expression while preserving the meaning of the original message.

[1591] "Application Example 1"

[1592] (Claim 1)

[1593] means for receiving a message input by a user;

[1594] means for analyzing the sentiment of received messages;

[1595] Depending on the analysis results, a means of transforming offensive or rude language into constructive and kind language;

[1596] means for returning the converted message to the user;

[1597] A means to compare the pre-converted and post-converted messages using a generative AI model to improve the quality of the reply while preserving the meaning of the original message; and

[1598] A means to link the converted message with other applications as a prompt sentence,

[1599] A system including:

[1600] (Claim 2)

[1601] 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

[1602] (Claim 3)

[1603] The system of claim 1 uses an algorithm that converts a message into a more gentle and friendly expression while preserving the meaning of the original message.

[1604] "Example 2: Combining Emotion Engines"

[1605] (Claim 1)

[1606] means for receiving a message input by a user;

[1607] means for analyzing the sentiment of received messages;

[1608] A means of recognizing analytics results and real-time user sentiment and transforming offensive or rude language into constructive and kind language; and

[1609] means for returning the converted message to the user;

[1610] A system including:

[1611] (Claim 2)

[1612] 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

[1613] (Claim 3)

[1614] The system of claim 1 uses an algorithm that converts a message into a more gentle and friendly expression while preserving the meaning of the original message.

[1615] "Application example 2 when combining emotion engines"

[1616] (Claim 1)

[1617] means for receiving a message input by a user;

[1618] means for analyzing the sentiment of received messages;

[1619] Depending on the analysis results, a means of transforming offensive or rude language into constructive and kind language;

[1620] means for returning the converted message to the user;

[1621] A means of recognizing the user's real-time emotions;

[1622] A means of optimizing messages based on real-time user sentiment data;

[1623] a means for transforming the message based on the prompt using a generative AI model;

[1624] A system including:

[1625] (Claim 2)

[1626] 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

[1627] (Claim 3)

[1628] The system of claim 1 uses an algorithm that converts a message into a more gentle and friendly expression while preserving the meaning of the original message. [Explanation of symbols]

[1629] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving a message input by a user; means for analyzing the sentiment of received messages; Depending on the analysis results, a means of transforming offensive or rude language into constructive and kind language; means for returning the converted message to the user; A system including:

2. 10. The system of claim 1, wherein the system classifies the sentiment of a received message as positive, negative, or neutral.

3. 10. The system of claim 1, which uses an algorithm to convert a message into a more gentle and friendly expression while preserving the meaning of the original message.

Images