System

Abstract

A system is provided.SOLUTION: A system, comprising: means for receiving a post; means for detecting a language of the received post; means for translating the received post from the detected language to another specified language; and means for delivering the translated post based on user preferences.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] Currently, international communication through social networking sites (SNS) faces language barriers, making it difficult for users to understand content posted in different languages. This limits the ability of users around the world to effectively share information and communicate with each other. The purpose of this invention is to solve this problem and enable smooth communication through social networking sites, even in different language environments. [Means for solving the problem]

[0005] The present invention provides a system including means for accepting posts, means for detecting the language of the accepted posts, means for translating the detected language into another specified language, and means for distributing the translated posts based on the user's settings. Specifically, a server receives messages posted by users to an SNS and automatically detects the language of the post. Next, the server uses artificial intelligence to translate the detected language into another specified language and distributes the translation results based on each user's language settings. This allows users around the world to view content posted in different languages ​​in a language they can understand, enabling smooth communication across language barriers.

[0006] "Post" refers to a message or content sent by a User on a social media platform.

[0007] "Means for accepting" refers to the function of accepting posts from users and transmitting the data to the server.

[0008] "Means for detecting language" refers to the function of analyzing the content of received posts and determining the language in which the post is written.

[0009] "Means for translation" refers to a function that performs processing to convert the posted content from the detected language into another specified language.

[0010] "Means of distribution" refers to the function of sending translated posts to each user's device based on their settings.

[0011] "User Settings" refers to the display language and other individual settings specified by each User in their account.

[0012] "Artificial intelligence" refers to a system that automatically processes language translation using machine learning and natural language processing technology. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

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

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

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

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0028] 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.

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

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] This invention provides a system that combines a social networking platform with chat generation AI, allowing users to view content posted in different languages ​​in a language they can understand. Below, we will explain the program processing of this system in natural language.

[0035] System configuration

[0036] 1. User Submissions

[0037] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[0038] 2. Receipt of Submissions

[0039] The device receives the user's input and sends it to the SNS's server.

[0040] 3. Language Detection

[0041] The server receives the post and analyzes its content to detect the language it is in. For example, it receives the post "Today is a great day" and identifies it as Japanese.

[0042] 4. Generating a translation request

[0043] Based on the detected language (Japanese in this case), the server creates and sends a translation request to the chat generation AI.

[0044] 5. Execution of the translation

[0045] The Chat generative AI translates based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0046] 6. Receiving translation results

[0047] The server receives and stores the translation results.

[0048] 7. Distribution Preparation

[0049] The server checks each user's language preference and prepares a translated post in the appropriate language, for example, "Today is a wonderful day" for a user with English preferences.

[0050] 8. Delivery of translation results

[0051] The server sends the translated post to the user's device.

[0052] 9. Displaying translation results

[0053] The device then displays the translation results to the user. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0054] Specific examples

[0055] A specific example is as follows:

[0056] 1. User A posts on social media in Japanese, "Today is a wonderful day."

[0057] Posts are sent from the device to the server.

[0058] 2. The server receives the post and detects that the language is Japanese.

[0059] 3. The server sends a translation request from Japanese to English to the chat generation AI.

[0060] 4. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day."

[0061] 5. The server receives the translation and prepares the translated post for English users.

[0062] 6. The server sends the translated post to User B's device.

[0063] 7. User B's device displays "Today is a wonderful day."

[0064] This system enables smooth communication through SNS even between users who speak different languages, and allows information to be shared across language barriers, leading to richer international exchange.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[0068] Step 2:

[0069] The device receives the user's input and sends it to the SNS server as posting data.

[0070] Step 3:

[0071] The server receives the posted data and stores it in a database.

[0072] Step 4:

[0073] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[0074] Step 5:

[0075] Based on the language identification results, the server generates a translation request to the chat generation AI.

[0076] Step 6:

[0077] The server sends this request to the chat generation AI, which includes the original text, the original language, and the language to translate it into.

[0078] Step 7:

[0079] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0080] Step 8:

[0081] The server receives and stores the translation results from the chat generation AI.

[0082] Step 9:

[0083] The server checks each user's language preference and prepares to display the translated post in that language, for example, "Today is a wonderful day" for a user with English preferences, or "Aujourd'hui est une journée merveilleuse" for a user with French preferences.

[0084] Step 10:

[0085] The server sends the translation results to the user's terminal based on each user's language settings.

[0086] Step 11:

[0087] The device receives the translation and displays the post in the user's preferred language. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0088] Example 1

[0089] 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."

[0090] With the development of modern communication technology, smooth communication between users who speak different languages ​​is required. However, current social networking platforms lack a means for users to easily understand content posted in different languages. This makes it difficult for users to share information between them, and is a barrier to international communication.

[0091] 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.

[0092] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for distributing the translated post based on user settings, a terminal for accepting user input, means for analyzing the received post content, means for generating a prompt sentence and sending it to a language model, means for saving the translation result from the language model, and a terminal for displaying the translation result, thereby enabling users who speak different languages ​​to communicate smoothly through the SNS.

[0093] A "means for accepting posts" is a part of the system that has the function of receiving and processing messages entered by users.

[0094] The "means for detecting the language of received posts" is a part of the system that has the ability to automatically analyze and identify the language of received messages.

[0095] A "means for translating from a detected language to another specified language" is a part of a system that has the functionality to translate messages in a specified language into another language.

[0096] A "means for delivering translated posts based on user preferences" is a part of a system that has the functionality to deliver translated messages in the appropriate format based on the user's language preferences.

[0097] A "terminal that receives user input" refers to a device (e.g., a smartphone or PC) that allows a user to input a message.

[0098] The "means for analyzing the received post content" is a part of the system that has the function of analyzing the information in the received message in detail and extracting the necessary data.

[0099] The "means for generating a prompt sentence and sending it to a language model" is a part of the system that has the function of generating a message in the input format required by the language model for translation and sending it to the language model.

[0100] The "means for storing translation results from a language model" is a part of a system that has the functionality to store translation results generated by a language model in a database or other storage device.

[0101] "Device displaying translation results" refers to a device (e.g., a smartphone or PC) that displays the translated message to the user.

[0102] This invention provides a system that allows users who speak different languages ​​to communicate smoothly by combining an SNS platform with chat generation AI. The detailed configuration and implementation method of this system are described below.

[0103] Overall system configuration

[0104] 1. User's device

[0105] Users access the SNS platform using devices such as smartphones or PCs and enter messages.

[0106] 2. Server

[0107] The SNS platform server receives the message sent from the user's device, detects the language, generates a translation request, receives and stores the translation result, and delivers the translation result into the appropriate language.

[0108] 3. Chat generation AI

[0109] The chat generation AI (e.g., GPT-4) receives translation requests from the server and translates them into the specified language.

[0110] Specific functions of the system

[0111] 1. Entering and Submitting User Submissions

[0112] A user enters "Today is a wonderful day" in Japanese into the input field of a social networking application and presses the send button.

[0113] The device receives the message and sends it to the SNS server.

[0114] 2. Receiving posts and detecting language

[0115] The server receives the message "Today is a great day" sent from the terminal.

[0116] The server analyzes the received message and detects that the language used is Japanese.

[0117] 3. Creating and sending a translation request

[0118] The server generates a translation request from Japanese to English based on the language detection results.

[0119] The server sends the generated translation request to the chat generation AI.

[0120] Example prompt: "Please translate the following Japanese sentence into English: Today is a wonderful day."

[0121] 4. Execution of the translation

[0122] The chat generation AI processes the request received from the server and translates "Today is a wonderful day" into "Today is a wonderful day."

[0123] 5. Receiving and saving translation results

[0124] The server receives the translation result "Today is a wonderful day" from the chat generation AI.

[0125] The server stores the translation results in a database.

[0126] 6. Preparing for distribution of translation results

[0127] The server sees that User B's preference is English and prepares the post translated into English.

[0128] 7. Delivery of translation results

[0129] The server sends the prepared translation result "Today is a wonderful day" to User B's device.

[0130] 8. Displaying translation results

[0131] The terminal displays the translation results received to User B.

[0132] Display: "Today is a wonderful day"

[0133] This system will enable users who speak different languages ​​to communicate smoothly through SNS, realizing international information sharing.

[0134] This allows users to view content posted in different languages ​​in a language they can understand, facilitating international communication through SNS. As a multilingual SNS platform, this system will greatly improve the convenience of communication between users.

[0135] Hardware and software used

[0136] Hardware: SNS platform servers, users' smartphones and PCs

[0137] Software: Social networking applications, chat generation AI (e.g., GPT-4)

[0138] In this way, the present invention makes it possible to communicate between multiple languages, which has been difficult in the past, by using specific hardware and software configurations.

[0139] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0140] Step 1:

[0141] A user enters a message into the input field of an SNS application and presses the send button. At this time, the Japanese message "Today is a wonderful day" is entered into the user's device as input. The device generates a request to send the message to the SNS server and sends it to the server. As an output, a request to send the message to the server is generated and arrives at the server.

[0142] Step 2:

[0143] The server receives the message sent from the terminal and analyzes its contents. The data received as input is the Japanese message "Today is a wonderful day." The server uses its language analysis function to detect that the language used in this message is Japanese and extracts the language information. As output, the server generates the language information that the message is in Japanese.

[0144] Step 3:

[0145] The server generates a translation request based on the detected language information. The input is the language information being Japanese and the original message "Today is a wonderful day." The server generates a prompt sentence and sends the translation request to the language model. This prompt sentence is "Please translate the following Japanese sentence into English: Today is a wonderful day." The output is a translation request generated and sent to the language model.

[0146] Step 4:

[0147] The chat generation AI processes the translation request received from the server. The input is the translation request prompt, "Please translate the following Japanese sentence into English: Today is a wonderful day." The AI ​​performs translation processing based on this prompt, translating the message from Japanese to English. The output is the translation result, "Today is a wonderful day."

[0148] Step 5:

[0149] The server receives the translation result from the chat generation AI. The input is the translation result "Today is a wonderful day." The server saves this translation result in a database. The output is the saved translation result.

[0150] Step 6:

[0151] The server checks each user's language preference. The input includes User B's language preference and the saved translation "Today is a wonderful day." The server verifies that User B's language preference is English and prepares the appropriate translation for this user. The output is the post translated into English.

[0152] Step 7:

[0153] The server sends the prepared translation result to User B's device. The input is the prepared translation result "Today is a wonderful day." The server creates a message including the translation result and sends it to User B's device. The output is the message delivered to User B's device.

[0154] Step 8:

[0155] The device displays the translation result it received to User B. The input is the translation result "Today is a wonderful day" sent from the server. The device displays this message on the screen. The output is "Today is a wonderful day" displayed on User B's device, allowing the user to view the translation result.

[0156] (Application example 1)

[0157] 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."

[0158] When users of different languages ​​use content distribution services, it is difficult to provide subtitles for video and audio content in multiple languages. Language differences can be a barrier, especially when communicating and consuming content between international users, degrading the user experience. Furthermore, because translation quality and timeliness are important, there is a demand for high-precision, fast translation.

[0159] 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.

[0160] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted posts, means for translating the detected language into another specified language, means for distributing the translated posts based on user settings, and means for automatically generating translated subtitles for video or audio content. This enables multilingual subtitles for video or audio content to be automatically generated and displayed when users who speak different languages ​​use the content distribution service.

[0161] "Posting" refers to a user entering content on a social media platform and disseminating it to other users.

[0162] "Language detection means" refers to technology or programming used to identify the language of an accepted submission.

[0163] "Translation means" refers to technology or programs used to convert content written in a particular language into another specified language.

[0164] "Means of delivery" refers to the technology and programs used to deliver translated content to users' devices.

[0165] "Video or audio content" refers to media formats that convey information visually and aurally.

[0166] "Means for automatic subtitling generation" refers to technology or programs that generate and display text in a specified language for video or audio content.

[0167] "Generative artificial intelligence" refers to advanced machine learning models that generate text and translate based on large amounts of data.

[0168] "User Settings" refers to options and settings that a user can individually set for their language and display format.

[0169] This invention provides a system that combines a social networking platform with a generative AI model to enable users to view content posted in different languages ​​in a language they understand. It also includes a means for automatically generating multilingual subtitles in a content distribution service. A specific embodiment of this system will be described.

[0170] System configuration

[0171] 1. Submission acceptance

[0172] The server accepts posts entered by a user on the SNS application. For example, the user enters "Today is a great day" and presses the post button.

[0173] 2. Language Detection

[0174] The server detects the language of the received post using tools such as the Google Translate API. For example, if the post says "Today is a great day," it will identify it as Japanese.

[0175] 3. Generating a translation request

[0176] The server sends a translation request to the generative AI model based on the detected language and the user's settings. For example, when translating from Japanese to English, it translates "Today is a wonderful day."

[0177] 4. Delivery of translation results

[0178] The translated post is stored on a server and delivered to the device that displays the post based on the user's language settings—for example, a user with English settings might see "Today is a wonderful day."

[0179] 5. Application to video and audio content

[0180] The server automatically generates translated subtitles for video or audio content using a generative AI model based on the user's language settings, allowing users of different languages ​​to watch the same content with subtitles in the specified language.

[0181] Hardware and software used

[0182] The system uses the following hardware and software:

[0183] Server: Web server required to run the SNS platform (e.g. AWS EC2)

[0184] Device: The smartphone, tablet, or computer used by the user

[0185] Software: Google Translate API, generative AI models (e.g., GPT-4)

[0186] Specific examples

[0187] For example, if a user posts "Today is a wonderful day" in Japanese on a social media platform, the server receives the post and detects that it is in Japanese. The server then requests a Japanese-to-English translation from the generative AI model, translates it to "Today is a wonderful day," and delivers it to User B's device. At this time, if User B is watching video content, the generated English subtitles will also be displayed.

[0188] Prompt Sentence Examples

[0189] "Piza o tsuika chūmon shimasu" wo eigo ni hon'yaku shite kudasai.

[0190] Translate "Piza o tsuika chūmon shimasu" into English.

[0191] This allows users who speak different languages ​​to enjoy the same content, promoting international exchange.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Step 1:

[0194] The user uses a device to input a post on the SNS platform and presses the post button. This is the initial input to the system. Specifically, for example, the user might input "Today is a wonderful day" in Japanese.

[0195] Step 2:

[0196] The device receives the user's input and sends it to the server, which then forwards the input data to the SNS platform's server.

[0197] Step 3:

[0198] The server analyzes the content of the received post and detects the language of the post using the Google Translate API, etc. For example, it identifies a post saying "Today is a great day" as Japanese. The detected language is output.

[0199] Step 4:

[0200] The server sends a translation request to the generative AI model based on the detected language to another specified language, where the input is the detected language and the language to translate to (e.g., Japanese to English), and the output is a request to the generative AI model.

[0201] Step 5:

[0202] The generative AI model performs a translation based on the request, for example, translating "Today is a wonderful day" in Japanese to "Today is a wonderful day" in English. The output is the translated text.

[0203] Step 6:

[0204] The server receives and stores the translation results from the generative AI model, where the input is the translated text and the output is the stored translation data.

[0205] Step 7:

[0206] The server checks the user's language preference and prepares the translated text for delivery in the appropriate language. For example, an English translation is prepared for a user with English preferences. The input is the user's language preference and the stored translation data, and the output is the data ready for delivery.

[0207] Step 8:

[0208] The server sends the translated post to the user's device, where the data is prepared for distribution.

[0209] Step 9:

[0210] The device displays the received translation result to the user. For example, a user with English settings might see "Today is a wonderful day" on their device. The input is the received translation data, and the output is the displayed text.

[0211] 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.

[0212] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Below, we will explain the program processing of this system in natural language.

[0213] System configuration

[0214] 1. User Submissions

[0215] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[0216] 2. Receipt of Submissions

[0217] The device receives the user's input and sends it to the SNS server as posting data.

[0218] 3. Language Detection

[0219] The server receives the posted data, analyzes its content, and detects the language. For example, it detects that "Today is a wonderful day" is Japanese.

[0220] 4. Emotional Recognition

[0221] The server sends the posted data to the emotion engine to detect the user's emotion, for example, recognizing the emotion of joy from the post.

[0222] 5. Generating a translation request

[0223] The server generates a translation request to the chat generation AI based on the detected language (Japanese in this case) and the recognized emotion.

[0224] 6. Execution of the translation

[0225] The Chat generative AI translates requests based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further incorporating the emotion of joy.

[0226] 7. Receiving translation results

[0227] The server receives and stores the translation results from the chat generation AI.

[0228] 8. Distribution Preparation

[0229] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[0230] 9. Delivery of translation results

[0231] The server sends the translation results to the user's terminal based on each user's language settings.

[0232] 10. Displaying translation results

[0233] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[0234] Specific examples

[0235] A specific example is as follows:

[0236] If user A posts "Today is a wonderful day" in Japanese on social media:

[0237] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[0238] Posts are sent from the device to the server.

[0239] 2. The server receives the post and detects that the language is Japanese.

[0240] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[0241] 4. The server sends a translation request from Japanese to English to the chat generation AI, and also reflects the emotion of joy.

[0242] 5. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day 😊".

[0243] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[0244] 7. The server sends the translated post to User B's device.

[0245] 8. User B's device displays "Today is a wonderful day 😊".

[0246] This system enables users of different languages ​​to communicate smoothly while sharing emotions across language barriers. By combining it with an emotion engine, translated content is expressed more naturally and emotionally, improving the user experience.

[0247] The processing flow will be explained below.

[0248] Step 1:

[0249] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[0250] Step 2:

[0251] The device receives the user's input and sends it to the SNS server as posting data.

[0252] Step 3:

[0253] The server receives the posted data and stores it in a database.

[0254] Step 4:

[0255] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[0256] Step 5:

[0257] The server sends the post data to the emotion engine based on the language identification results. The emotion engine analyzes the text data of the post and identifies the user's emotion. For example, it recognizes positive emotion (joy) from a post that says, "Today is a great day."

[0258] Step 6:

[0259] Based on the emotion recognition results, the server generates a translation request to the chat generation AI. The request includes the original text, Japanese (the source language), English (the target language), and the identified emotion.

[0260] Step 7:

[0261] The server sends a translation request to the chat generation AI.

[0262] Step 8:

[0263] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further reflecting the emotion of joy.

[0264] Step 9:

[0265] The server receives and stores the translation results from the chat generation AI. The translation results express emotions such as "Today is a wonderful day 😊."

[0266] Step 10:

[0267] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[0268] Step 11:

[0269] The server sends the translation results to the user's terminal based on each user's language settings.

[0270] Step 12:

[0271] The device receives the translation and displays the post in the user's language and emotion settings. For example, a user with English settings would see "Today is a wonderful day 😊" on their device.

[0272] Through these steps, users who speak different languages ​​can communicate smoothly through social networking sites while sharing their emotions. By combining this with an emotion engine, emotions are reflected in the translated content, enabling more natural and empathetic communication.

[0273] Example 2

[0274] 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."

[0275] In today's communication environment, multilingual communication has become essential, but language barriers still exist. Furthermore, simple translations tend to lose emotion and nuance, which can degrade the user experience. For this reason, there is a need for a system that can transcend language barriers while faithfully conveying emotion and nuance.

[0276] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0277] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for recognizing the emotion of the post, means for translating the post into another specified language based on the detected language and the recognized emotion, and means for distributing the translated post based on user settings, thereby enabling smooth communication between users who speak different languages ​​without losing emotion or nuance.

[0278] The "means for accepting posts" is a component that has the role of sending messages entered by users on the SNS application to the system.

[0279] The "means for detecting the language of a received post" is a component that has the function of automatically determining the language of a message received by the system.

[0280] The "means for recognizing the emotions of posts" is a component that has the function of analyzing the user's emotions and nuances from received messages and identifying those emotions.

[0281] The "means for translating into another specified language based on the detected language and the recognized emotion" is a component that has the function of performing an appropriate translation based on the language and emotion of the original text.

[0282] The "means for delivering translated posts based on user settings" is a component that has the function of delivering messages that have completed translation processing in accordance with the user's language settings.

[0283] "Generative AI models" refer to the machine learning algorithms used for natural language processing and translation, which play a key role in the system's translation capabilities.

[0284] A "prompt sentence" refers to the input sentence format used to give instructions to a generative AI model for translation or emotion recognition.

[0285] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Specific embodiments of this system are described below.

[0286] System configuration

[0287] 1. User submission input

[0288] A user types a message into an SNS application and presses the post button. For example, the user types "Today is a wonderful day" in Japanese.

[0289] 2. Receiving posted data

[0290] The device receives the user's input and sends the posted data to the SNS server. For example, the device sends a message to the server saying, "Today is a great day."

[0291] 3. Language Detection

[0292] The server receives the posted data, analyzes its content, and detects the language. To recognize that the posted data is in Japanese, the server uses a natural language processing library.

[0293] 4. Emotional Recognition

[0294] The server sends the posted data to the emotion engine to detect the user's emotion. For example, the server sends the text "Today is a great day" to the emotion engine, and recognizes the emotion of "joy" as a result.

[0295] 5. Generating a translation request

[0296] The server generates a translation request to the generative AI model based on the detected language (Japanese in this case) and the recognized emotion. Specifically, it generates a prompt sentence containing a "translation request from Japanese to English" and "emotion: joy" and sends it to the generative AI model.

[0297] 6. Execution of the translation

[0298] The generative AI model processes the translation based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊".

[0299] 7. Receiving translation results

[0300] The server receives the translation result from the generative AI model and stores it. For example, the server receives the translation result "Today is a wonderful day 😊" and stores it in the database.

[0301] 8. Distribution Preparation

[0302] The server checks each user's language setting and prepares to display the translated post in that language and emotion, for example, preparing to display the translated "Today is a wonderful day 😊" for a user with an English setting.

[0303] 9. Delivery of translation results

[0304] The server sends the translation results to the device based on each user's language setting. For example, the server sends the message "Today is a wonderful day 😊" to the device of a user with English settings.

[0305] 10. Displaying translation results

[0306] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[0307] Specific examples

[0308] As a specific example, if user A posts "Today is a wonderful day" in Japanese on an SNS, the process will proceed as follows:

[0309] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[0310] Posts are sent from the device to the server.

[0311] 2. The server receives the post and detects that the language is Japanese.

[0312] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[0313] 4. The server sends a translation request from Japanese to English to the generated AI model, and also reflects the emotion of joy.

[0314] 5. The generative AI model translates "Today is a wonderful day" to "Today is a wonderful day 😊".

[0315] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[0316] 7. The server sends the translated post to User B's device.

[0317] 8. User B's device displays "Today is a wonderful day 😊".

[0318] This system enables smooth communication between users of different languages ​​without losing emotion or nuance.

[0319] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0320] Step 1:

[0321] The user enters a message in the SNS application and presses the post button.

[0322] - Input: A message entered by the user into a social networking app (e.g., "Today is a great day")

[0323] - Output: Post data generated by the device

[0324] Step 2:

[0325] The device receives the user's input and sends the posted data to the SNS server.

[0326] - Input: Post data entered by the user

[0327] - Output: Post data sent to the server

[0328] Step 3:

[0329] The server receives the posted data and analyzes its content to detect the language.

[0330] - Input: Post data received by the server

[0331] - Data processing: Analyze language using natural language processing libraries

[0332] - Output: Detected language (e.g. Japanese)

[0333] Step 4:

[0334] The server sends the posted data to the emotion engine to detect the user's emotions.

[0335] - Input: Server-detected language and post data

[0336] - Data calculation: Analyze the sentiment of posts using the sentiment engine

[0337] - Output: Perceived emotion (e.g., joy)

[0338] Step 5:

[0339] The server generates a translation request to a generative AI model based on the detected language and recognized sentiment.

[0340] - Input: Detected language (Japanese) and recognized emotion (joy)

[0341] - Data calculation: Prompt sentence generation (e.g., "Japanese to English translation request" "Emotion: joy")

[0342] - Output: Generated prompt statement

[0343] Step 6:

[0344] The generative AI model performs translation processing based on the request.

[0345] - Input: Prompt sent from the server

[0346] - Data calculation: Translation processing using generative AI models (e.g., translating "Today is a wonderful day" to "Today is a wonderful day 😊")

[0347] - Output: Translated text

[0348] Step 7:

[0349] The server receives the translation results from the generative AI model and stores them.

[0350] - Input: Translation results returned by the generative AI model

[0351] - Data processing: Saving translation results

[0352] - Output: Saved translation data

[0353] Step 8:

[0354] The server checks each user's language preference and prepares the translated post to be displayed in that language and emotion.

[0355] - Input: saved translation data and user language settings

[0356] - Data calculation: Preparing display based on user language settings (e.g., "Today is a wonderful day 😊")

[0357] - Output: Data ready for display

[0358] Step 9:

[0359] The server sends the translation results to the terminal based on each user's language settings.

[0360] - Input: Data ready to display

[0361] - Output: Translation results sent to the user's device

[0362] Step 10:

[0363] The terminal displays the translation result received to the user.

[0364] - Input: Translation results sent from the server

[0365] - Output: The translation result displayed on the user's device (e.g., "Today is a wonderful day 😊")

[0366] (Application example 2)

[0367] 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."

[0368] The purpose of this invention is to enable smooth communication between users who speak different languages ​​on social networking platforms. Conventional technologies have had the problem of losing emotion or producing unnatural expressions simply by translating text. This problem is particularly pronounced in situations where emotional expression is important, resulting in a decline in the quality of communication. Therefore, there is a need to provide a system that recognizes the user's emotions and reflects them in the translation results, thereby enabling natural, emotionally rich communication.

[0369] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for recognizing the emotion of the accepted post, means for reflecting the recognized emotion in the translation result, and means for delivering the translated post based on the user's settings. This enables users who speak different languages ​​to communicate with each other in a rich and emotional way, overcoming language barriers.

[0370] "Posting" refers to the act of a user typing and publishing a message on a social media platform.

[0371] "Means for detecting language" is a function that determines the language in which a user's post is written.

[0372] The "translation means" is a function for converting a detected language into another specified language.

[0373] "Means for recognizing emotions" is a function that analyzes and identifies emotions from user posts.

[0374] "Means for reflecting emotions in translation results" refers to a function that takes into account recognized emotions and includes them in the translation results.

[0375] "User settings" are the display language and other display option settings that the user has preselected.

[0376] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and performs natural language processing, image generation, and other tasks.

[0377] A "prompt" is a document or keyword used as input to a generative AI model.

[0378] This invention is a system that translates messages posted on a social networking platform and distributes them to other users in a format that reflects their emotions. Specifically, the system performs a series of processes: accepting posts, detecting the language, recognizing emotions, translating, and finally displaying the translation results based on the user's settings. A specific example of this system is described in detail below.

[0379] System Configuration

[0380] The system includes a server, a terminal, an emotion engine, and a generative AI model. The role of each part and the specific processing steps are explained below.

[0381] Terminal

[0382] The terminal is a device that accepts user posts. When a user enters a message on the terminal and presses the post button, the data is sent to the server. For example, if user A posts "Today is a wonderful day" in Japanese, this post is sent from the terminal to the server.

[0383] server

[0384] The server analyzes the received post data and detects the language. For example, the server detects that the post "Today is a wonderful day" is in Japanese. The server then sends the post data to the emotion engine to recognize emotions. The server detects the emotion of joy from the post.

[0385] The server then generates a translation request to a generative AI model based on the detected language (Japanese) and the recognized emotion (joy). An example of a generative AI model is a large-scale transformer model. The generative AI model translates the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊."

[0386] When the translation result is sent back to the server, the server prepares it to be displayed in the appropriate language and emotion based on the user's settings. For example, User B, who has English settings, will see "Today is a wonderful day 😊" on their device.

[0387] Hardware and software used

[0388] Devices: Smartphones, tablets, computers, etc.

[0389] Server: A high-performance server machine

[0390] Emotion engine: Natural language processing library (e.g. NLTK, spaCy)

[0391] Generative AI models: Large-scale transformer models (e.g., GPT-3)

[0392] Specific examples

[0393] As a concrete example, let's look at the flow when User A posts "Today is a wonderful day" in Japanese. First, the device sends the posted data to the server. The server detects that the language is Japanese and recognizes the emotion of joy using the emotion engine. The server then sends a translation request from Japanese to English to the generative AI model, and receives the translation result "Today is a wonderful day 😊" that reflects the emotion. This translation result is then sent to User B's device, where it is displayed on User B's device.

[0394] Prompt Sentence Examples

[0395] "Translate the Japanese text 'Today is a wonderful day' to English. Make sure to include the user's emotion, which is joy, in the translation."

[0396] This provides a system that allows users who speak different languages ​​to communicate smoothly, including sharing emotions.

[0397] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0398] Step 1:

[0399] The user enters a message into the terminal and presses the post button. As input, the text entered by the user in Japanese, "Today is a wonderful day," is obtained. As output, this text is sent to the server as post data.

[0400] Step 2:

[0401] The server detects the language of the submitted data it receives. The input is text data received from the user, and the output detects that the language is Japanese. Specifically, it runs a language detection algorithm to identify the language of the text.

[0402] Step 3:

[0403] The server sends the post data to the emotion engine to recognize the emotion. Using the post data as input, the emotion detected as output is recognized as "joy." The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract emotions from text.

[0404] Step 4:

[0405] The server generates a translation request to the generative AI model based on the detected language (Japanese) and the recognized emotion (joy). It uses the detected language and emotion, and the source text as input, and generates a translation request as output. Specifically, it generates a prompt sentence and provides it to the generative AI model (e.g., GPT-3).

[0406] Step 5:

[0407] The generative AI model translates based on the translation request. It receives the generated prompt as input and obtains the translation result "Today is a wonderful day 😊" as output. The generative AI model uses a large-scale Transformer model to simultaneously translate between languages ​​and reflect sentiment.

[0408] Step 6:

[0409] The server stores the translation results received from the generative AI model and prepares them for display based on the user's settings. It uses the translation results and user settings as input and obtains displayable data as output. Specifically, it formats the translation results with the appropriate language and sentiment.

[0410] Step 7:

[0411] The server sends the translated post to the user's device, using the processed data as input and sending the data to the user's device as output. Specifically, the data is sent over the network to the appropriate device.

[0412] Step 8:

[0413] The user's device displays the translation results it receives. It uses the data sent from the server as input and displays the translation results to the user as output. Specifically, it renders the text on the device's display.

[0414] 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.

[0415] 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.

[0416] 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.

[0417] [Second embodiment]

[0418] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0419] 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.

[0420] 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).

[0421] 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.

[0422] 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.

[0423] 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).

[0424] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0425] 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.

[0426] 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.

[0427] 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.

[0428] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0429] 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."

[0430] This invention provides a system that combines a social networking platform with chat generation AI, allowing users to view content posted in different languages ​​in a language they can understand. Below, we will explain the program processing of this system in natural language.

[0431] System configuration

[0432] 1. User Submissions

[0433] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[0434] 2. Receipt of Submissions

[0435] The device receives the user's input and sends it to the SNS's server.

[0436] 3. Language Detection

[0437] The server receives the post and analyzes its content to detect the language it is in. For example, it receives the post "Today is a great day" and identifies it as Japanese.

[0438] 4. Generating a translation request

[0439] Based on the detected language (Japanese in this case), the server creates and sends a translation request to the chat generation AI.

[0440] 5. Execution of the translation

[0441] The Chat generative AI translates based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0442] 6. Receiving translation results

[0443] The server receives and stores the translation results.

[0444] 7. Distribution Preparation

[0445] The server checks each user's language preference and prepares a translated post in the appropriate language, for example, "Today is a wonderful day" for a user with English preferences.

[0446] 8. Delivery of translation results

[0447] The server sends the translated post to the user's device.

[0448] 9. Displaying translation results

[0449] The device then displays the translation results to the user. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0450] Specific examples

[0451] A specific example is as follows:

[0452] 1. User A posts on social media in Japanese, "Today is a wonderful day."

[0453] Posts are sent from the device to the server.

[0454] 2. The server receives the post and detects that the language is Japanese.

[0455] 3. The server sends a translation request from Japanese to English to the chat generation AI.

[0456] 4. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day."

[0457] 5. The server receives the translation and prepares the translated post for English users.

[0458] 6. The server sends the translated post to User B's device.

[0459] 7. User B's device displays "Today is a wonderful day."

[0460] This system enables smooth communication through SNS even between users who speak different languages, and allows information to be shared across language barriers, leading to richer international exchange.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[0464] Step 2:

[0465] The device receives the user's input and sends it to the SNS server as posting data.

[0466] Step 3:

[0467] The server receives the posted data and stores it in a database.

[0468] Step 4:

[0469] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[0470] Step 5:

[0471] Based on the language identification results, the server generates a translation request to the chat generation AI.

[0472] Step 6:

[0473] The server sends this request to the chat generation AI, which includes the original text, the original language, and the language to translate it into.

[0474] Step 7:

[0475] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0476] Step 8:

[0477] The server receives and stores the translation results from the chat generation AI.

[0478] Step 9:

[0479] The server checks each user's language preference and prepares to display the translated post in that language, for example, "Today is a wonderful day" for a user with English preferences, or "Aujourd'hui est une journée merveilleuse" for a user with French preferences.

[0480] Step 10:

[0481] The server sends the translation results to the user's terminal based on each user's language settings.

[0482] Step 11:

[0483] The device receives the translation and displays the post in the user's preferred language. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0484] Example 1

[0485] 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."

[0486] With the development of modern communication technology, smooth communication between users who speak different languages ​​is required. However, current social networking platforms lack a means for users to easily understand content posted in different languages. This makes it difficult for users to share information between them, and is a barrier to international communication.

[0487] 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.

[0488] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for distributing the translated post based on user settings, a terminal for accepting user input, means for analyzing the received post content, means for generating a prompt sentence and sending it to a language model, means for saving the translation result from the language model, and a terminal for displaying the translation result, thereby enabling users who speak different languages ​​to communicate smoothly through the SNS.

[0489] A "means for accepting posts" is a part of the system that has the function of receiving and processing messages entered by users.

[0490] The "means for detecting the language of received posts" is a part of the system that has the ability to automatically analyze and identify the language of received messages.

[0491] A "means for translating from a detected language to another specified language" is a part of a system that has the functionality to translate messages in a specified language into another language.

[0492] The "means for delivering translated posts based on user preferences" is a part of the system that has the functionality to deliver translated messages in the appropriate format based on the user's language preferences.

[0493] A "terminal that receives user input" refers to a device (e.g., a smartphone or PC) that allows a user to input a message.

[0494] The "means for analyzing the received post content" is a part of the system that has the function of analyzing the information in the received message in detail and extracting the necessary data.

[0495] The "means for generating a prompt sentence and sending it to a language model" is a part of the system that has the function of generating a message in the input format required by the language model for translation and sending it to the language model.

[0496] The "means for storing translation results from a language model" is a part of a system that has the functionality to store translation results generated by a language model in a database or other storage device.

[0497] "Device displaying translation results" refers to a device (e.g., a smartphone or PC) that displays the translated message to the user.

[0498] This invention provides a system that allows users who speak different languages ​​to communicate smoothly by combining an SNS platform with chat generation AI. The detailed configuration and implementation method of this system are described below.

[0499] Overall system configuration

[0500] 1. User's device

[0501] Users access the SNS platform using devices such as smartphones or PCs and enter messages.

[0502] 2. Server

[0503] The SNS platform server receives the message sent from the user's device, detects the language, generates a translation request, receives and stores the translation result, and delivers the translation result into the appropriate language.

[0504] 3. Chat generation AI

[0505] The chat generation AI (e.g., GPT-4) receives translation requests from the server and translates them into the specified language.

[0506] Specific functions of the system

[0507] 1. Entering and Submitting User Submissions

[0508] A user enters "Today is a wonderful day" in Japanese into the input field of a social networking application and presses the send button.

[0509] The device receives the message and sends it to the SNS server.

[0510] 2. Receiving posts and detecting language

[0511] The server receives the message "Today is a great day" sent from the terminal.

[0512] The server analyzes the received message and detects that the language used is Japanese.

[0513] 3. Creating and sending a translation request

[0514] The server generates a translation request from Japanese to English based on the language detection results.

[0515] The server sends the generated translation request to the chat generation AI.

[0516] Example prompt: "Please translate the following Japanese sentence into English: Today is a wonderful day."

[0517] 4. Execution of the translation

[0518] The chat generation AI processes the request received from the server and translates "Today is a wonderful day" into "Today is a wonderful day."

[0519] 5. Receiving and saving translation results

[0520] The server receives the translation result "Today is a wonderful day" from the chat generation AI.

[0521] The server stores the translation results in a database.

[0522] 6. Preparing for distribution of translation results

[0523] The server sees that User B's preference is English and prepares the post translated into English.

[0524] 7. Delivery of translation results

[0525] The server sends the prepared translation result "Today is a wonderful day" to User B's device.

[0526] 8. Displaying translation results

[0527] The terminal displays the translation results received to User B.

[0528] Display: "Today is a wonderful day"

[0529] This system will enable users who speak different languages ​​to communicate smoothly through SNS, realizing international information sharing.

[0530] This allows users to view content posted in different languages ​​in a language they can understand, facilitating international communication through SNS. As a multilingual SNS platform, this system will greatly improve the convenience of communication between users.

[0531] Hardware and software used

[0532] Hardware: SNS platform servers, users' smartphones and PCs

[0533] Software: Social networking applications, chat generation AI (e.g., GPT-4)

[0534] In this way, the present invention makes it possible to communicate between multiple languages, which has previously been difficult, by using specific hardware and software configurations.

[0535] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0536] Step 1:

[0537] A user enters a message into the input field of an SNS application and presses the send button. At this time, the Japanese message "Today is a wonderful day" is entered into the user's device as input. The device generates a request to send the message to the SNS server and sends it to the server. As an output, a request to send the message to the server is generated and arrives at the server.

[0538] Step 2:

[0539] The server receives the message sent from the terminal and analyzes its contents. The data received as input is the Japanese message "Today is a wonderful day." The server uses its language analysis function to detect that the language used in this message is Japanese and extracts the language information. As output, the server generates the language information that the message is in Japanese.

[0540] Step 3:

[0541] The server generates a translation request based on the detected language information. The input is the language information being Japanese and the original message "Today is a wonderful day." The server generates a prompt sentence and sends the translation request to the language model. This prompt sentence is "Please translate the following Japanese sentence into English: Today is a wonderful day." The output is a translation request generated and sent to the language model.

[0542] Step 4:

[0543] The chat generation AI processes the translation request received from the server. The input is the translation request prompt, "Please translate the following Japanese sentence into English: Today is a wonderful day." The AI ​​performs translation processing based on this prompt, translating the message from Japanese to English. The output is the translation result, "Today is a wonderful day."

[0544] Step 5:

[0545] The server receives the translation result from the chat generation AI. The input is the translation result "Today is a wonderful day." The server saves this translation result in a database. The output is the saved translation result.

[0546] Step 6:

[0547] The server checks each user's language preference. The input includes User B's language preference and the saved translation "Today is a wonderful day." The server verifies that User B's language preference is English and prepares the appropriate translation for this user. The output is the post translated into English.

[0548] Step 7:

[0549] The server sends the prepared translation result to User B's device. The input is the prepared translation result "Today is a wonderful day." The server creates a message including the translation result and sends it to User B's device. The output is the message delivered to User B's device.

[0550] Step 8:

[0551] The device displays the translation result it received to User B. The input is the translation result "Today is a wonderful day" sent from the server. The device displays this message on the screen. The output is "Today is a wonderful day" displayed on User B's device, allowing the user to view the translation result.

[0552] (Application example 1)

[0553] 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."

[0554] When users of different languages ​​use content distribution services, it is difficult to provide subtitles for video and audio content in multiple languages. Language differences can be a barrier, especially when communicating and consuming content between international users, degrading the user experience. Furthermore, because translation quality and timeliness are important, there is a demand for high-precision, fast translation.

[0555] 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.

[0556] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted posts, means for translating the detected language into another specified language, means for distributing the translated posts based on user settings, and means for automatically generating translated subtitles for video or audio content. This enables multilingual subtitles for video or audio content to be automatically generated and displayed when users who speak different languages ​​use the content distribution service.

[0557] "Posting" refers to a user entering content on a social media platform and disseminating it to other users.

[0558] "Language detection means" refers to technology or programming used to identify the language of an accepted submission.

[0559] "Translation means" refers to technology or programs used to convert content written in a particular language into another specified language.

[0560] "Means of delivery" refers to the technology and programs used to deliver translated content to users' devices.

[0561] "Video or audio content" refers to media formats that convey information visually and aurally.

[0562] "Means for automatic subtitling generation" refers to technology or programs that generate and display text in a specified language for video or audio content.

[0563] "Generative artificial intelligence" refers to advanced machine learning models that generate text and translate based on large amounts of data.

[0564] "User Settings" refers to options and settings that a user can individually set for their language and display format.

[0565] This invention provides a system that combines a social networking platform with a generative AI model to enable users to view content posted in different languages ​​in a language they understand. It also includes a means for automatically generating multilingual subtitles in a content distribution service. A specific embodiment of this system will be described.

[0566] System configuration

[0567] 1. Submission acceptance

[0568] The server accepts posts entered by a user on the SNS application. For example, the user enters "Today is a great day" and presses the post button.

[0569] 2. Language Detection

[0570] The server detects the language of the received post using tools such as the Google Translate API. For example, if the post says "Today is a great day," it will identify it as Japanese.

[0571] 3. Generating a translation request

[0572] The server sends a translation request to the generative AI model based on the detected language and the user's settings. For example, when translating from Japanese to English, it translates "Today is a wonderful day."

[0573] 4. Delivery of translation results

[0574] The translated post is stored on a server and delivered to the device that displays the post based on the user's language settings—for example, a user with English settings might see "Today is a wonderful day."

[0575] 5. Application to video and audio content

[0576] The server automatically generates translated subtitles for video or audio content using a generative AI model based on the user's language settings, allowing users of different languages ​​to watch the same content with subtitles in the specified language.

[0577] Hardware and software used

[0578] The system uses the following hardware and software:

[0579] Server: Web server required to run the SNS platform (e.g. AWS EC2)

[0580] Device: The smartphone, tablet, or computer used by the user

[0581] Software: Google Translate API, generative AI models (e.g., GPT-4)

[0582] Specific examples

[0583] For example, if a user posts "Today is a wonderful day" in Japanese on a social media platform, the server receives the post and detects that it is in Japanese. The server then requests a Japanese-to-English translation from the generative AI model, translates it to "Today is a wonderful day," and delivers it to User B's device. At this time, if User B is watching video content, the generated English subtitles will also be displayed.

[0584] Prompt Sentence Examples

[0585] "Piza o tsuika chūmon shimasu" wo eigo ni hon'yaku shite kudasai.

[0586] Translate "Piza o tsuika chūmon shimasu" into English.

[0587] This allows users who speak different languages ​​to enjoy the same content, promoting international exchange.

[0588] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0589] Step 1:

[0590] The user uses a device to input a post on the SNS platform and presses the post button. This is the initial input to the system. Specifically, for example, the user might input "Today is a wonderful day" in Japanese.

[0591] Step 2:

[0592] The device receives the user's input and sends it to the server, which then forwards the input data to the SNS platform's server.

[0593] Step 3:

[0594] The server analyzes the content of the received post and detects the language of the post using the Google Translate API, etc. For example, it identifies a post saying "Today is a great day" as Japanese. The detected language is output.

[0595] Step 4:

[0596] The server sends a translation request to the generative AI model based on the detected language to another specified language, where the input is the detected language and the language to translate to (e.g., Japanese to English), and the output is a request to the generative AI model.

[0597] Step 5:

[0598] The generative AI model performs a translation based on the request, for example, translating "Today is a wonderful day" in Japanese to "Today is a wonderful day" in English. The output is the translated text.

[0599] Step 6:

[0600] The server receives and stores the translation results from the generative AI model, where the input is the translated text and the output is the stored translation data.

[0601] Step 7:

[0602] The server checks the user's language preference and prepares the translated text for delivery in the appropriate language. For example, an English translation is prepared for a user with English preferences. The input is the user's language preference and the stored translation data, and the output is the data ready for delivery.

[0603] Step 8:

[0604] The server sends the translated post to the user's device, where the data is prepared for distribution.

[0605] Step 9:

[0606] The device displays the received translation result to the user. For example, a user with English settings might see "Today is a wonderful day" on their device. The input is the received translation data, and the output is the displayed text.

[0607] 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.

[0608] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Below, we will explain the program processing of this system in natural language.

[0609] System configuration

[0610] 1. User Submissions

[0611] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[0612] 2. Receipt of Submissions

[0613] The device receives the user's input and sends it to the SNS server as posting data.

[0614] 3. Language Detection

[0615] The server receives the posted data, analyzes its content, and detects the language. For example, it detects that "Today is a wonderful day" is Japanese.

[0616] 4. Emotional Recognition

[0617] The server sends the posted data to the emotion engine to detect the user's emotion, for example, recognizing the emotion of joy from the post.

[0618] 5. Generating a translation request

[0619] The server generates a translation request to the chat generation AI based on the detected language (Japanese in this case) and the recognized emotion.

[0620] 6. Execution of the translation

[0621] The Chat generative AI translates requests based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further incorporating the emotion of joy.

[0622] 7. Receiving translation results

[0623] The server receives and stores the translation results from the chat generation AI.

[0624] 8. Distribution Preparation

[0625] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[0626] 9. Delivery of translation results

[0627] The server sends the translation results to the user's terminal based on each user's language settings.

[0628] 10. Displaying translation results

[0629] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[0630] Specific examples

[0631] A specific example is as follows:

[0632] If user A posts "Today is a wonderful day" in Japanese on social media:

[0633] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[0634] Posts are sent from the device to the server.

[0635] 2. The server receives the post and detects that the language is Japanese.

[0636] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[0637] 4. The server sends a translation request from Japanese to English to the chat generation AI, and also reflects the emotion of joy.

[0638] 5. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day 😊".

[0639] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[0640] 7. The server sends the translated post to User B's device.

[0641] 8. User B's device displays "Today is a wonderful day 😊".

[0642] This system enables users of different languages ​​to communicate smoothly while sharing emotions across language barriers. By combining it with an emotion engine, translated content is expressed more naturally and emotionally, improving the user experience.

[0643] The processing flow will be explained below.

[0644] Step 1:

[0645] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[0646] Step 2:

[0647] The device receives the user's input and sends it to the SNS server as posting data.

[0648] Step 3:

[0649] The server receives the posted data and stores it in a database.

[0650] Step 4:

[0651] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[0652] Step 5:

[0653] The server sends the post data to the emotion engine based on the language identification results. The emotion engine analyzes the text data of the post and identifies the user's emotion. For example, it recognizes positive emotion (joy) from a post that says, "Today is a great day."

[0654] Step 6:

[0655] Based on the emotion recognition results, the server generates a translation request to the chat generation AI. The request includes the original text, Japanese (the source language), English (the target language), and the identified emotion.

[0656] Step 7:

[0657] The server sends a translation request to the chat generation AI.

[0658] Step 8:

[0659] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further reflecting the emotion of joy.

[0660] Step 9:

[0661] The server receives and stores the translation results from the chat generation AI. The translation results express emotions such as "Today is a wonderful day 😊."

[0662] Step 10:

[0663] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[0664] Step 11:

[0665] The server sends the translation results to the user's terminal based on each user's language settings.

[0666] Step 12:

[0667] The device receives the translation and displays the post in the user's language and emotion settings. For example, a user with English settings would see "Today is a wonderful day 😊" on their device.

[0668] Through these steps, users who speak different languages ​​can communicate smoothly through social networking sites while sharing their emotions. By combining this with an emotion engine, emotions are reflected in the translated content, enabling more natural and empathetic communication.

[0669] Example 2

[0670] 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."

[0671] In today's communication environment, multilingual communication has become essential, but language barriers still exist. Furthermore, simple translations tend to lose emotion and nuance, which can degrade the user experience. For this reason, there is a need for a system that can transcend language barriers while faithfully conveying emotion and nuance.

[0672] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0673] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for recognizing the emotion of the post, means for translating the post into another specified language based on the detected language and the recognized emotion, and means for distributing the translated post based on user settings, thereby enabling smooth communication between users who speak different languages ​​without losing emotion or nuance.

[0674] The "means for accepting posts" is a component that has the role of sending messages entered by users on the SNS application to the system.

[0675] The "means for detecting the language of a received post" is a component that has the function of automatically determining the language of a message received by the system.

[0676] The "means for recognizing the emotions of posts" is a component that has the function of analyzing the user's emotions and nuances from received messages and identifying those emotions.

[0677] The "means for translating into another specified language based on the detected language and the recognized emotion" is a component that has the function of performing an appropriate translation based on the language and emotion of the original text.

[0678] The "means for delivering translated posts based on user settings" is a component that has the function of delivering messages that have completed translation processing in accordance with the user's language settings.

[0679] "Generative AI models" refer to the machine learning algorithms used for natural language processing and translation, which play a key role in the system's translation capabilities.

[0680] A "prompt sentence" refers to the input sentence format used to give instructions to a generative AI model for translation or emotion recognition.

[0681] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Specific embodiments of this system are described below.

[0682] System configuration

[0683] 1. User submission input

[0684] A user types a message into an SNS application and presses the post button. For example, the user types "Today is a wonderful day" in Japanese.

[0685] 2. Receiving posted data

[0686] The device receives the user's input and sends the posted data to the SNS server. For example, the device sends a message to the server saying, "Today is a great day."

[0687] 3. Language Detection

[0688] The server receives the posted data, analyzes its content, and detects the language. To recognize that the posted data is in Japanese, the server uses a natural language processing library.

[0689] 4. Emotional Recognition

[0690] The server sends the posted data to the emotion engine to detect the user's emotion. For example, the server sends the text "Today is a great day" to the emotion engine, and recognizes the emotion of "joy" as a result.

[0691] 5. Generating a translation request

[0692] The server generates a translation request to the generative AI model based on the detected language (Japanese in this case) and the recognized emotion. Specifically, it generates a prompt sentence containing a "translation request from Japanese to English" and "emotion: joy" and sends it to the generative AI model.

[0693] 6. Execution of the translation

[0694] The generative AI model processes the translation based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊".

[0695] 7. Receiving translation results

[0696] The server receives the translation result from the generative AI model and stores it. For example, the server receives the translation result "Today is a wonderful day 😊" and stores it in the database.

[0697] 8. Distribution Preparation

[0698] The server checks each user's language setting and prepares to display the translated post in that language and emotion, for example, preparing to display the translated "Today is a wonderful day 😊" for a user with an English setting.

[0699] 9. Delivery of translation results

[0700] The server sends the translation results to the device based on each user's language setting. For example, the server sends the message "Today is a wonderful day 😊" to the device of a user with English settings.

[0701] 10. Displaying translation results

[0702] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[0703] Specific examples

[0704] As a specific example, if user A posts "Today is a wonderful day" in Japanese on an SNS, the process will proceed as follows:

[0705] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[0706] Posts are sent from the device to the server.

[0707] 2. The server receives the post and detects that the language is Japanese.

[0708] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[0709] 4. The server sends a translation request from Japanese to English to the generated AI model, and also reflects the emotion of joy.

[0710] 5. The generative AI model translates "Today is a wonderful day" to "Today is a wonderful day 😊".

[0711] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[0712] 7. The server sends the translated post to User B's device.

[0713] 8. User B's device displays "Today is a wonderful day 😊".

[0714] This system enables smooth communication between users of different languages ​​without losing emotion or nuance.

[0715] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0716] Step 1:

[0717] The user enters a message in the SNS application and presses the post button.

[0718] - Input: A message entered by the user into a social networking app (e.g., "Today is a great day")

[0719] - Output: Post data generated by the device

[0720] Step 2:

[0721] The device receives the user's input and sends the posted data to the SNS server.

[0722] - Input: Post data entered by the user

[0723] - Output: Post data sent to the server

[0724] Step 3:

[0725] The server receives the posted data and analyzes its content to detect the language.

[0726] - Input: Post data received by the server

[0727] - Data processing: Analyze language using natural language processing libraries

[0728] - Output: Detected language (e.g. Japanese)

[0729] Step 4:

[0730] The server sends the posted data to the emotion engine to detect the user's emotions.

[0731] - Input: Server-detected language and post data

[0732] - Data calculation: Analyze the sentiment of posts using the sentiment engine

[0733] - Output: Perceived emotion (e.g., joy)

[0734] Step 5:

[0735] The server generates a translation request to a generative AI model based on the detected language and recognized sentiment.

[0736] - Input: Detected language (Japanese) and recognized emotion (joy)

[0737] - Data calculation: Prompt sentence generation (e.g., "Japanese to English translation request" "Emotion: joy")

[0738] - Output: Generated prompt statement

[0739] Step 6:

[0740] The generative AI model performs translation processing based on the request.

[0741] - Input: Prompt sent from the server

[0742] - Data calculation: Translation processing using generative AI models (e.g., translating "Today is a wonderful day" to "Today is a wonderful day 😊")

[0743] - Output: Translated text

[0744] Step 7:

[0745] The server receives the translation results from the generative AI model and stores them.

[0746] - Input: Translation results returned by the generative AI model

[0747] - Data processing: Saving translation results

[0748] - Output: Saved translation data

[0749] Step 8:

[0750] The server checks each user's language preference and prepares the translated post to be displayed in that language and emotion.

[0751] - Input: saved translation data and user language settings

[0752] - Data calculation: Preparing display based on user language settings (e.g., "Today is a wonderful day 😊")

[0753] - Output: Data ready for display

[0754] Step 9:

[0755] The server sends the translation results to the terminal based on each user's language settings.

[0756] - Input: Data ready to display

[0757] - Output: Translation results sent to the user's device

[0758] Step 10:

[0759] The terminal displays the translation result received to the user.

[0760] - Input: Translation results sent from the server

[0761] - Output: The translation result displayed on the user's device (e.g., "Today is a wonderful day 😊")

[0762] (Application example 2)

[0763] 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."

[0764] The purpose of this invention is to enable smooth communication between users who speak different languages ​​on social networking platforms. Conventional technologies have had the problem of losing emotion or producing unnatural expressions simply by translating text. This problem is particularly pronounced in situations where emotional expression is important, resulting in a decline in the quality of communication. Therefore, there is a need to provide a system that recognizes the user's emotions and reflects them in the translation results, thereby enabling natural, emotionally rich communication.

[0765] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for recognizing the emotion of the accepted post, means for reflecting the recognized emotion in the translation result, and means for delivering the translated post based on the user's settings. This enables users who speak different languages ​​to communicate with each other in a rich and emotional way, overcoming language barriers.

[0766] "Posting" refers to the act of a user typing and publishing a message on a social media platform.

[0767] "Means for detecting language" is a function that determines the language in which a user's post is written.

[0768] The "translation means" is a function for converting a detected language into another specified language.

[0769] "Means for recognizing emotions" is a function that analyzes and identifies emotions from user posts.

[0770] "Means for reflecting emotions in translation results" refers to a function that takes into account recognized emotions and includes them in the translation results.

[0771] "User settings" are the display language and other display option settings that the user has preselected.

[0772] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and performs natural language processing, image generation, and other tasks.

[0773] A "prompt" is a document or keyword used as input to a generative AI model.

[0774] This invention is a system that translates messages posted on a social networking platform and distributes them to other users in a format that reflects their emotions. Specifically, the system performs a series of processes: accepting posts, detecting the language, recognizing emotions, translating, and finally displaying the translation results based on the user's settings. A specific example of this system is described in detail below.

[0775] System Configuration

[0776] The system includes a server, a terminal, an emotion engine, and a generative AI model. The role of each part and the specific processing steps are explained below.

[0777] Terminal

[0778] The terminal is a device that accepts user posts. When a user enters a message on the terminal and presses the post button, the data is sent to the server. For example, if user A posts "Today is a wonderful day" in Japanese, this post is sent from the terminal to the server.

[0779] server

[0780] The server analyzes the received post data and detects the language. For example, the server detects that the post "Today is a wonderful day" is in Japanese. The server then sends the post data to the emotion engine to recognize emotions. The server detects the emotion of joy from the post.

[0781] The server then generates a translation request to a generative AI model based on the detected language (Japanese) and the recognized emotion (joy). An example of a generative AI model is a large-scale transformer model. The generative AI model translates the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊."

[0782] When the translation result is sent back to the server, the server prepares it to be displayed in the appropriate language and emotion based on the user's settings. For example, User B, who has English settings, will see "Today is a wonderful day 😊" on their device.

[0783] Hardware and software used

[0784] Devices: Smartphones, tablets, computers, etc.

[0785] Server: A high-performance server machine

[0786] Emotion engine: Natural language processing library (e.g. NLTK, spaCy)

[0787] Generative AI models: Large-scale transformer models (e.g., GPT-3)

[0788] Specific examples

[0789] As a concrete example, let's look at the flow when User A posts "Today is a wonderful day" in Japanese. First, the device sends the posted data to the server. The server detects that the language is Japanese and recognizes the emotion of joy using the emotion engine. The server then sends a translation request from Japanese to English to the generative AI model, and receives the translation result "Today is a wonderful day 😊" that reflects the emotion. This translation result is then sent to User B's device, where it is displayed on User B's device.

[0790] Prompt Sentence Examples

[0791] "Translate the Japanese text 'Today is a wonderful day' to English. Make sure to include the user's emotion, which is joy, in the translation."

[0792] This provides a system that allows users who speak different languages ​​to communicate smoothly, including sharing emotions.

[0793] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0794] Step 1:

[0795] The user enters a message into the terminal and presses the post button. As input, the text entered by the user in Japanese, "Today is a wonderful day," is obtained. As output, this text is sent to the server as post data.

[0796] Step 2:

[0797] The server detects the language of the submitted data it receives. The input is text data received from the user, and the output detects that the language is Japanese. Specifically, it runs a language detection algorithm to identify the language of the text.

[0798] Step 3:

[0799] The server sends the post data to the emotion engine to recognize the emotion. Using the post data as input, the emotion detected as output is recognized as "joy." The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract emotions from text.

[0800] Step 4:

[0801] The server generates a translation request to the generative AI model based on the detected language (Japanese) and the recognized emotion (joy). It uses the detected language and emotion, and the source text as input, and generates a translation request as output. Specifically, it generates a prompt sentence and provides it to the generative AI model (e.g., GPT-3).

[0802] Step 5:

[0803] The generative AI model translates based on the translation request. It receives the generated prompt as input and obtains the translation result "Today is a wonderful day 😊" as output. The generative AI model uses a large-scale Transformer model to simultaneously translate between languages ​​and reflect sentiment.

[0804] Step 6:

[0805] The server stores the translation results received from the generative AI model and prepares them for display based on the user's settings. It uses the translation results and user settings as input and obtains displayable data as output. Specifically, it formats the translation results with the appropriate language and sentiment.

[0806] Step 7:

[0807] The server sends the translated post to the user's device, using the processed data as input and sending the data to the user's device as output. Specifically, the data is sent over the network to the appropriate device.

[0808] Step 8:

[0809] The user's device displays the translation results it receives. It uses the data sent from the server as input and displays the translation results to the user as output. Specifically, it renders the text on the device's display.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] [Third embodiment]

[0814] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0815] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0816] 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).

[0817] 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.

[0818] 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.

[0819] 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).

[0820] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0821] 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.

[0822] 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.

[0823] 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.

[0824] 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.

[0825] 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."

[0826] This invention provides a system that combines a social networking platform with chat generation AI, allowing users to view content posted in different languages ​​in a language they can understand. Below, we will explain the program processing of this system in natural language.

[0827] System configuration

[0828] 1. User Submissions

[0829] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[0830] 2. Receipt of Submissions

[0831] The device receives the user's input and sends it to the SNS's server.

[0832] 3. Language Detection

[0833] The server receives the post and analyzes its content to detect the language it is in. For example, it receives the post "Today is a great day" and identifies it as Japanese.

[0834] 4. Generating a translation request

[0835] Based on the detected language (Japanese in this case), the server creates and sends a translation request to the chat generation AI.

[0836] 5. Execution of the translation

[0837] The Chat generative AI translates based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0838] 6. Receiving translation results

[0839] The server receives and stores the translation results.

[0840] 7. Distribution Preparation

[0841] The server checks each user's language preference and prepares a translated post in the appropriate language, for example, "Today is a wonderful day" for a user with English preferences.

[0842] 8. Delivery of translation results

[0843] The server sends the translated post to the user's device.

[0844] 9. Displaying translation results

[0845] The device then displays the translation results to the user. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0846] Specific examples

[0847] A specific example is as follows:

[0848] 1. User A posts on social media in Japanese, "Today is a wonderful day."

[0849] Posts are sent from the device to the server.

[0850] 2. The server receives the post and detects that the language is Japanese.

[0851] 3. The server sends a translation request from Japanese to English to the chat generation AI.

[0852] 4. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day."

[0853] 5. The server receives the translation and prepares the translated post for English users.

[0854] 6. The server sends the translated post to User B's device.

[0855] 7. User B's device displays "Today is a wonderful day."

[0856] This system enables smooth communication through SNS even between users who speak different languages, and allows information to be shared across language barriers, leading to richer international exchange.

[0857] The processing flow will be explained below.

[0858] Step 1:

[0859] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[0860] Step 2:

[0861] The device receives the user's input and sends it to the SNS server as posting data.

[0862] Step 3:

[0863] The server receives the posted data and stores it in a database.

[0864] Step 4:

[0865] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[0866] Step 5:

[0867] Based on the language identification results, the server generates a translation request to the chat generation AI.

[0868] Step 6:

[0869] The server sends this request to the chat generation AI, which includes the original text, the original language, and the language to translate it into.

[0870] Step 7:

[0871] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[0872] Step 8:

[0873] The server receives and stores the translation results from the chat generation AI.

[0874] Step 9:

[0875] The server checks each user's language preference and prepares to display the translated post in that language, for example, "Today is a wonderful day" for a user with English preferences, or "Aujourd'hui est une journée merveilleuse" for a user with French preferences.

[0876] Step 10:

[0877] The server sends the translation results to the user's terminal based on each user's language settings.

[0878] Step 11:

[0879] The device receives the translation and displays the post in the user's preferred language. For example, a user with English settings will see "Today is a wonderful day" on their device.

[0880] Example 1

[0881] 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."

[0882] With the development of modern communication technology, smooth communication between users who speak different languages ​​is required. However, current social networking platforms lack a means for users to easily understand content posted in different languages. This makes it difficult for users to share information between them, and is a barrier to international communication.

[0883] 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.

[0884] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for distributing the translated post based on user settings, a terminal for accepting user input, means for analyzing the received post content, means for generating a prompt sentence and sending it to a language model, means for saving the translation result from the language model, and a terminal for displaying the translation result, thereby enabling users who speak different languages ​​to communicate smoothly through the SNS.

[0885] A "means for accepting posts" is a part of the system that has the function of receiving and processing messages entered by users.

[0886] The "means for detecting the language of received posts" is a part of the system that has the ability to automatically analyze and identify the language of received messages.

[0887] A "means for translating from a detected language to another specified language" is a part of a system that has the functionality to translate messages in a specified language into another language.

[0888] The "means for delivering translated posts based on user preferences" is a part of the system that has the functionality to deliver translated messages in the appropriate format based on the user's language preferences.

[0889] A "terminal that receives user input" refers to a device (e.g., a smartphone or PC) that allows a user to input a message.

[0890] The "means for analyzing the received post content" is a part of the system that has the function of analyzing the information in the received message in detail and extracting the necessary data.

[0891] The "means for generating a prompt sentence and sending it to a language model" is a part of the system that has the function of generating a message in the input format required by the language model for translation and sending it to the language model.

[0892] The "means for storing translation results from a language model" is a part of a system that has the functionality to store translation results generated by a language model in a database or other storage device.

[0893] "Device displaying translation results" refers to a device (e.g., a smartphone or PC) that displays the translated message to the user.

[0894] This invention provides a system that allows users who speak different languages ​​to communicate smoothly by combining an SNS platform with chat generation AI. The detailed configuration and implementation method of this system are described below.

[0895] Overall system configuration

[0896] 1. User's device

[0897] Users access the SNS platform using devices such as smartphones or PCs and enter messages.

[0898] 2. Server

[0899] The SNS platform server receives the message sent from the user's device, detects the language, generates a translation request, receives and stores the translation result, and delivers the translation result into the appropriate language.

[0900] 3. Chat generation AI

[0901] The chat generation AI (e.g., GPT-4) receives translation requests from the server and translates them into the specified language.

[0902] Specific functions of the system

[0903] 1. Entering and Submitting User Submissions

[0904] A user enters "Today is a wonderful day" in Japanese into the input field of a social networking application and presses the send button.

[0905] The device receives the message and sends it to the SNS server.

[0906] 2. Receiving posts and detecting language

[0907] The server receives the message "Today is a great day" sent from the terminal.

[0908] The server analyzes the received message and detects that the language used is Japanese.

[0909] 3. Creating and sending a translation request

[0910] The server generates a translation request from Japanese to English based on the language detection results.

[0911] The server sends the generated translation request to the chat generation AI.

[0912] Example prompt: "Please translate the following Japanese sentence into English: Today is a wonderful day."

[0913] 4. Execution of the translation

[0914] The chat generation AI processes the request received from the server and translates "Today is a wonderful day" into "Today is a wonderful day."

[0915] 5. Receiving and saving translation results

[0916] The server receives the translation result "Today is a wonderful day" from the chat generation AI.

[0917] The server stores the translation results in a database.

[0918] 6. Preparing for distribution of translation results

[0919] The server sees that User B's preference is English and prepares the post translated into English.

[0920] 7. Delivery of translation results

[0921] The server sends the prepared translation result "Today is a wonderful day" to User B's device.

[0922] 8. Displaying translation results

[0923] The terminal displays the translation results received to User B.

[0924] Display: "Today is a wonderful day"

[0925] This system will enable users who speak different languages ​​to communicate smoothly through SNS, realizing international information sharing.

[0926] This allows users to view content posted in different languages ​​in a language they can understand, facilitating international communication through SNS. As a multilingual SNS platform, this system will greatly improve the convenience of communication between users.

[0927] Hardware and software used

[0928] Hardware: SNS platform servers, users' smartphones and PCs

[0929] Software: Social networking applications, chat generation AI (e.g., GPT-4)

[0930] In this way, the present invention makes it possible to communicate between multiple languages, which has previously been difficult, by using specific hardware and software configurations.

[0931] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0932] Step 1:

[0933] A user enters a message into the input field of an SNS application and presses the send button. At this time, the Japanese message "Today is a wonderful day" is entered into the user's device as input. The device generates a request to send the message to the SNS server and sends it to the server. As an output, a request to send the message to the server is generated and arrives at the server.

[0934] Step 2:

[0935] The server receives the message sent from the terminal and analyzes its contents. The data received as input is the Japanese message "Today is a wonderful day." The server uses its language analysis function to detect that the language used in this message is Japanese and extracts the language information. As output, the server generates the language information that the message is in Japanese.

[0936] Step 3:

[0937] The server generates a translation request based on the detected language information. The input is the language information being Japanese and the original message "Today is a wonderful day." The server generates a prompt sentence and sends the translation request to the language model. This prompt sentence is "Please translate the following Japanese sentence into English: Today is a wonderful day." The output is a translation request generated and sent to the language model.

[0938] Step 4:

[0939] The chat generation AI processes the translation request received from the server. The input is the translation request prompt, "Please translate the following Japanese sentence into English: Today is a wonderful day." The AI ​​performs translation processing based on this prompt, translating the message from Japanese to English. The output is the translation result, "Today is a wonderful day."

[0940] Step 5:

[0941] The server receives the translation result from the chat generation AI. The input is the translation result "Today is a wonderful day." The server saves this translation result in a database. The output is the saved translation result.

[0942] Step 6:

[0943] The server checks each user's language preference. The input includes User B's language preference and the saved translation "Today is a wonderful day." The server verifies that User B's language preference is English and prepares the appropriate translation for this user. The output is the post translated into English.

[0944] Step 7:

[0945] The server sends the prepared translation result to User B's device. The input is the prepared translation result "Today is a wonderful day." The server creates a message including the translation result and sends it to User B's device. The output is the message delivered to User B's device.

[0946] Step 8:

[0947] The device displays the translation result it received to User B. The input is the translation result "Today is a wonderful day" sent from the server. The device displays this message on the screen. The output is "Today is a wonderful day" displayed on User B's device, allowing the user to view the translation result.

[0948] (Application example 1)

[0949] 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."

[0950] When users of different languages ​​use content distribution services, it is difficult to provide subtitles for video and audio content in multiple languages. Language differences can be a barrier, especially when communicating and consuming content between international users, degrading the user experience. Furthermore, because translation quality and timeliness are important, there is a demand for high-precision, fast translation.

[0951] 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.

[0952] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted posts, means for translating the detected language into another specified language, means for distributing the translated posts based on user settings, and means for automatically generating translated subtitles for video or audio content. This enables multilingual subtitles for video or audio content to be automatically generated and displayed when users who speak different languages ​​use the content distribution service.

[0953] "Posting" refers to a user entering content on a social media platform and disseminating it to other users.

[0954] "Language detection means" refers to technology or programming used to identify the language of an accepted submission.

[0955] "Translation means" refers to technology or programs used to convert content written in a particular language into another specified language.

[0956] "Means of delivery" refers to the technology and programs used to deliver translated content to users' devices.

[0957] "Video or audio content" refers to media formats that convey information visually and aurally.

[0958] "Means for automatic subtitling generation" refers to technology or programs that generate and display text in a specified language for video or audio content.

[0959] "Generative artificial intelligence" refers to advanced machine learning models that generate text and translate based on large amounts of data.

[0960] "User Settings" refers to options and settings that a user can individually set for their language and display format.

[0961] This invention provides a system that combines a social networking platform with a generative AI model to enable users to view content posted in different languages ​​in a language they understand. It also includes a means for automatically generating multilingual subtitles in a content distribution service. A specific embodiment of this system will be described.

[0962] System configuration

[0963] 1. Submission acceptance

[0964] The server accepts posts entered by a user on the SNS application. For example, the user enters "Today is a great day" and presses the post button.

[0965] 2. Language Detection

[0966] The server detects the language of the received post using tools such as the Google Translate API. For example, if the post says "Today is a great day," it will identify it as Japanese.

[0967] 3. Generating a translation request

[0968] The server sends a translation request to the generative AI model based on the detected language and the user's settings. For example, when translating from Japanese to English, it translates "Today is a wonderful day."

[0969] 4. Delivery of translation results

[0970] The translated post is stored on a server and delivered to the device that displays the post based on the user's language settings—for example, a user with English settings might see "Today is a wonderful day."

[0971] 5. Application to video and audio content

[0972] The server automatically generates translated subtitles for video or audio content using a generative AI model based on the user's language settings, allowing users of different languages ​​to watch the same content with subtitles in the specified language.

[0973] Hardware and software used

[0974] The system uses the following hardware and software:

[0975] Server: Web server required to run the SNS platform (e.g. AWS EC2)

[0976] Device: The smartphone, tablet, or computer used by the user

[0977] Software: Google Translate API, generative AI models (e.g., GPT-4)

[0978] Specific examples

[0979] For example, if a user posts "Today is a wonderful day" in Japanese on a social media platform, the server receives the post and detects that it is in Japanese. The server then requests a Japanese-to-English translation from the generative AI model, translates it to "Today is a wonderful day," and delivers it to User B's device. At this time, if User B is watching video content, the generated English subtitles will also be displayed.

[0980] Prompt Sentence Examples

[0981] "Piza o tsuika chūmon shimasu" wo eigo ni hon'yaku shite kudasai.

[0982] Translate "Piza o tsuika chūmon shimasu" into English.

[0983] This allows users who speak different languages ​​to enjoy the same content, promoting international exchange.

[0984] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0985] Step 1:

[0986] The user uses a device to input a post on the SNS platform and presses the post button. This is the initial input to the system. Specifically, for example, the user might input "Today is a wonderful day" in Japanese.

[0987] Step 2:

[0988] The device receives the user's input and sends it to the server, which then forwards the input data to the SNS platform's server.

[0989] Step 3:

[0990] The server analyzes the content of the received post and detects the language of the post using the Google Translate API, etc. For example, it identifies a post saying "Today is a great day" as Japanese. The detected language is output.

[0991] Step 4:

[0992] The server sends a translation request to the generative AI model based on the detected language to another specified language, where the input is the detected language and the language to translate to (e.g., Japanese to English), and the output is a request to the generative AI model.

[0993] Step 5:

[0994] The generative AI model performs a translation based on the request, for example, translating "Today is a wonderful day" in Japanese to "Today is a wonderful day" in English. The output is the translated text.

[0995] Step 6:

[0996] The server receives and stores the translation results from the generative AI model, where the input is the translated text and the output is the stored translation data.

[0997] Step 7:

[0998] The server checks the user's language preference and prepares the translated text for delivery in the appropriate language. For example, an English translation is prepared for a user with English preferences. The input is the user's language preference and the stored translation data, and the output is the data ready for delivery.

[0999] Step 8:

[1000] The server sends the translated post to the user's device, where the data is prepared for distribution.

[1001] Step 9:

[1002] The device displays the received translation result to the user. For example, a user with English settings might see "Today is a wonderful day" on their device. The input is the received translation data, and the output is the displayed text.

[1003] 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.

[1004] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Below, we will explain the program processing of this system in natural language.

[1005] System configuration

[1006] 1. User Submissions

[1007] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[1008] 2. Receipt of Submissions

[1009] The device receives the user's input and sends it to the SNS server as posting data.

[1010] 3. Language Detection

[1011] The server receives the posted data, analyzes its content, and detects the language. For example, it detects that "Today is a wonderful day" is Japanese.

[1012] 4. Emotional Recognition

[1013] The server sends the posted data to the emotion engine to detect the user's emotion, for example, recognizing the emotion of joy from the post.

[1014] 5. Generating a translation request

[1015] The server generates a translation request to the chat generation AI based on the detected language (Japanese in this case) and the recognized emotion.

[1016] 6. Execution of the translation

[1017] The Chat generative AI translates requests based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further incorporating the emotion of joy.

[1018] 7. Receiving translation results

[1019] The server receives and stores the translation results from the chat generation AI.

[1020] 8. Distribution Preparation

[1021] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[1022] 9. Delivery of translation results

[1023] The server sends the translation results to the user's terminal based on each user's language settings.

[1024] 10. Displaying translation results

[1025] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1026] Specific examples

[1027] A specific example is as follows:

[1028] If user A posts "Today is a wonderful day" in Japanese on social media:

[1029] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[1030] Posts are sent from the device to the server.

[1031] 2. The server receives the post and detects that the language is Japanese.

[1032] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[1033] 4. The server sends a translation request from Japanese to English to the chat generation AI, and also reflects the emotion of joy.

[1034] 5. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day 😊".

[1035] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[1036] 7. The server sends the translated post to User B's device.

[1037] 8. User B's device displays "Today is a wonderful day 😊".

[1038] This system enables users of different languages ​​to communicate smoothly while sharing emotions across language barriers. By combining it with an emotion engine, translated content is expressed more naturally and emotionally, improving the user experience.

[1039] The processing flow will be explained below.

[1040] Step 1:

[1041] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[1042] Step 2:

[1043] The device receives the user's input and sends it to the SNS server as posting data.

[1044] Step 3:

[1045] The server receives the posted data and stores it in a database.

[1046] Step 4:

[1047] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[1048] Step 5:

[1049] The server sends the post data to the emotion engine based on the language identification results. The emotion engine analyzes the text data of the post and identifies the user's emotion. For example, it recognizes positive emotion (joy) from a post that says, "Today is a great day."

[1050] Step 6:

[1051] Based on the emotion recognition results, the server generates a translation request to the chat generation AI. The request includes the original text, Japanese (the source language), English (the target language), and the identified emotion.

[1052] Step 7:

[1053] The server sends a translation request to the chat generation AI.

[1054] Step 8:

[1055] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further reflecting the emotion of joy.

[1056] Step 9:

[1057] The server receives and stores the translation results from the chat generation AI. The translation results express emotions such as "Today is a wonderful day 😊."

[1058] Step 10:

[1059] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[1060] Step 11:

[1061] The server sends the translation results to the user's terminal based on each user's language settings.

[1062] Step 12:

[1063] The device receives the translation and displays the post in the user's language and emotion settings. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1064] Through these steps, users who speak different languages ​​can communicate smoothly through social networking sites while sharing their emotions. By combining this with an emotion engine, emotions are reflected in the translated content, enabling more natural and empathetic communication.

[1065] Example 2

[1066] 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."

[1067] In today's communication environment, multilingual communication has become essential, but language barriers still exist. Furthermore, simple translations tend to lose emotion and nuance, which can degrade the user experience. For this reason, there is a need for a system that can transcend language barriers while faithfully conveying emotion and nuance.

[1068] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1069] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for recognizing the emotion of the post, means for translating the post into another specified language based on the detected language and the recognized emotion, and means for distributing the translated post based on user settings, thereby enabling smooth communication between users who speak different languages ​​without losing emotion or nuance.

[1070] The "means for accepting posts" is a component that has the role of sending messages entered by users on the SNS application to the system.

[1071] The "means for detecting the language of a received post" is a component that has the function of automatically determining the language of a message received by the system.

[1072] The "means for recognizing the emotions of posts" is a component that has the function of analyzing the user's emotions and nuances from received messages and identifying those emotions.

[1073] The "means for translating into another specified language based on the detected language and the recognized emotion" is a component that has the function of performing an appropriate translation based on the language and emotion of the original text.

[1074] The "means for delivering translated posts based on user settings" is a component that has the function of delivering messages that have completed translation processing in accordance with the user's language settings.

[1075] "Generative AI models" refer to the machine learning algorithms used for natural language processing and translation, which play a key role in the system's translation capabilities.

[1076] A "prompt sentence" refers to the input sentence format used to give instructions to a generative AI model for translation or emotion recognition.

[1077] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Specific embodiments of this system are described below.

[1078] System configuration

[1079] 1. User submission input

[1080] A user types a message into an SNS application and presses the post button. For example, the user types "Today is a wonderful day" in Japanese.

[1081] 2. Receiving posted data

[1082] The device receives the user's input and sends the posted data to the SNS server. For example, the device sends a message to the server saying, "Today is a great day."

[1083] 3. Language Detection

[1084] The server receives the posted data, analyzes its content, and detects the language. To recognize that the posted data is in Japanese, the server uses a natural language processing library.

[1085] 4. Emotional Recognition

[1086] The server sends the posted data to the emotion engine to detect the user's emotion. For example, the server sends the text "Today is a great day" to the emotion engine, and recognizes the emotion of "joy" as a result.

[1087] 5. Generating a translation request

[1088] The server generates a translation request to the generative AI model based on the detected language (Japanese in this case) and the recognized emotion. Specifically, it generates a prompt sentence containing a "translation request from Japanese to English" and "emotion: joy" and sends it to the generative AI model.

[1089] 6. Execution of the translation

[1090] The generative AI model processes the translation based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊".

[1091] 7. Receiving translation results

[1092] The server receives the translation result from the generative AI model and stores it. For example, the server receives the translation result "Today is a wonderful day 😊" and stores it in the database.

[1093] 8. Distribution Preparation

[1094] The server checks each user's language setting and prepares to display the translated post in that language and emotion, for example, preparing to display the translated "Today is a wonderful day 😊" for a user with an English setting.

[1095] 9. Delivery of translation results

[1096] The server sends the translation results to the device based on each user's language setting. For example, the server sends the message "Today is a wonderful day 😊" to the device of a user with English settings.

[1097] 10. Displaying translation results

[1098] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1099] Specific examples

[1100] As a specific example, if user A posts "Today is a wonderful day" in Japanese on an SNS, the process will proceed as follows:

[1101] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[1102] Posts are sent from the device to the server.

[1103] 2. The server receives the post and detects that the language is Japanese.

[1104] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[1105] 4. The server sends a translation request from Japanese to English to the generated AI model, and also reflects the emotion of joy.

[1106] 5. The generative AI model translates "Today is a wonderful day" to "Today is a wonderful day 😊".

[1107] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[1108] 7. The server sends the translated post to User B's device.

[1109] 8. User B's device displays "Today is a wonderful day 😊".

[1110] This system enables smooth communication between users of different languages ​​without losing emotion or nuance.

[1111] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1112] Step 1:

[1113] The user enters a message in the SNS application and presses the post button.

[1114] - Input: A message entered by the user into a social networking app (e.g., "Today is a great day")

[1115] - Output: Post data generated by the device

[1116] Step 2:

[1117] The device receives the user's input and sends the posted data to the SNS server.

[1118] - Input: Post data entered by the user

[1119] - Output: Post data sent to the server

[1120] Step 3:

[1121] The server receives the posted data and analyzes its content to detect the language.

[1122] - Input: Post data received by the server

[1123] - Data processing: Analyze language using natural language processing libraries

[1124] - Output: Detected language (e.g. Japanese)

[1125] Step 4:

[1126] The server sends the posted data to the emotion engine to detect the user's emotions.

[1127] - Input: Server-detected language and post data

[1128] - Data calculation: Analyze the sentiment of posts using the sentiment engine

[1129] - Output: Perceived emotion (e.g., joy)

[1130] Step 5:

[1131] The server generates a translation request to a generative AI model based on the detected language and recognized sentiment.

[1132] - Input: Detected language (Japanese) and recognized emotion (joy)

[1133] - Data calculation: Prompt sentence generation (e.g., "Japanese to English translation request" "Emotion: joy")

[1134] - Output: Generated prompt statement

[1135] Step 6:

[1136] The generative AI model performs translation processing based on the request.

[1137] - Input: Prompt sent from the server

[1138] - Data calculation: Translation processing using generative AI models (e.g., translating "Today is a wonderful day" to "Today is a wonderful day 😊")

[1139] - Output: translated text

[1140] Step 7:

[1141] The server receives the translation results from the generative AI model and stores them.

[1142] - Input: Translation results returned by the generative AI model

[1143] - Data processing: Saving translation results

[1144] - Output: Saved translation data

[1145] Step 8:

[1146] The server checks each user's language preference and prepares the translated post to be displayed in that language and emotion.

[1147] - Input: saved translation data and user language settings

[1148] - Data calculation: Preparing display based on user language settings (e.g., "Today is a wonderful day 😊")

[1149] - Output: Data ready for display

[1150] Step 9:

[1151] The server sends the translation results to the terminal based on each user's language settings.

[1152] - Input: Data ready to display

[1153] - Output: Translation results sent to the user's device

[1154] Step 10:

[1155] The terminal displays the translation result received to the user.

[1156] - Input: Translation results sent from the server

[1157] - Output: The translation result displayed on the user's device (e.g., "Today is a wonderful day 😊")

[1158] (Application example 2)

[1159] 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."

[1160] The purpose of this invention is to enable smooth communication between users who speak different languages ​​on social networking platforms. Conventional technologies have had the problem of losing emotion or producing unnatural expressions simply by translating text. This problem is particularly pronounced in situations where emotional expression is important, resulting in a decline in the quality of communication. Therefore, there is a need to provide a system that recognizes the user's emotions and reflects them in the translation results, thereby enabling natural, emotionally rich communication.

[1161] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for recognizing the emotion of the accepted post, means for reflecting the recognized emotion in the translation result, and means for delivering the translated post based on the user's settings. This enables users who speak different languages ​​to communicate with each other in a rich and emotional way, overcoming language barriers.

[1162] "Posting" refers to the act of a user typing and publishing a message on a social media platform.

[1163] "Means for detecting language" is a function that determines the language in which a user's post is written.

[1164] The "translation means" is a function for converting a detected language into another specified language.

[1165] "Means for recognizing emotions" is a function that analyzes and identifies emotions from user posts.

[1166] "Means for reflecting emotions in translation results" refers to a function that takes into account recognized emotions and includes them in the translation results.

[1167] "User settings" are the display language and other display option settings that the user has preselected.

[1168] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and performs natural language processing, image generation, and other tasks.

[1169] A "prompt" is a document or keyword used as input to a generative AI model.

[1170] This invention is a system that translates messages posted on a social networking platform and distributes them to other users in a format that reflects their emotions. Specifically, the system performs a series of processes: accepting posts, detecting the language, recognizing emotions, translating, and finally displaying the translation results based on the user's settings. A specific example of this system is described in detail below.

[1171] System Configuration

[1172] The system includes a server, a terminal, an emotion engine, and a generative AI model. The role of each part and the specific processing steps are explained below.

[1173] Terminal

[1174] The terminal is a device that accepts user posts. When a user enters a message on the terminal and presses the post button, the data is sent to the server. For example, if user A posts "Today is a wonderful day" in Japanese, this post is sent from the terminal to the server.

[1175] server

[1176] The server analyzes the received post data and detects the language. For example, the server detects that the post "Today is a wonderful day" is in Japanese. The server then sends the post data to the emotion engine to recognize emotions. The server detects the emotion of joy from the post.

[1177] The server then generates a translation request to a generative AI model based on the detected language (Japanese) and the recognized emotion (joy). An example of a generative AI model is a large-scale transformer model. The generative AI model translates the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊."

[1178] When the translation result is sent back to the server, the server prepares it to be displayed in the appropriate language and emotion based on the user's settings. For example, User B, who has English settings, will see "Today is a wonderful day 😊" on their device.

[1179] Hardware and software used

[1180] Devices: Smartphones, tablets, computers, etc.

[1181] Server: A high-performance server machine

[1182] Emotion engine: Natural language processing library (e.g. NLTK, spaCy)

[1183] Generative AI models: Large-scale transformer models (e.g., GPT-3)

[1184] Specific examples

[1185] As a concrete example, let's look at the flow when User A posts "Today is a wonderful day" in Japanese. First, the device sends the posted data to the server. The server detects that the language is Japanese and recognizes the emotion of joy using the emotion engine. The server then sends a translation request from Japanese to English to the generative AI model, and receives the translation result "Today is a wonderful day 😊" that reflects the emotion. This translation result is then sent to User B's device, where it is displayed on User B's device.

[1186] Prompt Sentence Examples

[1187] "Translate the Japanese text 'Today is a wonderful day' to English. Make sure to include the user's emotion, which is joy, in the translation."

[1188] This provides a system that allows users who speak different languages ​​to communicate smoothly, including sharing emotions.

[1189] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1190] Step 1:

[1191] The user enters a message into the terminal and presses the post button. As input, the text entered by the user in Japanese, "Today is a wonderful day," is obtained. As output, this text is sent to the server as post data.

[1192] Step 2:

[1193] The server detects the language of the submitted data it receives. The input is text data received from the user, and the output detects that the language is Japanese. Specifically, it runs a language detection algorithm to identify the language of the text.

[1194] Step 3:

[1195] The server sends the post data to the emotion engine to recognize the emotion. Using the post data as input, the emotion detected as output is recognized as "joy." The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract emotions from text.

[1196] Step 4:

[1197] The server generates a translation request to the generative AI model based on the detected language (Japanese) and the recognized emotion (joy). It uses the detected language and emotion, and the source text as input, and generates a translation request as output. Specifically, it generates a prompt sentence and provides it to the generative AI model (e.g., GPT-3).

[1198] Step 5:

[1199] The generative AI model translates based on the translation request. It receives the generated prompt as input and obtains the translation result "Today is a wonderful day 😊" as output. The generative AI model uses a large-scale Transformer model to simultaneously translate between languages ​​and reflect sentiment.

[1200] Step 6:

[1201] The server stores the translation results received from the generative AI model and prepares them for display based on the user's settings. It uses the translation results and user settings as input and obtains displayable data as output. Specifically, it formats the translation results with the appropriate language and sentiment.

[1202] Step 7:

[1203] The server sends the translated post to the user's device, using the processed data as input and sending the data to the user's device as output. Specifically, the data is sent over the network to the appropriate device.

[1204] Step 8:

[1205] The user's device displays the translation results it receives. It uses the data sent from the server as input and displays the translation results to the user as output. Specifically, it renders the text on the device's display.

[1206] 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.

[1207] 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.

[1208] 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.

[1209] [Fourth embodiment]

[1210] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1211] 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.

[1212] 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).

[1213] 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.

[1214] 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.

[1215] 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).

[1216] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1217] 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.

[1218] 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.

[1219] 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.

[1220] 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.

[1221] 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.

[1222] 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."

[1223] This invention provides a system that combines a social networking platform with chat generation AI, allowing users to view content posted in different languages ​​in a language they can understand. Below, we will explain the program processing of this system in natural language.

[1224] System configuration

[1225] 1. User Submissions

[1226] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[1227] 2. Receipt of Submissions

[1228] The device receives the user's input and sends it to the SNS's server.

[1229] 3. Language Detection

[1230] The server receives the post and analyzes its content to detect the language it is in. For example, it receives the post "Today is a great day" and identifies it as Japanese.

[1231] 4. Generating a translation request

[1232] Based on the detected language (Japanese in this case), the server creates and sends a translation request to the chat generation AI.

[1233] 5. Execution of the translation

[1234] The Chat generative AI translates based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[1235] 6. Receiving translation results

[1236] The server receives and stores the translation results.

[1237] 7. Distribution Preparation

[1238] The server checks each user's language preference and prepares a translated post in the appropriate language, for example, "Today is a wonderful day" for a user with English preferences.

[1239] 8. Delivery of translation results

[1240] The server sends the translated post to the user's device.

[1241] 9. Displaying translation results

[1242] The device then displays the translation results to the user. For example, a user with English settings will see "Today is a wonderful day" on their device.

[1243] Specific examples

[1244] A specific example is as follows:

[1245] 1. User A posts on social media in Japanese, "Today is a wonderful day."

[1246] Posts are sent from the device to the server.

[1247] 2. The server receives the post and detects that the language is Japanese.

[1248] 3. The server sends a translation request from Japanese to English to the chat generation AI.

[1249] 4. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day."

[1250] 5. The server receives the translation and prepares the translated post for English users.

[1251] 6. The server sends the translated post to User B's device.

[1252] 7. User B's device displays "Today is a wonderful day."

[1253] This system enables smooth communication through SNS even between users who speak different languages, and allows information to be shared across language barriers, leading to richer international exchange.

[1254] The processing flow will be explained below.

[1255] Step 1:

[1256] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[1257] Step 2:

[1258] The device receives the user's input and sends it to the SNS server as posting data.

[1259] Step 3:

[1260] The server receives the posted data and stores it in a database.

[1261] Step 4:

[1262] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[1263] Step 5:

[1264] Based on the language identification results, the server generates a translation request to the chat generation AI.

[1265] Step 6:

[1266] The server sends this request to the chat generation AI, which includes the original text, the original language, and the language to translate it into.

[1267] Step 7:

[1268] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day."

[1269] Step 8:

[1270] The server receives and stores the translation results from the chat generation AI.

[1271] Step 9:

[1272] The server checks each user's language preference and prepares to display the translated post in that language, for example, "Today is a wonderful day" for a user with English preferences, or "Aujourd'hui est une journée merveilleuse" for a user with French preferences.

[1273] Step 10:

[1274] The server sends the translation results to the user's terminal based on each user's language settings.

[1275] Step 11:

[1276] The device receives the translation and displays the post in the user's preferred language. For example, a user with English settings will see "Today is a wonderful day" on their device.

[1277] Example 1

[1278] 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."

[1279] With the development of modern communication technology, smooth communication between users who speak different languages ​​is required. However, current social networking platforms lack a means for users to easily understand content posted in different languages. This makes it difficult for users to share information between them, and is a barrier to international communication.

[1280] 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.

[1281] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for distributing the translated post based on user settings, a terminal for accepting user input, means for analyzing the received post content, means for generating a prompt sentence and sending it to a language model, means for saving the translation result from the language model, and a terminal for displaying the translation result, thereby enabling users who speak different languages ​​to communicate smoothly through the SNS.

[1282] A "means for accepting posts" is a part of the system that has the function of receiving and processing messages entered by users.

[1283] The "means for detecting the language of received posts" is a part of the system that has the ability to automatically analyze and identify the language of received messages.

[1284] A "means for translating from a detected language to another specified language" is a part of a system that has the functionality to translate messages in a specified language into another language.

[1285] The "means for delivering translated posts based on user preferences" is a part of the system that has the functionality to deliver translated messages in the appropriate format based on the user's language preferences.

[1286] A "terminal that receives user input" refers to a device (e.g., a smartphone or PC) that allows a user to input a message.

[1287] The "means for analyzing the received post content" is a part of the system that has the function of analyzing the information in the received message in detail and extracting the necessary data.

[1288] The "means for generating a prompt sentence and sending it to a language model" is a part of the system that has the function of generating a message in the input format required by the language model for translation and sending it to the language model.

[1289] The "means for storing translation results from a language model" is a part of a system that has the functionality to store translation results generated by a language model in a database or other storage device.

[1290] "Device displaying translation results" refers to a device (e.g., a smartphone or PC) that displays the translated message to the user.

[1291] This invention provides a system that allows users who speak different languages ​​to communicate smoothly by combining an SNS platform with chat generation AI. The detailed configuration and implementation method of this system are described below.

[1292] Overall system configuration

[1293] 1. User's device

[1294] Users access the SNS platform using devices such as smartphones or PCs and enter messages.

[1295] 2. Server

[1296] The SNS platform server receives the message sent from the user's device, detects the language, generates a translation request, receives and stores the translation result, and delivers the translation result into the appropriate language.

[1297] 3. Chat generation AI

[1298] The chat generation AI (e.g., GPT-4) receives translation requests from the server and translates them into the specified language.

[1299] Specific functions of the system

[1300] 1. Entering and Submitting User Submissions

[1301] A user enters "Today is a wonderful day" in Japanese into the input field of a social networking application and presses the send button.

[1302] The device receives the message and sends it to the SNS server.

[1303] 2. Receiving posts and detecting language

[1304] The server receives the message "Today is a great day" sent from the terminal.

[1305] The server analyzes the received message and detects that the language used is Japanese.

[1306] 3. Creating and sending a translation request

[1307] The server generates a translation request from Japanese to English based on the language detection results.

[1308] The server sends the generated translation request to the chat generation AI.

[1309] Example prompt: "Please translate the following Japanese sentence into English: Today is a wonderful day."

[1310] 4. Execution of the translation

[1311] The chat generation AI processes the request received from the server and translates "Today is a wonderful day" into "Today is a wonderful day."

[1312] 5. Receiving and saving translation results

[1313] The server receives the translation result "Today is a wonderful day" from the chat generation AI.

[1314] The server stores the translation results in a database.

[1315] 6. Preparing for distribution of translation results

[1316] The server sees that User B's preference is English and prepares the post translated into English.

[1317] 7. Delivery of translation results

[1318] The server sends the prepared translation result "Today is a wonderful day" to User B's device.

[1319] 8. Displaying translation results

[1320] The terminal displays the translation results received to User B.

[1321] Display: "Today is a wonderful day"

[1322] This system will enable users who speak different languages ​​to communicate smoothly through SNS, realizing international information sharing.

[1323] This allows users to view content posted in different languages ​​in a language they can understand, facilitating international communication through SNS. As a multilingual SNS platform, this system will greatly improve the convenience of communication between users.

[1324] Hardware and software used

[1325] Hardware: SNS platform servers, users' smartphones and PCs

[1326] Software: Social networking applications, chat generation AI (e.g., GPT-4)

[1327] In this way, the present invention makes it possible to communicate between multiple languages, which has previously been difficult, by using specific hardware and software configurations.

[1328] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1329] Step 1:

[1330] A user enters a message into the input field of an SNS application and presses the send button. At this time, the Japanese message "Today is a wonderful day" is entered into the user's device as input. The device generates a request to send the message to the SNS server and sends it to the server. As an output, a request to send the message to the server is generated and arrives at the server.

[1331] Step 2:

[1332] The server receives the message sent from the terminal and analyzes its contents. The data received as input is the Japanese message "Today is a wonderful day." The server uses its language analysis function to detect that the language used in this message is Japanese and extracts the language information. As output, the server generates the language information that the message is in Japanese.

[1333] Step 3:

[1334] The server generates a translation request based on the detected language information. The input is the language information being Japanese and the original message "Today is a wonderful day." The server generates a prompt sentence and sends the translation request to the language model. This prompt sentence is "Please translate the following Japanese sentence into English: Today is a wonderful day." The output is a translation request generated and sent to the language model.

[1335] Step 4:

[1336] The chat generation AI processes the translation request received from the server. The input is the translation request prompt, "Please translate the following Japanese sentence into English: Today is a wonderful day." The AI ​​performs translation processing based on this prompt, translating the message from Japanese to English. The output is the translation result, "Today is a wonderful day."

[1337] Step 5:

[1338] The server receives the translation result from the chat generation AI. The input is the translation result "Today is a wonderful day." The server saves this translation result in a database. The output is the saved translation result.

[1339] Step 6:

[1340] The server checks each user's language preference. The input includes User B's language preference and the saved translation "Today is a wonderful day." The server verifies that User B's language preference is English and prepares the appropriate translation for this user. The output is the post translated into English.

[1341] Step 7:

[1342] The server sends the prepared translation result to User B's device. The input is the prepared translation result "Today is a wonderful day." The server creates a message including the translation result and sends it to User B's device. The output is the message delivered to User B's device.

[1343] Step 8:

[1344] The device displays the translation result it received to User B. The input is the translation result "Today is a wonderful day" sent from the server. The device displays this message on the screen. The output is "Today is a wonderful day" displayed on User B's device, allowing the user to view the translation result.

[1345] (Application example 1)

[1346] 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."

[1347] When users of different languages ​​use content distribution services, it is difficult to provide subtitles for video and audio content in multiple languages. Language differences can be a barrier, especially when communicating and consuming content between international users, degrading the user experience. Furthermore, because translation quality and timeliness are important, there is a demand for high-precision, fast translation.

[1348] 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.

[1349] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted posts, means for translating the detected language into another specified language, means for distributing the translated posts based on user settings, and means for automatically generating translated subtitles for video or audio content. This enables multilingual subtitles for video or audio content to be automatically generated and displayed when users who speak different languages ​​use the content distribution service.

[1350] "Posting" refers to a user entering content on a social media platform and disseminating it to other users.

[1351] "Language detection means" refers to technology or programming used to identify the language of an accepted submission.

[1352] "Translation means" refers to technology or programs used to convert content written in a particular language into another specified language.

[1353] "Means of delivery" refers to the technology and programs used to deliver translated content to users' devices.

[1354] "Video or audio content" refers to media formats that convey information visually and aurally.

[1355] "Means for automatic subtitling generation" refers to technology or programs that generate and display text in a specified language for video or audio content.

[1356] "Generative artificial intelligence" refers to advanced machine learning models that generate text and translate based on large amounts of data.

[1357] "User Settings" refers to options and settings that a user can individually set for their language and display format.

[1358] This invention provides a system that combines a social networking platform with a generative AI model to enable users to view content posted in different languages ​​in a language they understand. It also includes a means for automatically generating multilingual subtitles in a content distribution service. A specific embodiment of this system will be described.

[1359] System configuration

[1360] 1. Submission acceptance

[1361] The server accepts posts entered by a user on the SNS application. For example, the user enters "Today is a great day" and presses the post button.

[1362] 2. Language Detection

[1363] The server detects the language of the received post using tools such as the Google Translate API. For example, if the post says "Today is a great day," it will identify it as Japanese.

[1364] 3. Generating a translation request

[1365] The server sends a translation request to the generative AI model based on the detected language and the user's settings. For example, when translating from Japanese to English, it translates "Today is a wonderful day."

[1366] 4. Delivery of translation results

[1367] The translated post is stored on a server and delivered to the device that displays the post based on the user's language settings—for example, a user with English settings might see "Today is a wonderful day."

[1368] 5. Application to video and audio content

[1369] The server automatically generates translated subtitles for video or audio content using a generative AI model based on the user's language settings, allowing users of different languages ​​to watch the same content with subtitles in the specified language.

[1370] Hardware and software used

[1371] The system uses the following hardware and software:

[1372] Server: Web server required to run the SNS platform (e.g. AWS EC2)

[1373] Device: The smartphone, tablet, or computer used by the user

[1374] Software: Google Translate API, generative AI models (e.g., GPT-4)

[1375] Specific examples

[1376] For example, if a user posts "Today is a wonderful day" in Japanese on a social media platform, the server receives the post and detects that it is in Japanese. The server then requests a Japanese-to-English translation from the generative AI model, translates it to "Today is a wonderful day," and delivers it to User B's device. At this time, if User B is watching video content, the generated English subtitles will also be displayed.

[1377] Prompt Sentence Examples

[1378] "Piza o tsuika chūmon shimasu" wo eigo ni hon'yaku shite kudasai.

[1379] Translate "Piza o tsuika chūmon shimasu" into English.

[1380] This allows users who speak different languages ​​to enjoy the same content, promoting international exchange.

[1381] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1382] Step 1:

[1383] The user uses a device to input a post on the SNS platform and presses the post button. This is the initial input to the system. Specifically, for example, the user might input "Today is a wonderful day" in Japanese.

[1384] Step 2:

[1385] The device receives the user's input and sends it to the server, which then forwards the input data to the SNS platform's server.

[1386] Step 3:

[1387] The server analyzes the content of the received post and detects the language of the post using the Google Translate API, etc. For example, it identifies a post saying "Today is a great day" as Japanese. The detected language is output.

[1388] Step 4:

[1389] The server sends a translation request to the generative AI model based on the detected language to another specified language, where the input is the detected language and the language to translate to (e.g., Japanese to English), and the output is a request to the generative AI model.

[1390] Step 5:

[1391] The generative AI model performs a translation based on the request, for example, translating "Today is a wonderful day" in Japanese to "Today is a wonderful day" in English. The output is the translated text.

[1392] Step 6:

[1393] The server receives and stores the translation results from the generative AI model, where the input is the translated text and the output is the stored translation data.

[1394] Step 7:

[1395] The server checks the user's language preference and prepares the translated text for delivery in the appropriate language. For example, an English translation is prepared for a user with English preferences. The input is the user's language preference and the stored translation data, and the output is the data ready for delivery.

[1396] Step 8:

[1397] The server sends the translated post to the user's device, where the data is prepared for distribution.

[1398] Step 9:

[1399] The device displays the received translation result to the user. For example, a user with English settings might see "Today is a wonderful day" on their device. The input is the received translation data, and the output is the displayed text.

[1400] 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.

[1401] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Below, we will explain the program processing of this system in natural language.

[1402] System configuration

[1403] 1. User Submissions

[1404] A user types a message into a social networking app and presses the post button. For example, a user posts "Today is a wonderful day" in Japanese.

[1405] 2. Receipt of Submissions

[1406] The device receives the user's input and sends it to the SNS server as posting data.

[1407] 3. Language Detection

[1408] The server receives the posted data, analyzes its content, and detects the language. For example, it detects that "Today is a wonderful day" is Japanese.

[1409] 4. Emotional Recognition

[1410] The server sends the posted data to the emotion engine to detect the user's emotion, for example, recognizing the emotion of joy from the post.

[1411] 5. Generating a translation request

[1412] The server generates a translation request to the chat generation AI based on the detected language (Japanese in this case) and the recognized emotion.

[1413] 6. Execution of the translation

[1414] The Chat generative AI translates requests based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further incorporating the emotion of joy.

[1415] 7. Receiving translation results

[1416] The server receives and stores the translation results from the chat generation AI.

[1417] 8. Distribution Preparation

[1418] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[1419] 9. Delivery of translation results

[1420] The server sends the translation results to the user's terminal based on each user's language settings.

[1421] 10. Displaying translation results

[1422] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1423] Specific examples

[1424] A specific example is as follows:

[1425] If user A posts "Today is a wonderful day" in Japanese on social media:

[1426] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[1427] Posts are sent from the device to the server.

[1428] 2. The server receives the post and detects that the language is Japanese.

[1429] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[1430] 4. The server sends a translation request from Japanese to English to the chat generation AI, and also reflects the emotion of joy.

[1431] 5. Chat generation AI translates "Today is a wonderful day" into "Today is a wonderful day 😊".

[1432] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[1433] 7. The server sends the translated post to User B's device.

[1434] 8. User B's device displays "Today is a wonderful day 😊".

[1435] This system enables users of different languages ​​to communicate smoothly while sharing emotions across language barriers. By combining it with an emotion engine, translated content is expressed more naturally and emotionally, improving the user experience.

[1436] The processing flow will be explained below.

[1437] Step 1:

[1438] A user logs into a social media app, types a message, and presses the post button. For example, they might type, "Today is a great day."

[1439] Step 2:

[1440] The device receives the user's input and sends it to the SNS server as posting data.

[1441] Step 3:

[1442] The server receives the posted data and stores it in a database.

[1443] Step 4:

[1444] The server analyzes the posted data and identifies the language it is written in. For example, it detects that "Today is a wonderful day" is Japanese.

[1445] Step 5:

[1446] The server sends the post data to the emotion engine based on the language identification results. The emotion engine analyzes the text data of the post and identifies the user's emotion. For example, it recognizes positive emotion (joy) from a post that says, "Today is a great day."

[1447] Step 6:

[1448] Based on the emotion recognition results, the server generates a translation request to the chat generation AI. The request includes the original text, Japanese (the source language), English (the target language), and the identified emotion.

[1449] Step 7:

[1450] The server sends a translation request to the chat generation AI.

[1451] Step 8:

[1452] The chat generation AI receives the request and translates the text into the specified language, for example, translating "Today is a wonderful day" into "Today is a wonderful day," further reflecting the emotion of joy.

[1453] Step 9:

[1454] The server receives and stores the translation results from the chat generation AI. The translation results express emotions such as "Today is a wonderful day 😊."

[1455] Step 10:

[1456] The server checks each user's language preference and prepares the translated post to display in that language and emotion, for example, "Today is a wonderful day 😊" for a user with English preferences.

[1457] Step 11:

[1458] The server sends the translation results to the user's terminal based on each user's language settings.

[1459] Step 12:

[1460] The device receives the translation and displays the post in the user's language and emotion settings. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1461] Through these steps, users who speak different languages ​​can communicate smoothly through social networking sites while sharing their emotions. By combining this with an emotion engine, emotions are reflected in the translated content, enabling more natural and empathetic communication.

[1462] Example 2

[1463] 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."

[1464] In today's communication environment, multilingual communication has become essential, but language barriers still exist. Furthermore, simple translations tend to lose emotion and nuance, which can degrade the user experience. For this reason, there is a need for a system that can transcend language barriers while faithfully conveying emotion and nuance.

[1465] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1466] In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for recognizing the emotion of the post, means for translating the post into another specified language based on the detected language and the recognized emotion, and means for distributing the translated post based on user settings, thereby enabling smooth communication between users who speak different languages ​​without losing emotion or nuance.

[1467] The "means for accepting posts" is a component that has the role of sending messages entered by users on the SNS application to the system.

[1468] The "means for detecting the language of a received post" is a component that has the function of automatically determining the language of a message received by the system.

[1469] The "means for recognizing the emotions of posts" is a component that has the function of analyzing the user's emotions and nuances from received messages and identifying those emotions.

[1470] The "means for translating into another specified language based on the detected language and the recognized emotion" is a component that has the function of performing an appropriate translation based on the language and emotion of the original text.

[1471] The "means for delivering translated posts based on user settings" is a component that has the function of delivering messages that have completed translation processing in accordance with the user's language settings.

[1472] "Generative AI models" refer to the machine learning algorithms used for natural language processing and translation, which play a key role in the system's translation capabilities.

[1473] A "prompt sentence" refers to the input sentence format used to give instructions to a generative AI model for translation or emotion recognition.

[1474] This invention provides a system that automatically translates user posts on a social networking platform and incorporates an emotion engine that recognizes the user's emotions, allowing the translated posts to reflect the user's emotions. Specific embodiments of this system are described below.

[1475] System configuration

[1476] 1. User submission input

[1477] A user types a message into an SNS application and presses the post button. For example, the user types "Today is a wonderful day" in Japanese.

[1478] 2. Receiving posted data

[1479] The device receives the user's input and sends the posted data to the SNS server. For example, the device sends a message to the server saying, "Today is a great day."

[1480] 3. Language Detection

[1481] The server receives the posted data, analyzes its content, and detects the language. To recognize that the posted data is in Japanese, the server uses a natural language processing library.

[1482] 4. Emotional Recognition

[1483] The server sends the posted data to the emotion engine to detect the user's emotion. For example, the server sends the text "Today is a great day" to the emotion engine, and recognizes the emotion of "joy" as a result.

[1484] 5. Generating a translation request

[1485] The server generates a translation request to the generative AI model based on the detected language (Japanese in this case) and the recognized emotion. Specifically, it generates a prompt sentence containing a "translation request from Japanese to English" and "emotion: joy" and sends it to the generative AI model.

[1486] 6. Execution of the translation

[1487] The generative AI model processes the translation based on the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊".

[1488] 7. Receiving translation results

[1489] The server receives the translation result from the generative AI model and stores it. For example, the server receives the translation result "Today is a wonderful day 😊" and stores it in the database.

[1490] 8. Distribution Preparation

[1491] The server checks each user's language setting and prepares to display the translated post in that language and emotion, for example, preparing to display the translated "Today is a wonderful day 😊" for a user with an English setting.

[1492] 9. Delivery of translation results

[1493] The server sends the translation results to the device based on each user's language setting. For example, the server sends the message "Today is a wonderful day 😊" to the device of a user with English settings.

[1494] 10. Displaying translation results

[1495] The device will then display the translation results to the user. For example, a user with English settings will see "Today is a wonderful day 😊" on their device.

[1496] Specific examples

[1497] As a specific example, if user A posts "Today is a wonderful day" in Japanese on an SNS, the process will proceed as follows:

[1498] 1. User A types "Today is a wonderful day" in Japanese and presses the send button.

[1499] Posts are sent from the device to the server.

[1500] 2. The server receives the post and detects that the language is Japanese.

[1501] 3. The server sends the posted data to the emotion engine and recognizes the emotion of joy.

[1502] 4. The server sends a translation request from Japanese to English to the generated AI model, and also reflects the emotion of joy.

[1503] 5. The generative AI model translates "Today is a wonderful day" to "Today is a wonderful day 😊".

[1504] 6. The server receives the translation result and prepares "Today is a wonderful day 😊" for the English user.

[1505] 7. The server sends the translated post to User B's device.

[1506] 8. User B's device displays "Today is a wonderful day 😊".

[1507] This system enables smooth communication between users of different languages ​​without losing emotion or nuance.

[1508] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1509] Step 1:

[1510] The user enters a message in the SNS application and presses the post button.

[1511] - Input: A message entered by the user into a social networking app (e.g., "Today is a great day")

[1512] - Output: Post data generated by the device

[1513] Step 2:

[1514] The device receives the user's input and sends the posted data to the SNS server.

[1515] - Input: Post data entered by the user

[1516] - Output: Post data sent to the server

[1517] Step 3:

[1518] The server receives the posted data and analyzes its content to detect the language.

[1519] - Input: Post data received by the server

[1520] - Data processing: Analyze language using natural language processing libraries

[1521] - Output: Detected language (e.g. Japanese)

[1522] Step 4:

[1523] The server sends the posted data to the emotion engine to detect the user's emotions.

[1524] - Input: Server-detected language and post data

[1525] - Data calculation: Analyze the sentiment of posts using the sentiment engine

[1526] - Output: Perceived emotion (e.g., joy)

[1527] Step 5:

[1528] The server generates a translation request to a generative AI model based on the detected language and recognized sentiment.

[1529] - Input: Detected language (Japanese) and recognized emotion (joy)

[1530] - Data calculation: Prompt sentence generation (e.g., "Japanese to English translation request" "Emotion: joy")

[1531] - Output: Generated prompt statement

[1532] Step 6:

[1533] The generative AI model performs translation processing based on the request.

[1534] - Input: Prompt sent from the server

[1535] - Data calculation: Translation processing using generative AI models (e.g., translating "Today is a wonderful day" to "Today is a wonderful day 😊")

[1536] - Output: translated text

[1537] Step 7:

[1538] The server receives the translation results from the generative AI model and stores them.

[1539] - Input: Translation results returned by the generative AI model

[1540] - Data processing: Saving translation results

[1541] - Output: Saved translation data

[1542] Step 8:

[1543] The server checks each user's language preference and prepares the translated post to be displayed in that language and emotion.

[1544] - Input: saved translation data and user language settings

[1545] - Data calculation: Preparing display based on user language settings (e.g., "Today is a wonderful day 😊")

[1546] - Output: Data ready for display

[1547] Step 9:

[1548] The server sends the translation results to the terminal based on each user's language settings.

[1549] - Input: Data ready to display

[1550] - Output: Translation results sent to the user's device

[1551] Step 10:

[1552] The terminal displays the translation result received to the user.

[1553] - Input: Translation results sent from the server

[1554] - Output: The translation result displayed on the user's device (e.g., "Today is a wonderful day 😊")

[1555] (Application example 2)

[1556] 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."

[1557] The purpose of this invention is to enable smooth communication between users who speak different languages ​​on social networking platforms. Conventional technologies have had the problem of losing emotion or producing unnatural expressions simply by translating text. This problem is particularly pronounced in situations where emotional expression is important, resulting in a decline in the quality of communication. Therefore, there is a need to provide a system that recognizes the user's emotions and reflects them in the translation results, thereby enabling natural, emotionally rich communication.

[1558] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for accepting posts, means for detecting the language of the accepted post, means for translating the detected language into another specified language, means for recognizing the emotion of the accepted post, means for reflecting the recognized emotion in the translation result, and means for delivering the translated post based on the user's settings. This enables users who speak different languages ​​to communicate with each other in a rich and emotional way, overcoming language barriers.

[1559] "Posting" refers to the act of a user typing and publishing a message on a social media platform.

[1560] "Means for detecting language" is a function that determines the language in which a user's post is written.

[1561] The "translation means" is a function for converting a detected language into another specified language.

[1562] "Means for recognizing emotions" is a function that analyzes and identifies emotions from user posts.

[1563] "Means for reflecting emotions in translation results" refers to a function that takes into account recognized emotions and includes them in the translation results.

[1564] "User settings" are the display language and other display option settings that the user has preselected.

[1565] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and performs natural language processing, image generation, and other tasks.

[1566] A "prompt" is a document or keyword used as input to a generative AI model.

[1567] This invention is a system that translates messages posted on a social networking platform and distributes them to other users in a format that reflects their emotions. Specifically, the system performs a series of processes: accepting posts, detecting the language, recognizing emotions, translating, and finally displaying the translation results based on the user's settings. A specific example of this system is described in detail below.

[1568] System Configuration

[1569] The system includes a server, a terminal, an emotion engine, and a generative AI model. The role of each part and the specific processing steps are explained below.

[1570] Terminal

[1571] The terminal is a device that accepts user posts. When a user enters a message on the terminal and presses the post button, the data is sent to the server. For example, if user A posts "Today is a wonderful day" in Japanese, this post is sent from the terminal to the server.

[1572] server

[1573] The server analyzes the received post data and detects the language. For example, the server detects that the post "Today is a wonderful day" is in Japanese. The server then sends the post data to the emotion engine to recognize emotions. The server detects the emotion of joy from the post.

[1574] The server then generates a translation request to a generative AI model based on the detected language (Japanese) and the recognized emotion (joy). An example of a generative AI model is a large-scale transformer model. The generative AI model translates the request, for example, translating "Today is a wonderful day" into "Today is a wonderful day 😊."

[1575] When the translation result is sent back to the server, the server prepares it to be displayed in the appropriate language and emotion based on the user's settings. For example, User B, who has English settings, will see "Today is a wonderful day 😊" on their device.

[1576] Hardware and software used

[1577] Devices: Smartphones, tablets, computers, etc.

[1578] Server: A high-performance server machine

[1579] Emotion engine: Natural language processing library (e.g. NLTK, spaCy)

[1580] Generative AI models: Large-scale transformer models (e.g., GPT-3)

[1581] Specific examples

[1582] As a concrete example, let's look at the flow when User A posts "Today is a wonderful day" in Japanese. First, the device sends the posted data to the server. The server detects that the language is Japanese and recognizes the emotion of joy using the emotion engine. The server then sends a translation request from Japanese to English to the generative AI model, and receives the translation result "Today is a wonderful day 😊" that reflects the emotion. This translation result is then sent to User B's device, where it is displayed on User B's device.

[1583] Prompt Sentence Examples

[1584] "Translate the Japanese text 'Today is a wonderful day' to English. Make sure to include the user's emotion, which is joy, in the translation."

[1585] This provides a system that allows users who speak different languages ​​to communicate smoothly, including sharing emotions.

[1586] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1587] Step 1:

[1588] The user enters a message into the terminal and presses the post button. As input, the text entered by the user in Japanese, "Today is a wonderful day," is obtained. As output, this text is sent to the server as post data.

[1589] Step 2:

[1590] The server detects the language of the submitted data it receives. The input is text data received from the user, and the output detects that the language is Japanese. Specifically, it runs a language detection algorithm to identify the language of the text.

[1591] Step 3:

[1592] The server sends the post data to the emotion engine to recognize the emotion. Using the post data as input, the emotion detected as output is recognized as "joy." The emotion engine uses a natural language processing library (e.g., NLTK or spaCy) to extract emotions from text.

[1593] Step 4:

[1594] The server generates a translation request to the generative AI model based on the detected language (Japanese) and the recognized emotion (joy). It uses the detected language and emotion, and the source text as input, and generates a translation request as output. Specifically, it generates a prompt sentence and provides it to the generative AI model (e.g., GPT-3).

[1595] Step 5:

[1596] The generative AI model translates based on the translation request. It receives the generated prompt as input and obtains the translation result "Today is a wonderful day 😊" as output. The generative AI model uses a large-scale Transformer model to simultaneously translate between languages ​​and reflect sentiment.

[1597] Step 6:

[1598] The server stores the translation results received from the generative AI model and prepares them for display based on the user's settings. It uses the translation results and user settings as input and obtains displayable data as output. Specifically, it formats the translation results with the appropriate language and sentiment.

[1599] Step 7:

[1600] The server sends the translated post to the user's device, using the processed data as input and sending the data to the user's device as output. Specifically, the data is sent over the network to the appropriate device.

[1601] Step 8:

[1602] The user's device displays the translation results it receives. It uses the data sent from the server as input and displays the translation results to the user as output. Specifically, it renders the text on the device's display.

[1603] 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.

[1604] 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.

[1605] 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.

[1606] 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.

[1607] FIG. 9 illustrates 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 behaviors 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.

[1608] 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.

[1609] 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).

[1610] 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.

[1611] 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."

[1612] 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.

[1613] 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).

[1614] 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.

[1615] 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.

[1616] 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.

[1617] 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.

[1618] 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.

[1619] 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.

[1620] 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.

[1621] 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.

[1622] 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.

[1623] 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.

[1624] The following is further disclosed regarding the above embodiment.

[1625] (Claim 1)

[1626] a means for accepting submissions; and

[1627] a means for detecting the language of an accepted submission;

[1628] means for translating from the detected language to another specified language;

[1629] means for distributing the translated posts based on user preferences;

[1630] A system including:

[1631] (Claim 2)

[1632] 10. The system of claim 1, wherein the translation means uses artificial intelligence.

[1633] (Claim 3)

[1634] 2. The system according to claim 1, further comprising means for displaying the translation result in an appropriate language based on the user's settings.

[1635] "Example 1"

[1636] (Claim 1)

[1637] a means for accepting submissions; and

[1638] a means for detecting the language of an accepted submission;

[1639] means for translating from the detected language to another specified language;

[1640] means for distributing the translated posts based on user preferences;

[1641] a terminal for receiving user input;

[1642] a means for analyzing received posts;

[1643] means for generating and sending prompt sentences to a language model;

[1644] a means for storing translation results from the language model;

[1645] A device that displays the translation results;

[1646] A system including:

[1647] (Claim 2)

[1648] 10. The system of claim 1, wherein the translation means uses artificial intelligence.

[1649] (Claim 3)

[1650] 2. The system according to claim 1, further comprising means for displaying the translation result in an appropriate language based on the user's settings.

[1651] "Application Example 1"

[1652] (Claim 1)

[1653] a means for accepting submissions; and

[1654] a means for detecting the language of an accepted submission;

[1655] means for translating from the detected language to another specified language;

[1656] means for distributing the translated posts based on user preferences;

[1657] means for automatically generating translated subtitles for video or audio content;

[1658] A system including:

[1659] (Claim 2)

[1660] 2. The system of claim 1, wherein the translation means uses generative artificial intelligence.

[1661] (Claim 3)

[1662] 2. The system according to claim 1, further comprising means for displaying the translation result in an appropriate language based on the user's settings.

[1663] "Example 2: Combining Emotion Engines"

[1664] (Claim 1)

[1665] a means for accepting submissions; and

[1666] a means for detecting the language of an accepted submission;

[1667] A means of recognizing the sentiment of a post;

[1668] means for translating the detected language and the recognized emotion into another specified language;

[1669] means for distributing the translated posts based on user preferences;

[1670] A system including:

[1671] (Claim 2)

[1672] 10. The system of claim 1, wherein the translation means uses a generative AI model.

[1673] (Claim 3)

[1674] 2. The system according to claim 1, further comprising means for displaying the translation result in an appropriate language based on the user's settings.

[1675] "Application example 2 when combining emotion engines"

[1676] (Claim 1)

[1677] a means for accepting submissions; and

[1678] a means for detecting the language of an accepted submission;

[1679] means for translating from the detected language to another specified language;

[1680] a means for recognizing the sentiment of received posts;

[1681] A means of incorporating the recognized emotions into the translation results;

[1682] means for distributing the translated posts based on user preferences;

[1683] A system including:

[1684] (Claim 2)

[1685] 10. The system of claim 1, wherein the translation means uses a generative AI model.

[1686] (Claim 3)

[1687] 10. The system of claim 1, further comprising means for displaying the translation results in appropriate language and emotion based on user settings. [Explanation of symbols]

[1688] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for accepting submissions; and a means for detecting the language of an accepted submission; means for translating from the detected language to another specified language; means for distributing the translated posts based on user preferences; A system including:

2. 10. The system of claim 1, wherein said translation means uses artificial intelligence.

3. 2. The system of claim 1, further comprising means for displaying the translation results in an appropriate language based on the user's settings.

Images