System

Abstract

To provide a system.SOLUTION: A system includes means for generating a button for sending a message in a specific language, means for inputting an input message as a prompt sentence to a generative AI model when the button is pressed, means for the generative AI model to translate the input message, and means for sending the translated message to a transmission destination.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, when communicating with foreigners on messenger apps or social media, users have to go through the trouble of copying and pasting messages translated using external services, etc. This poses a challenge as it makes it difficult to communicate smoothly across language barriers. [Means for solving the problem]

[0005] To solve this problem, this invention provides a system that creates a new "Send in XX language" button next to the message sending means in messenger apps and SNS, and when the button is pressed, automatically translates the message into XX language and sends the translated message. This allows users to translate and send the message directly within the app at the same time without using an external service, further lowering the language barrier. [Brief explanation of the drawings]

[0006] [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. 2 is a sequence diagram showing a flow of processing in the data processing system according to the first embodiment of the first form example. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Embodiment 1. [Figure 13] FIG. 10 is a sequence diagram showing a processing flow of a data processing system in a second embodiment of the second form example. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Embodiment Example 2. [Figure 15] FIG. 10 is a sequence diagram showing the flow of processing in a data processing system according to a third embodiment of the third embodiment. [Figure 16] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Embodiment 3. [Figure 17] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the first embodiment of the first form example when an emotion engine is combined. [Figure 18] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1 of Form Example 1 when an emotion engine is combined. [Figure 19] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the second embodiment of the second form example when an emotion engine is combined. [Figure 20] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 of Form Example 2 when an emotion engine is combined. [Figure 21] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in the third embodiment of the third form example when an emotion engine is combined. [Figure 22] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 3 of Form Example 3 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0007] 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.

[0008] First, the terms used in the following description will be explained.

[0009] 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, the 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), an APU (Accelerated Processing Unit), or a TPU (TENSOR PROCESSING UNIT (registered trademark)).

[0010] 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.

[0011] 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.

[0012] 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.

[0013] 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."

[0014] [First embodiment]

[0015] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] 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.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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.

[0025] 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.

[0026] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0027] "Example 1"

[0028] In one embodiment of the present invention, a "Send in English" button is placed next to the message input field on the message sending screen of a messenger app or SNS. When a user inputs a message and presses the "Send in English" button, the input message is automatically translated into English and the translated message is sent.

[0029] "Example 2"

[0030] Additionally, we provide a function that automatically selects the language of the "Send in" button from available languages ​​based on the user's settings. For example, if a user frequently communicates with people who speak French, the "Send in French" button will be automatically generated based on the user's settings.

[0031] "Example 3"

[0032] It also provides a function that selects the best translation based on the context of the message entered by the user. For example, if the user enters "I'm fine," it will be translated as "I'm fine." However, if the user enters "It's fine," it will be translated as "It's fine." In this way, the same "fine" will be translated differently depending on the context.

[0033] The processing flow of each embodiment will be described below.

[0034] "Example 1"

[0035] Step 1: The user opens a messaging app or social media message sending screen.

[0036] Step 2: Make sure there is a "Send in English" button next to the message input field.

[0037] Step 3: User types message and presses "Send in English" button.

[0038] Step 4: The system will automatically translate the message you entered into English.

[0039] Step 5: The system sends the translated message.

[0040] "Example 2"

[0041] Step 1: The user opens the settings screen of a messenger app or social networking site.

[0042] Step 2: The user selects the language from the available options under the "Send in" button. Step 3: The user saves the settings and returns to the message sending screen.

[0043] Step 4: Make sure there is a "Send in XXX" button in the user's language of choice next to the message input field.

[0044] "Example 3"

[0045] Step 1: The user types a message.

[0046] Step 2: The system takes the user's message and selects the best translation based on the context of the message.

[0047] Step 3: The system displays the translation it has selected to the user.

[0048] Step 4: The user presses the "Send in XXX language" button, and the system sends the selected translation.

[0049] Example 1

[0050] Next, a description will be given of Example 1 of Form 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."

[0051] With conventional messaging apps and social networking sites, users had to manually translate messages when sending them in different languages, which was a time-consuming process. Furthermore, the accuracy of the translations and the appropriate translations based on the context were often lacking, which could impair the smoothness of communication. To solve these problems, a system was needed that would allow users to easily translate and send messages in a specific language.

[0052] 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.

[0053] In this invention, the server includes a message sending means, a "Send in specific language" button generating means located next to the message sending means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, a means for inputting the message into a generative AI model as a prompt sentence, a means for the generative AI model to translate the message, and a means for sending the translated message to a destination. This allows a user to easily translate and send a message into a specific language.

[0054] "Message sending means" is a function that provides an interface for users to input and send messages.

[0055] The "specific language send button generating means" is a function that is placed next to the message sending means and generates a button for sending a message in a specific language selected by the user.

[0056] "Automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[0057] A "generative AI model" is an artificial intelligence model for natural language processing that has the ability to translate input text into other languages.

[0058] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0059] The "means for sending a translated message" is a function for sending a message translated by the automatic translation means to a specified destination.

[0060] "Context-based optimal translation" refers to providing the most appropriate translation by taking into account the content and intent of the message entered by the user.

[0061] This invention provides a system that enables users to easily translate and send messages into a specific language in messenger apps and social networking sites. Specific embodiments of this system are described below.

[0062] First, a user opens a messaging app or social networking site's message sending screen and enters a message in the message input field. For example, they might enter "Hello, how are you?". Next, the user presses the "Send in specific language" button located next to the message input field. This action starts the process of translating the entered message into the specific language.

[0063] The device sends the message entered by the user to the server. At this time, the message is sent to the server in an appropriate format. The server inputs the received message as a prompt sentence to a generative AI model (e.g., GPT-4 (registered trademark)). Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[0064] The generative AI model receives the prompt sentence and translates the message into a specific language. For example, it translates "Hello, how are you?" into "Hello, how are you?" The server sends the translated message received from the generative AI model to the device. At this time, the translated message is sent to the device in the appropriate format.

[0065] The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend. In this way, a message entered by the user in Japanese is automatically translated into a specific language and sent.

[0066] As a concrete example, consider a case where a user wants to send a message to a friend saying, "Hello, how are you?" The user enters "Hello, how are you?" in the message input field and presses the "Send in specific language" button. The system translates this message into "Hello, how are you?" and sends it to the friend.

[0067] Example prompts to input to a generative AI model:

[0068] "Please translate 'Hello, how are you?' into English."

[0069] This system allows users to easily translate and send messages into specific languages. The hardware used includes the user's device (smartphone, tablet, PC, etc.) and a server. The software used includes a messenger app, a social networking application, and a generative AI model (e.g., GPT-4 (registered trademark)).

[0070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0071] Step 1:

[0072] The user enters a message in the message input field.

[0073] Type: "Hello, how are you?"

[0074] Output: The input message "Hello, how are you?"

[0075] Specific actions: The user opens the message sending screen of a messenger app or social networking site and types "Hello, how are you?" in the message input field.

[0076] Step 2:

[0077] The user presses the "Send in specific language" button.

[0078] Input: User presses the "Submit in a specific language" button

[0079] Output: An event that the submit button was pressed

[0080] What it does: The user presses the "Send in specific language" button located next to the message input field, which starts the process of translating the entered message into the specific language.

[0081] Step 3:

[0082] The terminal sends the input message to the server.

[0083] Input: The message "Hello, how are you?" is entered and the event of pressing the send button

[0084] Output: Message sent to server: "Hello, how are you?"

[0085] Specific operation: The terminal sends the message entered by the user to the server, where the message is sent in the appropriate format.

[0086] Step 4:

[0087] The server inputs the message as a prompt into the generation AI model.

[0088] Input: Message sent to server: "Hello, how are you?"

[0089] Output: The prompt sent to the generative AI model: "Please translate 'Hello, how are you?' into English."

[0090] Specific operation: The server inputs the received message into a generative AI model (e.g., GPT-4 (registered trademark)) as a prompt sentence. Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[0091] Step 5:

[0092] A generative AI model translates the message into English.

[0093] Input: Prompt sentence: "Please translate 'Hello, how are you?' into English."

[0094] Output: The translated message "Hello, how are you?"

[0095] Specific behavior: The generative AI model receives a prompt and translates the message into a specific language, for example, translating "Hello, how are you?" into "Hello, how are you?"

[0096] Step 6:

[0097] The server sends the translated message to the terminal.

[0098] Input: Translated message "Hello, how are you?"

[0099] Output: The translated message "Hello, how are you?" sent to the terminal.

[0100] Specific operation: The server sends the translated message received from the generative AI model to the device, where the translated message is sent to the device in the appropriate format.

[0101] Step 7:

[0102] The device sends the translated message to the destination.

[0103] Input: The translated message "Hello, how are you?" sent to the terminal

[0104] Output: The message "Hello, how are you?" sent to the destination.

[0105] Specific operation: The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend.

[0106] (Application example 1)

[0107] Next, a description will be given of Application Example 1 of Embodiment Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0108] With the current trend toward globalization, communication with foreign customers has become increasingly important in brick-and-mortar stores. However, the language barrier makes it difficult for store staff to communicate smoothly with foreign customers. To solve this problem, real-time language translation is necessary, but current technology is not sufficient.

[0109] 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.

[0110] In this invention, the server includes a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a voice input means, a means for converting voice acquired by the voice input means into text, a means for translating the text into XX language, and a means for outputting the translated text by voice. This enables smooth real-time communication between store staff and foreign customers in physical stores.

[0111] A "message sending means" is an interface that allows a user to input and send a text message.

[0112] The "means for generating a send button in XX language" is a function that generates a button for translating and sending a message into a language selected by the user.

[0113] "Means for automatic translation" is a function for automatically translating an input text message into a specified language.

[0114] The "means for sending an automatically translated message" is a function for sending a translated message to the other party.

[0115] The "voice input means" is an interface for capturing the user's voice and acquiring it as digital data.

[0116] The "means for converting voice to text" is a function for converting acquired voice data into text data.

[0117] "Means for translating text into XX language" is a function for translating text data into a specified language.

[0118] The "means for audibly outputting the translated text" is a function for playing back the translated text as a voice.

[0119] To implement the present invention, the following system configuration and program are required.

[0120] First, the server provides a message sending means, which is an interface for users to input and send text messages. Next, the server provides a "send in XX language" button generation means, which is a function that generates a button for translating and sending a message into a language selected by the user.

[0121] When a user presses the "Send in XX language" button, the server uses an automatic translation means to automatically translate the entered text message into the specified language, and this translated message is sent to the recipient by a means for sending an automatically translated message.

[0122] Furthermore, the server provides a voice input means, which is an interface for capturing the user's voice and acquiring it as digital data, and the acquired voice data is converted into text data by a voice-to-text conversion means.

[0123] The converted text data is translated into a specified language by a means for translating text into a certain language. The translated text is reproduced as audio by a means for outputting the translated text as audio.

[0124] To realize this system, the following hardware and software are used:

[0125] Hardware: Smart glasses (capable of audio input and output)

[0126] software:

[0127] speech_recognition library: convert speech to text

[0128] googletrans library: translate text

[0129] pyttsx3 library: convert text to speech

[0130] For example, when a store clerk says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted to text using the speech_recognition library, and the text is translated into English using the googletrans library. The translated text is converted to speech using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[0131] Example prompts to input to a generative AI model:

[0132] When a user says "Where is this product located?", convert the speech to text, translate it into English, and play it aloud as "Where is this product located?"

[0133] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0134] Step 1:

[0135] The user speaks to the smart glasses. The microphone in the smart glasses captures the voice and obtains it as voice data. This voice data becomes the input.

[0136] Step 2:

[0137] The voice data acquired by the device (smart glasses) is converted into text data using the speech_recognition library. The voice data is analyzed and the corresponding text data is generated. This text data is the output.

[0138] Step 3:

[0139] Translates text data generated by the device into a specified language (e.g., English) using the googletrans library. It takes text data as input and generates translated text data. This translated text data is the output.

[0140] Step 4:

[0141] The device converts the translated text data into speech data using the pyttsx3 library, takes the translated text data as input, and generates the corresponding speech data, which is the output.

[0142] Step 5:

[0143] The device then plays the generated audio data through the smart glasses' speaker, allowing the user to listen to the translated audio.

[0144] Specifically, when a user says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted into text data, "Where is this product located?", using the speech_recognition library. The text data is translated into English, "Where is this product located?", using the googletrans library. The translated text data is converted into audio data using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[0145] Example 2

[0146] Next, a description will be given of Example 2 of Form 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."

[0147] In conventional messaging apps and social networking sites, when users wanted to send messages in different languages, they had to translate them manually, which was a time-consuming process. Furthermore, since there was no function to automatically select frequently used languages, users had to select the language each time, which was inconvenient. Furthermore, the quality of the translations was inconsistent, so the translations sometimes did not fit the context of the message.

[0148] 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.

[0149] In this invention, the server includes means for acquiring user setting information, means for sending a prompt sentence to the generative AI model based on the acquired setting information, means for generating a "Send in XX language" button based on a response from the generative AI model, means for displaying the generated "Send in XX language" button on a user interface, and means for sending a message when the user clicks the "Send in XX language" button. This makes it possible to automatically select a language frequently used by the user and perform appropriate translation.

[0150] "User Settings" refers to the individual language and other customization information that a User sets within the System.

[0151] A "generative AI model" is a model designed to perform specific tasks using artificial intelligence. In this case, it generates prompts based on user settings and generates appropriate language selection buttons.

[0152] A "prompt sentence" refers to an input sentence that gives specific instructions or questions to a generative AI model.

[0153] "Send in XX language button" refers to a button on the interface that allows a user to send a message in a specific language of their choice.

[0154] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[0155] "Means for sending messages" refers to the functionality for sending messages entered by the user in the selected language.

[0156] This invention relates to a system for messenger apps and SNS that automatically selects a language frequently used by a user and performs appropriate translation. Specific embodiments of this system are described below.

[0157] Server Processing

[0158] The server retrieves the user's preference information from a database. Specifically, it uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. For example, it executes an SQL query that retrieves the corresponding language preference using the user ID as a key.

[0159] Next, the server sends a prompt to a generative AI model (e.g., OpenAI's GPT-4®) based on the acquired user settings. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language.

[0160] Example prompt sentence:

[0161] "Based on the user's preferences, select the language they frequently use and generate a corresponding submit button. For example, if the user selects French, generate a 'Submit in French' button."

[0162] The server that receives the response from the generative AI model generates the data to display on the user interface. Specifically, it generates HTML and JavaScript code and creates buttons corresponding to the language selected by the user.

[0163] Terminal handling

[0164] The terminal receives the data sent from the server and reflects it in the user interface. Specifically, it dynamically generates and displays a "Submit in French" button using front-end technologies such as JavaScript (registered trademark) and React.

[0165] User operations

[0166] Users select the languages ​​they frequently use in the settings screen. For example, if a user selects French, a "Send in French" button will be automatically generated from next time. Users can click this button to send messages in the selected language.

[0167] Specific examples

[0168] For example, if a user selects "French" in the settings screen, the next time a "Send in French" button is automatically generated, the user can click this button to send the message in French.

[0169] In this way, it is possible to automatically select the language that the user frequently uses and provide the appropriate translation, which saves the user the trouble of manually selecting the language and allows for smoother communication.

[0170] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0171] Step 1:

[0172] Get user settings information

[0173] The server retrieves the user's preference information from a database. A user ID is given as input. The server uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. Specifically, it executes an SQL query using the user ID as a key to retrieve the corresponding language preference. The user's preference information (e.g., French) is obtained as output.

[0174] Step 2:

[0175] Generate and send prompts

[0176] The server sends a prompt to the generative AI model based on the acquired user preference information. The user preference information is given as input. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language. An example of a specific prompt is, "Based on the user's preference, please select the frequently used language and generate a corresponding submit button. For example, if the user selects French, please generate a 'Submit in French' button." The output is a response from the generative AI model.

[0177] Step 3:

[0178] Generate language selection button

[0179] The server generates a "Submit in XX language" button based on the response from the generative AI model. The response from the generative AI model is given as input. The server generates HTML and JavaScript (registered trademark) code to create a button corresponding to the language selected by the user. The output is the data for the generated "Submit in XX language" button.

[0180] Step 4:

[0181] Displaying the language selection button

[0182] The terminal receives the data sent from the server and reflects it in the user interface. As input, it receives the data for the "Send in XX language" button sent from the server. The terminal uses front-end technologies such as JavaScript (registered trademark) and React to dynamically generate and display the "Send in French" button. As output, it obtains the button displayed in the user interface.

[0183] Step 5:

[0184] Sending a message

[0185] The user clicks the generated "Send in French" button. The user's click event is given as input. When the click event occurs, a message is sent in the selected language. Specifically, a JavaScript (registered trademark) event listener is used to catch the button click event and process the message sending. The message sent in the selected language is obtained as output.

[0186] (Application example 2)

[0187] Next, a description will be given of Application Example 2 of Form 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."

[0188] In conventional messaging apps and social networking sites, users had to translate messages manually when communicating in different languages, which was inefficient. Furthermore, they lacked the ability to automatically learn frequently used languages ​​and send messages in the appropriate language, which made them less convenient for users.

[0189] The specific processing by the specific 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 a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a means for acquiring user setting information, a means for learning frequently used languages ​​based on the setting information, a means for automatically generating the "Send in XX language" button based on the learned languages, a means for translating the message in real time, and a means for sending the translated message. This makes it possible to automatically learn languages ​​frequently used by the user and send messages in the appropriate language.

[0190] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[0191] The "means for generating a send button in a specific language" is a function that automatically generates a button for sending a message in a specific language based on the user's settings.

[0192] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[0193] "Transmission means" is a function for sending translated messages to other users.

[0194] "Means for acquiring user setting information" refers to a function for acquiring the language and other setting information previously set by the user.

[0195] The "means for learning frequently used languages" is a function for learning the languages ​​that a user frequently uses based on the user's past message history and setting information.

[0196] "Means for real-time translation" refers to a function that translates a message into a specified language in real time as the user types the message.

[0197] The system for implementing this invention comprises a server and a user terminal. The server includes a message sending means, a "Send in XX language" button generating means, an automatic translation means, a sending means, a means for acquiring user setting information, a means for learning frequently used languages, and a means for translating in real time.

[0198] Program processing explanation

[0199] Hardware used

[0200] Server: Any cloud server (e.g., AWS (registered trademark), Google (registered trademark) Cloud)

[0201] Client: Smartphone (iOS or ANDROID (registered trademark))

[0202] Software used

[0203] Framework: Flask (Python)

[0204] Translation API: Googletrans (Python wrapper for Google Translate API)

[0205] Data processing and calculation

[0206] 1. How to get user configuration information:

[0207] The server retrieves the user's pre-selected language and other preferences, which are used to learn frequently used languages ​​based on the user's past message history and preferences.

[0208] 2. Ways to learn frequently used languages:

[0209] The server learns the user's most frequently used languages ​​based on their past message history and preferences, and automatically generates a button to identify the user's most frequently used language and send messages in that language.

[0210] 3. How to generate a "Submit in ____ language" button:

[0211] The server automatically generates a button for sending messages in a specific language based on the user's preferences, which is displayed when the user types a message.

[0212] 4. Real-time translation methods:

[0213] As users type their messages, the server translates them into the language of their choice in real time, allowing users to instantly see the translated message.

[0214] 5. Automatic translation methods:

[0215] When a user presses the "Send in X language" button, the server automatically translates the message into the specified language using a translation API such as GoogleTrans.

[0216] 6. Means of transmission:

[0217] The translated messages are then sent to other users via a server, allowing users of different languages ​​to communicate smoothly.

[0218] Specific examples

[0219] For example, if the user is "user1" and the message they send is "Hello, how are you?", the server will translate this message into French and return it as "Bonjour, comment ça va?".

[0220] Prompt Sentence Examples

[0221] User ID: user1

[0222] Message: Hello, how are you?

[0223] When you send this prompt to the server, you will get a response like this:

[0224] {

[0225] "original_message": "Hello, how are you?",

[0226] "translated_message": "Bonjour, comment ça va?",

[0227] "language": "fr"

[0228] }

[0229] In this way, a system can be realized that automatically selects a language based on the user's settings, and translates and sends messages.

[0230] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0231] Step 1:

[0232] The user inputs a message. The user inputs a text message using the message sending means of the smartphone. The input message is sent to the server.

[0233] Step 2:

[0234] The server retrieves the user's preference information from a database and identifies the user's frequently used languages, including languages ​​the user has used in the past and languages ​​that the user has previously set.

[0235] Step 3:

[0236] The server learns frequently used languages. Based on the acquired configuration information, the server learns the languages ​​most frequently used by users. Based on this learning result, it generates a "Send in XX language" button for sending messages in a specific language.

[0237] Step 4:

[0238] The server generates a "Send in XX language" button. Based on the learning results, the server automatically generates a button for sending messages in the language the user frequently uses and displays it on the user's device.

[0239] Step 5:

[0240] The user presses the "Send in XX" button. By pressing the generated "Send in XX" button, the user instructs the message they entered to be translated into a specific language.

[0241] Step 6:

[0242] The server translates messages in real time. The server translates messages entered by users into the specified language in real time. This translation uses a translation API such as Google Trans.

[0243] Step 7:

[0244] The server sends the translated message, returns the translated message to the user's terminal for the user to review, and then sends the translated message to other users.

[0245] Step 8:

[0246] The other user receives the translated message. The other user receives and displays the translated message sent from the server. This allows users who speak different languages ​​to communicate smoothly.

[0247] Example 3

[0248] Next, a third embodiment of the third embodiment will be described. 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."

[0249] In conventional messaging apps and social networking sites, it was difficult to provide appropriate translations based on the context when users sent messages. This was especially difficult when the same word had different meanings depending on the context.

[0250] The specification process by the specification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send with Translation" button generating means located next to the message sending means, a means for automatically translating the message when the "Send with Translation" button is pressed, a means for sending the automatically translated message, a means for receiving a message input by a user, a means for inputting a message to a generative AI model, a means for the generative AI model to generate a translation based on the context, a means for receiving the generated translation result, and a means for displaying the translation result to the user. This makes it possible to provide an optimal translation based on the context of the message input by the user.

[0251] The "message sending means" is a means for sending a message input by a user to other users.

[0252] The "translation and send button generating means" is a means for generating a button for a user to translate and send a message.

[0253] The "means for automatic translation" is a means for automatically translating a message input by a user into a specified language.

[0254] The "means for sending an automatically translated message" is a means for sending a translated message to another user.

[0255] The "means for receiving a message input by a user" refers to a means by which the server receives a message input by a user.

[0256] "Means for inputting a message into a generative AI model" refers to means for inputting a received message into a generative AI model.

[0257] "Means by which a generative AI model generates a context-based translation" refers to the means by which a generative AI model analyzes the context of a message and generates an optimal translation.

[0258] "Means for receiving the generated translation results" refers to means for receiving the translation results generated from the generative AI model.

[0259] The "means for displaying the translation result to the user" refers to a means for displaying the received translation result to the user.

[0260] This invention relates to a system that optimally translates a message entered by a user based on its context. The system includes a message sending means, a translation send button generating means, an automatic translation means, a means for sending the automatically translated message, a means for receiving a message entered by a user, a means for inputting the message to a generative AI model, a means for the generative AI model to generate a translation based on its context, a means for receiving the generated translation result, and a means for displaying the translation result to a user.

[0261] Hardware and software used

[0262] This system uses a server, a terminal, and a generative AI model. Specifically, the server receives messages, inputs them into the generative AI model, and receives and sends translation results. The terminal inputs messages from users and displays translation results. The generative AI model uses, for example, OpenAI's GPT-4 (registered trademark).

[0263] Data processing and calculation

[0264] The server receives the message entered by the user on the device and inputs the message into the generative AI model. The generative AI model analyzes the context of the message and generates the optimal translation. The generated translation result is returned to the server, which then sends it to the device. The device displays the translation result to the user.

[0265] Specific examples

[0266] As a concrete example, consider the case where a user types "I'm fine." In this case, the server uses a generative AI model to generate the translation "I'm fine." On the other hand, if the user types "It's fine," the server generates the translation "It's fine."

[0267] Prompt Sentence Examples

[0268] "If a user types 'I'm fine.', translate this to 'I'm fine.'"

[0269] "If the user types 'It's fine.', translate this to 'That's fine.'"

[0270] In this way, the system can appropriately translate the user's input message based on the context. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0271] Step 1:

[0272] The user inputs a message. The user inputs a message into the input field of the terminal. For example, the user inputs "I'm fine." This input message becomes the input data for the next processing step.

[0273] Step 2:

[0274] The terminal sends a message to the server. The terminal sends the message entered by the user to the server using an HTTP request. In this case, the input data is the message entered by the user, and the output data is the message sent to the server.

[0275] Step 3:

[0276] The server receives the message. The server receives the message sent from the terminal and temporarily stores it in memory. The input data is the message sent from the terminal, and the output data is the message stored in the server.

[0277] Step 4:

[0278] The server inputs a message to the generative AI model. The server calls the API to input the received message to the generative AI model. The input data is the message stored on the server, and the output data is the message passed to the generative AI model.

[0279] Step 5:

[0280] The generative AI model generates a translation based on the context. The generative AI model analyzes the context of the input message and generates the optimal translation. For example, the generative AI model generates the translation "I'm fine." for the message "I'm fine." The input data is the message passed to the generative AI model, and the output data is the generated translation result.

[0281] Step 6:

[0282] The server receives the translation results. The server receives the translation results generated by the generative AI model and stores them in memory. The input data is the translation results returned by the generative AI model, and the output data is the translation results stored on the server.

[0283] Step 7:

[0284] The server sends the translation results to the terminal. The server sends the translation results to the terminal using an HTTP response. The input data is the translation results stored on the server, and the output data is the translation results sent to the terminal.

[0285] Step 8:

[0286] The terminal displays the translation result to the user. The terminal displays the translation result received from the server to the user. For example, the translation result "I'm fine" is displayed on the terminal screen. The input data is the translation result sent to the terminal, and the output data is the translation result displayed to the user.

[0287] (Application example 3)

[0288] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0289] Traditional messaging apps and social media platforms often lack accurate translations based on context when users communicate in different languages. Virtual stores also lack accurate translations when users search for product information or contact customer support, resulting in a poor user experience.

[0290] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a message sending means, a "Send in Language" button generating means located next to the message sending means, a means for automatically translating the message into a language when the "Send in Language" button is pressed, a means for sending the automatically translated message, a means for selecting an optimal translation based on the context of a message entered by a user, and a means for providing an optimal translation based on the context when a user searches for product information or makes an inquiry to customer support in a virtual store. This makes it possible to provide accurate translations based on the context when users communicate in different languages, improving the user experience in the virtual store.

[0291] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[0292] The "send in language button generating means" is a function that generates a button for sending a message in a language selected by the user.

[0293] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[0294] "Transmission means" is a function for sending automatically translated messages to other users.

[0295] "Means for selecting the best translation based on context" is a function that analyzes the context of the message entered by the user and selects the translation that best suits that context.

[0296] A "virtual store" is a virtual store that offers products and services over the Internet.

[0297] "Means for searching for product information" is a function that allows users to search for information about products and services offered in the virtual store.

[0298] "Mechanisms for contacting customer support" refers to the functionality that allows users to report questions or issues to the virtual store's support team.

[0299] To implement this invention, the following system configuration is required: The system includes a message sending means, a language send button generating means, an automatic translation means, a sending means, a means for selecting the most appropriate translation based on the context, a means for searching for product information in a virtual store, and a means for making an inquiry to customer support.

[0300] The server receives messages entered by users and uses a generative AI model to provide the best translation based on the context. Specifically, it uses Hugging Face's Transformers library to translate from English to Japanese. When a user enters a message, it is sent to the server, which then uses the generative AI model to translate it based on the context.

[0301] The terminal provides an interface for a user to input and send a message. The terminal includes a message sending means and a send in language button generating means, and allows the user to send a message in a language selected by the user. When the user presses the "send in language" button, the message is automatically translated and sent to another user through the sending means.

[0302] As a concrete example, suppose a user types "Can I return this item?" and presses the "Send in Japanese" button. In this case, the server uses the generative AI model to translate it into "Can I return this item?" and sends the translated message to other users via the sending means.

[0303] The same process applies to virtual stores when users search for product information or contact customer support. For example, if a user types "I need help with my order," the server translates it to "I need help with my order" and provides appropriate support.

[0304] An example of a prompt sentence to input to the generative AI model would be "Translate the following English sentence to Japanese considering the context: 'I need help with my order.'" Using this prompt sentence, the server can provide the best translation based on the context.

[0305] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0306] Step 1:

[0307] A user inputs a message on a terminal. The user inputs a text message using a message sending means and presses a send button in a language. The input is the user's text message and the output is the inputted message displayed on the terminal.

[0308] Step 2:

[0309] The terminal sends the input message to the server. The terminal sends the input message to the server along with the send button information in the language pressed by the user. The input is the user's text message and the selected language, and the output is the message data sent to the server.

[0310] Step 3:

[0311] The server receives the message and performs a translation based on the context. The server analyzes the received message and uses a generative AI model to perform the optimal translation based on the context. The input is the message data sent to the server, and the output is the translated message. Specifically, the translation is performed using Hugging Face's Transformers library.

[0312] Step 4:

[0313] The server sends the translated message to the terminal. The server sends the translated message to the terminal and displays it to the user. The input is the translated message and the output is the translated message sent to the terminal.

[0314] Step 5:

[0315] The terminal displays the translated message and transmits it to other users through the transmitting means. The terminal displays the translated message to the user, and after the user confirms it, transmits the message to other users through the transmitting means. The input is the translated message, and the output is the message transmitted to other users.

[0316] Step 6:

[0317] Other users check the received message. Other users check the received message and reply if necessary. The input is the received message and the output is the other users' checked message.

[0318] In this way, messages entered by users are optimally translated based on the context and accurately conveyed to other users.

[0319] 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.

[0320] "Example 1"

[0321] One embodiment of the present invention provides a message translation system incorporating an emotion engine. In this system, when a user types a message on a messaging app or social networking site, the emotion engine recognizes the user's emotion. The emotion engine infers the user's emotion from, for example, the context of the message, the use of emojis, and the typing speed. The emotion engine then adjusts the translation of the message based on the user's emotion. For example, if a user types "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression: "Today is the best day ever!"

[0322] "Example 2"

[0323] In another embodiment of the present invention, a system is provided that includes emotion recognition means for an emotion engine to recognize a user's emotion, and emotion adjustment means for adjusting the translation of a message based on the recognized emotion. The emotion recognition means infers the user's emotion from the context of the user's message, the use of emojis, typing speed, etc. The emotion adjustment means adjusts the translation of the message based on the recognized emotion. For example, if the user types "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, such as "Today is the worst day...".

[0324] "Example 3"

[0325] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. In this system, the emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. For example, if the user inputs "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression, "Today is the best day ever!". On the other hand, if the user inputs "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, "Today is the worst day...".

[0326] The processing flow of each embodiment will be described below.

[0327] "Example 1"

[0328] Step 1: The user types a message in a messaging app or social networking site.

[0329] Step 2: The sentiment engine infers the user's sentiment based on the context of the message, emoji use, typing speed, etc.

[0330] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[0331] "Example 2"

[0332] Step 1: The user types a message.

[0333] Step 2: The emotion recognition tool infers the user's emotions from the context of the message, the use of emojis, typing speed, etc.

[0334] Step 3: Emotion regulation measures adjust the translation of the message based on the perceived emotion.

[0335] "Example 3"

[0336] Step 1: The user types a message.

[0337] Step 2: The emotion engine recognizes the user's emotion.

[0338] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[0339] Example 1

[0340] Next, a description will be given of Example 1 of Form 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."

[0341] In conventional messaging apps and social networking sites, when users translate and send messages into other languages, the quality of the translation often lacks emotional expression, resulting in problems such as users not being able to accurately convey their intentions or emotions.In addition, the time and effort required for users to translate manually made these services inconvenient.

[0342] The specification process by the specification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send in specific language" button generating means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and a means for adjusting the translation based on the result of the emotion recognition means. This makes it possible to send a high-quality translated message that reflects the user's emotion.

[0343] The "message sending means" is a function that provides an interface for users to input and send messages.

[0344] The "means for generating a send button in a specific language" is a function for generating a button that is placed next to the message input field and that is used to send a message in a specific language selected by the user.

[0345] The "automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[0346] The "emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emoticons, typing speed, etc.

[0347] The "translation adjustment means" is a function that adjusts the expression of the translated message based on the results of the emotion recognition means.

[0348] MODE FOR CARRYING OUT THE INVENTION

[0349] This invention is a system that places a "Send in specific language" button on the message sending screen of a messenger app or SNS, and when a user inputs a message and presses the "Send in specific language" button, the input message is automatically translated into the specific language and the translated message is sent. It also includes a function that recognizes the user's emotions and adjusts the translation based on those emotions by combining it with an emotion engine.

[0350] Hardware and software used

[0351] Hardware:

[0352] Device: The device on which the user types the message (smartphone, tablet, PC, etc.)

[0353] Server: A remote server for message translation and emotion recognition

[0354] software:

[0355] Messenger apps and social networking applications

[0356] Emotion engine (e.g. EmotionAI)

[0357] Translation APIs (e.g., Google Translate API, DeepL API)

[0358] Data processing and calculation

[0359] 1. Get the message:

[0360] The terminal receives the message entered by the user.

[0361] 2. Emotion Recognition:

[0362] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[0363] 3. Message Translation:

[0364] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[0365] 4. Sending translated messages:

[0366] The device sends the translated message.

[0367] Specific examples

[0368] When a user types "Today is the best day ever!" into a messenger app and presses the "Send in a specific language" button, the following process occurs:

[0369] 1. The user types, "Today is the best day ever!"

[0370] 2. The user presses the "Send in specific language" button.

[0371] 3. Your device will get the message "Today is a great day!"

[0372] 4. The server uses "EmotionAI" to recognize the emotion of joy from the message.

[0373] 5. The server uses the Google Translate API to translate "Today is the best day ever!" to "Today is the best day ever!"

[0374] 6. The device sends the message "Today is the best day ever!"

[0375] Prompt Sentence Examples

[0376] "Design a system that translates messages typed by a user in a messenger app into a specific language and adjusts the message appropriately based on emotion. For example, if a user types, 'Today is the best day ever!', the emotion engine should recognize joy and translate the message to, 'Today is the best day ever!'"

[0377] In this way, a system is realized that appropriately translates and transmits a message input by a user based on emotion.

[0378] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0379] Step 1:

[0380] The user enters a message in the message input field of a messenger app or social networking site.

[0381] Input: The user types, "Today is the best day ever!"

[0382] What happens: A user types a message using a keyboard or touchscreen.

[0383] Output: "Today is the best day ever!" is displayed in the message input field.

[0384] Step 2:

[0385] The user presses the "Send in specific language" button located next to the message input field.

[0386] Input: User clicks the "Submit in specific language" button.

[0387] Specific action: The user presses a button using a mouse or touchscreen.

[0388] Output: A button pressed event is fired.

[0389] Step 3:

[0390] The terminal receives the message entered by the user.

[0391] Input: The event of a button being pressed and the message being entered: "Today is a great day!"

[0392] Specific operation: The device reads text data from the message input field.

[0393] Output: The retrieved message "Today is a great day!" is stored in memory.

[0394] Step 4:

[0395] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[0396] Input: The retrieved message "Today is the best day!"

[0397] What it does: The server uses an emotion engine (e.g., EmotionAI) to analyze the message context, detect emojis, and analyze typing speed.

[0398] Output: The emotion engine recognizes the user's emotion as "joy."

[0399] Step 5:

[0400] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[0401] Input: Emotion engine result "Joy" and original message "Today is the best day ever!"

[0402] Specific operation: The server uses a translation API (e.g., Google Translate API) to perform translation that takes emotional expressions into account.

[0403] Output: The translated message "Today is the best day ever!".

[0404] Step 6:

[0405] The device sends the translated message.

[0406] Input: The translated message "Today is the best day ever!".

[0407] Specific behavior: The device uses the message sending function to send the translated message.

[0408] Output: The sent message "Today is the best day ever!" will appear in the recipient's message inbox.

[0409] (Application example 1)

[0410] Next, a description will be given of Application Example 1 of Embodiment 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."

[0411] In existing messaging apps and social media, when users send messages in different languages, the quality of the translation often does not accurately reflect the user's feelings, which can lead to poor communication quality. Furthermore, because translations do not take the user's feelings into consideration, there is a risk of misunderstandings or discomfort.

[0412] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a message sending means for a messenger app or SNS, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the translation based on the sentiment analysis means. This enables natural translation that takes the user's sentiment into consideration.

[0413] "Messenger apps and social networking sites" are software applications that allow users to send and receive text messages, images, videos, etc.

[0414] The "message sending means" is a function for sending a message input by a user to other users.

[0415] The "means for generating a send button in a specific language" is a function that generates a button that allows a user to send a message in a specific language.

[0416] "Means for automatic translation" refers to a function that automatically translates a message entered by a user into a specified language.

[0417] "Means for sending automatically translated messages" refers to a function for sending translated messages to other users.

[0418] "Emotion analysis means" is a function that analyzes a user's emotions from messages entered by the user and other information.

[0419] The "means for adjusting the translation" is a function for adjusting the translated message based on the emotional information obtained by the emotional analysis means.

[0420] To implement this invention, we need to build a system that translates messages in a way that takes into account emotions when users send them on messenger apps or social networking sites. This system includes the following main components:

[0421] 1. Message sending method

[0422] It provides an interface for users to type and send messages. This can be a messaging or social networking application that runs on a device such as a smartphone or computer.

[0423] 2. How to generate a "Send in XX language" button

[0424] Creates a "Send in ____ language" button that is placed next to the message sending method. This button allows users to send messages in a specific language. The language is automatically selected from the available options based on the user's settings.

[0425] 3. Means of automatic translation

[0426] When a user presses the "Send in XX language" button, the input message is automatically translated into the specified language. This function is achieved using a generative AI model. Specifically, a translation model using the Transformers library is used.

[0427] 4. How to send automatically translated messages

[0428] Send translated messages to other users. This feature will also be implemented as part of messaging apps and social networking applications.

[0429] 5. Emotion analysis method

[0430] Analyzes user sentiment from user-entered messages and other information. This functionality is achieved using a generative AI model, specifically a sentiment analysis model using the Transformers library.

[0431] 6. Means of adjusting the translation

[0432] The translated message is adjusted based on the emotional information obtained by the emotional analysis means, for example, if a positive emotional state is detected, the translated message is changed to a more positive expression.

[0433] Hardware and software used

[0434] Hardware: Servers (cloud servers and on-premise servers), smartphones, computers

[0435] Software: Python, Flask, Transformers library

[0436] Specific examples

[0437] If a user types "Today is the best day ever!" and presses the "Send in English" button, the sentiment analyzer will recognize the joy and the message will be translated as "Today is the best day ever!" and sent.

[0438] Prompt Sentence Examples

[0439] "Today is the best day!"

[0440] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0441] Step 1:

[0442] A user opens a messaging app or social networking application and types a message, which is then saved in text format on the device.

[0443] Step 2:

[0444] The user presses the "Send in XX language" button. This action causes the device to send the entered message to the server. The data sent includes the message text and the language selected by the user.

[0445] Step 3:

[0446] The server passes the received message text to a sentiment analyzer, which uses a generative AI model to analyze the message's sentiment. The input is the message text, and the output is a sentiment label (e.g., positive, negative, neutral).

[0447] Step 4:

[0448] The server passes the message text to the translation means based on the emotion label obtained from the emotion analysis means. The translation means uses a generative AI model to translate the message into the specified language. The input is the message text and the emotion label, and the output is the translated message text.

[0449] Step 5:

[0450] The server adjusts the translated message text based on the emotion label. For example, if a positive emotion is detected, it changes the translated message to a more positive expression. The input is the translated message text and the emotion label, and the output is the adjusted message text.

[0451] Step 6:

[0452] The server sends the adjusted message text to the terminal, which displays the received message and sends it to other users. The input is the adjusted message text, and the output is the displayed and sent message.

[0453] Example 2

[0454] Next, a description will be given of Example 2 of Form 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."

[0455] In conventional messaging apps and social media, when users wanted to send messages in a different language, they had to translate them manually, which was a time-consuming process. Furthermore, because translations were done without taking into account the user's feelings, there was a risk of conveying unintended nuances. This led to issues such as a decline in the quality of communication.

[0456] 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.

[0457] In this invention, the server includes a message sending means, a "Send in XX language" button generating means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and an emotion adjustment means for adjusting the message translation based on the recognized emotion. This makes it possible to automatically select a language frequently used by the user and provide an appropriate translation according to the emotion.

[0458] The "message sending means" is a function for sending a message input by a user to other users.

[0459] The "means for generating a send button in XX language" is a function that automatically selects an appropriate language from selectable languages ​​based on the user's settings and generates a button for sending a message in that language.

[0460] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[0461] "Emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emojis, typing speed, etc.

[0462] "Emotion adjustment means" is a function that adjusts the translation of a message based on the perceived emotions of the user.

[0463] The present invention provides a system that reduces the effort required for users to send messages in different languages ​​and provides appropriate translations according to emotions. Specific embodiments of this system are described below.

[0464] Hardware and software used

[0465] Server: Responsible for data processing and storage. Specifically, it uses cloud services (e.g., AWS (registered trademark), Google Cloud Platform).

[0466] Device: The device used by the user, such as a smartphone, tablet, or PC.

[0467] Generative AI model: A model for natural language processing, such as OpenAI's GPT-3 (registered trademark) or GPT-4 (registered trademark).

[0468] Emotion engine: An engine for recognizing user emotions, for example, using IBM Watson (registered trademark) emotion analysis API.

[0469] System processing flow

[0470] 1. User language settings

[0471] Users select their preferred language in the application settings screen.

[0472] The terminal transmits the selected language information to the server.

[0473] The server stores the received language information in a database.

[0474] Specific operation: When the user selects "French" on the settings screen and presses the save button, the device sends that information to the server.

[0475] 2. Creating a language selection button

[0476] The server will generate a submit button in the appropriate language based on the user's language setting information the next time the site is accessed.

[0477] Based on the information received by the terminal from the server, a "Send in French" button is displayed on the user interface.

[0478] Specific behavior: When the user restarts the app, the device retrieves the "Send in French" button information from the server and displays it on the screen.

[0479] 3. Enter your message

[0480] The user enters text into the message input field.

[0481] The terminal transmits the input message to the server in real time.

[0482] What it does: When a user types "Today is the worst day...", the text is immediately sent to the server.

[0483] 4. Emotion recognition

[0484] The server sends the received message to the emotion engine.

[0485] The emotion engine infers a user's emotions from the context of the message, the use of emojis, typing speed, and more.

[0486] The server receives the results from the emotion engine and stores the emotion information.

[0487] Specific behavior: The emotion engine recognizes "sadness" from the message "Today is the worst day..."

[0488] 5. Translation Adjustment

[0489] The server sends a translation request to a generative AI model based on the recognized emotion.

[0490] The generative AI model generates appropriate translations based on the emotion.

[0491] The server sends the generated translation to the device.

[0492] Specific operation: The generative AI model generates the translation "Today is the worst day..." and the server sends it to the device.

[0493] 6. Sending a Message

[0494] The user presses the "Send in French" button.

[0495] The device sends the translated message to the server.

[0496] The server receives the message and sends it to the specified recipient.

[0497] Specific operation: When the user presses the "Send in French" button, the device sends the message "Aujourd'hui est le pire jour..." to the server, which then sends it to the recipient.

[0498] Prompt Sentence Examples

[0499] Please explain the feature that allows users to set their frequently used language and automatically generate submit buttons in that language on subsequent visits.

[0500] "Please explain the functionality of recognizing user emotions and adjusting message translation based on the recognized emotions."

[0501] To improve user convenience, the system automatically selects frequently used languages ​​and provides appropriate translations based on sentiment.

[0502] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0503] Step 1:

[0504] Users select their preferred language in the application settings screen.

[0505] Input: The language selected by the user in the settings screen (e.g. French)

[0506] Output: Selected language information

[0507] Specific operation: When the user selects "French" on the settings screen and presses the save button, the device sends that information to the server.

[0508] Step 2:

[0509] The terminal transmits the selected language information to the server.

[0510] Input: Selected language information

[0511] Output: Language information sent to the server

[0512] Specific operation: The terminal sends the user's selected language information to the server.

[0513] Step 3:

[0514] The server stores the received language information in a database.

[0515] Input: Language information sent to the server

[0516] Output: Language information stored in a database

[0517] Specific operation: The server stores the received language information in a database.

[0518] Step 4:

[0519] The server will generate a submit button in the appropriate language based on the user's language setting information the next time the site is accessed.

[0520] Input: Language information stored in a database

[0521] Output: Submit button information in generated language

[0522] Specific operation: The server retrieves language information from the database and generates information for the "Submit in French" button.

[0523] Step 5:

[0524] Based on the information received by the terminal from the server, a "Send in French" button is displayed on the user interface.

[0525] Input: Submit button information in generated language

[0526] Output: A submit button displayed in the user interface

[0527] Specific behavior: When the user restarts the app, the device retrieves the "Send in French" button information from the server and displays it on the screen.

[0528] Step 6:

[0529] The user enters text into the message input field.

[0530] Input: A message typed by the user (e.g., "Today is the worst day...")

[0531] Output: The input message

[0532] What happens: The user types, "Today is the worst day..."

[0533] Step 7:

[0534] The terminal transmits the input message to the server in real time.

[0535] Input: The message entered

[0536] Output: Message sent to the server

[0537] Specific operation: The terminal sends the input message to the server.

[0538] Step 8:

[0539] The server sends the received message to the emotion engine.

[0540] Input: The message sent to the server

[0541] Output: The message sent to the emotion engine

[0542] Specific operation: The server sends the received message to the emotion engine.

[0543] Step 9:

[0544] The emotion engine infers a user's emotions from the context of the message, the use of emojis, typing speed, and more.

[0545] Input: Message sent to the emotion engine

[0546] Output: Inferred emotion information (e.g., sadness)

[0547] Specific behavior: The emotion engine recognizes "sadness" from the message "Today is the worst day..."

[0548] Step 10:

[0549] The server receives the results from the emotion engine and stores the emotion information.

[0550] Input: Inferred emotion information

[0551] Output: Stored emotion information

[0552] Specific operation: The server receives emotion information from the emotion engine and stores it in a database.

[0553] Step 11:

[0554] The server sends a translation request to a generative AI model based on the recognized emotion.

[0555] Input: Stored emotion information, user message

[0556] Output: The translation request sent to the generative AI model

[0557] Specific operation: The server sends emotion information and the user's message to the generative AI model.

[0558] Step 12:

[0559] The generative AI model generates appropriate translations based on the emotion.

[0560] Input: The translation request sent to the generative AI model

[0561] Output: The generated translation (e.g., "Today is the worst day...")

[0562] Specific behavior: The generative AI model generates the translation "Today is the worst day..."

[0563] Step 13:

[0564] The server sends the generated translation to the device.

[0565] Input: Generated translation

[0566] Output: Translation sent to the device

[0567] Specific operation: The server sends the generated translation to the device.

[0568] Step 14:

[0569] The user presses the "Send in French" button.

[0570] Input: User actions

[0571] Output: Send button press information

[0572] Specific behavior: The user presses the "Send in French" button.

[0573] Step 15:

[0574] The device sends the translated message to the server.

[0575] Input: Submit button press information, generated translation

[0576] Output: The translated message sent to the server

[0577] Specific operation: The terminal sends the message "Aujourd'hui est le pire jour..." to the server.

[0578] Step 16:

[0579] The server receives the message and sends it to the specified recipient.

[0580] Input: The translated message sent to the server

[0581] Output: Message sent to recipient

[0582] Specific operation: The server sends the translated message to the recipient.

[0583] (Application example 2)

[0584] Next, a description will be given of Application Example 2 of Form 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."

[0585] Conventional messaging apps and social networking sites lack the ability to automatically select a user's frequently used language or adjust message translations based on the user's emotions, limiting the user experience. There is also a need for more natural and appropriate communication by recognizing user emotions and adjusting translations accordingly. Furthermore, content distribution services also face the challenge of improving the viewing experience by adjusting subtitles and audio translations based on the user's emotions.

[0586] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0587] In this invention, the server includes a message sending means for a messenger app or SNS, a "Send in XX language" button generating means placed next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing a user's emotion, an emotion adjustment means for adjusting the translation of the message based on the recognized emotion, a language selection means for automatically selecting a language based on the user's settings and past usage history, and a translation adjustment means for adjusting the translation of subtitles and audio based on the user's emotion. This makes it possible to automatically select a language frequently used by the user and adjust the translation of messages and content based on the user's emotion.

[0588] The "message sending means" is an interface that allows a user to input and send a message.

[0589] The "means for generating a send button in a specific language" is a function that automatically generates a button for sending a message in a specific language based on the user's settings and usage history.

[0590] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[0591] "Emotion recognition means" is a technology for recognizing emotions from user input, facial expressions, voice, etc.

[0592] "Emotion adjustment means" is a function that adjusts the translation or display of a message based on the recognized emotion.

[0593] "Language selection means" is a function that automatically selects the most appropriate language based on the user's settings and past usage history.

[0594] The "translation adjustment means" is a function that adjusts the subtitle and audio translation based on the user's emotions.

[0595] The following system configuration will be described as an embodiment of the present invention.

[0596] System Configuration

[0597] This system includes a means for sending messages in a messenger app or SNS, a means for generating a send button in a certain language, an automatic translation means, an emotion recognition means, an emotion adjustment means, a language selection means, and a translation adjustment means.

[0598] Message sending method

[0599] An interface that allows users to input and send messages. It is implemented as an application on smartphones and PCs.

[0600] How to generate a send button in XX language

[0601] This feature automatically generates buttons for sending messages in specific languages ​​based on the user's settings and usage history. The language that the user frequently uses is automatically selected and displayed as the button.

[0602] Automatic translation means

[0603] This function automatically translates messages entered by users into a specified language. It is implemented using a translation API such as Google Translator.

[0604] emotion recognition means

[0605] It is a technology for recognizing emotions from user input, facial expressions, voice, etc. It uses software such as OpenCV and EmotionRecognizer to analyze data from cameras and microphones.

[0606] emotional regulation means

[0607] This feature adjusts the translation and display of messages based on the recognized emotion: for example, if the user has a sad expression, the translated message will be adjusted accordingly.

[0608] Language selection method

[0609] This function automatically selects the optimal language based on the user's settings and past usage history. Content will also be automatically selected from the next time onwards based on the user's viewing history and settings information.

[0610] Translation Adjustment Method

[0611] This feature adjusts subtitle and audio translations based on the user's emotions. For example, if the user looks sad, the subtitle translation will also adjust accordingly.

[0612] Program processing explanation

[0613] The server captures data from the camera and microphone and recognizes the user's emotions using OpenCV and EmotionRecognizer. Based on the recognized emotions, it translates the message using a translation API such as Google Translator and displays the adjusted message using the emotion adjustment mechanism. The language selection mechanism automatically selects the most appropriate language based on the user's settings and past usage history.

[0614] Specific examples

[0615] If a user frequently speaks French, the application will automatically provide content in French, and if the user has a sad expression, the subtitle translation will adjust accordingly.

[0616] Prompt Sentence Examples

[0617] Develop a smartphone application that automatically selects the language that the user frequently uses and uses an emotion engine to recognize the user's emotions and adjust the translation accordingly. For example, if the user frequently uses French, the application will automatically provide content in French. Also, if the user has a sad expression, the subtitle translation will also adjust accordingly.

[0618] In this way, a system can be realized that provides optimal content based on the user's preferences and emotions.

[0619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0620] Step 1:

[0621] The server captures the user's video and audio data from the device through the camera and microphone. The input is the user's video and audio data, and the output is the captured raw data, which is used in subsequent processing steps.

[0622] Step 2:

[0623] The server analyzes the captured video and audio data using OpenCV and EmotionRecognizer to recognize the user's emotions. The input is the captured raw data, and the output is the recognized emotional information. Specifically, it analyzes the user's facial expressions and tone of voice and classifies emotions into categories such as "joy," "sadness," and "anger."

[0624] Step 3:

[0625] The server automatically selects the most appropriate language using a language selection method based on the user's settings and past usage history. The input is the user's settings and usage history, and the output is the selected language information. Specifically, the server retrieves the user's frequently used language from a database and applies it to future content.

[0626] Step 4:

[0627] The user enters a message and presses the send button. The input is the message entered by the user, and the output is the event information that the send button was pressed. Specifically, this includes the action of the user entering a message in the text box and pressing the "Send in XX language" button.

[0628] Step 5:

[0629] When the send button is pressed, the server uses a translation API such as Google Translator to automatically translate the entered message into the selected language. The input is the message entered by the user and the selected language information, and the output is the translated message. Specifically, the server sends the message and the target language to the API and receives the translation result.

[0630] Step 6:

[0631] The server adjusts the translated message based on the recognized emotion using an emotion adjustment means. The input is the translated message and the recognized emotion information, and the output is the adjusted message. Specifically, if the user expresses sad emotion, the translated message is adjusted to a negative expression accordingly.

[0632] Step 7:

[0633] The server sends the adjusted message through the message sending means, where the input is the adjusted message and the output is the sent message. Specifically, the server sends the adjusted message to the recipient and displays a notification of the completion of the message sending on the user's interface.

[0634] Step 8:

[0635] The server adjusts the subtitle and audio translations in the content distribution service based on the user's emotions. The input is the user's emotional information and content data, and the output is the adjusted subtitles and audio. Specifically, if the user has a sad expression, the subtitle translation is also adjusted accordingly.

[0636] In this way, a system can be realized that provides optimal content based on the user's preferences and emotions.

[0637] Example 3

[0638] Next, a third embodiment of the third embodiment will be described. 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."

[0639] Conventional message translation systems often fail to produce accurate translation results because they simply translate without considering the context or emotion of the message entered by the user. Furthermore, because the translation cannot reflect the user's emotions, the quality of communication declines. This makes it difficult to convey the exact meaning and emotion intended by the user.

[0640] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[0641] In this invention, the server includes a message sending means, a "Send in translation language" button generating means located next to the message sending means, a means for automatically translating the message into the translation language when the "Send in translation language" button is pressed, a means for sending the automatically translated message, a context analysis means for selecting an optimal translation based on the context of a message input by a user, and an emotion analysis means for recognizing the user's emotion and adjusting the translation based on that emotion. This makes it possible to provide an optimal translation of a user's input message based on the context and emotion.

[0642] The "message sending means" is a function for sending a message input by a user to other users.

[0643] The "means for generating a send button in a translated language" is a function for generating a button for sending a message in a translated language selected by the user.

[0644] The "automatic translation means" is a function that automatically translates a message input by a user into a specified translation language.

[0645] The "context analysis means" is a function that analyzes the context of the message entered by the user and selects the most appropriate translation based on that context.

[0646] "Emotion analysis means" is a function that recognizes the emotion of the message entered by the user and adjusts the translation based on that emotion.

[0647] "Transmission means" is a function for transmitting a translated message to other users.

[0648] "User settings" are settings that the user makes to customize the system's operation, translation language, etc.

[0649] The "translation language" is the language that the user uses to translate the message.

[0650] MODE FOR CARRYING OUT THE INVENTION

[0651] The present invention is a system that provides an optimal translation of a message input by a user based on the context and emotion. A specific embodiment of this system will be described below.

[0652] System configuration

[0653] The system mainly consists of the following components:

[0654] 1. Message sending method

[0655] 2. How to generate a submit button in a translated language

[0656] 3. Automatic Translation Methods

[0657] 4. Context analysis means

[0658] 5. Emotion analysis method

[0659] 6. Means of transmission

[0660] Hardware and software used

[0661] The Server uses the following hardware and software to receive, parse, translate, and send messages:

[0662] Server hardware: A server with a powerful processor and sufficient memory

[0663] Software: Python, NLTK library, Google Natural Language API

[0664] The terminal uses the following hardware and software to allow users to input messages and display translation results:

[0665] Device hardware: smartphones, tablets, PCs, etc.

[0666] Software: Messenger apps, social networking apps

[0667] System Operation

[0668] A user inputs a message into an input field of a terminal. For example, the user inputs "I'm fine." The terminal sends this message to a server. The server sends the received message to a context analysis means, which analyzes the context of the message. The context analysis means analyzes the context using, for example, Python's NLTK library, and translates the message "I'm fine." into "I'm fine."

[0669] The server then sends the translation result to the sentiment analyzer, which uses Google's Natural Language API to analyze the sentiment of the message. For example, if the user types "Today is the best day ever!" and the sentiment analyzer recognizes joy, the translation will be adjusted to "Today is the best day ever!"

[0670] Finally, the server sends the adjusted translation result to the terminal, and the terminal displays the translation result to the user, who can check the translation result on the terminal screen.

[0671] Specific examples

[0672] As a concrete example, consider the case where a user types "I'm fine." This message is sent to the server, where the context analyzer translates it as "I'm fine." Next, if the user types "Today is the best day ever!", the emotion analyzer recognizes joy and the server translates it as "Today is the best day ever!"

[0673] Example prompt sentence:

[0674] If a user types "I'm fine," choose the appropriate translation based on the context and analyze the sentiment using sentiment analysis tools.

[0675] If a user types "Today is a great day!", use sentiment analysis to recognize the sentiment and choose the appropriate translation.

[0676] This system supports more natural and appropriate communication by providing an optimal translation of the user's input message based on the context and emotion. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0677] Step 1:

[0678] The user enters a message.

[0679] The user inputs a message into an input field of the terminal, for example, "I'm fine." The input message is passed to the message sending means of the terminal.

[0680] Step 2:

[0681] The device sends a message to the server.

[0682] The terminal sends the message entered by the user to the server. At this time, the message is sent as an HTTP request. The input is the user's message, and the output is an HTTP request to the server.

[0683] Step 3:

[0684] The server receives the message and sends it to the context analysis means.

[0685] The server sends the received message to a context analyzer, which analyzes the context using, for example, Python's NLTK library. The input is the user's message, and the output is data to the context analyzer.

[0686] Step 4:

[0687] A context analyzer analyzes the context of the message and selects an appropriate translation.

[0688] The context analysis means analyzes the context of the message and selects an appropriate translation. For example, the message "I'm fine" is translated as "I'm fine." The input is the user's message and the output is the translation result.

[0689] Step 5:

[0690] The server sends the translation result to the emotion analysis means.

[0691] The server sends the translation result obtained from the context analysis means to the sentiment analysis means. The sentiment analysis means analyzes sentiment using, for example, Google's (registered trademark) Natural Language API. The input is the translation result, and the output is data sent to the sentiment analysis means.

[0692] Step 6:

[0693] A sentiment analyzer analyzes the sentiment of the message and adjusts the translation.

[0694] The sentiment analyzer analyzes the sentiment of the message and adjusts the translation. For example, if the message "Today is the best day ever!" is recognized as expressing joy, the translation is adjusted to "Today is the best day ever!" The input is the translation result, and the output is the adjusted translation result.

[0695] Step 7:

[0696] The server sends the final translation results to the terminal.

[0697] The server sends the final translation result obtained from the sentiment analysis means to the terminal. At this time, the translation result is sent as an HTTP response. The input is the adjusted translation result, and the output is the HTTP response to the terminal.

[0698] Step 8:

[0699] The terminal displays the translation results to the user.

[0700] The terminal displays the translation results received from the server to the user. The user can check the translation results on the terminal screen. The input is the translation result from the server, and the output is what is displayed to the user.

[0701] (Application example 3)

[0702] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[0703] Conventional message translation systems perform translation without considering the context or emotion of the message entered by the user, resulting in unnatural translations or an inability to accurately convey the user's intentions. Furthermore, since the translation cannot reflect emotion, it is difficult to convey the user's feelings appropriately.

[0704] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a message sending means, a "Send in Language" button generating means located next to the message sending means, a means for automatically translating the message into a language when the "Send in Language" button is pressed, a means for sending the automatically translated message, a means for selecting an optimal translation based on the context of a message entered by a user, and a means for recognizing the user's emotion using an emotion engine and adjusting the translation based on the emotion. This enables natural and appropriate translation that takes into account the context and emotion of the message entered by the user.

[0705] A "message sending means" is an interface that allows a user to input text messages and send them to other users.

[0706] The "send in language button generating means" is a function that generates a button for sending a message in a language selected by the user.

[0707] "Means for automatic translation" is a function that automatically translates an input message into a specified language.

[0708] The "means for sending automatically translated messages" is a function for sending automatically translated messages to other users.

[0709] The "means for selecting the best translation based on the context" is a function that analyzes the context of the input message and selects the translation that is most appropriate for that context.

[0710] An "emotion engine" is a system that recognizes emotions from messages entered by users and adjusts translations based on those emotions.

[0711] The "means for adjusting translation based on emotion" is a function for adjusting the translated message based on the emotion of the user as recognized by the emotion engine.

[0712] A system for implementing the present invention includes a message sending means, a language send button generating means, a means for automatically translating, a means for sending the automatically translated message, a means for selecting an optimal translation based on context, an emotion engine, and a means for adjusting the translation based on emotion.

[0713] System configuration

[0714] The server uses the following hardware and software:

[0715] Hardware: The server uses a computer system with a powerful processor and sufficient memory, such as an Intel Xeon processor or AMD EPYC processor.

[0716] Software: The server uses the Python programming language and the transformers natural language processing library, as well as the nltk library for sentiment analysis.

[0717] Processing flow

[0718] 1. Message sending means: Provides an interface for users to enter text messages. This is an application that runs on a mobile device such as a smartphone or tablet.

[0719] 2. Send in Language Button Generator: Generates a button to send a message in the language selected by the user. This button is automatically selected based on the user's settings.

[0720] 3. Automated translation: Automatically translate user-entered messages into a specified language using a generative AI model.

[0721] 4. How to send an automatically translated message: Send an automatically translated message to other users.

[0722] 5. A means of selecting the best translation based on context: Analyze the context of the message entered by the user and select the translation that best suits that context.

[0723] 6. Emotion engine: Recognizes emotions from the user's input message and adjusts the translation based on those emotions.

[0724] 7. Means for tailoring translation based on emotion: tailoring the translated message based on the user's emotion as recognized by the emotion engine.

[0725] Specific examples

[0726] For example, if a user is watching a movie and a character says, "I'm so happy today!", the emotion engine will recognize the positive emotion and adjust the translation to "I'm so happy today!"

[0727] Example prompts to input to a generative AI model:

[0728] "Translate the following text to Japanese and adjust the translation based on the detected emotion: 'I'm so happy today!'"

[0729] In this way, the user's viewing experience can be enriched.

[0730] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0731] Step 1:

[0732] The user inputs a message on the terminal. The input message is sent to the server in text format. The input data is the text message input by the user.

[0733] Step 2:

[0734] The server analyzes the received message and performs natural language processing (NLP) to understand the context. This process uses the transformers library. The input data is the text message entered by the user, and the output data is the analysis result including context information.

[0735] Step 3:

[0736] The server uses a generative AI model to generate the optimal translation based on the analysis results. Specifically, it uses a translation model from the transformers library to translate the message into the specified language. The input data is the analysis results including contextual information, and the output data is the translated text message.

[0737] Step 4:

[0738] The server passes the translated message to an emotion engine to recognize the user's emotion. The emotion engine uses the nltk library. The input data is the translated text message, and the output data is an emotion label (e.g., positive, negative).

[0739] Step 5:

[0740] The server adjusts the translated message based on the emotion label. For example, for positive emotion, it adds "Yo da!" to the translated message, and for negative emotion, it adds "Da...". The input data are the emotion label and the translated text message, and the output data is the adjusted translated message.

[0741] Step 6:

[0742] The server sends the adjusted translation message to the terminal, and the terminal displays the received message to the user. The input data is the adjusted translation message, and the output data is the text message displayed to the user.

[0743] Step 7:

[0744] The user checks the displayed message and replies if necessary. The reply message is then sent again via the process from step 1. The input data is the reply message entered by the user, and the output data is the translated and adjusted reply message.

[0745] 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.

[0746] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ) 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.

[0747] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0748] 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.

[0749] [Second embodiment]

[0750] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0751] 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.

[0752] 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).

[0753] 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.

[0754] 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.

[0755] 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).

[0756] 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.

[0757] 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.

[0758] 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.

[0759] 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.

[0760] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0761] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0762] "Example 1"

[0763] In one embodiment of the present invention, a "Send in English" button is placed next to the message input field on the message sending screen of a messenger app or SNS. When a user inputs a message and presses the "Send in English" button, the input message is automatically translated into English and the translated message is sent.

[0764] "Example 2"

[0765] Additionally, we provide a function that automatically selects the language of the "Send in" button from available languages ​​based on the user's settings. For example, if a user frequently communicates with people who speak French, the "Send in French" button will be automatically generated based on the user's settings.

[0766] "Example 3"

[0767] It also provides a function that selects the best translation based on the context of the message entered by the user. For example, if the user enters "I'm fine," it will be translated as "I'm fine." However, if the user enters "It's fine," it will be translated as "It's fine." In this way, the same "fine" will be translated differently depending on the context.

[0768] The processing flow of each embodiment will be described below.

[0769] "Example 1"

[0770] Step 1: The user opens a messaging app or social media message sending screen.

[0771] Step 2: Make sure there is a "Send in English" button next to the message input field.

[0772] Step 3: User types message and presses "Send in English" button.

[0773] Step 4: The system will automatically translate the message you entered into English.

[0774] Step 5: The system sends the translated message.

[0775] "Example 2"

[0776] Step 1: The user opens the settings screen of a messenger app or social networking site.

[0777] Step 2: The user selects the language from the available options under the "Send in" button. Step 3: The user saves the settings and returns to the message sending screen.

[0778] Step 4: Make sure there is a "Send in XXX" button in the user's language of choice next to the message input field.

[0779] "Example 3"

[0780] Step 1: The user types a message.

[0781] Step 2: The system takes the user's message and selects the best translation based on the context of the message.

[0782] Step 3: The system displays the translation it has selected to the user.

[0783] Step 4: The user presses the "Send in XXX language" button, and the system sends the selected translation.

[0784] Example 1

[0785] Next, a description will be given of Example 1 of Form 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."

[0786] With conventional messaging apps and social networking sites, users had to manually translate messages when sending them in different languages, which was a time-consuming process. Furthermore, the accuracy of the translations and the appropriate translations based on the context were often lacking, which could impair the smoothness of communication. To solve these problems, a system was needed that would allow users to easily translate and send messages in a specific language.

[0787] 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.

[0788] In this invention, the server includes a message sending means, a "Send in specific language" button generating means located next to the message sending means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, a means for inputting the message into a generative AI model as a prompt sentence, a means for the generative AI model to translate the message, and a means for sending the translated message to a destination. This allows a user to easily translate and send a message into a specific language.

[0789] "Message sending means" is a function that provides an interface for users to input and send messages.

[0790] The "specific language send button generating means" is a function that is placed next to the message sending means and generates a button for sending a message in a specific language selected by the user.

[0791] "Automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[0792] A "generative AI model" is an artificial intelligence model for natural language processing that has the ability to translate input text into other languages.

[0793] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[0794] The "means for sending a translated message" is a function for sending a message translated by the automatic translation means to a specified destination.

[0795] "Context-based optimal translation" refers to providing the most appropriate translation by taking into account the content and intent of the message entered by the user.

[0796] This invention provides a system that enables users to easily translate and send messages into a specific language in messenger apps and social networking sites. Specific embodiments of this system are described below.

[0797] First, a user opens a messaging app or social networking site's message sending screen and enters a message in the message input field. For example, they might enter "Hello, how are you?". Next, the user presses the "Send in specific language" button located next to the message input field. This action starts the process of translating the entered message into the specific language.

[0798] The device sends the message entered by the user to the server. At this time, the message is sent to the server in an appropriate format. The server inputs the received message as a prompt sentence to a generative AI model (e.g., GPT-4 (registered trademark)). Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[0799] The generative AI model receives the prompt sentence and translates the message into a specific language. For example, it translates "Hello, how are you?" into "Hello, how are you?" The server sends the translated message received from the generative AI model to the device. At this time, the translated message is sent to the device in the appropriate format.

[0800] The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend. In this way, a message entered by the user in Japanese is automatically translated into a specific language and sent.

[0801] As a concrete example, consider a case where a user wants to send a message to a friend saying, "Hello, how are you?" The user enters "Hello, how are you?" in the message input field and presses the "Send in specific language" button. The system translates this message into "Hello, how are you?" and sends it to the friend.

[0802] Example prompts to input to a generative AI model:

[0803] "Please translate 'Hello, how are you?' into English."

[0804] This system allows users to easily translate and send messages into specific languages. The hardware used includes the user's device (smartphone, tablet, PC, etc.) and a server. The software used includes a messenger app, a social networking application, and a generative AI model (e.g., GPT-4 (registered trademark)).

[0805] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0806] Step 1:

[0807] The user enters a message in the message input field.

[0808] Type: "Hello, how are you?"

[0809] Output: The input message "Hello, how are you?"

[0810] Specific actions: The user opens the message sending screen of a messenger app or social networking site and types "Hello, how are you?" in the message input field.

[0811] Step 2:

[0812] The user presses the "Send in specific language" button.

[0813] Input: User presses the "Submit in a specific language" button

[0814] Output: An event that the submit button was pressed.

[0815] What it does: The user presses the "Send in specific language" button located next to the message input field, which starts the process of translating the entered message into the specific language.

[0816] Step 3:

[0817] The terminal sends the input message to the server.

[0818] Input: The message "Hello, how are you?" is entered and the event of pressing the send button

[0819] Output: Message sent to server: "Hello, how are you?"

[0820] Specific operation: The terminal sends the message entered by the user to the server, where the message is sent in the appropriate format.

[0821] Step 4:

[0822] The server inputs the message as a prompt into the generation AI model.

[0823] Input: Message sent to server: "Hello, how are you?"

[0824] Output: The prompt sent to the generative AI model: "Please translate 'Hello, how are you?' into English."

[0825] Specific operation: The server inputs the received message into a generative AI model (e.g., GPT-4 (registered trademark)) as a prompt sentence. Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[0826] Step 5:

[0827] A generative AI model translates the message into English.

[0828] Input: Prompt sentence: "Please translate 'Hello, how are you?' into English."

[0829] Output: The translated message "Hello, how are you?"

[0830] Specific behavior: The generative AI model receives a prompt and translates the message into a specific language, for example, translating "Hello, how are you?" into "Hello, how are you?"

[0831] Step 6:

[0832] The server sends the translated message to the terminal.

[0833] Input: Translated message "Hello, how are you?"

[0834] Output: The translated message "Hello, how are you?" sent to the terminal.

[0835] Specific operation: The server sends the translated message received from the generative AI model to the device, where the translated message is sent to the device in the appropriate format.

[0836] Step 7:

[0837] The device sends the translated message to the destination.

[0838] Input: The translated message "Hello, how are you?" sent to the terminal

[0839] Output: The message "Hello, how are you?" sent to the destination.

[0840] Specific operation: The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend.

[0841] (Application example 1)

[0842] Next, a description will be given of Application Example 1 of Form 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."

[0843] With the current trend toward globalization, communication with foreign customers has become increasingly important in brick-and-mortar stores. However, the language barrier makes it difficult for store staff to communicate smoothly with foreign customers. To solve this problem, real-time language translation is necessary, but current technology is not sufficient.

[0844] 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.

[0845] In this invention, the server includes a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a voice input means, a means for converting voice acquired by the voice input means into text, a means for translating the text into XX language, and a means for outputting the translated text by voice. This enables smooth real-time communication between store staff and foreign customers in physical stores.

[0846] A "message sending means" is an interface that allows a user to input and send a text message.

[0847] The "means for generating a send button in XX language" is a function that generates a button for translating and sending a message into a language selected by the user.

[0848] "Means for automatic translation" is a function for automatically translating an input text message into a specified language.

[0849] The "means for sending an automatically translated message" is a function for sending a translated message to the other party.

[0850] The "voice input means" is an interface for capturing the user's voice and acquiring it as digital data.

[0851] The "means for converting voice to text" is a function for converting acquired voice data into text data.

[0852] "Means for translating text into XX language" is a function for translating text data into a specified language.

[0853] The "means for audibly outputting the translated text" is a function for playing back the translated text as a voice.

[0854] To implement the present invention, the following system configuration and program are required.

[0855] First, the server provides a message sending means, which is an interface for users to input and send text messages. Next, the server provides a "send in XX language" button generation means, which is a function that generates a button for translating and sending a message into a language selected by the user.

[0856] When a user presses the "Send in XX language" button, the server uses an automatic translation means to automatically translate the entered text message into the specified language, and this translated message is sent to the recipient by a means for sending an automatically translated message.

[0857] Furthermore, the server provides a voice input means, which is an interface for capturing the user's voice and acquiring it as digital data, and the acquired voice data is converted into text data by a voice-to-text conversion means.

[0858] The converted text data is translated into a specified language by a means for translating text into a certain language. The translated text is reproduced as audio by a means for outputting the translated text as audio.

[0859] To realize this system, the following hardware and software are used:

[0860] Hardware: Smart glasses (capable of audio input and output)

[0861] software:

[0862] speech_recognition library: convert speech to text

[0863] googletrans library: translate text

[0864] pyttsx3 library: convert text to speech

[0865] For example, when a store clerk says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted to text using the speech_recognition library, and the text is translated into English using the googletrans library. The translated text is converted to speech using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[0866] Example prompts to input to a generative AI model:

[0867] When a user says "Where is this product located?", convert the speech to text, translate it into English, and play it aloud as "Where is this product located?"

[0868] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0869] Step 1:

[0870] The user speaks to the smart glasses. The microphone in the smart glasses captures the voice and obtains it as voice data. This voice data becomes the input.

[0871] Step 2:

[0872] The voice data acquired by the device (smart glasses) is converted into text data using the speech_recognition library. The voice data is analyzed and the corresponding text data is generated. This text data is the output.

[0873] Step 3:

[0874] Translates text data generated by the device into a specified language (e.g., English) using the googletrans library. It takes text data as input and generates translated text data. This translated text data is the output.

[0875] Step 4:

[0876] The device converts the translated text data into speech data using the pyttsx3 library, takes the translated text data as input, and generates the corresponding speech data, which is the output.

[0877] Step 5:

[0878] The device then plays the generated audio data through the smart glasses' speaker, allowing the user to listen to the translated audio.

[0879] Specifically, when a user says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted into text data, "Where is this product located?", using the speech_recognition library. The text data is translated into English, "Where is this product located?", using the googletrans library. The translated text data is converted into audio data using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[0880] Example 2

[0881] Next, a description will be given of Example 2 of Form 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."

[0882] In conventional messaging apps and social networking sites, when users wanted to send messages in different languages, they had to translate them manually, which was a time-consuming process. Furthermore, since there was no function to automatically select frequently used languages, users had to select the language each time, which was inconvenient. Furthermore, the quality of the translations was inconsistent, so the translations sometimes did not fit the context of the message.

[0883] 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.

[0884] In this invention, the server includes means for acquiring user setting information, means for sending a prompt sentence to the generative AI model based on the acquired setting information, means for generating a "Send in XX language" button based on a response from the generative AI model, means for displaying the generated "Send in XX language" button on a user interface, and means for sending a message when the user clicks the "Send in XX language" button. This makes it possible to automatically select a language frequently used by the user and perform appropriate translation.

[0885] "User Settings" refers to the individual language and other customization information that a User sets within the System.

[0886] A "generative AI model" is a model designed to perform specific tasks using artificial intelligence. In this case, it generates prompts based on user settings and generates appropriate language selection buttons.

[0887] A "prompt sentence" refers to an input sentence that gives specific instructions or questions to a generative AI model.

[0888] "Send in XX language button" refers to a button on the interface that allows a user to send a message in a specific language of their choice.

[0889] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[0890] "Means for sending messages" refers to the functionality for sending messages entered by the user in the selected language.

[0891] This invention relates to a system for messenger apps and SNS that automatically selects a language frequently used by a user and performs appropriate translation. Specific embodiments of this system are described below.

[0892] Server Processing

[0893] The server retrieves the user's preference information from a database. Specifically, it uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. For example, it executes an SQL query that retrieves the corresponding language preference using the user ID as a key.

[0894] The server then sends a prompt to a generative AI model (e.g., OpenAI's GPT-4 (registered trademark)) based on the user's preferences. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language.

[0895] Example prompt sentence:

[0896] "Based on the user's preferences, select the language they frequently use and generate a corresponding submit button. For example, if the user selects French, generate a 'Submit in French' button."

[0897] The server that receives the response from the generative AI model generates the data to display on the user interface. Specifically, it generates HTML and JavaScript code and creates buttons corresponding to the language selected by the user.

[0898] Terminal handling

[0899] The terminal receives the data sent from the server and reflects it in the user interface. Specifically, it dynamically generates and displays a "Submit in French" button using front-end technologies such as JavaScript (registered trademark) and React.

[0900] User operations

[0901] Users select the languages ​​they frequently use in the settings screen. For example, if a user selects French, a "Send in French" button will be automatically generated from next time. Users can click this button to send messages in the selected language.

[0902] Specific examples

[0903] For example, if a user selects "French" in the settings screen, the next time a "Send in French" button is automatically generated, the user can click this button to send the message in French.

[0904] In this way, it is possible to automatically select the language that the user frequently uses and provide the appropriate translation, which saves the user the trouble of manually selecting the language and allows for smoother communication.

[0905] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0906] Step 1:

[0907] Get user settings information

[0908] The server retrieves the user's preference information from a database. A user ID is given as input. The server uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. Specifically, it executes an SQL query using the user ID as a key to retrieve the corresponding language preference. The user's preference information (e.g., French) is obtained as output.

[0909] Step 2:

[0910] Generate and send prompts

[0911] The server sends a prompt to the generative AI model based on the acquired user preference information. The user preference information is given as input. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language. An example of a specific prompt is, "Based on the user's preference, please select the frequently used language and generate a corresponding submit button. For example, if the user selects French, please generate a 'Submit in French' button." The output is a response from the generative AI model.

[0912] Step 3:

[0913] Generate language selection button

[0914] The server generates a "Submit in XX language" button based on the response from the generative AI model. The response from the generative AI model is given as input. The server generates HTML and JavaScript (registered trademark) code to create a button corresponding to the language selected by the user. The output is the data for the generated "Submit in XX language" button.

[0915] Step 4:

[0916] Displaying the language selection button

[0917] The terminal receives the data sent from the server and reflects it in the user interface. As input, it receives the data for the "Send in XX language" button sent from the server. The terminal uses front-end technologies such as JavaScript (registered trademark) and React to dynamically generate and display the "Send in French" button. As output, it obtains the button displayed in the user interface.

[0918] Step 5:

[0919] Sending a message

[0920] The user clicks the generated "Send in French" button. The user's click event is given as input. When the click event occurs, a message is sent in the selected language. Specifically, a JavaScript (registered trademark) event listener is used to catch the button click event and process the message sending. The message sent in the selected language is obtained as output.

[0921] (Application example 2)

[0922] Next, a description will be given of Application Example 2 of Form 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."

[0923] In conventional messaging apps and social networking sites, users had to translate messages manually when communicating in different languages, which was inefficient. Furthermore, they lacked the ability to automatically learn frequently used languages ​​and send messages in the appropriate language, which made them less convenient for users.

[0924] The specific processing by the specific 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 a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a means for acquiring user setting information, a means for learning frequently used languages ​​based on the setting information, a means for automatically generating the "Send in XX language" button based on the learned languages, a means for translating the message in real time, and a means for sending the translated message. This makes it possible to automatically learn languages ​​frequently used by the user and send messages in the appropriate language.

[0925] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[0926] The "means for generating a send button in a specific language" is a function that automatically generates a button for sending a message in a specific language based on the user's settings.

[0927] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[0928] "Transmission means" is a function for sending translated messages to other users.

[0929] "Means for acquiring user setting information" refers to a function for acquiring the language and other setting information previously set by the user.

[0930] The "means for learning frequently used languages" is a function for learning the languages ​​that a user frequently uses based on the user's past message history and setting information.

[0931] "Means for real-time translation" refers to a function that translates a message into a specified language in real time as the user types the message.

[0932] The system for implementing this invention comprises a server and a user terminal. The server includes a message sending means, a "Send in XX language" button generating means, an automatic translation means, a sending means, a means for acquiring user setting information, a means for learning frequently used languages, and a means for translating in real time.

[0933] Program processing explanation

[0934] Hardware used

[0935] Server: Any cloud server (e.g. AWS (registered trademark), Google Cloud)

[0936] Client: Smartphone (iOS or ANDROID (registered trademark))

[0937] Software used

[0938] Framework: Flask (Python)

[0939] Translation API: Googletrans (Python wrapper for Google Translate API)

[0940] Data processing and calculation

[0941] 1. How to get user configuration information:

[0942] The server retrieves the user's pre-selected language and other preferences, which are used to learn frequently used languages ​​based on the user's past message history and preferences.

[0943] 2. Ways to learn frequently used languages:

[0944] The server learns the user's most frequently used languages ​​based on their past message history and preferences, and automatically generates a button to identify the user's most frequently used language and send messages in that language.

[0945] 3. How to generate a "Submit in ____ language" button:

[0946] The server automatically generates a button for sending messages in a specific language based on the user's preferences, which is displayed when the user types a message.

[0947] 4. Real-time translation methods:

[0948] As users type their messages, the server translates them into the language of their choice in real time, allowing users to instantly see the translated message.

[0949] 5. Automatic translation methods:

[0950] When a user presses the "Send in X language" button, the server automatically translates the message into the specified language using a translation API such as GoogleTrans.

[0951] 6. Means of transmission:

[0952] The translated messages are then sent to other users via a server, allowing users of different languages ​​to communicate smoothly.

[0953] Specific examples

[0954] For example, if the user is "user1" and the message they send is "Hello, how are you?", the server will translate this message into French and return it as "Bonjour, comment ça va?".

[0955] Prompt Sentence Examples

[0956] User ID: user1

[0957] Message: Hello, how are you?

[0958] When you send this prompt to the server, you will get a response like this:

[0959] {

[0960] "original_message": "Hello, how are you?",

[0961] "translated_message": "Bonjour, comment ça va?",

[0962] "language": "fr"

[0963] }

[0964] In this way, a system can be realized that automatically selects a language based on the user's settings, and translates and sends messages.

[0965] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0966] Step 1:

[0967] The user inputs a message. The user inputs a text message using the message sending means of the smartphone. The input message is sent to the server.

[0968] Step 2:

[0969] The server retrieves the user's preference information from a database and identifies the user's frequently used languages, including languages ​​the user has used in the past and languages ​​that the user has previously set.

[0970] Step 3:

[0971] The server learns frequently used languages. Based on the acquired configuration information, the server learns the languages ​​most frequently used by users. Based on this learning result, it generates a "Send in XX language" button for sending messages in a specific language.

[0972] Step 4:

[0973] The server generates a "Send in XX language" button. Based on the learning results, the server automatically generates a button for sending messages in the language the user frequently uses and displays it on the user's device.

[0974] Step 5:

[0975] The user presses the "Send in XX language" button. By pressing the generated "Send in XX language" button, the user instructs the message they entered to be translated into a specific language.

[0976] Step 6:

[0977] The server translates messages in real time. The server translates messages entered by users into the specified language in real time. This translation uses a translation API such as Google Trans.

[0978] Step 7:

[0979] The server sends the translated message, returns the translated message to the user's terminal for the user to review, and then sends the translated message to other users.

[0980] Step 8:

[0981] The other user receives the translated message. The other user receives and displays the translated message sent from the server. This allows users who speak different languages ​​to communicate smoothly.

[0982] Example 3

[0983] Next, a description will be given of Example 3 of Form Example 3. 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."

[0984] In conventional messaging apps and social networking sites, it was difficult to provide appropriate translations based on the context when users sent messages. This was especially difficult when the same word had different meanings depending on the context.

[0985] The specification process by the specification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send with Translation" button generating means located next to the message sending means, a means for automatically translating the message when the "Send with Translation" button is pressed, a means for sending the automatically translated message, a means for receiving a message input by a user, a means for inputting a message to a generative AI model, a means for the generative AI model to generate a translation based on the context, a means for receiving the generated translation result, and a means for displaying the translation result to the user. This makes it possible to provide an optimal translation based on the context of the message input by the user.

[0986] The "message sending means" is a means for sending a message input by a user to other users.

[0987] The "translation and send button generating means" is a means for generating a button for a user to translate and send a message.

[0988] The "means for automatic translation" is a means for automatically translating a message input by a user into a specified language.

[0989] The "means for sending an automatically translated message" is a means for sending a translated message to another user.

[0990] The "means for receiving a message input by a user" refers to a means by which the server receives a message input by a user.

[0991] "Means for inputting a message into a generative AI model" refers to means for inputting a received message into a generative AI model.

[0992] "Means by which a generative AI model generates a context-based translation" refers to the means by which a generative AI model analyzes the context of a message and generates an optimal translation.

[0993] "Means for receiving the generated translation results" refers to means for receiving the translation results generated from the generative AI model.

[0994] The "means for displaying the translation result to the user" refers to a means for displaying the received translation result to the user.

[0995] This invention relates to a system that optimally translates a message entered by a user based on its context. The system includes a message sending means, a translation send button generating means, an automatic translation means, a means for sending the automatically translated message, a means for receiving a message entered by a user, a means for inputting the message to a generative AI model, a means for the generative AI model to generate a translation based on its context, a means for receiving the generated translation result, and a means for displaying the translation result to a user.

[0996] Hardware and software used

[0997] This system uses a server, a terminal, and a generative AI model. Specifically, the server receives messages, inputs them into the generative AI model, and receives and sends translation results. The terminal inputs messages from users and displays translation results. The generative AI model uses, for example, OpenAI's GPT-4 (registered trademark).

[0998] Data processing and calculation

[0999] The server receives the message entered by the user on the device and inputs the message into the generative AI model. The generative AI model analyzes the context of the message and generates the optimal translation. The generated translation result is returned to the server, which then sends it to the device. The device displays the translation result to the user.

[1000] Specific examples

[1001] As a concrete example, consider the case where a user types "I'm fine." In this case, the server uses a generative AI model to generate the translation "I'm fine." On the other hand, if the user types "It's fine," the server generates the translation "It's fine."

[1002] Prompt Sentence Examples

[1003] "If a user types 'I'm fine.', translate this to 'I'm fine.'"

[1004] "If the user types 'It's fine.', translate this to 'That's fine.'"

[1005] In this way, the system can appropriately translate the user's input message based on the context. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1006] Step 1:

[1007] The user inputs a message. The user inputs a message into the input field of the terminal. For example, the user inputs "I'm fine." This input message becomes the input data for the next processing step.

[1008] Step 2:

[1009] The terminal sends a message to the server. The terminal sends the message entered by the user to the server using an HTTP request. In this case, the input data is the message entered by the user, and the output data is the message sent to the server.

[1010] Step 3:

[1011] The server receives the message. The server receives the message sent from the terminal and temporarily stores it in memory. The input data is the message sent from the terminal, and the output data is the message stored in the server.

[1012] Step 4:

[1013] The server inputs a message to the generative AI model. The server calls the API to input the received message to the generative AI model. The input data is the message stored on the server, and the output data is the message passed to the generative AI model.

[1014] Step 5:

[1015] The generative AI model generates a translation based on the context. The generative AI model analyzes the context of the input message and generates the optimal translation. For example, the generative AI model generates the translation "I'm fine." for the message "I'm fine." The input data is the message passed to the generative AI model, and the output data is the generated translation result.

[1016] Step 6:

[1017] The server receives the translation results. The server receives the translation results generated by the generative AI model and stores them in memory. The input data is the translation results returned by the generative AI model, and the output data is the translation results stored on the server.

[1018] Step 7:

[1019] The server sends the translation results to the terminal. The server sends the translation results to the terminal using an HTTP response. The input data is the translation results stored on the server, and the output data is the translation results sent to the terminal.

[1020] Step 8:

[1021] The terminal displays the translation result to the user. The terminal displays the translation result received from the server to the user. For example, the translation result "I'm fine" is displayed on the terminal screen. The input data is the translation result sent to the terminal, and the output data is the translation result displayed to the user.

[1022] (Application example 3)

[1023] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1024] Traditional messaging apps and social media platforms often lack accurate translations based on context when users communicate in different languages. Virtual stores also lack accurate translations when users search for product information or contact customer support, resulting in a poor user experience.

[1025] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a message sending means, a "Send in Language" button generating means located next to the message sending means, a means for automatically translating the message into a language when the "Send in Language" button is pressed, a means for sending the automatically translated message, a means for selecting an optimal translation based on the context of a message entered by a user, and a means for providing an optimal translation based on the context when a user searches for product information or makes an inquiry to customer support in a virtual store. This makes it possible to provide accurate translations based on the context when users communicate in different languages, improving the user experience in the virtual store.

[1026] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[1027] The "send in language button generating means" is a function that generates a button for sending a message in a language selected by the user.

[1028] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1029] "Transmission means" is a function for sending automatically translated messages to other users.

[1030] "Means for selecting the best translation based on context" is a function that analyzes the context of the message entered by the user and selects the translation that best suits that context.

[1031] A "virtual store" is a virtual store that offers products and services over the Internet.

[1032] "Means for searching for product information" is a function that allows users to search for information about products and services offered in the virtual store.

[1033] "Mechanisms for contacting customer support" refers to the functionality that allows users to report questions or issues to the virtual store's support team.

[1034] To implement this invention, the following system configuration is required: The system includes a message sending means, a language send button generating means, an automatic translation means, a sending means, a means for selecting the most appropriate translation based on the context, a means for searching for product information in a virtual store, and a means for making an inquiry to customer support.

[1035] The server receives messages entered by users and uses a generative AI model to provide the best translation based on the context. Specifically, it uses Hugging Face's Transformers library to translate from English to Japanese. When a user enters a message, it is sent to the server, which then uses the generative AI model to translate it based on the context.

[1036] The terminal provides an interface for a user to input and send a message. The terminal includes a message sending means and a send in language button generating means, and allows the user to send a message in a language selected by the user. When the user presses the "send in language" button, the message is automatically translated and sent to another user through the sending means.

[1037] As a concrete example, suppose a user types "Can I return this item?" and presses the "Send in Japanese" button. In this case, the server uses the generative AI model to translate it into "Can I return this item?" and sends the translated message to other users via the sending means.

[1038] The same process applies to virtual stores when users search for product information or contact customer support. For example, if a user types "I need help with my order," the server translates it to "I need help with my order" and provides appropriate support.

[1039] An example of a prompt sentence to input to the generative AI model would be "Translate the following English sentence to Japanese considering the context: 'I need help with my order.'" Using this prompt sentence, the server can provide the best translation based on the context.

[1040] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1041] Step 1:

[1042] A user inputs a message on a terminal. The user inputs a text message using a message sending means and presses a send button in a language. The input is the user's text message and the output is the inputted message displayed on the terminal.

[1043] Step 2:

[1044] The terminal sends the input message to the server. The terminal sends the input message to the server along with the send button information in the language pressed by the user. The input is the user's text message and the selected language, and the output is the message data sent to the server.

[1045] Step 3:

[1046] The server receives the message and performs a translation based on the context. The server analyzes the received message and uses a generative AI model to perform the optimal translation based on the context. The input is the message data sent to the server, and the output is the translated message. Specifically, the translation is performed using Hugging Face's Transformers library.

[1047] Step 4:

[1048] The server sends the translated message to the terminal. The server sends the translated message to the terminal and displays it to the user. The input is the translated message and the output is the translated message sent to the terminal.

[1049] Step 5:

[1050] The terminal displays the translated message and transmits it to other users through the transmitting means. The terminal displays the translated message to the user, and after the user confirms it, transmits the message to other users through the transmitting means. The input is the translated message, and the output is the message transmitted to other users.

[1051] Step 6:

[1052] Other users check the received message. Other users check the received message and reply if necessary. The input is the received message and the output is the other users' checked message.

[1053] In this way, messages entered by users are optimally translated based on the context and accurately conveyed to other users.

[1054] 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.

[1055] "Example 1"

[1056] One embodiment of the present invention provides a message translation system incorporating an emotion engine. In this system, when a user types a message on a messaging app or social networking site, the emotion engine recognizes the user's emotion. The emotion engine infers the user's emotion from, for example, the context of the message, the use of emojis, and the typing speed. The emotion engine then adjusts the translation of the message based on the user's emotion. For example, if a user types "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression: "Today is the best day ever!"

[1057] "Example 2"

[1058] In another embodiment of the present invention, a system is provided that includes emotion recognition means for an emotion engine to recognize a user's emotion, and emotion adjustment means for adjusting the translation of a message based on the recognized emotion. The emotion recognition means infers the user's emotion from the context of the user's message, the use of emojis, typing speed, etc. The emotion adjustment means adjusts the translation of the message based on the recognized emotion. For example, if the user types "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, such as "Today is the worst day...".

[1059] "Example 3"

[1060] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. In this system, the emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. For example, if the user inputs "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression, "Today is the best day ever!". On the other hand, if the user inputs "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, "Today is the worst day...".

[1061] The processing flow of each embodiment will be described below.

[1062] "Example 1"

[1063] Step 1: The user types a message in a messaging app or social networking site.

[1064] Step 2: The sentiment engine infers the user's sentiment based on the context of the message, emoji use, typing speed, etc.

[1065] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[1066] "Example 2"

[1067] Step 1: The user types a message.

[1068] Step 2: The emotion recognition tool infers the user's emotions from the context of the message, the use of emojis, typing speed, etc.

[1069] Step 3: Emotion regulation measures adjust the translation of the message based on the perceived emotion.

[1070] "Example 3"

[1071] Step 1: The user types a message.

[1072] Step 2: The emotion engine recognizes the user's emotion.

[1073] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[1074] Example 1

[1075] Next, a description will be given of Example 1 of Form 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."

[1076] In conventional messaging apps and social networking sites, when users translated and sent messages into other languages, the quality of the translation often lacked emotional expression, resulting in problems with the user's intentions and emotions not being conveyed accurately. Furthermore, the user had to take the time and effort to translate manually, making it inconvenient.

[1077] The specification process by the specification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send in specific language" button generating means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and a means for adjusting the translation based on the result of the emotion recognition means. This makes it possible to send a high-quality translated message that reflects the user's emotion.

[1078] The "message sending means" is a function that provides an interface for users to input and send messages.

[1079] The "means for generating a send button in a specific language" is a function for generating a button that is placed next to the message input field and that is used to send a message in a specific language selected by the user.

[1080] The "automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[1081] The "emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emoticons, typing speed, etc.

[1082] The "translation adjustment means" is a function that adjusts the expression of the translated message based on the results of the emotion recognition means.

[1083] MODE FOR CARRYING OUT THE INVENTION

[1084] This invention is a system that places a "Send in specific language" button on the message sending screen of a messenger app or SNS, and when a user inputs a message and presses the "Send in specific language" button, the input message is automatically translated into the specific language and the translated message is sent. It also includes a function that recognizes the user's emotions and adjusts the translation based on those emotions by combining it with an emotion engine.

[1085] Hardware and software used

[1086] Hardware:

[1087] Device: The device on which the user types the message (smartphone, tablet, PC, etc.)

[1088] Server: A remote server for message translation and emotion recognition

[1089] software:

[1090] Messenger apps and social networking applications

[1091] Emotion engine (e.g. EmotionAI)

[1092] Translation APIs (e.g., Google Translate API, DeepL API)

[1093] Data processing and calculation

[1094] 1. Get the message:

[1095] The terminal receives the message entered by the user.

[1096] 2. Emotion Recognition:

[1097] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[1098] 3. Message Translation:

[1099] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[1100] 4. Sending translated messages:

[1101] The device sends the translated message.

[1102] Specific examples

[1103] When a user types "Today is the best day ever!" into a messenger app and presses the "Send in a specific language" button, the following process occurs:

[1104] 1. The user types, "Today is a great day!"

[1105] 2. The user presses the "Send in specific language" button.

[1106] 3. Your device will get the message "Today is a great day!"

[1107] 4. The server uses "EmotionAI" to recognize the emotion of joy from the message.

[1108] 5. The server uses the Google Translate API to translate "Today is the best day ever!" to "Today is the best day ever!"

[1109] 6. The device sends the message "Today is the best day ever!"

[1110] Prompt Sentence Examples

[1111] "Design a system that translates messages typed by a user in a messenger app into a specific language and adjusts the message appropriately based on emotion. For example, if a user types, 'Today is the best day ever!', the emotion engine should recognize joy and translate the message to, 'Today is the best day ever!'"

[1112] In this way, a system is realized that appropriately translates and transmits a message input by a user based on emotion.

[1113] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1114] Step 1:

[1115] The user enters a message in the message input field of a messenger app or social networking site.

[1116] Input: The user types, "Today is the best day ever!"

[1117] What happens: A user types a message using a keyboard or touchscreen.

[1118] Output: "Today is the best day ever!" is displayed in the message input field.

[1119] Step 2:

[1120] The user presses the "Send in specific language" button located next to the message input field.

[1121] Input: User clicks the "Submit in specific language" button.

[1122] Specific action: The user presses a button using a mouse or touchscreen.

[1123] Output: A button pressed event is fired.

[1124] Step 3:

[1125] The terminal receives the message entered by the user.

[1126] Input: The event of a button being pressed and the message being entered: "Today is a great day!"

[1127] Specific operation: The device reads text data from the message input field.

[1128] Output: The retrieved message "Today is a great day!" is stored in memory.

[1129] Step 4:

[1130] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[1131] Input: The retrieved message "Today is the best day!"

[1132] What it does: The server uses an emotion engine (e.g., EmotionAI) to analyze the message context, detect emojis, and analyze typing speed.

[1133] Output: The emotion engine recognizes the user's emotion as "joy."

[1134] Step 5:

[1135] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[1136] Input: Emotion engine result "Joy" and original message "Today is the best day ever!"

[1137] Specific operation: The server uses a translation API (e.g., Google Translate API) to perform translation that takes emotional expressions into account.

[1138] Output: The translated message "Today is the best day ever!".

[1139] Step 6:

[1140] The device sends the translated message.

[1141] Input: The translated message "Today is the best day ever!".

[1142] Specific behavior: The device uses the message sending function to send the translated message.

[1143] Output: The sent message "Today is the best day ever!" will appear in the recipient's message inbox.

[1144] (Application example 1)

[1145] Next, a description will be given of Application Example 1 of Form 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."

[1146] In existing messaging apps and social media, when users send messages in different languages, the quality of the translation often does not accurately reflect the user's feelings, which can lead to poor communication quality. Furthermore, because translations do not take the user's feelings into consideration, there is a risk of misunderstandings or discomfort.

[1147] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a message sending means for a messenger app or SNS, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the translation based on the sentiment analysis means. This enables natural translation that takes the user's sentiment into consideration.

[1148] "Messenger apps and social networking sites" are software applications that allow users to send and receive text messages, images, videos, etc.

[1149] The "message sending means" is a function for sending a message input by a user to other users.

[1150] The "means for generating a send button in a specific language" is a function that generates a button that allows a user to send a message in a specific language.

[1151] "Means for automatic translation" refers to a function that automatically translates a message entered by a user into a specified language.

[1152] "Means for sending automatically translated messages" refers to a function for sending translated messages to other users.

[1153] "Emotion analysis means" is a function that analyzes a user's emotions from messages entered by the user and other information.

[1154] The "means for adjusting the translation" is a function for adjusting the translated message based on the emotional information obtained by the emotional analysis means.

[1155] To implement this invention, we need to build a system that translates messages in a way that takes into account emotions when users send them on messenger apps or social networking sites. This system includes the following main components:

[1156] 1. Message sending method

[1157] It provides an interface for users to type and send messages. This can be a messaging or social networking application that runs on a device such as a smartphone or computer.

[1158] 2. How to generate a "Send in XX language" button

[1159] Creates a "Send in ____ language" button that is placed next to the message sending method. This button allows users to send messages in a specific language. The language is automatically selected from the available options based on the user's settings.

[1160] 3. Means of automatic translation

[1161] When a user presses the "Send in XX language" button, the input message is automatically translated into the specified language. This function is achieved using a generative AI model. Specifically, a translation model using the Transformers library is used.

[1162] 4. How to send automatically translated messages

[1163] Send translated messages to other users. This feature will also be implemented as part of messaging apps and social networking applications.

[1164] 5. Emotion analysis method

[1165] Analyzes user sentiment from user-entered messages and other information. This functionality is achieved using a generative AI model, specifically a sentiment analysis model using the Transformers library.

[1166] 6. Means of adjusting the translation

[1167] The translated message is adjusted based on the emotional information obtained by the emotional analysis means, for example, if a positive emotional state is detected, the translated message is changed to a more positive expression.

[1168] Hardware and software used

[1169] Hardware: Servers (cloud servers and on-premise servers), smartphones, computers

[1170] Software: Python, Flask, Transformers library

[1171] Specific examples

[1172] If a user types "Today is the best day ever!" and presses the "Send in English" button, the sentiment analyzer will recognize the joy and the message will be translated as "Today is the best day ever!" and sent.

[1173] Prompt Sentence Examples

[1174] "Today is the best day!"

[1175] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1176] Step 1:

[1177] A user opens a messaging app or social networking application and types a message, which is then saved in text format on the device.

[1178] Step 2:

[1179] The user presses the "Send in XX language" button. This action causes the device to send the entered message to the server. The data sent includes the message text and the language selected by the user.

[1180] Step 3:

[1181] The server passes the received message text to a sentiment analyzer, which uses a generative AI model to analyze the message's sentiment. The input is the message text, and the output is a sentiment label (e.g., positive, negative, neutral).

[1182] Step 4:

[1183] The server passes the message text to the translation means based on the emotion label obtained from the emotion analysis means. The translation means uses a generative AI model to translate the message into the specified language. The input is the message text and the emotion label, and the output is the translated message text.

[1184] Step 5:

[1185] The server adjusts the translated message text based on the emotion label. For example, if a positive emotion is detected, it changes the translated message to a more positive expression. The input is the translated message text and the emotion label, and the output is the adjusted message text.

[1186] Step 6:

[1187] The server sends the adjusted message text to the terminal, which displays the received message and sends it to other users. The input is the adjusted message text, and the output is the displayed and sent message.

[1188] Example 2

[1189] Next, a description will be given of Example 2 of Form 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."

[1190] In conventional messaging apps and social media, when users wanted to send messages in a different language, they had to translate them manually, which was a time-consuming process. Furthermore, because translations were done without taking into account the user's feelings, there was a risk of conveying unintended nuances. This led to issues such as a decline in the quality of communication.

[1191] 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.

[1192] In this invention, the server includes a message sending means, a "Send in XX language" button generating means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and an emotion adjustment means for adjusting the message translation based on the recognized emotion. This makes it possible to automatically select a language frequently used by the user and provide an appropriate translation according to the emotion.

[1193] The "message sending means" is a function for sending a message input by a user to other users.

[1194] The "means for generating a send button in XX language" is a function that automatically selects an appropriate language from selectable languages ​​based on the user's settings and generates a button for sending a message in that language.

[1195] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1196] "Emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emojis, typing speed, etc.

[1197] "Emotion adjustment means" is a function that adjusts the translation of a message based on the perceived emotion of the user.

[1198] The present invention provides a system that reduces the effort required for users to send messages in different languages ​​and provides appropriate translations according to emotions. Specific embodiments of this system are described below.

[1199] Hardware and software used

[1200] Server: Responsible for data processing and storage. Specifically, it uses cloud services (e.g., AWS (registered trademark), Google Cloud Platform).

[1201] Device: The device used by the user, such as a smartphone, tablet, or PC.

[1202] Generative AI model: A model for natural language processing. For example, OpenAI's GPT-3 or GPT-4 (registered trademark) is used.

[1203] Sentiment engine: An engine for recognizing user emotions, for example, using IBM Watson's sentiment analysis API.

[1204] System processing flow

[1205] 1. User language settings

[1206] Users select their preferred language in the application settings screen.

[1207] The terminal transmits the selected language information to the server.

[1208] The server stores the received language information in a database.

[1209] Specific operation: When the user selects "French" on the settings screen and presses the save button, the device sends that information to the server.

[1210] 2. Creating a language selection button

[1211] The server will generate a submit button in the appropriate language based on the user's language setting information the next time the site is accessed.

[1212] Based on the information received by the terminal from the server, a "Send in French" button is displayed on the user interface.

[1213] Specific behavior: When the user restarts the app, the device retrieves the "Send in French" button information from the server and displays it on the screen.

[1214] 3. Enter your message

[1215] The user enters text into the message input field.

[1216] The terminal transmits the input message to the server in real time.

[1217] What it does: When a user types "Today is the worst day...", the text is immediately sent to the server.

[1218] 4. Emotion recognition

[1219] The server sends the received message to the emotion engine.

[1220] The emotion engine infers a user's emotions from the context of the message, the use of emojis, typing speed, and more.

[1221] The server receives the results from the emotion engine and stores the emotion information.

[1222] Specific behavior: The emotion engine recognizes "sadness" from the message "Today is the worst day..."

[1223] 5. Translation Adjustment

[1224] The server sends a translation request to a generative AI model based on the recognized emotion.

[1225] The generative AI model generates appropriate translations based on the emotion.

[1226] The server sends the generated translation to the device.

[1227] Specific operation: The generative AI model generates the translation "Today is the worst day..." and the server sends it to the device.

[1228] 6. Sending a Message

[1229] The user presses the "Send in French" button.

[1230] The device sends the translated message to the server.

[1231] The server receives the message and sends it to the specified recipient.

[1232] Specific operation: When the user presses the "Send in French" button, the device sends the message "Aujourd'hui est le pire jour..." to the server, which then sends it to the recipient.

[1233] Prompt Sentence Examples

[1234] Please explain the feature that allows users to set their frequently used language and automatically generate submit buttons in that language on subsequent visits.

[1235] "Please explain the functionality of recognizing user emotions and adjusting message translation based on the recognized emotions."

[1236] To improve user convenience, the system automatically selects frequently used languages ​​and provides appropriate translations based on sentiment.

[1237] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1238] Step 1:

[1239] Users select their preferred language in the application settings screen.

[1240] Input: The language selected by the user in the settings screen (e.g. French)

[1241] Output: Selected language information

[1242] Specific operation: When the user selects "French" on the settings screen and presses the save button, the device sends that information to the server.

[1243] Step 2:

[1244] The terminal transmits the selected language information to the server.

[1245] Input: Selected language information

[1246] Output: Language information sent to the server

[1247] Specific operation: The terminal sends the user's selected language information to the server.

[1248] Step 3:

[1249] The server stores the received language information in a database.

[1250] Input: Language information sent to the server

[1251] Output: Language information stored in a database

[1252] Specific operation: The server stores the received language information in a database.

[1253] Step 4:

[1254] The server will generate a submit button in the appropriate language based on the user's language setting information the next time the site is accessed.

[1255] Input: Language information stored in a database

[1256] Output: Submit button information in generated language

[1257] Specific operation: The server retrieves language information from the database and generates information for the "Submit in French" button.

[1258] Step 5:

[1259] Based on the information received by the terminal from the server, a "Send in French" button is displayed on the user interface.

[1260] Input: Submit button information in generated language

[1261] Output: A submit button displayed in the user interface

[1262] Specific behavior: When the user restarts the app, the device retrieves the "Send in French" button information from the server and displays it on the screen.

[1263] Step 6:

[1264] The user enters text into the message input field.

[1265] Input: A message typed by the user (e.g., "Today is the worst day...")

[1266] Output: The input message

[1267] What happens: The user types, "Today is the worst day..."

[1268] Step 7:

[1269] The terminal transmits the input message to the server in real time.

[1270] Input: The message entered

[1271] Output: Message sent to the server

[1272] Specific operation: The terminal sends the input message to the server.

[1273] Step 8:

[1274] The server sends the received message to the emotion engine.

[1275] Input: The message sent to the server

[1276] Output: The message sent to the emotion engine

[1277] Specific operation: The server sends the received message to the emotion engine.

[1278] Step 9:

[1279] The emotion engine infers a user's emotions from the context of the message, the use of emojis, typing speed, and more.

[1280] Input: Message sent to the emotion engine

[1281] Output: Inferred emotion information (e.g., sadness)

[1282] Specific behavior: The emotion engine recognizes "sadness" from the message "Today is the worst day..."

[1283] Step 10:

[1284] The server receives the results from the emotion engine and stores the emotion information.

[1285] Input: Inferred emotion information

[1286] Output: Stored emotion information

[1287] Specific operation: The server receives emotion information from the emotion engine and stores it in a database.

[1288] Step 11:

[1289] The server sends a translation request to a generative AI model based on the recognized emotion.

[1290] Input: Stored emotion information, user message

[1291] Output: The translation request sent to the generative AI model

[1292] Specific operation: The server sends emotion information and the user's message to the generative AI model.

[1293] Step 12:

[1294] The generative AI model generates appropriate translations based on the emotion.

[1295] Input: The translation request sent to the generative AI model

[1296] Output: The generated translation (e.g., "Today is the worst day...")

[1297] Specific behavior: The generative AI model generates the translation "Today is the worst day..."

[1298] Step 13:

[1299] The server sends the generated translation to the device.

[1300] Input: Generated translation

[1301] Output: Translation sent to the device

[1302] Specific operation: The server sends the generated translation to the device.

[1303] Step 14:

[1304] The user presses the "Send in French" button.

[1305] Input: User actions

[1306] Output: Send button press information

[1307] Specific behavior: The user presses the "Send in French" button.

[1308] Step 15:

[1309] The device sends the translated message to the server.

[1310] Input: Submit button press information, generated translation

[1311] Output: The translated message sent to the server

[1312] Specific operation: The terminal sends the message "Aujourd'hui est le pire jour..." to the server.

[1313] Step 16:

[1314] The server receives the message and sends it to the specified recipient.

[1315] Input: The translated message sent to the server

[1316] Output: Message sent to recipient

[1317] Specific operation: The server sends the translated message to the recipient.

[1318] (Application example 2)

[1319] Next, a description will be given of Application Example 2 of Form 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."

[1320] Conventional messaging apps and social networking sites lack the ability to automatically select a user's frequently used language or adjust message translations based on the user's emotions, limiting the user experience. There is also a need for more natural and appropriate communication by recognizing user emotions and adjusting translations accordingly. Furthermore, content distribution services also face the challenge of improving the viewing experience by adjusting subtitles and audio translations based on the user's emotions.

[1321] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1322] In this invention, the server includes a message sending means for a messenger app or SNS, a "Send in XX language" button generating means placed next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing a user's emotion, an emotion adjustment means for adjusting the translation of the message based on the recognized emotion, a language selection means for automatically selecting a language based on the user's settings and past usage history, and a translation adjustment means for adjusting the translation of subtitles and audio based on the user's emotion. This makes it possible to automatically select a language frequently used by the user and adjust the translation of messages and content based on the user's emotion.

[1323] The "message sending means" is an interface that allows a user to input and send a message.

[1324] The "means for generating a send button in a specific language" is a function that automatically generates a button for sending a message in a specific language based on the user's settings and usage history.

[1325] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1326] "Emotion recognition means" is a technology for recognizing emotions from user input, facial expressions, voice, etc.

[1327] "Emotion adjustment means" is a function that adjusts the translation or display of a message based on the recognized emotion.

[1328] "Language selection means" is a function that automatically selects the most appropriate language based on the user's settings and past usage history.

[1329] The "translation adjustment means" is a function that adjusts the subtitle and audio translation based on the user's emotions.

[1330] The following system configuration will be described as an embodiment of the present invention.

[1331] System Configuration

[1332] This system includes a means for sending messages in a messenger app or SNS, a means for generating a send button in a certain language, an automatic translation means, an emotion recognition means, an emotion adjustment means, a language selection means, and a translation adjustment means.

[1333] Message sending method

[1334] An interface that allows users to input and send messages. It is implemented as an application on smartphones and PCs.

[1335] How to generate a send button in XX language

[1336] This feature automatically generates buttons for sending messages in specific languages ​​based on the user's settings and usage history. The language that the user frequently uses is automatically selected and displayed as the button.

[1337] Automatic translation means

[1338] This function automatically translates messages entered by users into a specified language. It is implemented using a translation API such as Google Translator.

[1339] emotion recognition means

[1340] It is a technology for recognizing emotions from user input, facial expressions, voice, etc. It uses software such as OpenCV and EmotionRecognizer to analyze data from cameras and microphones.

[1341] emotional regulation means

[1342] This feature adjusts the translation and display of messages based on the recognized emotion: for example, if the user has a sad expression, the translated message will be adjusted accordingly.

[1343] Language selection method

[1344] This function automatically selects the optimal language based on the user's settings and past usage history. Content will also be automatically selected from the next time onwards based on the user's viewing history and settings information.

[1345] Translation Adjustment Method

[1346] This feature adjusts subtitle and audio translations based on the user's emotions. For example, if the user looks sad, the subtitle translation will also adjust accordingly.

[1347] Program processing explanation

[1348] The server captures data from the camera and microphone and recognizes the user's emotions using OpenCV and EmotionRecognizer. Based on the recognized emotions, it translates the message using a translation API such as Google Translator and displays the adjusted message using the emotion adjustment mechanism. The language selection mechanism automatically selects the most appropriate language based on the user's settings and past usage history.

[1349] Specific examples

[1350] If a user frequently speaks French, the application will automatically provide content in French, and if the user has a sad expression, the subtitle translation will adjust accordingly.

[1351] Prompt Sentence Examples

[1352] Develop a smartphone application that automatically selects the language that the user frequently uses and uses an emotion engine to recognize the user's emotions and adjust the translation accordingly. For example, if the user frequently uses French, the application will automatically provide content in French. Also, if the user has a sad expression, the subtitle translation will also adjust accordingly.

[1353] In this way, a system can be realized that provides optimal content based on the user's preferences and emotions.

[1354] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1355] Step 1:

[1356] The server captures the user's video and audio data from the device through the camera and microphone. The input is the user's video and audio data, and the output is the captured raw data, which is used in subsequent processing steps.

[1357] Step 2:

[1358] The server analyzes the captured video and audio data using OpenCV and EmotionRecognizer to recognize the user's emotions. The input is the captured raw data, and the output is the recognized emotional information. Specifically, it analyzes the user's facial expressions and tone of voice and classifies emotions into categories such as "joy," "sadness," and "anger."

[1359] Step 3:

[1360] The server automatically selects the most appropriate language using a language selection method based on the user's settings and past usage history. The input is the user's settings and usage history, and the output is the selected language information. Specifically, the server retrieves the user's frequently used language from a database and applies it to future content.

[1361] Step 4:

[1362] The user enters a message and presses the send button. The input is the message entered by the user, and the output is the event information that the send button was pressed. Specifically, this includes the action of the user entering a message in the text box and pressing the "Send in XX language" button.

[1363] Step 5:

[1364] When the send button is pressed, the server uses a translation API such as Google Translator to automatically translate the entered message into the selected language. The input is the message entered by the user and the selected language information, and the output is the translated message. Specifically, the server sends the message and the target language to the API and receives the translation result.

[1365] Step 6:

[1366] The server adjusts the translated message based on the recognized emotion using an emotion adjustment means. The input is the translated message and the recognized emotion information, and the output is the adjusted message. Specifically, if the user expresses sad emotion, the translated message is adjusted to a negative expression accordingly.

[1367] Step 7:

[1368] The server sends the adjusted message through the message sending means, where the input is the adjusted message and the output is the sent message. Specifically, the server sends the adjusted message to the recipient and displays a notification of the completion of the message sending on the user's interface.

[1369] Step 8:

[1370] The server adjusts the subtitle and audio translations in the content distribution service based on the user's emotions. The input is the user's emotional information and content data, and the output is the adjusted subtitles and audio. Specifically, if the user has a sad expression, the subtitle translation is also adjusted accordingly.

[1371] In this way, a system can be realized that provides optimal content based on the user's preferences and emotions.

[1372] Example 3

[1373] Next, a description will be given of Example 3 of Form Example 3. 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."

[1374] Conventional message translation systems often fail to produce accurate translation results because they simply translate without considering the context or emotion of the message entered by the user. Furthermore, because the translation cannot reflect the user's emotions, the quality of communication declines. This makes it difficult to convey the exact meaning and emotion intended by the user.

[1375] The specific processing by the specific processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means.

[1376] In this invention, the server includes a message sending means, a "Send in translation language" button generating means located next to the message sending means, a means for automatically translating the message into the translation language when the "Send in translation language" button is pressed, a means for sending the automatically translated message, a context analysis means for selecting an optimal translation based on the context of a message input by a user, and an emotion analysis means for recognizing the user's emotion and adjusting the translation based on that emotion. This makes it possible to provide an optimal translation of a user's input message based on the context and emotion.

[1377] The "message sending means" is a function for sending a message input by a user to other users.

[1378] The "means for generating a send button in a translated language" is a function for generating a button for sending a message in a translated language selected by the user.

[1379] The "automatic translation means" is a function that automatically translates a message input by a user into a specified translation language.

[1380] The "context analysis means" is a function that analyzes the context of the message entered by the user and selects the most appropriate translation based on that context.

[1381] "Emotion analysis means" is a function that recognizes the emotion of the message entered by the user and adjusts the translation based on that emotion.

[1382] "Transmission means" is a function for transmitting a translated message to other users.

[1383] "User settings" are settings that the user makes to customize the system's operation, translation language, etc.

[1384] The "translation language" is the language that the user uses to translate the message.

[1385] MODE FOR CARRYING OUT THE INVENTION

[1386] The present invention is a system that provides an optimal translation of a message input by a user based on the context and emotion. A specific embodiment of this system will be described below.

[1387] System configuration

[1388] The system mainly consists of the following components:

[1389] 1. Message sending method

[1390] 2. How to generate a submit button in a translated language

[1391] 3. Automatic Translation Methods

[1392] 4. Context analysis means

[1393] 5. Emotion analysis method

[1394] 6. Means of transmission

[1395] Hardware and software used

[1396] The Server uses the following hardware and software to receive, parse, translate, and send messages:

[1397] Server hardware: A server with a powerful processor and sufficient memory

[1398] Software: Python, NLTK library, Google Natural Language API

[1399] The terminal uses the following hardware and software to allow users to input messages and display translation results:

[1400] Device hardware: smartphones, tablets, PCs, etc.

[1401] Software: Messenger apps, social networking apps

[1402] System Operation

[1403] A user inputs a message into an input field of a terminal. For example, the user inputs "I'm fine." The terminal sends this message to a server. The server sends the received message to a context analysis means, which analyzes the context of the message. The context analysis means analyzes the context using, for example, Python's NLTK library, and translates the message "I'm fine." into "I'm fine."

[1404] The server then sends the translation result to the sentiment analyzer, which uses Google's Natural Language API to analyze the sentiment of the message. For example, if the user types "Today is the best day ever!" and the sentiment analyzer recognizes joy, the translation will be adjusted to "Today is the best day ever!"

[1405] Finally, the server sends the adjusted translation result to the terminal, and the terminal displays the translation result to the user, who can check the translation result on the terminal screen.

[1406] Specific examples

[1407] As a concrete example, consider the case where a user types "I'm fine." This message is sent to the server, where the context analyzer translates it as "I'm fine." Next, if the user types "Today is the best day ever!", the emotion analyzer recognizes joy and the server translates it as "Today is the best day ever!"

[1408] Example prompt sentence:

[1409] If a user types "I'm fine," choose the appropriate translation based on the context and analyze the sentiment using sentiment analysis tools.

[1410] If a user types "Today is a great day!", use sentiment analysis to recognize the sentiment and choose the appropriate translation.

[1411] This system supports more natural and appropriate communication by providing an optimal translation of the user's input message based on the context and emotion. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1412] Step 1:

[1413] The user enters a message.

[1414] The user inputs a message into an input field of the terminal, for example, "I'm fine." The input message is passed to the message sending means of the terminal.

[1415] Step 2:

[1416] The device sends a message to the server.

[1417] The terminal sends the message entered by the user to the server. At this time, the message is sent as an HTTP request. The input is the user's message, and the output is an HTTP request to the server.

[1418] Step 3:

[1419] The server receives the message and sends it to the context analysis means.

[1420] The server sends the received message to a context analyzer, which analyzes the context using, for example, Python's NLTK library. The input is the user's message, and the output is data to the context analyzer.

[1421] Step 4:

[1422] A context analyzer analyzes the context of the message and selects an appropriate translation.

[1423] The context analysis means analyzes the context of the message and selects an appropriate translation. For example, the message "I'm fine" is translated as "I'm fine." The input is the user's message and the output is the translation result.

[1424] Step 5:

[1425] The server sends the translation result to the emotion analysis means.

[1426] The server sends the translation result obtained from the context analysis means to the sentiment analysis means. The sentiment analysis means analyzes sentiment using, for example, Google's (registered trademark) Natural Language API. The input is the translation result, and the output is data sent to the sentiment analysis means.

[1427] Step 6:

[1428] A sentiment analyzer analyzes the sentiment of the message and adjusts the translation.

[1429] The sentiment analyzer analyzes the sentiment of the message and adjusts the translation. For example, if the message "Today is the best day ever!" is recognized as expressing joy, the translation is adjusted to "Today is the best day ever!" The input is the translation result, and the output is the adjusted translation result.

[1430] Step 7:

[1431] The server sends the final translation results to the terminal.

[1432] The server sends the final translation result obtained from the sentiment analysis means to the terminal. At this time, the translation result is sent as an HTTP response. The input is the adjusted translation result, and the output is the HTTP response to the terminal.

[1433] Step 8:

[1434] The terminal displays the translation results to the user.

[1435] The terminal displays the translation results received from the server to the user. The user can check the translation results on the terminal screen. The input is the translation result from the server, and the output is what is displayed to the user.

[1436] (Application example 3)

[1437] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1438] Conventional message translation systems perform translation without considering the context or emotion of the message entered by the user, resulting in unnatural translations or an inability to accurately convey the user's intentions. Furthermore, since the translation cannot reflect emotion, it is difficult to convey the user's feelings appropriately.

[1439] The specification process by the specification processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a message sending means, a "Send in Language" button generating means located next to the message sending means, a means for automatically translating the message into a language when the "Send in Language" button is pressed, a means for sending the automatically translated message, a means for selecting an optimal translation based on the context of a message entered by a user, and a means for recognizing the user's emotion using an emotion engine and adjusting the translation based on the emotion. This enables natural and appropriate translation that takes into account the context and emotion of the message entered by the user.

[1440] A "message sending means" is an interface that allows a user to input text messages and send them to other users.

[1441] The "send in language button generating means" is a function that generates a button for sending a message in a language selected by the user.

[1442] "Means for automatic translation" is a function that automatically translates an input message into a specified language.

[1443] The "means for sending automatically translated messages" is a function for sending automatically translated messages to other users.

[1444] The "means for selecting the best translation based on the context" is a function that analyzes the context of the input message and selects the translation that is most appropriate for that context.

[1445] An "emotion engine" is a system that recognizes emotions from messages entered by users and adjusts translations based on those emotions.

[1446] The "means for adjusting translation based on emotion" is a function for adjusting the translated message based on the emotion of the user as recognized by the emotion engine.

[1447] A system for implementing the present invention includes a message sending means, a language send button generating means, a means for automatically translating, a means for sending the automatically translated message, a means for selecting an optimal translation based on context, an emotion engine, and a means for adjusting the translation based on emotion.

[1448] System configuration

[1449] The server uses the following hardware and software:

[1450] Hardware: The server uses a computer system with a powerful processor and sufficient memory, such as an Intel Xeon processor or AMD EPYC processor.

[1451] Software: The server uses the Python programming language and the transformers natural language processing library, as well as the nltk library for sentiment analysis.

[1452] Processing flow

[1453] 1. Message sending means: Provides an interface for users to enter text messages. This is an application that runs on a mobile device such as a smartphone or tablet.

[1454] 2. Send in Language Button Generator: Generates a button to send a message in the language selected by the user. This button is automatically selected based on the user's settings.

[1455] 3. Automated translation: Automatically translate user-entered messages into a specified language using a generative AI model.

[1456] 4. How to send an automatically translated message: Send an automatically translated message to other users.

[1457] 5. A means of selecting the best translation based on context: Analyze the context of the message entered by the user and select the translation that best suits that context.

[1458] 6. Emotion engine: Recognizes emotions from the user's input message and adjusts the translation based on those emotions.

[1459] 7. Means for tailoring translation based on emotion: tailoring the translated message based on the user's emotion as recognized by the emotion engine.

[1460] Specific examples

[1461] For example, if a user is watching a movie and a character says, "I'm so happy today!", the emotion engine will recognize the positive emotion and adjust the translation to "I'm so happy today!"

[1462] Example prompts to input to a generative AI model:

[1463] "Translate the following text to Japanese and adjust the translation based on the detected emotion: 'I'm so happy today!'"

[1464] In this way, the user's viewing experience can be enriched.

[1465] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1466] Step 1:

[1467] The user inputs a message on the terminal. The input message is sent to the server in text format. The input data is the text message input by the user.

[1468] Step 2:

[1469] The server analyzes the received message and performs natural language processing (NLP) to understand the context. This process uses the transformers library. The input data is the text message entered by the user, and the output data is the analysis result including context information.

[1470] Step 3:

[1471] The server uses a generative AI model to generate the optimal translation based on the analysis results. Specifically, it uses a translation model from the transformers library to translate the message into the specified language. The input data is the analysis results including contextual information, and the output data is the translated text message.

[1472] Step 4:

[1473] The server passes the translated message to an emotion engine to recognize the user's emotion. The emotion engine uses the nltk library. The input data is the translated text message, and the output data is an emotion label (e.g., positive, negative).

[1474] Step 5:

[1475] The server adjusts the translated message based on the emotion label. For example, for positive emotion, it adds "Yo da!" to the translated message, and for negative emotion, it adds "Da...". The input data are the emotion label and the translated text message, and the output data is the adjusted translated message.

[1476] Step 6:

[1477] The server sends the adjusted translation message to the terminal, and the terminal displays the received message to the user. The input data is the adjusted translation message, and the output data is the text message displayed to the user.

[1478] Step 7:

[1479] The user checks the displayed message and replies if necessary. The reply message is then sent again via the process from step 1. The input data is the reply message entered by the user, and the output data is the translated and adjusted reply message.

[1480] 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.

[1481] 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> ) 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.

[1482] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1483] 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.

[1484] [Third embodiment]

[1485] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1486] 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.

[1487] 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).

[1488] 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.

[1489] 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.

[1490] 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).

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1497] "Example 1"

[1498] In one embodiment of the present invention, a "Send in English" button is placed next to the message input field on the message sending screen of a messenger app or SNS. When a user inputs a message and presses the "Send in English" button, the input message is automatically translated into English and the translated message is sent.

[1499] "Example 2"

[1500] Additionally, we provide a function that automatically selects the language of the "Send in" button from available languages ​​based on the user's settings. For example, if a user frequently communicates with people who speak French, the "Send in French" button will be automatically generated based on the user's settings.

[1501] "Example 3"

[1502] It also provides a function that selects the best translation based on the context of the message entered by the user. For example, if the user enters "I'm fine," it will be translated as "I'm fine." However, if the user enters "It's fine," it will be translated as "It's fine." In this way, the same "fine" will be translated differently depending on the context.

[1503] The processing flow of each embodiment will be described below.

[1504] "Example 1"

[1505] Step 1: The user opens a messaging app or social media message sending screen.

[1506] Step 2: Make sure there is a "Send in English" button next to the message input field.

[1507] Step 3: User types message and presses "Send in English" button.

[1508] Step 4: The system will automatically translate the message you entered into English.

[1509] Step 5: The system sends the translated message.

[1510] "Example 2"

[1511] Step 1: The user opens the settings screen of a messenger app or social networking site.

[1512] Step 2: The user selects the language from the available options under the "Send in" button. Step 3: The user saves the settings and returns to the message sending screen.

[1513] Step 4: Make sure there is a "Send in XXX" button in the user's language of choice next to the message input field.

[1514] "Example 3"

[1515] Step 1: The user types a message.

[1516] Step 2: The system takes the user's message and selects the best translation based on the context of the message.

[1517] Step 3: The system displays the translation it has selected to the user.

[1518] Step 4: The user presses the "Send in XXX language" button, and the system sends the selected translation.

[1519] Example 1

[1520] Next, a description will be given of Example 1 of Form 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."

[1521] With conventional messaging apps and social networking sites, users had to manually translate messages when sending them in different languages, which was a time-consuming process. Furthermore, the accuracy of the translations and the appropriate translations based on the context were often lacking, which could impair the smoothness of communication. To solve these problems, a system was needed that would allow users to easily translate and send messages in a specific language.

[1522] 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.

[1523] In this invention, the server includes a message sending means, a "Send in specific language" button generating means located next to the message sending means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, a means for inputting the message into a generative AI model as a prompt sentence, a means for the generative AI model to translate the message, and a means for sending the translated message to a destination. This allows a user to easily translate and send a message into a specific language.

[1524] "Message sending means" is a function that provides an interface for users to input and send messages.

[1525] The "specific language send button generating means" is a function that is placed next to the message sending means and generates a button for sending a message in a specific language selected by the user.

[1526] "Automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[1527] A "generative AI model" is an artificial intelligence model for natural language processing that has the ability to translate input text into other languages.

[1528] A "prompt" is an instruction entered into a generative AI model to make it perform a specific task.

[1529] The "means for sending a translated message" is a function for sending a message translated by the automatic translation means to a specified destination.

[1530] "Context-based optimal translation" refers to providing the most appropriate translation by taking into account the content and intent of the message entered by the user.

[1531] This invention provides a system that enables users to easily translate and send messages into a specific language in messenger apps and social networking sites. Specific embodiments of this system are described below.

[1532] First, a user opens a messaging app or social networking site's message sending screen and enters a message in the message input field. For example, they might enter "Hello, how are you?". Next, the user presses the "Send in specific language" button located next to the message input field. This action starts the process of translating the entered message into the specific language.

[1533] The device sends the message entered by the user to the server. At this time, the message is sent to the server in an appropriate format. The server inputs the received message as a prompt sentence to a generative AI model (e.g., GPT-4 (registered trademark)). Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[1534] The generative AI model receives the prompt sentence and translates the message into a specific language. For example, it translates "Hello, how are you?" into "Hello, how are you?" The server sends the translated message received from the generative AI model to the device. At this time, the translated message is sent to the device in the appropriate format.

[1535] The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend. In this way, a message entered by the user in Japanese is automatically translated into a specific language and sent.

[1536] As a concrete example, consider a case where a user wants to send a message to a friend saying, "Hello, how are you?" The user enters "Hello, how are you?" in the message input field and presses the "Send in specific language" button. The system translates this message into "Hello, how are you?" and sends it to the friend.

[1537] Example prompts to input to a generative AI model:

[1538] "Please translate 'Hello, how are you?' into English."

[1539] This system allows users to easily translate and send messages into specific languages. The hardware used includes the user's device (smartphone, tablet, PC, etc.) and a server. The software used includes a messenger app, a social networking application, and a generative AI model (e.g., GPT-4 (registered trademark)).

[1540] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1541] Step 1:

[1542] The user enters a message in the message input field.

[1543] Type: "Hello, how are you?"

[1544] Output: The input message "Hello, how are you?"

[1545] Specific actions: The user opens the message sending screen of a messenger app or social networking site and types "Hello, how are you?" in the message input field.

[1546] Step 2:

[1547] The user presses the "Send in specific language" button.

[1548] Input: User presses the "Submit in a specific language" button

[1549] Output: An event that the submit button was pressed

[1550] What it does: The user presses the "Send in specific language" button located next to the message input field, which starts the process of translating the entered message into the specific language.

[1551] Step 3:

[1552] The terminal sends the input message to the server.

[1553] Input: The message "Hello, how are you?" is entered and the event of pressing the send button

[1554] Output: Message sent to server: "Hello, how are you?"

[1555] Specific operation: The terminal sends the message entered by the user to the server, where the message is sent in the appropriate format.

[1556] Step 4:

[1557] The server inputs the message as a prompt into the generation AI model.

[1558] Input: Message sent to server: "Hello, how are you?"

[1559] Output: The prompt sent to the generative AI model: "Please translate 'Hello, how are you?' into English."

[1560] Specific operation: The server inputs the received message into a generative AI model (e.g., GPT-4 (registered trademark)) as a prompt sentence. Specifically, the server sends the prompt sentence "Please translate 'Hello, how are you?' into English" to the generative AI model.

[1561] Step 5:

[1562] A generative AI model translates the message into English.

[1563] Input: Prompt sentence: "Please translate 'Hello, how are you?' into English."

[1564] Output: The translated message "Hello, how are you?"

[1565] Specific behavior: The generative AI model receives a prompt and translates the message into a specific language, for example, translating "Hello, how are you?" into "Hello, how are you?"

[1566] Step 6:

[1567] The server sends the translated message to the terminal.

[1568] Input: Translated message "Hello, how are you?"

[1569] Output: The translated message "Hello, how are you?" sent to the terminal.

[1570] Specific operation: The server sends the translated message received from the generative AI model to the device, where the translated message is sent to the device in the appropriate format.

[1571] Step 7:

[1572] The device sends the translated message to the destination.

[1573] Input: The translated message "Hello, how are you?" sent to the terminal

[1574] Output: The message "Hello, how are you?" sent to the destination.

[1575] Specific operation: The device receives the translated message from the server and sends it to the destination. For example, the message "Hello, how are you?" is sent to a friend.

[1576] (Application example 1)

[1577] Next, a description will be given of Application Example 1 of Form 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."

[1578] With the current trend toward globalization, communication with foreign customers has become increasingly important in brick-and-mortar stores. However, the language barrier makes it difficult for store staff to communicate smoothly with foreign customers. To solve this problem, real-time language translation is necessary, but current technology is not sufficient.

[1579] 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.

[1580] In this invention, the server includes a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a voice input means, a means for converting voice acquired by the voice input means into text, a means for translating the text into XX language, and a means for outputting the translated text by voice. This enables smooth real-time communication between store staff and foreign customers in physical stores.

[1581] A "message sending means" is an interface that allows a user to input and send a text message.

[1582] The "means for generating a send button in XX language" is a function that generates a button for translating and sending a message into a language selected by the user.

[1583] "Means for automatic translation" is a function for automatically translating an input text message into a specified language.

[1584] The "means for sending an automatically translated message" is a function for sending a translated message to the other party.

[1585] The "voice input means" is an interface for capturing the user's voice and acquiring it as digital data.

[1586] The "means for converting voice to text" is a function for converting acquired voice data into text data.

[1587] "Means for translating text into XX language" is a function for translating text data into a specified language.

[1588] The "means for audibly outputting the translated text" is a function for playing back the translated text as a voice.

[1589] To implement the present invention, the following system configuration and program are required.

[1590] First, the server provides a message sending means, which is an interface for users to input and send text messages. Next, the server provides a "send in XX language" button generation means, which is a function that generates a button for translating and sending a message into a language selected by the user.

[1591] When a user presses the "Send in XX language" button, the server uses an automatic translation means to automatically translate the entered text message into the specified language, and this translated message is sent to the recipient by a means for sending an automatically translated message.

[1592] Furthermore, the server provides a voice input means, which is an interface for capturing the user's voice and acquiring it as digital data, and the acquired voice data is converted into text data by a voice-to-text conversion means.

[1593] The converted text data is translated into a specified language by a means for translating text into a certain language. The translated text is reproduced as audio by a means for outputting the translated text as audio.

[1594] To realize this system, the following hardware and software are used:

[1595] Hardware: Smart glasses (capable of audio input and output)

[1596] software:

[1597] speech_recognition library: convert speech to text

[1598] googletrans library: translate text

[1599] pyttsx3 library: convert text to speech

[1600] For example, when a store clerk says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted to text using the speech_recognition library, and the text is translated into English using the googletrans library. The translated text is converted to speech using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[1601] Example prompts to input to a generative AI model:

[1602] When a user says "Where is this product located?", convert the speech to text, translate it into English, and play it aloud as "Where is this product located?"

[1603] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1604] Step 1:

[1605] The user speaks to the smart glasses. The microphone in the smart glasses captures the voice and obtains it as voice data. This voice data becomes the input.

[1606] Step 2:

[1607] The voice data acquired by the device (smart glasses) is converted into text data using the speech_recognition library. The voice data is analyzed and the corresponding text data is generated. This text data is the output.

[1608] Step 3:

[1609] Translates text data generated by the device into a specified language (e.g., English) using the googletrans library. It takes text data as input and generates translated text data. This translated text data is the output.

[1610] Step 4:

[1611] The device converts the translated text data into speech data using the pyttsx3 library, takes the translated text data as input, and generates the corresponding speech data, which is the output.

[1612] Step 5:

[1613] The device then plays the generated audio data through the smart glasses' speaker, allowing the user to listen to the translated audio.

[1614] Specifically, when a user says, "Where is this product located?", the voice is captured by the microphone on the smart glasses. The voice data is converted into text data, "Where is this product located?", using the speech_recognition library. The text data is translated into English, "Where is this product located?", using the googletrans library. The translated text data is converted into audio data using the pyttsx3 library, and the speaker on the smart glasses plays "Where is this product located?"

[1615] Example 2

[1616] Next, a description will be given of Example 2 of Form 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."

[1617] In conventional messaging apps and social networking sites, when users wanted to send messages in different languages, they had to translate them manually, which was a time-consuming process. Furthermore, since there was no function to automatically select frequently used languages, users had to select the language each time, which was inconvenient. Furthermore, the quality of the translations was inconsistent, so the translations sometimes did not fit the context of the message.

[1618] 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.

[1619] In this invention, the server includes means for acquiring user setting information, means for sending a prompt sentence to the generative AI model based on the acquired setting information, means for generating a "Send in XX language" button based on a response from the generative AI model, means for displaying the generated "Send in XX language" button on a user interface, and means for sending a message when the user clicks the "Send in XX language" button. This makes it possible to automatically select a language frequently used by the user and perform appropriate translation.

[1620] "User Settings" refers to the individual language and other customization information that a User sets within the System.

[1621] A "generative AI model" is a model designed to perform specific tasks using artificial intelligence. In this case, it generates prompts based on user settings and generates appropriate language selection buttons.

[1622] A "prompt sentence" refers to an input sentence that gives specific instructions or questions to a generative AI model.

[1623] "Send in XX language button" refers to a button on the interface that allows a user to send a message in a specific language of their choice.

[1624] "User interface" refers to the screen and operating means that allow a user to interact with a system.

[1625] "Means for sending messages" refers to the functionality for sending messages entered by the user in the selected language.

[1626] This invention relates to a system for messenger apps and SNS that automatically selects a language frequently used by a user and performs appropriate translation. Specific embodiments of this system are described below.

[1627] Server Processing

[1628] The server retrieves the user's preference information from a database. Specifically, it uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. For example, it executes an SQL query that retrieves the corresponding language preference using the user ID as a key.

[1629] The server then sends a prompt to a generative AI model (e.g., OpenAI's GPT-4 (registered trademark)) based on the user's preferences. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language.

[1630] Example prompt sentence:

[1631] "Based on the user's preferences, select the language they frequently use and generate a corresponding submit button. For example, if the user selects French, generate a 'Submit in French' button."

[1632] The server that receives the response from the generative AI model generates the data to display on the user interface. Specifically, it generates HTML and JavaScript code and creates buttons corresponding to the language selected by the user.

[1633] Terminal handling

[1634] The terminal receives the data sent from the server and reflects it in the user interface. Specifically, it dynamically generates and displays a "Submit in French" button using front-end technologies such as JavaScript (registered trademark) and React.

[1635] User operations

[1636] Users select the languages ​​they frequently use in the settings screen. For example, if a user selects French, a "Send in French" button will be automatically generated from next time. Users can click this button to send messages in the selected language.

[1637] Specific examples

[1638] For example, if a user selects "French" in the settings screen, the next time a "Send in French" button is automatically generated, the user can click this button to send the message in French.

[1639] In this way, it is possible to automatically select the language that the user frequently uses and provide the appropriate translation, which saves the user the trouble of manually selecting the language and allows for smoother communication.

[1640] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1641] Step 1:

[1642] Get user settings information

[1643] The server retrieves the user's preference information from a database. A user ID is given as input. The server uses a database management system such as MySQL (registered trademark) to retrieve information about the user's frequently used languages. Specifically, it executes an SQL query using the user ID as a key to retrieve the corresponding language preference. The user's preference information (e.g., French) is obtained as output.

[1644] Step 2:

[1645] Generate and send prompts

[1646] The server sends a prompt to the generative AI model based on the acquired user preference information. The user preference information is given as input. The prompt includes instructions to generate a "Submit in XX language" button based on the user's frequently used language. An example of a specific prompt is, "Based on the user's preference, please select the frequently used language and generate a corresponding submit button. For example, if the user selects French, please generate a 'Submit in French' button." The output is a response from the generative AI model.

[1647] Step 3:

[1648] Generate language selection button

[1649] The server generates a "Submit in XX language" button based on the response from the generative AI model. The response from the generative AI model is given as input. The server generates HTML and JavaScript (registered trademark) code to create a button corresponding to the language selected by the user. The output is the data for the generated "Submit in XX language" button.

[1650] Step 4:

[1651] Displaying the language selection button

[1652] The terminal receives the data sent from the server and reflects it in the user interface. As input, it receives the data for the "Send in XX language" button sent from the server. The terminal uses front-end technologies such as JavaScript (registered trademark) and React to dynamically generate and display the "Send in French" button. As output, it obtains the button displayed in the user interface.

[1653] Step 5:

[1654] Sending a message

[1655] The user clicks the generated "Send in French" button. The user's click event is given as input. When the click event occurs, a message is sent in the selected language. Specifically, a JavaScript (registered trademark) event listener is used to catch the button click event and process the message sending. The message sent in the selected language is obtained as output.

[1656] (Application example 2)

[1657] Next, a description will be given of Application Example 2 of Form 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."

[1658] In conventional messaging apps and social networking sites, users had to translate messages manually when communicating in different languages, which was inefficient. Furthermore, they lacked the ability to automatically learn frequently used languages ​​and send messages in the appropriate language, which made them less convenient for users.

[1659] The specific processing by the specific 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 a message sending means, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a means for acquiring user setting information, a means for learning frequently used languages ​​based on the setting information, a means for automatically generating the "Send in XX language" button based on the learned languages, a means for translating the message in real time, and a means for sending the translated message. This makes it possible to automatically learn languages ​​frequently used by the user and send messages in the appropriate language.

[1660] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[1661] The "means for generating a send button in a specific language" is a function that automatically generates a button for sending a message in a specific language based on the user's settings.

[1662] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1663] "Transmission means" is a function for sending translated messages to other users.

[1664] "Means for acquiring user setting information" refers to a function for acquiring the language and other setting information previously set by the user.

[1665] The "means for learning frequently used languages" is a function for learning the languages ​​that a user frequently uses based on the user's past message history and setting information.

[1666] "Means for real-time translation" refers to a function that translates a message into a specified language in real time as the user types the message.

[1667] The system for implementing this invention comprises a server and a user terminal. The server includes a message sending means, a "Send in XX language" button generating means, an automatic translation means, a sending means, a means for acquiring user setting information, a means for learning frequently used languages, and a means for translating in real time.

[1668] Program processing explanation

[1669] Hardware used

[1670] Server: Any cloud server (e.g. AWS (registered trademark), Google Cloud)

[1671] Client: Smartphone (iOS or ANDROID (registered trademark))

[1672] Software used

[1673] Framework: Flask (Python)

[1674] Translation API: Googletrans (Python wrapper for Google Translate API)

[1675] Data processing and calculation

[1676] 1. How to get user configuration information:

[1677] The server retrieves the user's pre-selected language and other preferences, which are used to learn frequently used languages ​​based on the user's past message history and preferences.

[1678] 2. Ways to learn frequently used languages:

[1679] The server learns the user's most frequently used languages ​​based on their past message history and preferences, and automatically generates a button to identify the user's most frequently used language and send messages in that language.

[1680] 3. How to generate a "Submit in ____ language" button:

[1681] The server automatically generates a button for sending messages in a specific language based on the user's preferences, which is displayed when the user types a message.

[1682] 4. Real-time translation methods:

[1683] As users type their messages, the server translates them into the language of their choice in real time, allowing users to instantly see the translated message.

[1684] 5. Automatic translation methods:

[1685] When a user presses the "Send in X language" button, the server automatically translates the message into the specified language using a translation API such as GoogleTrans.

[1686] 6. Means of transmission:

[1687] The translated messages are then sent to other users via a server, allowing users of different languages ​​to communicate smoothly.

[1688] Specific examples

[1689] For example, if the user is "user1" and the message they send is "Hello, how are you?", the server will translate this message into French and return it as "Bonjour, comment ça va?".

[1690] Prompt Sentence Examples

[1691] User ID: user1

[1692] Message: Hello, how are you?

[1693] When you send this prompt to the server, you will get a response like this:

[1694] {

[1695] "original_message": "Hello, how are you?",

[1696] "translated_message": "Bonjour, comment ça va?",

[1697] "language": "fr"

[1698] }

[1699] In this way, a system can be realized that automatically selects a language based on the user's settings, and translates and sends messages.

[1700] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1701] Step 1:

[1702] The user inputs a message. The user inputs a text message using the message sending means of the smartphone. The input message is sent to the server.

[1703] Step 2:

[1704] The server retrieves the user's preference information from a database and identifies the user's frequently used languages, including languages ​​the user has used in the past and languages ​​that the user has previously set.

[1705] Step 3:

[1706] The server learns frequently used languages. Based on the acquired configuration information, the server learns the languages ​​most frequently used by users. Based on this learning result, it generates a "Send in XX language" button for sending messages in a specific language.

[1707] Step 4:

[1708] The server generates a "Send in XX language" button. Based on the learning results, the server automatically generates a button for sending messages in the language the user frequently uses and displays it on the user's device.

[1709] Step 5:

[1710] The user presses the "Send in XX" button. By pressing the generated "Send in XX" button, the user instructs the message they entered to be translated into a specific language.

[1711] Step 6:

[1712] The server translates messages in real time. The server translates messages entered by users into the specified language in real time. This translation uses a translation API such as Google Trans.

[1713] Step 7:

[1714] The server sends the translated message, returns the translated message to the user's terminal for the user to review, and then sends the translated message to other users.

[1715] Step 8:

[1716] The other user receives the translated message. The other user receives and displays the translated message sent from the server. This allows users who speak different languages ​​to communicate smoothly.

[1717] Example 3

[1718] Next, a third embodiment of the third embodiment will be described. 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."

[1719] In conventional messaging apps and social networking sites, it was difficult to provide appropriate translations based on the context when users sent messages. This was especially difficult when the same word had different meanings depending on the context.

[1720] The specification process by the specification processing unit 290 of the data processing device 12 in the third embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send with Translation" button generating means located next to the message sending means, a means for automatically translating the message when the "Send with Translation" button is pressed, a means for sending the automatically translated message, a means for receiving a message input by a user, a means for inputting a message to a generative AI model, a means for the generative AI model to generate a translation based on the context, a means for receiving the generated translation result, and a means for displaying the translation result to the user. This makes it possible to provide an optimal translation based on the context of the message input by the user.

[1721] The "message sending means" is a means for sending a message input by a user to other users.

[1722] The "translation and send button generating means" is a means for generating a button for a user to translate and send a message.

[1723] The "means for automatic translation" is a means for automatically translating a message input by a user into a specified language.

[1724] The "means for sending an automatically translated message" is a means for sending a translated message to another user.

[1725] The "means for receiving a message input by a user" refers to a means by which the server receives a message input by a user.

[1726] "Means for inputting a message into a generative AI model" refers to means for inputting a received message into a generative AI model.

[1727] "Means by which a generative AI model generates a context-based translation" refers to the means by which a generative AI model analyzes the context of a message and generates an optimal translation.

[1728] "Means for receiving the generated translation results" refers to means for receiving the translation results generated from the generative AI model.

[1729] The "means for displaying the translation result to the user" refers to a means for displaying the received translation result to the user.

[1730] This invention relates to a system that optimally translates a message entered by a user based on its context. The system includes a message sending means, a translation send button generating means, an automatic translation means, a means for sending the automatically translated message, a means for receiving a message entered by a user, a means for inputting the message to a generative AI model, a means for the generative AI model to generate a translation based on its context, a means for receiving the generated translation result, and a means for displaying the translation result to a user.

[1731] Hardware and software used

[1732] This system uses a server, a terminal, and a generative AI model. Specifically, the server receives messages, inputs them into the generative AI model, and receives and sends translation results. The terminal inputs messages from users and displays translation results. The generative AI model uses, for example, OpenAI's GPT-4 (registered trademark).

[1733] Data processing and calculation

[1734] The server receives the message entered by the user on the device and inputs the message into the generative AI model. The generative AI model analyzes the context of the message and generates the optimal translation. The generated translation result is returned to the server, which then sends it to the device. The device displays the translation result to the user.

[1735] Specific examples

[1736] As a concrete example, consider the case where a user types "I'm fine." In this case, the server uses a generative AI model to generate the translation "I'm fine." On the other hand, if the user types "It's fine," the server generates the translation "It's fine."

[1737] Prompt Sentence Examples

[1738] "If a user types 'I'm fine.', translate this to 'I'm fine.'"

[1739] "If the user types 'It's fine.', translate this to 'That's fine.'"

[1740] In this way, the system can appropriately translate the user's input message based on the context. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1741] Step 1:

[1742] The user inputs a message. The user inputs a message into the input field of the terminal. For example, the user inputs "I'm fine." This input message becomes the input data for the next processing step.

[1743] Step 2:

[1744] The terminal sends a message to the server. The terminal sends the message entered by the user to the server using an HTTP request. In this case, the input data is the message entered by the user, and the output data is the message sent to the server.

[1745] Step 3:

[1746] The server receives the message. The server receives the message sent from the terminal and temporarily stores it in memory. The input data is the message sent from the terminal, and the output data is the message stored in the server.

[1747] Step 4:

[1748] The server inputs a message to the generative AI model. The server calls the API to input the received message to the generative AI model. The input data is the message stored on the server, and the output data is the message passed to the generative AI model.

[1749] Step 5:

[1750] The generative AI model generates a translation based on the context. The generative AI model analyzes the context of the input message and generates the optimal translation. For example, the generative AI model generates the translation "I'm fine." for the message "I'm fine." The input data is the message passed to the generative AI model, and the output data is the generated translation result.

[1751] Step 6:

[1752] The server receives the translation results. The server receives the translation results generated by the generative AI model and stores them in memory. The input data is the translation results returned by the generative AI model, and the output data is the translation results stored on the server.

[1753] Step 7:

[1754] The server sends the translation results to the terminal. The server sends the translation results to the terminal using an HTTP response. The input data is the translation results stored on the server, and the output data is the translation results sent to the terminal.

[1755] Step 8:

[1756] The terminal displays the translation result to the user. The terminal displays the translation result received from the server to the user. For example, the translation result "I'm fine" is displayed on the terminal screen. The input data is the translation result sent to the terminal, and the output data is the translation result displayed to the user.

[1757] (Application example 3)

[1758] Next, a description will be given of Application Example 3 of Form Example 3. 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."

[1759] Traditional messaging apps and social media platforms often lack accurate translations based on context when users communicate in different languages. Virtual stores also lack accurate translations when users search for product information or contact customer support, resulting in a poor user experience.

[1760] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 3 is realized by the following means. In this invention, the server includes a message sending means, a "Send in Language" button generating means located next to the message sending means, a means for automatically translating the message into a language when the "Send in Language" button is pressed, a means for sending the automatically translated message, a means for selecting an optimal translation based on the context of a message entered by a user, and a means for providing an optimal translation based on the context when a user searches for product information or makes an inquiry to customer support in a virtual store. This makes it possible to provide accurate translations based on the context when users communicate in different languages, improving the user experience in the virtual store.

[1761] "Message sending means" is a function that allows a user to input a text message and send it to other users.

[1762] The "send in language button generating means" is a function that generates a button for sending a message in a language selected by the user.

[1763] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1764] "Transmission means" is a function for sending automatically translated messages to other users.

[1765] "Means for selecting the best translation based on context" is a function that analyzes the context of the message entered by the user and selects the translation that best suits that context.

[1766] A "virtual store" is a virtual store that offers products and services over the Internet.

[1767] "Means for searching for product information" is a function that allows users to search for information about products and services offered in the virtual store.

[1768] "Mechanisms for contacting customer support" refers to the functionality that allows users to report questions or issues to the virtual store's support team.

[1769] To implement this invention, the following system configuration is required: The system includes a message sending means, a language send button generating means, an automatic translation means, a sending means, a means for selecting the most appropriate translation based on the context, a means for searching for product information in a virtual store, and a means for making an inquiry to customer support.

[1770] The server receives messages entered by users and uses a generative AI model to provide the best translation based on the context. Specifically, it uses Hugging Face's Transformers library to translate from English to Japanese. When a user enters a message, it is sent to the server, which then uses the generative AI model to translate it based on the context.

[1771] The terminal provides an interface for a user to input and send a message. The terminal includes a message sending means and a send in language button generating means, and allows the user to send a message in a language selected by the user. When the user presses the "send in language" button, the message is automatically translated and sent to another user through the sending means.

[1772] As a concrete example, suppose a user types "Can I return this item?" and presses the "Send in Japanese" button. In this case, the server uses the generative AI model to translate it into "Can I return this item?" and sends the translated message to other users via the sending means.

[1773] The same process applies to virtual stores when users search for product information or contact customer support. For example, if a user types "I need help with my order," the server translates it to "I need help with my order" and provides appropriate support.

[1774] An example of a prompt sentence to input to the generative AI model would be "Translate the following English sentence to Japanese considering the context: 'I need help with my order.'" Using this prompt sentence, the server can provide the best translation based on the context.

[1775] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1776] Step 1:

[1777] A user inputs a message on a terminal. The user inputs a text message using a message sending means and presses a send button in a language. The input is the user's text message and the output is the inputted message displayed on the terminal.

[1778] Step 2:

[1779] The terminal sends the input message to the server. The terminal sends the input message to the server along with the send button information in the language pressed by the user. The input is the user's text message and the selected language, and the output is the message data sent to the server.

[1780] Step 3:

[1781] The server receives the message and performs a translation based on the context. The server analyzes the received message and uses a generative AI model to perform the optimal translation based on the context. The input is the message data sent to the server, and the output is the translated message. Specifically, the translation is performed using Hugging Face's Transformers library.

[1782] Step 4:

[1783] The server sends the translated message to the terminal. The server sends the translated message to the terminal and displays it to the user. The input is the translated message and the output is the translated message sent to the terminal.

[1784] Step 5:

[1785] The terminal displays the translated message and transmits it to other users through the transmitting means. The terminal displays the translated message to the user, and after the user confirms it, transmits the message to other users through the transmitting means. The input is the translated message, and the output is the message transmitted to other users.

[1786] Step 6:

[1787] Other users check the received message. Other users check the received message and reply if necessary. The input is the received message and the output is the other users' checked message.

[1788] In this way, messages entered by users are optimally translated based on the context and accurately conveyed to other users.

[1789] 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.

[1790] "Example 1"

[1791] One embodiment of the present invention provides a message translation system incorporating an emotion engine. In this system, when a user types a message on a messaging app or social networking site, the emotion engine recognizes the user's emotion. The emotion engine infers the user's emotion from, for example, the context of the message, the use of emojis, and the typing speed. The emotion engine then adjusts the translation of the message based on the user's emotion. For example, if a user types "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression: "Today is the best day ever!"

[1792] "Example 2"

[1793] In another embodiment of the present invention, a system is provided that includes emotion recognition means for an emotion engine to recognize a user's emotion, and emotion adjustment means for adjusting the translation of a message based on the recognized emotion. The emotion recognition means infers the user's emotion from the context of the user's message, the use of emojis, typing speed, etc. The emotion adjustment means adjusts the translation of the message based on the recognized emotion. For example, if the user types "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, such as "Today is the worst day...".

[1794] "Example 3"

[1795] Furthermore, in another embodiment of the present invention, a system is provided in which an emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. In this system, the emotion engine recognizes a user's emotion and adjusts the translation of a message based on the emotion. For example, if the user inputs "Today is the best day ever!" and the emotion engine recognizes joy, the message is translated into a more positive expression, "Today is the best day ever!". On the other hand, if the user inputs "Today is the worst day..." and the emotion engine recognizes sadness, the message is translated into a more negative expression, "Today is the worst day...".

[1796] The processing flow of each embodiment will be described below.

[1797] "Example 1"

[1798] Step 1: The user types a message in a messaging app or social networking site.

[1799] Step 2: The sentiment engine infers the user's sentiment based on the context of the message, emoji use, typing speed, etc.

[1800] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[1801] "Example 2"

[1802] Step 1: The user types a message.

[1803] Step 2: The emotion recognition tool infers the user's emotions from the context of the message, the use of emojis, typing speed, etc.

[1804] Step 3: Emotion regulation measures adjust the translation of the message based on the perceived emotion.

[1805] "Example 3"

[1806] Step 1: The user types a message.

[1807] Step 2: The emotion engine recognizes the user's emotion.

[1808] Step 3: The emotion engine adjusts the translation of the message based on the emotion it recognizes.

[1809] Example 1

[1810] Next, a description will be given of Example 1 of Form 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."

[1811] In conventional messaging apps and social networking sites, when users translated and sent messages into other languages, the quality of the translation often lacked emotional expression, resulting in problems with the user's intentions and emotions not being conveyed accurately. Furthermore, the user had to take the time and effort to translate manually, making it inconvenient.

[1812] The specification process by the specification processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means. In this invention, the server includes a message sending means, a "Send in specific language" button generating means, a means for automatically translating the message into a specific language when the "Send in specific language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and a means for adjusting the translation based on the result of the emotion recognition means. This makes it possible to send a high-quality translated message that reflects the user's emotion.

[1813] The "message sending means" is a function that provides an interface for users to input and send messages.

[1814] The "means for generating a send button in a specific language" is a function for generating a button that is placed next to the message input field and that is used to send a message in a specific language selected by the user.

[1815] The "automatic translation means" is a function that automatically translates a message entered by a user into a specific language.

[1816] The "emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emoticons, typing speed, etc.

[1817] The "translation adjustment means" is a function that adjusts the expression of the translated message based on the results of the emotion recognition means.

[1818] MODE FOR CARRYING OUT THE INVENTION

[1819] This invention is a system that places a "Send in specific language" button on the message sending screen of a messenger app or SNS, and when a user inputs a message and presses the "Send in specific language" button, the input message is automatically translated into the specific language and the translated message is sent. It also includes a function that recognizes the user's emotions and adjusts the translation based on those emotions by combining it with an emotion engine.

[1820] Hardware and software used

[1821] Hardware:

[1822] Device: The device on which the user types the message (smartphone, tablet, PC, etc.)

[1823] Server: A remote server for message translation and emotion recognition

[1824] software:

[1825] Messenger apps and social networking applications

[1826] Emotion engine (e.g. EmotionAI)

[1827] Translation APIs (e.g., Google Translate API, DeepL API)

[1828] Data processing and calculation

[1829] 1. Get the message:

[1830] The terminal receives the message entered by the user.

[1831] 2. Emotion Recognition:

[1832] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[1833] 3. Message Translation:

[1834] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[1835] 4. Sending translated messages:

[1836] The device sends the translated message.

[1837] Specific examples

[1838] When a user types "Today is the best day ever!" into a messenger app and presses the "Send in a specific language" button, the following process occurs:

[1839] 1. The user types, "Today is the best day ever!"

[1840] 2. The user presses the "Send in specific language" button.

[1841] 3. Your device will get the message "Today is a great day!"

[1842] 4. The server uses "EmotionAI" to recognize the emotion of joy from the message.

[1843] 5. The server uses the Google Translate API to translate "Today is the best day ever!" to "Today is the best day ever!"

[1844] 6. The device sends the message "Today is the best day ever!"

[1845] Prompt Sentence Examples

[1846] "Design a system that translates messages typed by a user in a messenger app into a specific language and adjusts the message appropriately based on emotion. For example, if a user types, 'Today is the best day ever!', the emotion engine should recognize joy and translate the message to, 'Today is the best day ever!'"

[1847] In this way, a system is realized that appropriately translates and transmits a message input by a user based on emotion.

[1848] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1849] Step 1:

[1850] The user enters a message in the message input field of a messenger app or social networking site.

[1851] Input: The user types, "Today is the best day ever!"

[1852] What happens: A user types a message using a keyboard or touchscreen.

[1853] Output: "Today is the best day ever!" is displayed in the message input field.

[1854] Step 2:

[1855] The user presses the "Send in specific language" button located next to the message input field.

[1856] Input: User clicks the "Submit in specific language" button.

[1857] Specific action: The user presses a button using a mouse or touchscreen.

[1858] Output: A button pressed event is fired.

[1859] Step 3:

[1860] The terminal receives the message entered by the user.

[1861] Input: The event of a button being pressed and the message being entered: "Today is a great day!"

[1862] Specific operation: The device reads text data from the message input field.

[1863] Output: The retrieved message "Today is a great day!" is stored in memory.

[1864] Step 4:

[1865] The server uses an emotion engine to infer the user's emotions from the context of the user's message, the use of emojis, typing speed, etc.

[1866] Input: The retrieved message "Today is the best day!"

[1867] What it does: The server uses an emotion engine (e.g., EmotionAI) to analyze the message context, detect emojis, and analyze typing speed.

[1868] Output: The emotion engine recognizes the user's emotion as "joy."

[1869] Step 5:

[1870] The server translates the message into a specific language containing appropriate emotional expressions based on the results of the emotion engine.

[1871] Input: Emotion engine result "Joy" and original message "Today is the best day ever!"

[1872] Specific operation: The server uses a translation API (e.g., Google Translate API) to perform translation that takes emotional expressions into account.

[1873] Output: The translated message "Today is the best day ever!".

[1874] Step 6:

[1875] The device sends the translated message.

[1876] Input: The translated message "Today is the best day ever!".

[1877] Specific behavior: The device uses the message sending function to send the translated message.

[1878] Output: The sent message "Today is the best day ever!" will appear in the recipient's message inbox.

[1879] (Application example 1)

[1880] Next, a description will be given of Application Example 1 of Form 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."

[1881] In existing messaging apps and social media, when users send messages in different languages, the quality of the translation often does not accurately reflect the user's feelings, which can lead to poor communication quality. Furthermore, because translations do not take the user's feelings into consideration, there is a risk of misunderstandings or discomfort.

[1882] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. In this invention, the server includes a message sending means for a messenger app or SNS, a "Send in XX language" button generating means located next to the message sending means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, a sentiment analysis means for analyzing the user's sentiment, and a means for adjusting the translation based on the sentiment analysis means. This enables natural translation that takes the user's sentiment into consideration.

[1883] "Messenger apps and social networking sites" are software applications that allow users to send and receive text messages, images, videos, etc.

[1884] The "message sending means" is a function for sending a message input by a user to other users.

[1885] The "means for generating a send button in a specific language" is a function that generates a button that allows a user to send a message in a specific language.

[1886] "Means for automatic translation" refers to a function that automatically translates a message entered by a user into a specified language.

[1887] "Means for sending automatically translated messages" refers to a function for sending translated messages to other users.

[1888] "Emotion analysis means" is a function that analyzes a user's emotions from messages entered by the user and other information.

[1889] The "means for adjusting the translation" is a function for adjusting the translated message based on the emotional information obtained by the emotional analysis means.

[1890] To implement this invention, we need to build a system that translates messages in a way that takes into account emotions when users send them on messenger apps or social networking sites. This system includes the following main components:

[1891] 1. Message sending method

[1892] It provides an interface for users to type and send messages. This can be a messaging or social networking application that runs on a device such as a smartphone or computer.

[1893] 2. How to generate a "Send in XX language" button

[1894] Creates a "Send in ____ language" button that is placed next to the message sending method. This button allows users to send messages in a specific language. The language is automatically selected from the available options based on the user's settings.

[1895] 3. Means of automatic translation

[1896] When a user presses the "Send in XX language" button, the input message is automatically translated into the specified language. This function is achieved using a generative AI model. Specifically, a translation model using the Transformers library is used.

[1897] 4. How to send automatically translated messages

[1898] Send translated messages to other users. This feature will also be implemented as part of messaging apps and social networking applications.

[1899] 5. Emotion analysis method

[1900] Analyzes user sentiment from user-entered messages and other information. This functionality is achieved using a generative AI model, specifically a sentiment analysis model using the Transformers library.

[1901] 6. Means of adjusting the translation

[1902] The translated message is adjusted based on the emotional information obtained by the emotional analysis means. For example, if a positive emotional state is detected, the translated message is changed to a more positive expression.

[1903] Hardware and software used

[1904] Hardware: Servers (cloud servers and on-premise servers), smartphones, computers

[1905] Software: Python, Flask, Transformers library

[1906] Specific examples

[1907] If a user types "Today is the best day ever!" and presses the "Send in English" button, the sentiment analyzer will recognize the joy and the message will be translated as "Today is the best day ever!" and sent.

[1908] Prompt Sentence Examples

[1909] "Today is the best day!"

[1910] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1911] Step 1:

[1912] A user opens a messaging app or social networking application and types a message, which is then saved in text format on the device.

[1913] Step 2:

[1914] The user presses the "Send in XX language" button. This action causes the device to send the entered message to the server. The data sent includes the message text and the language selected by the user.

[1915] Step 3:

[1916] The server passes the received message text to a sentiment analyzer, which uses a generative AI model to analyze the message's sentiment. The input is the message text, and the output is a sentiment label (e.g., positive, negative, neutral).

[1917] Step 4:

[1918] The server passes the message text to the translation means based on the emotion label obtained from the emotion analysis means. The translation means uses a generative AI model to translate the message into the specified language. The input is the message text and the emotion label, and the output is the translated message text.

[1919] Step 5:

[1920] The server adjusts the translated message text based on the emotion label. For example, if a positive emotion is detected, it changes the translated message to a more positive expression. The input is the translated message text and the emotion label, and the output is the adjusted message text.

[1921] Step 6:

[1922] The server sends the adjusted message text to the terminal, which displays the received message and sends it to other users. The input is the adjusted message text, and the output is the displayed and sent message.

[1923] Example 2

[1924] Next, a description will be given of Example 2 of Form 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."

[1925] In conventional messaging apps and social media, when users wanted to send messages in a different language, they had to translate them manually, which was a time-consuming process. Furthermore, because translations were done without taking into account the user's feelings, there was a risk of conveying unintended nuances. This led to issues such as a decline in the quality of communication.

[1926] 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.

[1927] In this invention, the server includes a message sending means, a "Send in XX language" button generating means, a means for automatically translating the message into XX language when the "Send in XX language" button is pressed, a means for sending the automatically translated message, an emotion recognition means for recognizing the user's emotion, and an emotion adjustment means for adjusting the message translation based on the recognized emotion. This makes it possible to automatically select a language frequently used by the user and provide an appropriate translation according to the emotion.

[1928] The "message sending means" is a function for sending a message input by a user to other users.

[1929] The "means for generating a send button in XX language" is a function that automatically selects an appropriate language from selectable languages ​​based on the user's settings and generates a button for sending a message in that language.

[1930] "Automatic translation means" is a function that automatically translates a message entered by a user into a specified language.

[1931] "Emotion recognition means" is a function that infers a user's emotions from the context of the message entered by the user, the use of emojis, typing speed, etc.

[1932] "Emotion adjustment means" is a function that adjusts the translation of a message based on the perceived emotion of the user.

[1933] The present invention provides a system that reduces the effort required for users to send messages in different languages ​​and provides appropriate translations according to emotions. Specific embodiments of this system are described below.

[1934] Hardware and software used

[1935] Server: Responsible for data processing and storage. Specifically, it uses cloud services (e.g., AWS (registered trademark), Google Cloud Platform).

[1936] Device: The device used by the user, such as a smartphone, tablet, or PC.

[1937] Generative AI model: A model for natural language processing. For example, OpenAI's GPT-3 or GPT-4 (registered trademark) is used.

[1938] Sentiment engine: An engine for recognizing user emotions, for example, using IBM Watson's sentiment analysis API.

[1939] System processing flow

[1940] 1. User language settings

[1941] Users select their preferred language in the application settings screen.

[1942] The terminal transmits the selected language information to the server.

[1943] The server stores the received language information in a database.

[1944] Specific operation: When the user selects "French" on the settings screen and presses the save button, the device sends that information to the server.

[1945] 2. Creating a language selection button

[1946] The server will generate a submit button in the appropriate language based on the user's language setting information the next time the site is acc...

Claims

1. A means for generating a button for sending a message in a particular language; a means for inputting the input message as a prompt sentence to a generative AI model when the button is pressed; A means for translating an input message by the generative AI model; means for transmitting the translated message to a destination; A system including:

2. The means for generating the button automatically selects a particular language from the available languages ​​based on a user setting. The system of claim 1

3. A means for generating a button for sending a message in a particular language; means for translating an input message when the button is pressed; means for transmitting the translated message to a destination; A means for obtaining user configuration information; A means for learning frequently used languages ​​based on the setting information; Including, The means for generating the button generates the button based on a learned language. system.

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