system

Abstract

To solve the problem that language barriers and cultural differences are major obstacles when people all over the world communicate.SOLUTION: A system includes means for utilizing a generative AI to provide an SNS participable in a plurality of languages, means for understanding cultural background differences among users, means for allowing the plurality of users to guess each other's roots to provide a game property for providing an incentive if the guess of the roots is correct, means for translating user's submission contents into different languages, and means for transmitting translated through-hole contents to the other users' terminals.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, language barriers and cultural differences are major obstacles when people around the world communicate with each other. Preconceived notions and prejudices also limit interactions between people of different cultures and nationalities, preventing deeper understanding. [Means for solving the problem]

[0005] To solve this problem, we will use generative AI to provide a social networking site that can be used in any country's language. Furthermore, we will provide a means for users to understand the differences in their cultural backgrounds, and we will add a game element where users can guess each other's origins and receive incentives if they are correct. This will allow people to work together toward a common goal without preconceptions, foster connections that transcend borders and cultures, and contribute to world peace. [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] One embodiment of the present invention is a social networking system that utilizes generative AI. This system allows users to participate in the system in their own languages ​​and supports communication between users. The generative AI analyzes text posted by users and translates that text into other languages, enabling communication between users across language barriers.

[0029] "Example 2"

[0030] Furthermore, this system provides a means to understand differences in cultural backgrounds among users. Specifically, it has the ability to infer a user's cultural background from the text they post. For example, if a user posts, "I eat mochi on New Year's Day," the system will infer that the user is likely to have a Japanese cultural background.

[0031] "Example 3"

[0032] The system also features a gamification element, where users are asked to guess each other's origins and are given incentives if they are correct. Specifically, if a user correctly guesses another user's cultural background, they are given incentives such as points or badges. This encourages users to understand and respect other cultures.

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

[0034] "Example 1"

[0035] Step 1: Users post on social media in their own language.

[0036] Step 2: The generative AI analyzes the posted text.

[0037] Step 3: The generative AI translates the analyzed text into other languages.

[0038] Step 4: The translated text will be displayed to other users and you can start communicating with them.

[0039] "Example 2"

[0040] Step 1: A user posts on social media.

[0041] Step 2: The system infers the user's cultural background from the posted text.

[0042] Step 3: The guesses are reflected in the user's profile and are visible to other users.

[0043] "Example 3"

[0044] Step 1: Users participate in a game where they have to guess the cultural backgrounds of other users.

[0045] Step 2: Users submit their guesses.

[0046] Step 3: The system determines whether the guess is correct or incorrect.

[0047] Step 4: If the guess is correct, provide the user with an incentive (e.g., points or a badge).

[0048] Example 1

[0049] 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."

[0050] In today's social networking sites, there is a language barrier when communicating between users who speak different languages, making it difficult to have smooth interactions. It is also difficult for users to understand the differences in their cultural backgrounds, making it difficult to form communities that transcend national borders and cultures. Furthermore, there is a lack of ways to make communication more fun by adding a game element to the experience, such as users guessing each other's origins.

[0051] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, a means for analyzing text posted by users and translating the text into other languages, and a means for sending the translated text to other users' devices. This enables smooth communication between users who speak different languages, promotes mutual understanding of differences in cultural backgrounds, and promotes the formation of communities that transcend borders and cultures. Furthermore, the gamification element makes interactions between users more enjoyable.

[0052] "Generative AI" is an artificial intelligence technology that analyzes text posted by users and understands its content.

[0053] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0054] "User" means an individual or organization that uses SNS to post text and communicate with other users.

[0055] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[0056] "Roots" refers to the user's origins, such as place of birth, ethnicity, and cultural background.

[0057] An "incentive" is a reward or benefit that encourages users to take a particular action.

[0058] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[0059] "Text" refers to the written information that users post on social media.

[0060] "Translation" is the process of converting text written in one language into another.

[0061] A "terminal" is a device (smartphone, PC, etc.) that a user uses to access an SNS.

[0062] A "server" is a computer system used to operate a social networking site and process data.

[0063] This invention utilizes generative AI to provide a social networking system that allows participation in various languages. The system aims to support communication between users and overcome language barriers. Specific embodiments of this system are described below.

[0064] Hardware and Software Configuration

[0065] server

[0066] The server is the central computer system of the SNS system. The server is built using a virtual machine from a cloud service provider. Specifically, it uses a virtual machine instance from a common cloud service provider. The server includes the following main software components:

[0067] Generative AI model: We use a popular generative AI model (e.g., GPT-4®) to analyze user-submitted text.

[0068] Translation API: Use a common translation API (e.g., Cloud Translation API) to translate text into other languages.

[0069] Terminal

[0070] A terminal is a device that a user uses to access an SNS. Terminals include smartphones, tablets, PCs, etc. Terminals are connected to a server via the Internet.

[0071] User

[0072] A user is an individual or an organization that uses the SNS system to post text and communicate with other users.

[0073] System Operation

[0074] User Submissions

[0075] A user posts text to an SNS using a terminal. For example, the user posts, "It's nice weather today."

[0076] Sending a text

[0077] The device sends the posted text to the server, along with metadata such as the user ID and posting time.

[0078] Text Analysis

[0079] The server sends the received text to a generative AI model, which analyzes the content of the text. For example, the text "It's a nice day today" is analyzed as a "positive comment about the weather."

[0080] Executing the translation

[0081] Based on the analysis results, the server uses a translation API to translate the text into another language, for example, translating the Japanese phrase "Kyo wa ii tenki desu ne" (It's a nice day today) into English.

[0082] Sending translation results

[0083] The server sends the translated text to the other user's device, for example, an English-speaking user's device might display "It's a nice day today."

[0084] Specific examples

[0085] User A: Posts in Japanese, "It's a nice day today."

[0086] Server: Receives posts and analyzes them with a generative AI model.

[0087] Server: Use the translation API to translate "It's a nice day today" into English.

[0088] Server: Sends the translation result "It's a nice day today" to User B, who speaks English.

[0089] User B: Receives and understands "It's a nice day today" in English.

[0090] Prompt Sentence Examples

[0091] Prompt: If a user posts "It's a nice day today," translate this into English.

[0092] In this way, by utilizing generative AI and translation APIs, smooth communication between users who speak different languages ​​is achieved.

[0093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0094] Step 1:

[0095] A user posts text to a social networking site using a device. Specifically, the user opens the social networking site app, enters "It's a nice day today" in the text input field, and presses the post button. The input is the text data entered by the user, and the output is the text data sent from the device to the server.

[0096] Step 2:

[0097] The device sends the posted text to the server. Specifically, the device sends the text data entered by the user to the server via the Internet. The transmitted data also includes metadata such as the user ID and posting time. The input is the text data entered by the user, and the output is the text data sent to the server.

[0098] Step 3:

[0099] The server sends the received text to the generative AI model, which analyzes the text content. Specifically, the server sends the received text data to the API endpoint of the generative AI model and receives the analysis results. The input is the text data received by the server, and the output is the analysis results returned by the generative AI model.

[0100] Step 4:

[0101] The server uses the translation API to translate the text into another language based on the analysis results. Specifically, the server sends a request to the translation API to translate "It's a nice day today" based on the analysis results. The input is the analysis result from the generative AI model, and the output is the translation result returned by the translation API.

[0102] Step 5:

[0103] The server sends the translated text to the other user's device. Specifically, the server receives the translation result and sends "It's a nice day today" to the English-speaking user's device. The sent data also includes metadata such as the original text and translation time. The input is the translation result from the translation API, and the output is the translated text sent to the other user's device.

[0104] (Application example 1)

[0105] 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."

[0106] In today's global society, it is difficult for users who speak different languages ​​to communicate in real time. In particular, in social networking sites and chat applications, language barriers are a major factor hindering interaction between users. Differences in cultural backgrounds can also be an obstacle to communication. There is a need to solve these problems, deepen connections between users, and form communities that transcend borders and cultures.

[0107] 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.

[0108] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text entered by users into other languages ​​in real time, means for displaying the translated text to other users, and means for supporting real-time communication between users who speak different languages. This enables smooth communication that transcends language barriers, deepens connections between users, and enables the formation of a community that transcends borders and cultures.

[0109] "Generative AI" is an artificial intelligence technology that analyzes text entered by a user and translates it into other languages.

[0110] "SNS" stands for Social Networking Service, a platform for users to communicate online.

[0111] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[0112] "Roots" refers to the user's place of origin or ethnic background.

[0113] An "incentive" is a reward or benefit that encourages users to take a particular action.

[0114] "Real-time" means that information entered by a user is processed immediately and displayed to other users almost simultaneously.

[0115] "Translation" is the process of converting text written in one language into another.

[0116] "Communication" is the act of exchanging information and emotions between users.

[0117] "Connection" refers to the relationships and bonds between users.

[0118] A "community" is a group or group of users who share common interests or goals.

[0119] As an embodiment of the present invention, the following system can be constructed.

[0120] First, the server uses generative AI to provide a social networking site that can be used in various languages. Users access the site from their smartphones, computers, or other devices and enter text. The entered text is sent to the server, where it is translated into other languages ​​in real time by the generative AI model. The translated text is then displayed to other users.

[0121] The system uses the following hardware and software:

[0122] Server: Cloud server such as AWS (registered trademark) EC2 instance

[0123] Client: Smartphone (iOS or ANDROID (registered trademark)), PC

[0124] Software: Flask (a lightweight Python web framework), Transformers (a library of generative AI models provided by Hugging Face), MarianMTModel (Hugging Face's translation model)

[0125] The server receives the text entered by the user and translates it using a generative AI model. Specifically, the text entered by the user is tokenized and input into the generative AI model. The model translates the text and detokenizes the result back into text. The translated text is then displayed to other users.

[0126] For example, if User A (an English speaker) types "Hello, how are you?", the app sends this text to the server, where the generative AI model translates it to "Hello, how are you?" The translated text is then displayed to User B (a German speaker).

[0127] An example of a prompt is as follows:

[0128] "Translate the following text from English to German: "Hello, how are you?"

[0129] In this way, a system can be realized that allows users who speak different languages ​​to communicate smoothly in real time.

[0130] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0131] Step 1:

[0132] A user accesses an SNS application from a device and inputs text. The input text is sent to a server along with the user's language. The input data consists of the text and language input by the user.

[0133] Step 2:

[0134] The server analyzes the received text data and tokenizes it for input to the generative AI model. Specifically, it breaks the text down into words and phrases and converts them into a format that the model can understand. The input data is the text entered by the user, and the output data is the tokenized text.

[0135] Step 3:

[0136] The server inputs the tokenized text into the generative AI model for translation. The generative AI model translates the input text into the specified target language. The input data is the tokenized text, and the output data is the translated tokenized text.

[0137] Step 4:

[0138] The server detokenizes the translated tokenized text and returns it to its original text form. Specifically, it combines words and phrases to reconstruct natural-sounding sentences. The input data is the translated tokenized text, and the output data is the translated text.

[0139] Step 5:

[0140] The server sends the translated text to the original user and the target user. The target user receives and displays the translated text on their device. The input data is the translated text, and the output data is the text displayed on the target user's device.

[0141] Step 6:

[0142] The target user checks the translated text and replies if necessary. The returned text is sent back to the server, where it goes through the same processing steps and is then displayed to the original user. The input data is the text entered by the target user, and the output data is the text displayed on the original user's device.

[0143] In this way, a system is realized that allows users who speak different languages ​​to communicate smoothly in real time.

[0144] Example 2

[0145] 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."

[0146] In today's global society, it is important to facilitate communication between users with different cultural backgrounds. However, language barriers and cultural differences can be obstacles that make it difficult to deepen understanding and connections between users. Furthermore, a lack of tools for users to understand each other's cultural backgrounds can lead to misunderstandings and friction. Furthermore, there is a need for a game-like feature that allows users to guess each other's origins, allowing them to deepen cultural understanding while having fun.

[0147] 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.

[0148] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be used in the languages ​​of various countries; a means for understanding differences in cultural backgrounds between users; a means for providing a game element in which users guess each other's origins and are given incentives if they are correct; a means for analyzing text posted by users and guessing their cultural background; and a means for guessing the user's cultural background based on the analysis results and returning the result to the user. This makes it possible to facilitate communication between users with different cultural backgrounds and deepen cultural understanding.

[0149] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[0150] "SNS" is an abbreviation for Social Networking Service, an online platform for users to communicate over the Internet.

[0151] "Cultural background" refers to the totality of customs, values, traditions, etc. of the culture or society to which the user belongs.

[0152] "Analyzing text" means analyzing text data posted by users using natural language processing technology to understand its content.

[0153] An "incentive" is a reward or benefit offered to encourage users to take a particular action.

[0154] "Gameplay" refers to elements and mechanisms designed to allow users to participate in a fun way.

[0155] A "prompt" is an instruction given to a generative AI model, and includes instructions for specific analysis or generation.

[0156] "Analysis results" refers to the information or data obtained after a generative AI model analyzes text.

[0157] This invention is a system for understanding differences in cultural backgrounds between users and facilitating smooth communication. A specific embodiment of this system will be described below.

[0158] First, a user posts text to the system using their own device (smartphone, PC, etc.). For example, the user might type, "I eat rice cakes on New Year's Day."

[0159] The device then transmits the text entered by the user to the server, using an Internet connection to transmit the data, which is received by the server and stored in a database.

[0160] The server uses natural language processing (NLP) techniques to analyze the received text data. Specifically, it uses a generative AI model (e.g., GPT-4) to understand the content of the text. For analysis, it uses prompts such as:

[0161] "Infer the user's cultural background from this text"

[0162] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" and "rice cakes"). For example, if a user posts "I eat rice cakes on New Year's Day," the server detects keywords such as "New Year's Day" and "rice cakes" and determines that this is related to Japanese culture.

[0163] Finally, the server returns the prediction results to the user. The prediction results are packaged in JSON format and sent to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background."

[0164] This system will facilitate communication between users with different cultural backgrounds and deepen cultural understanding. In addition, by adding a game element to the system, users can guess each other's origins, promoting cultural understanding while having fun.

[0165] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0166] Step 1:

[0167] User posts text

[0168] A user uses their own device (smartphone, PC, etc.) to enter text into the system and click the send button. For example, they might enter "I eat rice cakes on New Year's Day." The input data is in text format.

[0169] Step 2:

[0170] The device sends the text to the server

[0171] The terminal packages the text entered by the user in JSON format and sends it to the server using an HTTP POST request: the input is the user's text data, and the output is an HTTP request to the server.

[0172] Step 3:

[0173] The server receives the text

[0174] The server receives the HTTP request sent from the terminal, extracts the text data, and stores it in a database. The input is the HTTP request, and the output is the text data stored in the database.

[0175] Step 4:

[0176] The server parses the text

[0177] The server inputs the saved text data into a generative AI model (GPT-4) and analyzes the content of the text. Specifically, it uses the following prompt: "Please infer the user's cultural background from this text." The input is text data obtained from the database, and the output is the analysis result from the generative AI model.

[0178] Step 5:

[0179] The server guesses the cultural background

[0180] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" or "rice cakes"). The input is the analysis results from the generative AI model, and the output is information about the inferred cultural background.

[0181] Step 6:

[0182] The server returns the guess to the user

[0183] To return the prediction results to the user, the server packages the results in JSON format and sends them to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background." The input is the prediction result information, and the output is the message displayed on the user's device.

[0184] (Application example 2)

[0185] 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."

[0186] In today's social networking sites, it is difficult to understand the differences in cultural backgrounds between users and provide appropriate content. There are also insufficient means for users to deepen communication by guessing each other's cultural backgrounds. Furthermore, there are insufficient means to support communication between users across language barriers. To solve these issues, a system is needed that can accurately guess a user's cultural background and provide personalized content based on that.

[0187] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for utilizing a generative AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, and a means for inferring cultural backgrounds from the content posted by users and recommending appropriate content based on that guess. This makes it possible to understand the cultural backgrounds of users and provide appropriate content. Furthermore, by users guessing each other's cultural backgrounds, communication can be deepened and communication support that overcomes language barriers can be realized.

[0188] "Generative AI" is an artificial intelligence technology that analyzes user input data and performs natural language processing.

[0189] An "SNS that can be accessed in various languages" is a social networking service that supports multiple languages ​​and allows users who speak different languages ​​to participate.

[0190] "Means for understanding differences in cultural backgrounds among users" refers to technology that analyzes text posted by users and behavioral data to infer their cultural backgrounds.

[0191] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a system in which users guess the cultural backgrounds of other users and are offered rewards or benefits if they are correct.

[0192] "Means of inferring cultural background from user posts and recommending appropriate content based on that" refers to technology that analyzes text posted by users and provides relevant content based on that cultural background.

[0193] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are provided with incentives if they are correct, and a means for inferring cultural backgrounds from user posts and recommending appropriate content based on that guess.

[0194] The server uses a generative AI model to analyze the content posted by the user and infer its cultural background. Specifically, it receives the text posted by the user as input and analyzes it using the generative AI model. For example, if a user posts "I eat rice cakes on New Year's Day," the server infers that the user has a Japanese cultural background. This analysis uses the OpenAI (registered trademark) API and the text-davinci-003 engine.

[0195] The server then recommends content appropriate to the user based on the inferred cultural background. For example, if a user is inferred to have a Japanese cultural background, it will recommend movies, music, articles, etc. related to Japan. This recommendation system is realized by referencing a pre-defined list of correspondences between cultural backgrounds and content.

[0196] To understand the differences in cultural backgrounds between users, the server uses technology to analyze the text and behavioral data posted by users and infer their cultural background, making it easier for users to understand each other's cultural backgrounds.

[0197] It also includes a gamification mechanism where users can guess each other's origins and receive incentives if they guess correctly. This system allows users to guess the cultural background of other users and provides rewards and benefits if they guess correctly. This promotes communication between users.

[0198] For example, if a user posts "I eat mochi on New Year's Day," the server will input the following prompt into the generative AI model:

[0199] User-submitted text: Eat rice cakes on New Year's Day

[0200] Guess this user's cultural background.

[0201] Based on this prompt, the generative AI model infers the user's cultural background and returns the results to the server, which then recommends appropriate content to the user.

[0202] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[0203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0204] Step 1:

[0205] The user enters the text to post on the SNS. The user posts, "I eat rice cakes on New Year's Day."

[0206] Step 2:

[0207] The server receives the user's posted text, which is "I eat rice cakes on New Year's Day."

[0208] Step 3:

[0209] The server generates a prompt for the generative AI model. The generated prompt is "Text posted by the user: I eat rice cakes on New Year's Day. Please guess this user's cultural background."

[0210] Step 4:

[0211] The server sends a prompt to the generative AI model and requests it to analyze it. The generative AI model analyzes the prompt and infers the user's cultural background.

[0212] Step 5:

[0213] The generative AI model returns the analysis result to the server, which is "has a Japanese cultural background."

[0214] Step 6:

[0215] The server then uses the analysis results to recommend content appropriate to the user. For example, a user with a Japanese cultural background would be recommended movies, music, articles, etc. related to Japan.

[0216] Step 7:

[0217] The server sends the recommended content to the user's device, which then displays the recommended content.

[0218] Step 8:

[0219] Users can browse and enjoy recommended content, including movies, music, and articles related to Japan.

[0220] Step 9:

[0221] Users participate in a game where they must guess the cultural backgrounds of other users. Users look at the posts of other users and guess their cultural backgrounds.

[0222] Step 10:

[0223] The server evaluates the user's guess and provides incentives if the user guesses correctly. If the user guesses correctly, they are given rewards or benefits.

[0224] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[0225] Example 3

[0226] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0227] On modern social media, there are few opportunities for users to understand and respect the differences in cultural backgrounds between them. It is also difficult to deepen communication across cultural and language barriers. Furthermore, there is no incentive for users to guess each other's origins and get it right, which makes it difficult to promote understanding and respect for other cultures.

[0228] 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.

[0229] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for users to log in to the system, means for displaying user profile information, means for users to view other users' profile information, means for users to guess other users' cultural backgrounds, means for evaluating the accuracy of guesses using a generative AI model, means for notifying users of the results of their guesses, and means for offering incentives for correct guesses, thereby enabling users to understand and respect other cultures.

[0230] "Generative AI" is a system that uses artificial intelligence technology to automatically generate content such as text and images.

[0231] "SNS" stands for Social Networking Service, a platform where users can interact and share information with other users online.

[0232] "User" means any individual or entity that accesses and uses the System.

[0233] "Profile information" is basic information about a user, including name, hometown, hobbies, etc.

[0234] "Cultural background" refers to the cultural and social background to which a user belongs, including place of origin, language, customs, values, etc.

[0235] "Incentives" are rewards or benefits offered to encourage user behavior, such as points or badges.

[0236] "Login" means the authentication process required for a user to access a system, including entering a username and password.

[0237] A "generative AI model" is a specific implementation of generative AI, an artificial intelligence model optimized for a specific task.

[0238] "Guessing" refers to the act of a user predicting the cultural background of another user.

[0239] "Evaluation" is the process of using a generative AI model to determine the accuracy of a user's guess.

[0240] "Notification" refers to the act of the server conveying information to the user, including displaying information on the screen and sending messages.

[0241] This invention is a social networking service system with a gamified element in which users guess each other's origins and receive incentives if they are correct. A specific embodiment of this system will be described below.

[0242] Hardware and software used

[0243] Hardware

[0244] Server: Responsible for database management and execution of generative AI models.

[0245] Terminal: The device (PC, smartphone, etc.) through which a user accesses the system.

[0246] software

[0247] Database: Manages user profile information and incentive information (e.g., MySQL (registered trademark)).

[0248] Generative AI models: Used to evaluate the accuracy of user guesses (e.g., OpenAI GPT-4).

[0249] Web application: Provides the user interface and accepts user interactions.

[0250] Explanation of program processing

[0251] 1. A user logs into the system

[0252] The user opens a browser on their device and accesses the system's login page, enters their username and password, and clicks the "Login" button. The server checks the entered information against the database, and if authentication is successful, redirects the user to the home page.

[0253] 2. The server displays the user's profile information

[0254] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Taro Tanaka," "Birthplace: Tokyo," and "Hobbies: Tea ceremony, anime" may be displayed.

[0255] 3. Users view other users' profile information

[0256] Users use the search function on the homepage to search for other users. The server retrieves the search results from the database and displays them on the device. Users can click on the displayed profile information to view details.

[0257] 4. Users infer other users' cultural backgrounds

[0258] Users can infer the cultural background of other users based on the profile information they have viewed. For example, they can infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" in the input form, and press the submit button.

[0259] 5. The server receives the guesses and evaluates them using a generative AI model

[0260] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate the accuracy of this guess." is input into the generative AI model.

[0261] 6. The server notifies the user of the result of the guess.

[0262] The server receives the evaluation results from the generative AI model and notifies the user of the results, for example, by displaying a message on the device screen such as "Your guess was correct. Points have been awarded."

[0263] 7. The server provides incentives for correct guesses

[0264] If the guess is correct, the server adds points or badges to the user's account, e.g., "10 points awarded." This information is stored in the database and reflected in the user's profile.

[0265] Examples of concrete examples and prompts

[0266] As a concrete example, consider a scenario in which User A wants to guess User B's cultural background. User A views User B's profile information (e.g., name, hometown, hobbies, etc.) and guesses that User B's cultural background is "Japan." After User A enters their guess, the server uses a generative AI model to evaluate the accuracy of their guess. If the guess is correct, User A is awarded points.

[0267] Example prompt sentence:

[0268] User A infers the cultural background of User B. User B's profile information is as follows:

[0269] Name: Tanaka Taro

[0270] Hometown: Tokyo

[0271] Hobbies: Tea ceremony, anime

[0272] User A guesses: "Japan"

[0273] Evaluate whether this assumption is accurate.

[0274] In this way, the user can understand and respect other cultures. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0275] Step 1:

[0276] A user logs into the system

[0277] The user opens a browser on the terminal and accesses the system's login page. He enters his username and password and clicks the "Login" button. The server checks the entered username and password against the database, and if authentication is successful, redirects the user to the home page. The input is the username and password, and the output is the authentication result. To check the database, the server executes an SQL query.

[0278] Step 2:

[0279] The server displays the user's profile information

[0280] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Tanaka Taro," "Hometown: Tokyo," and "Hobbies: Tea ceremony, anime" is displayed. The input is the user ID, and the output is the profile information. To retrieve the information from the database, the server executes an SQL query.

[0281] Step 3:

[0282] Users view other users' profile information

[0283] A user searches for other users using the search function on their homepage. The server retrieves the search results from the database and displays them on their device. The user clicks on the displayed profile information to view details. The input is the search query and the output is the search results. To retrieve information from the database, the server executes an SQL query.

[0284] Step 4:

[0285] Users infer the cultural background of other users

[0286] A user infers the cultural background of other users based on the profile information they have viewed. For example, they may infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" into the input form, and press the submit button. The input is the inference, and the output is the transmission of the inference result.

[0287] Step 5:

[0288] The server receives the guesses and evaluates them using a generative AI model

[0289] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate whether this guess is accurate." is input into the generative AI model. The input is the prompt sentence, and the output is the evaluation result. To execute the generative AI model, the server sends an API request.

[0290] Step 6:

[0291] The server notifies the user of the result of the guess.

[0292] The server receives the evaluation results from the generative AI model and notifies the user of the results. For example, a message such as "Your guess is correct. Points have been awarded." is displayed on the device screen. The input is the evaluation result, and the output is the notification message.

[0293] Step 7:

[0294] The server provides incentives for correct guesses

[0295] If the guess is correct, the server adds points or badges to the user's account. For example, "10 points awarded." This information is saved in the database and reflected in the user's profile. The input is the evaluation result, and the output is the awarded incentive. To update the database, the server executes an SQL query.

[0296] (Application example 3)

[0297] 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."

[0298] In modern society, it is important for people with different cultural backgrounds to understand each other. However, language barriers and cultural differences often become obstacles, making mutual understanding difficult. In addition, users lack motivation to learn about and respect other cultures. For this reason, there is a need for a system that promotes intercultural understanding and allows users to learn in a fun way.

[0299] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are given incentives if they are correct, means for determining whether users' guesses are correct and for awarding points and badges, and means for managing users' points and badges. This enables users to enjoy learning about other cultures and deepen mutual understanding.

[0300] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide appropriate responses and content based on user input.

[0301] "SNS" stands for Social Networking Service, a platform for users to communicate with other users online.

[0302] "Cultural background" refers to the culture, social environment, history, customs, values, etc. to which an individual belongs.

[0303] "Incentives" are rewards or benefits offered to encourage user behavior.

[0304] "Gamification" refers to elements of competition or challenge designed to allow users to participate in a fun way.

[0305] "Points" are a numerical evaluation given to users based on their activities and achievements.

[0306] A "badge" is a digital title or insignia that users can earn when they meet certain conditions.

[0307] "Guessing" is the act of predicting the cultural background of other users based on limited information.

[0308] "Verification" is the process of evaluating whether the user's guess is correct.

[0309] "Management" refers to tracking the status of a user's points and badges and updating them appropriately.

[0310] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for gamifying users' guesses about each other's origins and providing incentives if they are correct, a means for determining whether users' guesses are correct and for awarding points and badges, and a means for managing users' points and badges.

[0311] Program processing explanation

[0312] Hardware

[0313] Smartphone (iOS or Android)

[0314] software

[0315] Python

[0316] Mobile app development frameworks (React Native, Flutter (registered trademark), etc.)

[0317] Data processing and calculation

[0318] 1. User information management: The server creates a class to manage the user's name, cultural background, points, and badges, allowing it to track the user's basic information and game progress.

[0319] 2. Judgment of guesses: The server has a function that allows users to guess the cultural backgrounds of other users and judge whether their guesses are correct. This allows points to be added if the user's guess is correct.

[0320] 3. Points and badge management: The server adds points when users answer correctly and awards badges when they reach a certain number of points, allowing the server to evaluate users' activities and provide incentives.

[0321] Specific examples

[0322] For example, if user "A" correctly guesses user "B"'s cultural background as "American," user "A" will receive 10 points. When user "A" reaches 100 points, he or she will be awarded the badge "Cultural Expert."

[0323] Prompt Sentence Examples

[0324] I want to develop a game app where users guess the cultural backgrounds of other users and are awarded points and badges if they guess correctly. Please create a class that manages the user's name, cultural background, points, and badges, and a program that has a function to determine whether the guess is correct.

[0325] In this way, users can learn about other cultures in a fun way and deepen mutual understanding.

[0326] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0327] Step 1:

[0328] When a user logs in to the application, the server retrieves the user's basic information (name, cultural background, points, badges) from the database. The input is the user's login information, and the output is the user's basic information. Specifically, the server executes a database query to retrieve the user information.

[0329] Step 2:

[0330] Users view other users' profiles and posts to infer their cultural backgrounds. The input is the profile information of other users, and the output is the user's inference. Specifically, users check other users' information through the application interface.

[0331] Step 3:

[0332] The user inputs their guess about the cultural background into the application. The input is the user's guess, and the output is sending the guess data to the server. Specifically, the user enters their guess in the text box and presses the submit button.

[0333] Step 4:

[0334] The server determines whether the user's guess is correct. The input is the user's guess data and the target user's cultural background, and the output is the judgment result. Specifically, the server compares the guess data with the target user's cultural background to see if they match.

[0335] Step 5:

[0336] The server adds points if the user guesses correctly and awards a badge when a certain number of points is reached. The input is the judgment result, and the output is the updated user points and badge. Specifically, the server adds points and awards a badge when the conditions are met.

[0337] Step 6:

[0338] The server saves the updated user points and badges to the database. The input is the updated user points and badges, and the output is the result saved in the database. Specifically, the server executes a database query and updates the user information.

[0339] Step 7:

[0340] The server notifies the user of the updated points and badges. The input is the updated user points and badges, and the output is a notification message. Specifically, the server generates a notification message and sends it to the user's device.

[0341] Step 8:

[0342] The user receives a notification and checks the update of points and badges in the application. The input is the notification message and the output is the user's confirmation. In concrete terms, the user opens the application notification and checks the update of points and badges.

[0343] 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.

[0344] "Example 1"

[0345] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system analyzes emotions from the content posted by users and reflects the results in the user's profile. Specifically, the emotion engine analyzes text data posted by users on social networking sites and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise. The emotion results are then reflected in the user's profile, allowing other users to understand those emotions.

[0346] "Example 2"

[0347] The emotion engine also deepens understanding of emotions in communication between users, enabling richer communication. Specifically, when a user reacts to another user's post (e.g., "like" or comment), the emotion engine analyzes the emotion behind that reaction. Based on the results of this analysis, it provides feedback to deepen understanding of emotions between users.

[0348] "Example 3"

[0349] Furthermore, the emotion engine analyzes the emotions in the text posted by users and provides incentives to users based on the results. Specifically, if a user reacts to another user's post with joy or sympathy, that user is given incentives such as points or badges. This encourages users to express their emotions more proactively, stimulating communication on social media.

[0350] The processing flow of each embodiment will be described below.

[0351] "Example 1"

[0352] Step 1: A user posts text data on a social networking site.

[0353] Step 2: The emotion engine analyzes the posted text data and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise.

[0354] Step 3: The emotion engine applies the analysis results to the user's profile.

[0355] "Example 2"

[0356] Step 1: A user reacts to another user's post (e.g., "likes" or comments).

[0357] Step 2: The emotion engine analyzes the emotion behind the reaction.

[0358] Step 3: The emotion engine uses the analysis results to provide feedback to deepen understanding of emotions between users.

[0359] "Example 3"

[0360] Step 1: Users react to other users' posts expressing joy or empathy.

[0361] Step 2: The emotion engine analyzes the emotion behind the reaction.

[0362] Step 3: Based on the analysis results, the emotion engine provides incentives such as points or badges to the user.

[0363] Example 1

[0364] 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."

[0365] In today's social networking sites, communication between users who speak different languages ​​is difficult, and language barriers exist. It is also difficult to understand users' emotions, and misunderstandings can occur due to differences in cultural backgrounds. Furthermore, there is a lack of ways to deepen connections between users and form communities that transcend borders and cultures.

[0366] 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.

[0367] In this invention, the server includes a means for utilizing generative AI to provide a social networking service that can be used in various languages, a means for translating text posted by users into other languages, a means for analyzing emotions from user posts and reflecting the results in the user's profile, a means for understanding differences in cultural backgrounds between users, and a means for providing a gamification element in which users guess each other's origins and receive incentives if they are correct. This enables communication across language barriers, makes it easier to understand users' emotions, and reduces misunderstandings due to differences in cultural background. It also deepens connections between users and enables the formation of a community that transcends borders and cultures.

[0368] "Generative AI" is a system that uses artificial intelligence technology to generate, analyze, and translate data such as text and images.

[0369] "SNS" is an abbreviation for social networking service, a platform for users to communicate over the Internet.

[0370] "User" refers to an individual or organization that uses the SNS system.

[0371] "Text" refers to the written information posted by users on social media.

[0372] "Translation" is the process of converting text written in one language into another.

[0373] An "emotion engine" is software or an algorithm that analyzes emotions from text data and identifies those emotions.

[0374] A "profile" is a page or section on a social networking site that displays information about a user.

[0375] "Cultural background" refers to information about the culture and social environment to which the user belongs.

[0376] "Incentives" refer to rewards or benefits offered to encourage user behavior.

[0377] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[0378] This invention provides an SNS system that utilizes generation AI and allows participation in the languages ​​of various countries. A specific embodiment of this system will be described below.

[0379] First, a user accesses the SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system.

[0380] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The server inputs the received text data into a generative AI model (e.g., a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a lot of fun today!" is translated into English as "I had a great time today!"

[0381] The server then distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages.

[0382] The server then inputs the received text data into an emotion engine (e.g., a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The server then reflects this result in the user's profile, allowing other users to understand the user's emotional state.

[0383] As a concrete example, consider the case where User A posts in Japanese, "I had a great time today!". User A uses a smartphone to access a social networking application, enters text, and posts. The server receives this text and translates it into English using a generative AI model. The translated result, "I had a great time today!", is then distributed by the server to other users.

[0384] At the same time, the server uses an emotion engine to analyze the text "I had so much fun today!" and detects the emotion "joy." This result is reflected in User A's profile, allowing other users to understand User A's emotions.

[0385] An example prompt is:

[0386] "Translate user-submitted text into other languages. Original text: 'I had so much fun today!'"

[0387] "Analyze the sentiment of a user's post and return the result. Original text: 'Today was so much fun!'"

[0388] In this way, the server, terminal, and user work together to achieve communication and emotional sharing that transcends language barriers.

[0389] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0390] Step 1:

[0391] A user posts text to a social networking site.

[0392] A user accesses an SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system. The input is the text entered by the user, and the output is the text data sent to the server.

[0393] Step 2:

[0394] The server receives the posted text.

[0395] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The input is the text data sent by the user, and the output is the text data stored in the database.

[0396] Step 3:

[0397] The server uses a generative AI model to translate the text into other languages.

[0398] The server inputs the received text data into a generative AI model (for example, a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a great time today!" is translated into English as "I had a great time today!" The input is the received text data and the prompt, and the output is the translated text.

[0399] Step 4:

[0400] The server distributes the translated text to other users.

[0401] The server distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages. The input is the translated text, and the output is the text distributed to other users.

[0402] Step 5:

[0403] The server analyzes the sentiment of the text using an emotion engine.

[0404] The server inputs the received text data into an emotion engine (for example, a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The input is the received text data, and the output is the analyzed emotion data.

[0405] Step 6:

[0406] The server reflects the analysis results in the user's profile.

[0407] The server reflects the emotion results analyzed by the emotion engine in the user's profile, allowing other users to understand the user's emotional state. For example, a "joy" icon is displayed in the user's profile. The input is the analyzed emotion data, and the output is the emotion information reflected in the user's profile.

[0408] (Application example 1)

[0409] 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."

[0410] On modern social networking sites, communication between users is often limited by language barriers and differences in cultural backgrounds. It can also be difficult for other users to understand the sentiment behind a user's posts, which can lead to a decline in the quality of communication. Furthermore, the lack of real-time translation and sentiment analysis makes it difficult for users to deepen their connections.

[0411] The identification processing by the identification 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 means for utilizing a generative AI to provide a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for adding a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text posted by users in real time, means for analyzing emotions from the content posted by users and reflecting the results in the user's profile, and means for displaying the translated text and the results of the emotion analysis to other users. This enables communication across language barriers, deepens connections between users, and promotes the formation of communities that transcend borders and cultures.

[0412] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[0413] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0414] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[0415] "Incentives" are rewards or benefits offered to encourage user behavior.

[0416] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[0417] "Real-time" refers to the instantaneous processing of data and provision of information.

[0418] "Translation" is the conversion of text written in one language into another language.

[0419] "Sentiment analysis" is a technology that identifies and classifies user emotions from text data.

[0420] A "profile" is information that compiles a user's personal information and activity history.

[0421] A "community" is a group or group of users who share common interests or goals.

[0422] A system for implementing this invention has the following configuration: First, a user accesses an SNS using a device such as a smartphone or head-mounted display. Text data posted by the user is sent to a server.

[0423] The server uses a generative AI model to translate the posted text in real time, using, for example, the Google Translate API, and then converts the translated text into the user's language of choice.

[0424] The server then uses a sentiment analysis engine to analyze the sentiment of the posted text. For example, IBM Watson (registered trademark) is used for sentiment analysis. The results of the sentiment analysis (happiness, sadness, anger, surprise, etc.) are reflected in the user's profile.

[0425] The translated text and the results of the sentiment analysis are displayed to other users, who can see the translated text and the results of the sentiment analysis in real time.

[0426] As a concrete example, if user A posts "I had a great time today!", the server translates this text into English using the Google Translate API. The translation result is "I had a great time today!". Next, the server analyzes the sentiment of the text using IBM Watson's sentiment analysis engine. The sentiment analysis result is "joy." The translated text and the sentiment analysis result are displayed to other users.

[0427] Examples of prompts to input to a generative AI model include:

[0428] "Translate user-submitted text and analyze the sentiment. Text: 'Today was so much fun!'"

[0429] In this way, communication between users can be carried out smoothly across language barriers, making it easier to share emotions.

[0430] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0431] Step 1:

[0432] A user accesses an SNS using a device and posts text. The input is the text data entered by the user, and the output is the text data sent to the server.

[0433] Step 2:

[0434] The server inputs the received text data into the generative AI model and translates it in real time. The input is the text data posted by the user, and the output is the translated text data. Specifically, the server calls the Google Translate API to translate the text into the specified language.

[0435] Step 3:

[0436] The server inputs the translated text data into a sentiment analysis engine to perform sentiment analysis. The input is the translated text data, and the output is the result of the sentiment analysis. Specifically, the server uses IBM Watson's sentiment analysis engine to analyze the sentiment of the text.

[0437] Step 4:

[0438] The server reflects the results of the sentiment analysis in the user's profile. The input is the result of the sentiment analysis, and the output is the updated user's profile data. Specifically, the server updates the user's profile database and adds the results of the sentiment analysis.

[0439] Step 5:

[0440] The server displays the translated text and the results of sentiment analysis to other users. The input is the translated text data and the results of sentiment analysis, and the output is the information displayed on the other user's device. Specifically, the server sends the translated text and the results of sentiment analysis to the other user's device.

[0441] Step 6:

[0442] Other users use the device to check the translated text and the results of sentiment analysis. The input is the information sent from the server, and the output is the display data viewed by other users. Specifically, the device displays the information received from the server on the screen.

[0443] Example 2

[0444] 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."

[0445] In modern social networking services (SNS), misunderstandings and friction are likely to occur due to a lack of understanding of differences in cultural backgrounds and emotions between users. Communication between users with different languages ​​and cultures is also often hindered. Furthermore, there are a lack of ways for users to understand each other's cultural backgrounds and deepen empathy. This makes it difficult to form communities that transcend borders and cultures, limiting their contribution to world peace.

[0446] 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.

[0447] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be used in various languages; a means for understanding differences in cultural backgrounds among users; a means for inferring cultural backgrounds from text posted by users; a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct; and a means for analyzing emotions in communication between users and providing feedback. This allows users to deepen their understanding of each other's cultural backgrounds and emotions, enabling smooth communication between users with different languages ​​and cultures. Furthermore, by users understanding each other's cultural backgrounds and deepening empathy, the formation of communities that transcend borders and cultures can be promoted, contributing to world peace.

[0448] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[0449] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0450] "Means for understanding differences in cultural backgrounds among users" is a function that infers a user's cultural background from the text posted by the user and their behavior, and provides that information to other users.

[0451] The "means of inferring a user's cultural background from the text posted by the user" refers to an algorithm that uses natural language processing technology to analyze the content posted by the user and infer the user's cultural background.

[0452] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a function that allows users to guess the cultural background of other users and offers rewards or points if their guess is correct.

[0453] "Means for analyzing emotions in communication between users and providing feedback" refers to a function that analyzes users' reactions and comments, determines their emotions, and provides feedback to the users.

[0454] This invention utilizes generative AI to provide a social networking system that can be used in various languages. The system understands the differences in cultural backgrounds between users and includes a gamification feature that allows users to guess each other's origins and provides incentives for those who guess correctly. It also has a function that analyzes emotions in communication between users and provides feedback.

[0455] Hardware and software used

[0456] The server receives user posts and reaction data and stores them in a database. The server provides the computational resources to run a natural language processing (NLP) engine and a sentiment analysis engine. Specifically, it uses generative AI models such as Google's BERT and OpenAI's GPT-3 (registered trademark).

[0457] A device is a device that users use to access SNS and post or react to posts. Devices include smartphones, tablets, and PCs.

[0458] Users participate in social networking sites, post text, and react to other users' posts.

[0459] Specific operation of the system

[0460] Cultural background guessing function

[0461] A user posts, "I eat rice cakes on New Year's Day."

[0462] The server receives this post and stores it in a database.

[0463] The server sends the stored text data to the NLP engine.

[0464] The NLP engine extracts keywords such as "New Year's" and "mochi."

[0465] Based on the extracted keywords, the server infers that the user is likely to have a Japanese cultural background.

[0466] The server notifies the user, "From your posts, we infer that you are likely to have a Japanese cultural background."

[0467] Emotion analysis using an emotion engine

[0468] A user "likes" another user's post.

[0469] The server receives this reaction and stores it in a database.

[0470] The server transmits the stored reaction data to the emotion engine.

[0471] The emotion engine analyzes that the reaction of "like" is based on "joy" and "empathy."

[0472] The server informs the user, "This reaction is based on joy."

[0473] Prompt Sentence Examples

[0474] Cultural background speculation

[0475] Prompt: "Infer the user's cultural background from the text they post. For example, if the user posts, 'I eat mochi on New Year's Day,' infer that they are likely from a Japanese cultural background."

[0476] Emotion analysis

[0477] Prompt: "Analyze the emotions behind users' reactions to other users' posts. For example, analyze whether the reaction 'Like' is based on 'Joy' or 'Empathy'."

[0478] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0479] Cultural background guessing function

[0480] Step 1:

[0481] A user posts text to an SNS, for example, "I eat rice cakes on New Year's Day."

[0482] Input: Text data entered by the user.

[0483] Output: The text data sent to the server.

[0484] Step 2:

[0485] The server receives the text data posted by the user and stores it in a database.

[0486] Input: Text data submitted by the user.

[0487] Output: Text data stored in a database.

[0488] Step 3:

[0489] The server sends the stored text data to a natural language processing (NLP) engine, which uses generative AI models such as Google's BERT or OpenAI's GPT-3.

[0490] Input: Text data stored in a database.

[0491] Output: The text data sent to the NLP engine.

[0492] Step 4:

[0493] The NLP engine analyzes text data and extracts specific keywords and phrases, such as "New Year's" and "rice cakes."

[0494] Input: Text data sent to the NLP engine.

[0495] Output: Extracted keywords and phrases.

[0496] Step 5:

[0497] The server runs an algorithm to infer the user's cultural background based on the extracted keywords. For example, keywords like "New Year's" and "mochi" may suggest that the user is likely to have a Japanese cultural background.

[0498] Input: Extracted keywords or phrases.

[0499] Output: Inferred cultural background information.

[0500] Step 6:

[0501] The server then provides feedback to the user based on the results of its predictions, such as displaying a message like, "Based on your posts, we have determined that you are likely to have a Japanese cultural background."

[0502] Input: Inferred cultural background information.

[0503] Output: The feedback message that is displayed to the user.

[0504] Emotion analysis using an emotion engine

[0505] Step 1:

[0506] Users react to other users' posts (e.g., "like" or comment).

[0507] Input: User reaction data.

[0508] Output: The reaction data sent to the server.

[0509] Step 2:

[0510] The server receives the reaction data from the user and stores it in a database.

[0511] Input: Reaction data sent by the user.

[0512] Output: Reaction data stored in a database.

[0513] Step 3:

[0514] The server sends the stored reaction data to an emotion engine, which also uses generative AI models such as Google's BERT or OpenAI's GPT-3.

[0515] Input: Reaction data stored in the database.

[0516] Output: Reaction data sent to the emotion engine.

[0517] Step 4:

[0518] The emotion engine analyzes the reaction data and determines what emotion the reaction is based on. For example, it analyzes that a "like" reaction is based on "joy" or "empathy."

[0519] Input: Reaction data sent to the emotion engine.

[0520] Output: Determined emotion information.

[0521] Step 5:

[0522] The server uses the analysis results of the emotion engine to provide feedback to deepen understanding of emotions between users, for example, displaying a message such as "This reaction is based on joy."

[0523] Input: Determined emotion information.

[0524] Output: The feedback message that is displayed to the user.

[0525] (Application example 2)

[0526] 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."

[0527] In modern social networking sites, it is difficult to understand the differences in cultural backgrounds and feelings between users, which often leads to misunderstandings and friction. Furthermore, there is a lack of means for users with different cultural backgrounds to communicate smoothly. Furthermore, there is a problem of a decline in the quality of communication because feedback is not provided to users to help them understand the cultural backgrounds and feelings of other users.

[0528] 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.

[0529] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries; a means for understanding differences in cultural backgrounds between users; a means for providing a game element in which users guess each other's origins and are given incentives if they are correct; a means for guessing cultural backgrounds from text posted by users; a means for analyzing emotions based on user reactions; and a means for providing feedback to deepen understanding of emotions between users based on the analysis results. This makes it possible to understand differences in cultural backgrounds and emotions between users and promote smooth communication.

[0530] "Generative AI" is an artificial intelligence technology that generates new information and content based on user input data.

[0531] An "SNS that can be accessed in various languages" is a social networking service that supports multiple languages ​​and allows users who speak different languages ​​to participate.

[0532] "Means for understanding differences in cultural backgrounds among users" refers to technology that infers a user's cultural background from their posts and behavior and provides that information to other users.

[0533] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a system in which users guess the cultural background of other users and are offered rewards or benefits if their guess is correct.

[0534] "Means for inferring cultural background from text posted by a user" refers to a technology that analyzes text data posted by a user and infers the user's cultural background.

[0535] "Means for analyzing emotions based on user reactions" refers to technology that analyzes reactions (e.g., "likes" and comments) made by users to posts by other users and identifies emotions based on those reactions.

[0536] "Means for providing feedback to deepen understanding of emotions among users based on analysis results" refers to technology that provides users with information and advice to help them understand the emotions of other users based on the results of emotion analysis.

[0537] A system for carrying out this invention has the following configuration: The server utilizes a generative AI and includes means for providing a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for providing a game element in which users guess each other's origins and are offered incentives if they are correct, means for guessing cultural backgrounds from text posted by users, means for analyzing emotions based on user reactions, and means for providing feedback to deepen understanding of emotions between users based on the analysis results.

[0538] Explaining the program's processing in natural language

[0539] The server uses software such as Python, nltk, TextBlob, and langdetect to analyze the text posted by users. Specifically, it receives the text data posted by users as input, first detects the language, then checks whether certain keywords are included, and based on the results, infers the user's cultural background.

[0540] Furthermore, the TextBlob library is used to analyze the sentiment of the text and classify it as positive, negative, or neutral, and the analysis results are provided as feedback to users to help them understand the sentiment of other users.

[0541] Specific examples

[0542] For example, if a user posts, "I'm looking forward to eating rice cakes for New Year's!", the server will process it as follows:

[0543] 1. Receive the post text.

[0544] 2. Detect the language and confirm that it is Japanese.

[0545] 3. Detect keywords such as "New Year's" and "mochi" in the text and infer the user's cultural background as "Japan."

[0546] 4. Use the TextBlob library to analyze the sentiment of the text and determine it as positive.

[0547] 5. Based on the analysis results, provide feedback to other users such as "This user has a Japanese cultural background and positive feelings."

[0548] Prompt Sentence Examples

[0549] An example of a prompt sentence to input to the generative AI model is as follows:

[0550] Analyze the cultural context and sentiment of user-submitted text. Analyze the following text:

[0551] "I'm looking forward to eating mochi on New Year's!"

[0552] This prompt can be used to ask a generative AI model to analyze the text.

[0553] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0554] Step 1:

[0555] The user enters the post text.

[0556] Users post text through a social networking application, and this text becomes input data for the system.

[0557] Step 2:

[0558] The server receives the posted text.

[0559] The server receives the text data sent by the user and prepares it for the next processing step.

[0560] Step 3:

[0561] The server detects the language of the text.

[0562] The server uses the langdetect library to detect the language of the received text, for example, to determine whether the text is Japanese or English. The input is the user-submitted text, and the output is the detected language.

[0563] Step 4:

[0564] The server detects keywords in the text.

[0565] The server extracts keywords that indicate a specific cultural background (e.g., "New Year's Day," "mochi," etc.) from the text. The input is the user's posted text, and the output is the detected keywords.

[0566] Step 5:

[0567] The server infers the user's cultural background.

[0568] The server infers the user's cultural background based on the detected language and keywords. For example, if the language is Japanese and the keywords "New Year" and "mochi" are included, the server infers the user's cultural background as "Japan." The input is the detected language and keywords, and the output is the inferred cultural background.

[0569] Step 6:

[0570] The server analyzes the sentiment of the text.

[0571] The server uses the TextBlob library to analyze the sentiment of the posted text. Sentiment is classified as positive, negative, or neutral. The input is the user's posted text, and the output is the analyzed sentiment.

[0572] Step 7:

[0573] The server generates feedback based on the analysis results.

[0574] The server generates feedback for other users based on the inferred cultural background and analyzed emotions. For example, it provides feedback such as "This user has a Japanese cultural background and positive emotions." The input is the inferred cultural background and analyzed emotions, and the output is the generated feedback.

[0575] Step 8:

[0576] The server provides feedback to the user.

[0577] The server provides the generated feedback to other users to enhance sentiment understanding between users. The input is the generated feedback, and the output is the feedback provided to the users.

[0578] Example 3

[0579] Next, a description will be given of a third embodiment of the third embodiment. 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."

[0580] In modern social networking sites, it is difficult to understand and respect the differences in cultural backgrounds between users. Users also rarely express their feelings about other users' posts, which can lead to a lack of active communication. Furthermore, due to language barriers, communication between users who speak different languages ​​often does not proceed smoothly.

[0581] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for utilizing a generation AI to provide an SNS that can be participated in in various languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and provide incentives if they are correct, a means for analyzing the emotions in the text posted by users and providing incentives based on the results, and a means for providing incentives if a user reacts to another user's post showing joy or sympathy. This encourages users to understand and respect other cultures, activates communication on the SNS, and enables smooth communication across language barriers.

[0582] "Generative AI" is an artificial intelligence technology that uses natural language processing and machine learning to generate text and data.

[0583] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0584] "User" refers to an individual or organization that uses SNS.

[0585] "Cultural background" refers to information related to a user's culture, such as their country of origin, ethnicity, religion, and language.

[0586] "Incentives" refer to rewards or benefits offered to encourage user behavior.

[0587] "Emotion analysis" is a technology that identifies emotions from text data and evaluates the type and intensity of those emotions.

[0588] "Reactions" refer to comments or emotional expressions made by users in response to other users' posts.

[0589] "Points" refers to numerical rewards given to users.

[0590] A "badge" is a digital insignia that denotes a user's specific actions or achievements.

[0591] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[0592] "Emotion engine" refers to software or algorithms for analyzing the emotions in text data.

[0593] This invention is a gamified SNS system that allows users to guess the cultural backgrounds of other users and provides incentives for those who correctly guess. It also uses an emotion engine to analyze the emotions in users' posted text and provides incentives for those who react to other users' posts with joy or sympathy.

[0594] Hardware and software used

[0595] Server: Database management system (e.g., MySQL), sentiment analysis engine (e.g., IBM Watson Natural Language Understanding)

[0596] Device: The smartphone or PC used by the user

[0597] Generative AI models: Natural language generation models such as GPT-4

[0598] Specific operation of the system

[0599] 1. Guessing the User's Cultural Background

[0600] Users view other users' profiles

[0601] Users open the application and visit other users' profile pages, which display information such as their name, photo, and bio.

[0602] Users infer cultural background

[0603] Based on the displayed information, users can guess the cultural background of other users (e.g., country of origin, ethnicity, religion, etc.) and enter their guesses into the input form.

[0604] The device sends the guess results to the server

[0605] When the user inputs their guess and presses the send button, the device sends the data to the server, which includes the guess and the ID of the user who made the guess.

[0606] The server saves the guess results in a database and determines whether they are correct.

[0607] The server stores the received data in a database, and checks it against the correct data in the database to determine whether the guess is correct.

[0608] The server gives an incentive if the answer is correct.

[0609] If the guess is correct, the server will award the user points or badges, and the incentive information will be updated in the database and reflected in the user's account.

[0610] 2. Emotion analysis using an emotion engine

[0611] Users react to other users' posts

[0612] Users can view other users' posts and comment or react to them, which includes emotional text such as "like" or "agree."

[0613] The device sends the reaction text to the server

[0614] When a user sends a reaction, the device sends the text data to the server, which includes the text of the reaction and the ID of the user who sent the reaction.

[0615] The server uses an emotion analysis engine to analyze the emotions of the reactions.

[0616] The server sends the received reaction text to a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding), which analyzes the sentiment of the text and returns the results to the server.

[0617] The server detects emotions such as joy and empathy

[0618] The server receives the analysis results and checks whether the reactions contain emotions such as joy or empathy. If emotions such as joy or empathy are detected, the information is stored in a database.

[0619] The server provides incentives

[0620] If emotions of joy or empathy are detected, the server will award points or badges to the user, and the incentive information will be updated in the database and reflected in the user's account.

[0621] Specific examples

[0622] 1. A specific example of the cultural background guessing game

[0623] Users view other users' profiles

[0624] Example: User A opens the app and visits the profile page of User B. The profile displays "Name: Taro" and "Self-introduction: I love Japanese culture."

[0625] Users infer cultural background

[0626] Example: User A guesses that "User B is from Japan" and enters "Japan" into the input form.

[0627] The device sends the guess results to the server

[0628] Example: When User A presses the send button, the device sends "User ID: A" and "Guessed result: Japan" to the server.

[0629] The server saves the guess results in a database and determines whether they are correct.

[0630] Example: The server checks its database and verifies that User B is in fact from Japan.

[0631] The server gives an incentive if the answer is correct.

[0632] Example: The server awards 10 points to user A and updates the database.

[0633] 2. Specific examples of emotion analysis using emotion engines

[0634] Users react to other users' posts

[0635] Example: User C comments on User D's post "Today was a great day!" with "Great post!"

[0636] The device sends the reaction text to the server

[0637] Example: When user C submits a comment, the device sends "User ID: C" and "Comment: Great post!" to the server.

[0638] The server uses an emotion analysis engine to analyze the emotions of the reactions.

[0639] Example: The server sends comments to a sentiment analysis engine and receives the analysis results.

[0640] The server detects emotions such as joy and empathy

[0641] Example: A sentiment analysis engine detects the emotion "joy" in a comment and returns the result to the server.

[0642] The server provides incentives

[0643] Example: The server awards 5 points to user C and updates the database.

[0644] Example prompts for generative AI models

[0645] Prompt: "Describe a system that allows users to guess the cultural backgrounds of other users and offers incentives if they guess correctly."

[0646] In this way, a system is constructed that encourages users to understand and respect other cultures and activates communication on SNS. The flow of the identification process in the third embodiment will be described with reference to FIG.

[0647] Step 1:

[0648] Users view other users' profiles.

[0649] What it does: A user opens the app and visits another user's profile page, which displays information such as their name, photo, and bio.

[0650] Input: A user action requesting a profile page.

[0651] Output: Display of profile page.

[0652] Step 2:

[0653] The user infers cultural background.

[0654] Specific behavior: Based on the displayed information, the user guesses the cultural background of the other user (e.g., country of origin, ethnicity, religion, etc.) and enters the guess into the input form.

[0655] Input: The information shown on your profile page.

[0656] Output: The input data for the prediction results.

[0657] Step 3:

[0658] The device sends the guess result to the server.

[0659] Specific operation: When the user inputs the guess and presses the send button, the device sends the data to the server, including the guess and the ID of the user who made the guess.

[0660] Input: Input data for the prediction result.

[0661] Output: Sending data to the server.

[0662] Step 4:

[0663] The server stores the guess results in a database and determines whether they are correct.

[0664] Specific operation: The server stores the received data in a database. The server compares it with the correct data in the database and determines whether the guess is correct.

[0665] Input: Submitted guess result data.

[0666] Output: Correct answer result.

[0667] Step 5:

[0668] If the server answers correctly, an incentive is awarded.

[0669] What happens: If the guess is correct, the server will award the user points and badges, and the incentive information will be updated in the database and reflected in the user's account.

[0670] Input: Correct answer result.

[0671] Output: Incentives awarded and database updated.

[0672] Step 6:

[0673] Users react to other users' posts.

[0674] What it does: Users view other users' posts and comment or react to them. Reactions include emotional text such as "like" or "agree."

[0675] Input: Reactions to the post.

[0676] Output: Reaction text data.

[0677] Step 7:

[0678] The device sends the reaction text to the server.

[0679] What it does: When a user posts a reaction, the device sends the text data to the server, including the text of the reaction and the ID of the user who posted the reaction.

[0680] Input: Reaction text data.

[0681] Output: Sending data to the server.

[0682] Step 8:

[0683] The server analyzes the emotions of the reactions using an emotion analysis engine.

[0684] Specific operation: The server sends the received reaction text to a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding), which analyzes the sentiment of the text and returns the results to the server.

[0685] Input: Reaction text data.

[0686] Output: Emotion analysis results.

[0687] Step 9:

[0688] The server detects emotions such as joy and empathy.

[0689] Specific operation: The server receives the analysis results and checks whether the reaction contains emotions such as joy or empathy. If emotions such as joy or empathy are detected, the information is stored in a database.

[0690] Input: Sentiment analysis results.

[0691] Output: Emotion detection results stored in a database.

[0692] Step 10:

[0693] The server provides the incentives.

[0694] Specific behavior: If emotions of joy or empathy are detected, the server will award points or badges to the user. The incentive information will be updated in the database and reflected in the user's account.

[0695] Input: Emotion detection results.

[0696] Output: Incentives awarded and database updated.

[0697] (Application example 3)

[0698] 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."

[0699] On modern social media, users have limited opportunities to understand and respect other cultures. Furthermore, users' emotional expressions are not fully appreciated, which hinders communication. Furthermore, there is a lack of content for learning about other cultures, which leaves users with little motivation to learn about them.

[0700] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[0701] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for providing a game element in which users guess each other's origins and are offered incentives if they are correct, means for analyzing the sentiment of users' posted text and offering incentives if they respond positively, means for displaying rankings based on points and badges earned by users, and means for providing content for learning about different cultures. This encourages users to understand and respect other cultures, activates communication on the social networking service, and strengthens motivation to learn about different cultures.

[0702] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide appropriate responses and content based on user input.

[0703] "SNS" stands for Social Networking Service, a platform that allows users to communicate with other users online.

[0704] "Cultural background" is a general term for the culture, customs, language, history, etc. of the country or region to which the user belongs.

[0705] "Incentives" are rewards or benefits offered to encourage user behavior, such as points or badges.

[0706] "Gamification" is a characteristic of a system that allows users to participate in a fun way, providing a sense of competition and accomplishment.

[0707] "Sentiment analysis" is a technology that automatically analyzes emotions from text posted by users and determines the type and intensity of those emotions.

[0708] A "positive reaction" is a user's reaction to another user's post that shows joy or sympathy.

[0709] "Ranking" is a system that displays users' rankings based on the points and badges they have earned.

[0710] "Intercultural learning content" refers to information and educational materials provided to users to deepen their understanding of other cultures.

[0711] The following system configuration will be described as an embodiment of the present invention.

[0712] System Configuration

[0713] This system includes a server, a user device, and a generative AI model. The server provides a social networking service that can be used in various languages ​​and manages data to understand differences in cultural backgrounds among users. The user device is a device such as a smartphone or PC, and provides an interface for users to access the social networking service and post and react to messages. The generative AI model is used to analyze the text posted by users and determine their emotions.

[0714] Program processing

[0715] The server executes a program that includes the following means:

[0716] 1. SNS provision method:

[0717] The server provides a social networking site that can be used in various languages. Users can register their own cultural background and communicate with other users.

[0718] 2. Cultural background understanding tools:

[0719] The server manages the cultural background data registered by users and provides information that allows other users to guess their background.

[0720] 3. Incentive delivery methods:

[0721] The server provides incentives such as points and badges to users who correctly guess the cultural backgrounds of other users or who give positive reactions.

[0722] 4. Emotion analysis means:

[0723] The server analyzes the user's posted text using a generative AI model to determine the sentiment, and if a positive sentiment is detected, provides an incentive to the user.

[0724] 5. Ranking display method:

[0725] The server displays a ranking based on the points and badges that users have earned, allowing users to track their progress and compete with other users.

[0726] 6. Means of providing intercultural learning content:

[0727] The server provides content for users to learn about different cultures, thereby enabling users to deepen their understanding of other cultures.

[0728] Hardware and software used

[0729] Hardware: smartphones, PCs, servers

[0730] Software: Python, TextBlob (sentiment analysis library)

[0731] Specific examples

[0732] For example, if a user correctly guesses "user1"'s cultural background as "Japanese," they will receive 10 points and the "Culture Expert" badge. If "user2" posts a positive comment like "I love this app!", they will receive 5 points and the "Positive Vibes" badge.

[0733] Prompt Sentence Examples

[0734] "Create a system where users can guess the cultural backgrounds of other users and reward them with points or badges if they guess correctly. Also, add a feature that analyzes the text of users' posts and rewards them for positive reactions."

[0735] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[0736] Step 1:

[0737] A user accesses an SNS using a device and creates an account.

[0738] Input: User's basic information (name, email address, password)

[0739] Output: User account creation completion notification

[0740] Specific operation: The device enters the user's basic information into an input form and sends it to the server. The server stores the received information in a database and returns a notification to the device that account creation is complete.

[0741] Step 2:

[0742] Users register their cultural background.

[0743] Input: User's cultural background information (nationality, language, cultural characteristics)

[0744] Output: Notification of completion of cultural background information registration

[0745] Specific operation: The terminal enters the user's cultural background information into an input form and sends it to the server. The server stores the received information in a database and returns a registration completion notice to the terminal.

[0746] Step 3:

[0747] Users infer the cultural background of other users.

[0748] Input: User ID to be guessed, guessed cultural background

[0749] Output: Guessing result (correct / incorrect), incentives (points, badges)

[0750] Specific operation: The device sends the user ID to be guessed and the guessed cultural background to the server. The server retrieves the actual cultural background from the database and judges the guess result. If the answer is correct, points and badges are awarded to the user and the result is returned to the device.

[0751] Step 4:

[0752] The user enters the post text and performs sentiment analysis.

[0753] Input: Post text

[0754] Output: Sentiment analysis results (positive / negative / neutral), incentives (points, badges)

[0755] Specific operation: The device sends the user's posted text to the server. The server uses a generative AI model (TextBlob) to analyze the text's sentiment and determines the result. If a positive sentiment is detected, points or badges are awarded to the user and the result is returned to the device.

[0756] Step 5:

[0757] The server displays rankings based on the points and badges that users have earned.

[0758] Input: User points and badge information

[0759] Output: Ranking list

[0760] Specific operation: The server retrieves all users' points and badge information from the database, generates a ranking, and sends the generated ranking list to the terminal for display.

[0761] Step 6:

[0762] The server provides content for learning about different cultures.

[0763] Input: User request (type of culture you want to learn)

[0764] Output: Intercultural learning content

[0765] Specific operations: The terminal sends the user's request to the server. The server retrieves the relevant intercultural learning content from the database and sends it to the terminal. The terminal displays the received content to the user.

[0766] 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.

[0767] 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.

[0768] Another example of generative AI is Gemini (registered trademark) (Internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[0769] 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.

[0770] [Second embodiment]

[0771] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0772] 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.

[0773] 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).

[0774] 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.

[0775] 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.

[0776] 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).

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[0783] "Example 1"

[0784] One embodiment of the present invention is a social networking system that utilizes generative AI. This system allows users to participate in the system in their own languages ​​and supports communication between users. The generative AI analyzes text posted by users and translates that text into other languages, enabling communication between users across language barriers.

[0785] "Example 2"

[0786] Furthermore, this system provides a means to understand differences in cultural backgrounds among users. Specifically, it has the ability to infer a user's cultural background from the text they post. For example, if a user posts, "I eat mochi on New Year's Day," the system will infer that the user is likely to have a Japanese cultural background.

[0787] "Example 3"

[0788] The system also features a gamification element, where users are asked to guess each other's origins and are given incentives if they are correct. Specifically, if a user correctly guesses another user's cultural background, they are given incentives such as points or badges. This encourages users to understand and respect other cultures.

[0789] The processing flow of each embodiment will be described below.

[0790] "Example 1"

[0791] Step 1: Users post on social media in their own language.

[0792] Step 2: The generative AI analyzes the posted text.

[0793] Step 3: The generative AI translates the analyzed text into other languages.

[0794] Step 4: The translated text will be displayed to other users and you can start communicating with them.

[0795] "Example 2"

[0796] Step 1: A user posts on social media.

[0797] Step 2: The system infers the user's cultural background from the posted text.

[0798] Step 3: The guesses are reflected in the user's profile and are visible to other users.

[0799] "Example 3"

[0800] Step 1: Users participate in a game where they have to guess the cultural backgrounds of other users.

[0801] Step 2: Users submit their guesses.

[0802] Step 3: The system determines whether the guess is correct or incorrect.

[0803] Step 4: If the guess is correct, provide the user with an incentive (e.g., points or a badge).

[0804] Example 1

[0805] 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."

[0806] In today's social networking sites, there is a language barrier when communicating between users who speak different languages, making it difficult to have smooth interactions. It is also difficult for users to understand the differences in their cultural backgrounds, making it difficult to form communities that transcend national borders and cultures. Furthermore, there is a lack of ways to make communication more fun by adding a game element to the experience, such as users guessing each other's origins.

[0807] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, a means for analyzing text posted by users and translating the text into other languages, and a means for sending the translated text to other users' devices. This enables smooth communication between users who speak different languages, promotes mutual understanding of differences in cultural backgrounds, and promotes the formation of communities that transcend borders and cultures. Furthermore, the gamification element makes interactions between users more enjoyable.

[0808] "Generative AI" is an artificial intelligence technology that analyzes text posted by users and understands its content.

[0809] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[0810] "User" means an individual or organization that uses SNS to post text and communicate with other users.

[0811] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[0812] "Roots" refers to the user's origins, such as place of birth, ethnicity, and cultural background.

[0813] An "incentive" is a reward or benefit that encourages users to take a particular action.

[0814] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[0815] "Text" refers to the written information that users post on social media.

[0816] "Translation" is the process of converting text written in one language into another.

[0817] A "terminal" is a device (smartphone, PC, etc.) that a user uses to access an SNS.

[0818] A "server" is a computer system used to operate a social networking site and process data.

[0819] This invention utilizes generative AI to provide a social networking system that allows participation in various languages. The system aims to support communication between users and overcome language barriers. Specific embodiments of this system are described below.

[0820] Hardware and Software Configuration

[0821] server

[0822] The server is the central computer system of the SNS system. The server is built using a virtual machine from a cloud service provider. Specifically, it uses a virtual machine instance from a common cloud service provider. The server includes the following main software components:

[0823] Generative AI models: Use popular generative AI models (e.g., GPT-4) to analyze user-submitted text.

[0824] Translation API: Use a common translation API (e.g., Cloud Translation API) to translate text into other languages.

[0825] Terminal

[0826] A terminal is a device that a user uses to access an SNS. Terminals include smartphones, tablets, PCs, etc. Terminals are connected to a server via the Internet.

[0827] User

[0828] A user is an individual or an organization that uses the SNS system to post text and communicate with other users.

[0829] System Operation

[0830] User Submissions

[0831] A user posts text to an SNS using a terminal. For example, the user posts, "It's nice weather today."

[0832] Sending a text

[0833] The device sends the posted text to the server, along with metadata such as the user ID and posting time.

[0834] Text Analysis

[0835] The server sends the received text to a generative AI model, which analyzes the content of the text. For example, the text "It's a nice day today" is analyzed as a "positive comment about the weather."

[0836] Executing the translation

[0837] Based on the analysis results, the server uses a translation API to translate the text into another language, for example, translating the Japanese phrase "Kyo wa ii tenki desu ne" (It's a nice day today) into English.

[0838] Sending translation results

[0839] The server sends the translated text to the other user's device, for example, an English-speaking user's device might display "It's a nice day today."

[0840] Specific examples

[0841] User A: Posts in Japanese, "It's a nice day today."

[0842] Server: Receives posts and analyzes them with a generative AI model.

[0843] Server: Use the translation API to translate "It's a nice day today" into English.

[0844] Server: Sends the translation result "It's a nice day today" to User B, who speaks English.

[0845] User B: Receives and understands "It's a nice day today" in English.

[0846] Prompt Sentence Examples

[0847] Prompt: If a user posts "It's a nice day today," translate this into English.

[0848] In this way, by utilizing generative AI and translation APIs, smooth communication between users who speak different languages ​​is achieved.

[0849] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0850] Step 1:

[0851] A user posts text to a social networking site using a device. Specifically, the user opens the social networking site app, enters "It's a nice day today" in the text input field, and presses the post button. The input is the text data entered by the user, and the output is the text data sent from the device to the server.

[0852] Step 2:

[0853] The device sends the posted text to the server. Specifically, the device sends the text data entered by the user to the server via the Internet. The transmitted data also includes metadata such as the user ID and posting time. The input is the text data entered by the user, and the output is the text data sent to the server.

[0854] Step 3:

[0855] The server sends the received text to the generative AI model, which analyzes the text content. Specifically, the server sends the received text data to the API endpoint of the generative AI model and receives the analysis results. The input is the text data received by the server, and the output is the analysis results returned by the generative AI model.

[0856] Step 4:

[0857] The server uses the translation API to translate the text into another language based on the analysis results. Specifically, the server sends a request to the translation API to translate "It's a nice day today" based on the analysis results. The input is the analysis result from the generative AI model, and the output is the translation result returned by the translation API.

[0858] Step 5:

[0859] The server sends the translated text to the other user's device. Specifically, the server receives the translation result and sends "It's a nice day today" to the English-speaking user's device. The sent data also includes metadata such as the original text and translation time. The input is the translation result from the translation API, and the output is the translated text sent to the other user's device.

[0860] (Application example 1)

[0861] 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."

[0862] In today's global society, it is difficult for users who speak different languages ​​to communicate in real time. In particular, in social networking sites and chat applications, language barriers are a major factor hindering interaction between users. Differences in cultural backgrounds can also be an obstacle to communication. There is a need to solve these problems, deepen connections between users, and form communities that transcend borders and cultures.

[0863] 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.

[0864] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text entered by users into other languages ​​in real time, means for displaying the translated text to other users, and means for supporting real-time communication between users who speak different languages. This enables smooth communication that transcends language barriers, deepens connections between users, and enables the formation of a community that transcends borders and cultures.

[0865] "Generative AI" is an artificial intelligence technology that analyzes text entered by a user and translates it into other languages.

[0866] "SNS" stands for Social Networking Service, a platform for users to communicate online.

[0867] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[0868] "Roots" refers to the user's place of origin or ethnic background.

[0869] An "incentive" is a reward or benefit that encourages users to take a particular action.

[0870] "Real-time" means that information entered by a user is processed immediately and displayed to other users almost simultaneously.

[0871] "Translation" is the process of converting text written in one language into another.

[0872] "Communication" is the act of exchanging information and emotions between users.

[0873] "Connection" refers to the relationships and bonds between users.

[0874] A "community" is a group or group of users who share common interests or goals.

[0875] As an embodiment of the present invention, the following system can be constructed.

[0876] First, the server uses generative AI to provide a social networking site that can be used in various languages. Users access the site from their smartphones, computers, or other devices and enter text. The entered text is sent to the server, where it is translated into other languages ​​in real time by the generative AI model. The translated text is then displayed to other users.

[0877] The system uses the following hardware and software:

[0878] Server: A cloud server, such as an AWS EC2 instance

[0879] Client: Smartphone (iOS or Android), PC

[0880] Software: Flask (a lightweight Python web framework), Transformers (a library of generative AI models provided by Hugging Face), MarianMTModel (Hugging Face's translation model)

[0881] The server receives the text entered by the user and translates it using a generative AI model. Specifically, the text entered by the user is tokenized and input into the generative AI model. The model translates the text and detokenizes the result back into text. The translated text is then displayed to other users.

[0882] For example, if User A (an English speaker) types "Hello, how are you?", the app sends this text to the server, where the generative AI model translates it to "Hello, how are you?" The translated text is then displayed to User B (a German speaker).

[0883] An example of a prompt is as follows:

[0884] "Translate the following text from English to German: "Hello, how are you?"

[0885] In this way, a system can be realized that allows users who speak different languages ​​to communicate smoothly in real time.

[0886] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0887] Step 1:

[0888] A user accesses an SNS application from a device and inputs text. The input text is sent to a server along with the user's language. The input data consists of the text and language input by the user.

[0889] Step 2:

[0890] The server analyzes the received text data and tokenizes it for input to the generative AI model. Specifically, it breaks the text down into words and phrases and converts them into a format that the model can understand. The input data is the text entered by the user, and the output data is the tokenized text.

[0891] Step 3:

[0892] The server inputs the tokenized text into the generative AI model for translation. The generative AI model translates the input text into the specified target language. The input data is the tokenized text, and the output data is the translated tokenized text.

[0893] Step 4:

[0894] The server detokenizes the translated tokenized text and returns it to its original text form. Specifically, it combines words and phrases to reconstruct natural-sounding sentences. The input data is the translated tokenized text, and the output data is the translated text.

[0895] Step 5:

[0896] The server sends the translated text to the original user and the target user. The target user receives and displays the translated text on their device. The input data is the translated text, and the output data is the text displayed on the target user's device.

[0897] Step 6:

[0898] The target user checks the translated text and replies if necessary. The returned text is sent back to the server, where it goes through the same processing steps and is then displayed to the original user. The input data is the text entered by the target user, and the output data is the text displayed on the original user's device.

[0899] In this way, a system is realized that allows users who speak different languages ​​to communicate smoothly in real time.

[0900] Example 2

[0901] 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."

[0902] In today's global society, it is important to facilitate communication between users with different cultural backgrounds. However, language barriers and cultural differences can be obstacles that make it difficult to deepen understanding and connections between users. Furthermore, a lack of tools for users to understand each other's cultural backgrounds can lead to misunderstandings and friction. Furthermore, there is a need for a game-like feature that allows users to guess each other's origins, allowing them to deepen cultural understanding while having fun.

[0903] 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.

[0904] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be used in the languages ​​of various countries; a means for understanding differences in cultural backgrounds between users; a means for providing a game element in which users guess each other's origins and are given incentives if they are correct; a means for analyzing text posted by users and guessing their cultural background; and a means for guessing the user's cultural background based on the analysis results and returning the result to the user. This makes it possible to facilitate communication between users with different cultural backgrounds and deepen cultural understanding.

[0905] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[0906] "SNS" is an abbreviation for Social Networking Service, an online platform for users to communicate over the Internet.

[0907] "Cultural background" refers to the totality of customs, values, traditions, etc. of the culture or society to which the user belongs.

[0908] "Analyzing text" means analyzing text data posted by users using natural language processing technology to understand its content.

[0909] An "incentive" is a reward or benefit offered to encourage users to take a particular action.

[0910] "Gameplay" refers to elements and mechanisms designed to allow users to participate in a fun way.

[0911] A "prompt" is an instruction given to a generative AI model, and includes instructions for specific analysis or generation.

[0912] "Analysis results" refers to the information or data obtained after a generative AI model analyzes text.

[0913] This invention is a system for understanding differences in cultural backgrounds between users and facilitating smooth communication. A specific embodiment of this system will be described below.

[0914] First, a user posts text to the system using their own device (smartphone, PC, etc.). For example, the user might type, "I eat rice cakes on New Year's Day."

[0915] The device then transmits the text entered by the user to the server, using an Internet connection to transmit the data, which is received by the server and stored in a database.

[0916] The server uses natural language processing (NLP) techniques to analyze the received text data. Specifically, it uses a generative AI model (e.g., GPT-4) to understand the content of the text. For analysis, it uses prompts such as:

[0917] "Infer the user's cultural background from this text"

[0918] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" and "rice cakes"). For example, if a user posts "I eat rice cakes on New Year's Day," the server detects keywords such as "New Year's Day" and "rice cakes" and determines that this is related to Japanese culture.

[0919] Finally, the server returns the prediction results to the user. The prediction results are packaged in JSON format and sent to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background."

[0920] This system will facilitate communication between users with different cultural backgrounds and deepen cultural understanding. In addition, by adding a game element to the system, users can guess each other's origins, promoting cultural understanding while having fun.

[0921] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0922] Step 1:

[0923] User posts text

[0924] A user uses their own device (smartphone, PC, etc.) to enter text into the system and click the send button. For example, they might enter "I eat rice cakes on New Year's Day." The input data is in text format.

[0925] Step 2:

[0926] The device sends the text to the server

[0927] The terminal packages the text entered by the user in JSON format and sends it to the server using an HTTP POST request: the input is the user's text data, and the output is an HTTP request to the server.

[0928] Step 3:

[0929] The server receives the text

[0930] The server receives the HTTP request sent from the terminal, extracts the text data, and stores it in a database. The input is the HTTP request, and the output is the text data stored in the database.

[0931] Step 4:

[0932] The server parses the text

[0933] The server inputs the saved text data into a generative AI model (GPT-4) and analyzes the content of the text. Specifically, it uses the following prompt: "Please infer the user's cultural background from this text." The input is text data obtained from the database, and the output is the analysis result from the generative AI model.

[0934] Step 5:

[0935] The server guesses the cultural background

[0936] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" or "rice cakes"). The input is the analysis results from the generative AI model, and the output is information about the inferred cultural background.

[0937] Step 6:

[0938] The server returns the guess to the user

[0939] To return the prediction results to the user, the server packages the results in JSON format and sends them to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background." The input is the prediction result information, and the output is the message displayed on the user's device.

[0940] (Application example 2)

[0941] 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."

[0942] In today's social networking sites, it is difficult to understand the differences in cultural backgrounds between users and provide appropriate content. There are also insufficient means for users to deepen communication by guessing each other's cultural backgrounds. Furthermore, there are insufficient means to support communication between users across language barriers. To solve these issues, a system is needed that can accurately guess a user's cultural background and provide personalized content based on that.

[0943] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for utilizing a generative AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, and a means for inferring cultural backgrounds from the content posted by users and recommending appropriate content based on that guess. This makes it possible to understand the cultural backgrounds of users and provide appropriate content. Furthermore, by users guessing each other's cultural backgrounds, communication can be deepened and communication support that overcomes language barriers can be realized.

[0944] "Generative AI" is an artificial intelligence technology that analyzes user input data and performs natural language processing.

[0945] An "SNS that can be accessed in various languages" is a social networking service that supports multiple languages ​​and allows users who speak different languages ​​to participate.

[0946] "Means for understanding differences in cultural backgrounds among users" refers to technology that analyzes text posted by users and behavioral data to infer their cultural backgrounds.

[0947] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a system in which users guess the cultural backgrounds of other users and are offered rewards or benefits if they are correct.

[0948] "Means of inferring cultural background from user posts and recommending appropriate content based on that" refers to technology that analyzes text posted by users and provides relevant content based on that cultural background.

[0949] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are provided with incentives if they are correct, and a means for inferring cultural backgrounds from user posts and recommending appropriate content based on that guess.

[0950] The server uses a generative AI model to analyze the user's posts and infer their cultural background. Specifically, it receives the text posted by the user as input and analyzes it using the generative AI model. For example, if a user posts "I eat rice cakes on New Year's Day," the server infers that the user has a Japanese cultural background. This analysis uses the OpenAI API and the text-davinci-003 engine.

[0951] The server then recommends content appropriate to the user based on the inferred cultural background. For example, if a user is inferred to have a Japanese cultural background, it will recommend movies, music, articles, etc. related to Japan. This recommendation system is realized by referencing a pre-defined list of correspondences between cultural backgrounds and content.

[0952] To understand the differences in cultural backgrounds between users, the server uses technology to analyze the text and behavioral data posted by users and infer their cultural background, making it easier for users to understand each other's cultural backgrounds.

[0953] It also includes a gamification mechanism where users can guess each other's origins and receive incentives if they guess correctly. This system allows users to guess the cultural background of other users and provides rewards and benefits if they guess correctly. This promotes communication between users.

[0954] For example, if a user posts "I eat mochi on New Year's Day," the server will input the following prompt into the generative AI model:

[0955] User-submitted text: Eat rice cakes on New Year's Day

[0956] Guess this user's cultural background.

[0957] Based on this prompt, the generative AI model infers the user's cultural background and returns the results to the server, which then recommends appropriate content to the user.

[0958] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[0959] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0960] Step 1:

[0961] The user enters the text to post on the SNS. The user posts, "I eat rice cakes on New Year's Day."

[0962] Step 2:

[0963] The server receives the user's posted text, which is "I eat rice cakes on New Year's Day."

[0964] Step 3:

[0965] The server generates a prompt for the generative AI model. The generated prompt is "Text posted by the user: I eat rice cakes on New Year's Day. Please guess this user's cultural background."

[0966] Step 4:

[0967] The server sends a prompt to the generative AI model and requests it to analyze it. The generative AI model analyzes the prompt and infers the user's cultural background.

[0968] Step 5:

[0969] The generative AI model returns the analysis result to the server, which is "has a Japanese cultural background."

[0970] Step 6:

[0971] The server then uses the analysis results to recommend content appropriate to the user. For example, a user with a Japanese cultural background would be recommended movies, music, articles, etc. related to Japan.

[0972] Step 7:

[0973] The server sends the recommended content to the user's device, which then displays the recommended content.

[0974] Step 8:

[0975] Users can browse and enjoy recommended content, including movies, music, and articles related to Japan.

[0976] Step 9:

[0977] Users participate in a game where they must guess the cultural backgrounds of other users. Users look at the posts of other users and guess their cultural backgrounds.

[0978] Step 10:

[0979] The server evaluates the user's guess and provides incentives if the user guesses correctly. If the user guesses correctly, they are given rewards or benefits.

[0980] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[0981] Example 3

[0982] 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."

[0983] On modern social media, there are few opportunities for users to understand and respect the differences in cultural backgrounds between them. It is also difficult to deepen communication across cultural and language barriers. Furthermore, there is no incentive for users to guess each other's origins and get it right, which makes it difficult to promote understanding and respect for other cultures.

[0984] 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.

[0985] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for users to log in to the system, means for displaying user profile information, means for users to view other users' profile information, means for users to guess other users' cultural backgrounds, means for evaluating the accuracy of guesses using a generative AI model, means for notifying users of the results of their guesses, and means for offering incentives for correct guesses, thereby enabling users to understand and respect other cultures.

[0986] "Generative AI" is a system that uses artificial intelligence technology to automatically generate content such as text and images.

[0987] "SNS" stands for Social Networking Service, a platform where users can interact and share information with other users online.

[0988] "User" means any individual or entity that accesses and uses the System.

[0989] "Profile information" is basic information about a user, including name, hometown, hobbies, etc.

[0990] "Cultural background" refers to the cultural and social background to which a user belongs, including place of origin, language, customs, values, etc.

[0991] "Incentives" are rewards or benefits offered to encourage user behavior, such as points or badges.

[0992] "Login" means the authentication process required for a user to access a system, including entering a username and password.

[0993] A "generative AI model" is a specific implementation of generative AI, an artificial intelligence model optimized for a specific task.

[0994] "Guessing" refers to the act of a user predicting the cultural background of another user.

[0995] "Evaluation" is the process of using a generative AI model to determine the accuracy of a user's guess.

[0996] "Notification" refers to the act of the server conveying information to the user, including displaying information on the screen and sending messages.

[0997] This invention is a social networking service system with a gamified element in which users guess each other's origins and receive incentives if they are correct. A specific embodiment of this system will be described below.

[0998] Hardware and software used

[0999] Hardware

[1000] Server: Responsible for database management and execution of generative AI models.

[1001] Terminal: The device (PC, smartphone, etc.) through which a user accesses the system.

[1002] software

[1003] Database: Manages user profile information and incentive information (e.g., MySQL).

[1004] Generative AI models: Used to evaluate the accuracy of user guesses (e.g., OpenAI GPT-4).

[1005] Web application: Provides the user interface and accepts user interactions.

[1006] Explanation of program processing

[1007] 1. A user logs into the system

[1008] The user opens a browser on their device and accesses the system's login page, enters their username and password, and clicks the "Login" button. The server checks the entered information against the database, and if authentication is successful, redirects the user to the home page.

[1009] 2. The server displays the user's profile information

[1010] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Taro Tanaka," "Birthplace: Tokyo," and "Hobbies: Tea ceremony, anime" may be displayed.

[1011] 3. Users view other users' profile information

[1012] Users use the search function on the homepage to search for other users. The server retrieves the search results from the database and displays them on the device. Users can click on the displayed profile information to view details.

[1013] 4. Users infer other users' cultural backgrounds

[1014] Users can infer the cultural background of other users based on the profile information they have viewed. For example, they can infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" in the input form, and press the submit button.

[1015] 5. The server receives the guesses and evaluates them using a generative AI model

[1016] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate the accuracy of this guess." is input into the generative AI model.

[1017] 6. The server notifies the user of the result of the guess.

[1018] The server receives the evaluation results from the generative AI model and notifies the user of the results, for example, by displaying a message on the device screen such as "Your guess was correct. Points have been awarded."

[1019] 7. The server provides incentives for correct guesses

[1020] If the guess is correct, the server adds points or badges to the user's account, e.g., "10 points awarded." This information is stored in the database and reflected in the user's profile.

[1021] Examples of concrete examples and prompts

[1022] As a concrete example, consider a scenario in which User A wants to guess User B's cultural background. User A views User B's profile information (e.g., name, hometown, hobbies, etc.) and guesses that User B's cultural background is "Japan." After User A enters their guess, the server uses a generative AI model to evaluate the accuracy of their guess. If the guess is correct, User A is awarded points.

[1023] Example prompt sentence:

[1024] User A infers the cultural background of User B. User B's profile information is as follows:

[1025] Name: Tanaka Taro

[1026] Hometown: Tokyo

[1027] Hobbies: Tea ceremony, anime

[1028] User A guesses: "Japan"

[1029] Evaluate whether this assumption is accurate.

[1030] In this way, the user can understand and respect other cultures. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1031] Step 1:

[1032] A user logs into the system

[1033] The user opens a browser on the terminal and accesses the system's login page. He enters his username and password and clicks the "Login" button. The server checks the entered username and password against the database, and if authentication is successful, redirects the user to the home page. The input is the username and password, and the output is the authentication result. To check the database, the server executes an SQL query.

[1034] Step 2:

[1035] The server displays the user's profile information

[1036] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Tanaka Taro," "Hometown: Tokyo," and "Hobbies: Tea ceremony, anime" is displayed. The input is the user ID, and the output is the profile information. To retrieve the information from the database, the server executes an SQL query.

[1037] Step 3:

[1038] Users view other users' profile information

[1039] A user searches for other users using the search function on their homepage. The server retrieves the search results from the database and displays them on their device. The user clicks on the displayed profile information to view details. The input is the search query and the output is the search results. To retrieve information from the database, the server executes an SQL query.

[1040] Step 4:

[1041] Users infer the cultural background of other users

[1042] A user infers the cultural background of other users based on the profile information they have viewed. For example, they may infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" into the input form, and press the submit button. The input is the inference, and the output is the transmission of the inference result.

[1043] Step 5:

[1044] The server receives the guesses and evaluates them using a generative AI model

[1045] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate whether this guess is accurate." is input into the generative AI model. The input is the prompt sentence, and the output is the evaluation result. To execute the generative AI model, the server sends an API request.

[1046] Step 6:

[1047] The server notifies the user of the result of the guess.

[1048] The server receives the evaluation results from the generative AI model and notifies the user of the results. For example, a message such as "Your guess is correct. Points have been awarded." is displayed on the device screen. The input is the evaluation result, and the output is the notification message.

[1049] Step 7:

[1050] The server provides incentives for correct guesses

[1051] If the guess is correct, the server adds points or badges to the user's account. For example, "10 points awarded." This information is saved in the database and reflected in the user's profile. The input is the evaluation result, and the output is the awarded incentive. To update the database, the server executes an SQL query.

[1052] (Application example 3)

[1053] 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."

[1054] In modern society, it is important for people with different cultural backgrounds to understand each other. However, language barriers and cultural differences often become obstacles, making mutual understanding difficult. In addition, users lack motivation to learn about and respect other cultures. For this reason, there is a need for a system that promotes intercultural understanding and allows users to learn in a fun way.

[1055] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are given incentives if they are correct, means for determining whether users' guesses are correct and for awarding points and badges, and means for managing users' points and badges. This enables users to enjoy learning about other cultures and deepen mutual understanding.

[1056] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide appropriate responses and content based on user input.

[1057] "SNS" stands for Social Networking Service, a platform for users to communicate with other users online.

[1058] "Cultural background" refers to the culture, social environment, history, customs, values, etc. to which an individual belongs.

[1059] "Incentives" are rewards or benefits offered to encourage user behavior.

[1060] "Gamification" refers to elements of competition or challenge designed to allow users to participate in a fun way.

[1061] "Points" are a numerical evaluation given to users based on their activities and achievements.

[1062] A "badge" is a digital title or insignia that users can earn when they meet certain conditions.

[1063] "Guessing" is the act of predicting the cultural background of other users based on limited information.

[1064] "Verification" is the process of evaluating whether the user's guess is correct.

[1065] "Management" refers to tracking the status of a user's points and badges and updating them appropriately.

[1066] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for gamifying users' guesses about each other's origins and providing incentives if they are correct, a means for determining whether users' guesses are correct and for awarding points and badges, and a means for managing users' points and badges.

[1067] Program processing explanation

[1068] Hardware

[1069] Smartphone (iOS or Android)

[1070] software

[1071] Python

[1072] Mobile app development frameworks (React Native, Flutter, etc.)

[1073] Data processing and calculation

[1074] 1. User information management: The server creates a class to manage the user's name, cultural background, points, and badges, allowing it to track the user's basic information and game progress.

[1075] 2. Judgment of guesses: The server has a function that allows users to guess the cultural backgrounds of other users and judge whether their guesses are correct. This allows points to be added if the user's guess is correct.

[1076] 3. Points and badge management: The server adds points when users answer correctly and awards badges when they reach a certain number of points, allowing the server to evaluate users' activities and provide incentives.

[1077] Specific examples

[1078] For example, if user "A" correctly guesses user "B"'s cultural background as "American," user "A" will receive 10 points. When user "A" reaches 100 points, he or she will be awarded the badge "Cultural Expert."

[1079] Prompt Sentence Examples

[1080] I want to develop a game app where users guess the cultural backgrounds of other users and are awarded points and badges if they guess correctly. Please create a class that manages the user's name, cultural background, points, and badges, and a program that has a function to determine whether the guess is correct.

[1081] In this way, users can learn about other cultures in a fun way and deepen mutual understanding.

[1082] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1083] Step 1:

[1084] When a user logs in to the application, the server retrieves the user's basic information (name, cultural background, points, badges) from the database. The input is the user's login information, and the output is the user's basic information. Specifically, the server executes a database query to retrieve the user information.

[1085] Step 2:

[1086] Users view other users' profiles and posts to infer their cultural backgrounds. The input is the profile information of other users, and the output is the user's inference. Specifically, users check other users' information through the application interface.

[1087] Step 3:

[1088] The user inputs their guess about the cultural background into the application. The input is the user's guess, and the output is sending the guess data to the server. Specifically, the user enters their guess in the text box and presses the submit button.

[1089] Step 4:

[1090] The server determines whether the user's guess is correct. The input is the user's guess data and the target user's cultural background, and the output is the judgment result. Specifically, the server compares the guess data with the target user's cultural background to see if they match.

[1091] Step 5:

[1092] The server adds points if the user guesses correctly and awards a badge when a certain number of points is reached. The input is the judgment result, and the output is the updated user points and badge. Specifically, the server adds points and awards a badge when the conditions are met.

[1093] Step 6:

[1094] The server saves the updated user points and badges to the database. The input is the updated user points and badges, and the output is the result saved in the database. Specifically, the server executes a database query and updates the user information.

[1095] Step 7:

[1096] The server notifies the user of the updated points and badges. The input is the updated user points and badges, and the output is a notification message. Specifically, the server generates a notification message and sends it to the user's device.

[1097] Step 8:

[1098] The user receives a notification and checks the update of points and badges in the application. The input is the notification message and the output is the user's confirmation. In concrete terms, the user opens the application notification and checks the update of points and badges.

[1099] 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.

[1100] "Example 1"

[1101] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system analyzes emotions from the content posted by users and reflects the results in the user's profile. Specifically, the emotion engine analyzes text data posted by users on social networking sites and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise. The emotion results are then reflected in the user's profile, allowing other users to understand those emotions.

[1102] "Example 2"

[1103] The emotion engine also deepens understanding of emotions in communication between users, enabling richer communication. Specifically, when a user reacts to another user's post (e.g., "like" or comment), the emotion engine analyzes the emotion behind that reaction. Based on the results of this analysis, it provides feedback to deepen understanding of emotions between users.

[1104] "Example 3"

[1105] Furthermore, the emotion engine analyzes the emotions in the text posted by users and provides incentives to users based on the results. Specifically, if a user reacts to another user's post with joy or sympathy, that user is given incentives such as points or badges. This encourages users to express their emotions more proactively, stimulating communication on social media.

[1106] The processing flow of each embodiment will be described below.

[1107] "Example 1"

[1108] Step 1: A user posts text data on a social networking site.

[1109] Step 2: The emotion engine analyzes the posted text data and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise.

[1110] Step 3: The emotion engine applies the analysis results to the user's profile.

[1111] "Example 2"

[1112] Step 1: A user reacts to another user's post (e.g., "likes" or comments).

[1113] Step 2: The emotion engine analyzes the emotion behind the reaction.

[1114] Step 3: The emotion engine uses the analysis results to provide feedback to deepen understanding of emotions between users.

[1115] "Example 3"

[1116] Step 1: Users react to other users' posts expressing joy or empathy.

[1117] Step 2: The emotion engine analyzes the emotion behind the reaction.

[1118] Step 3: Based on the analysis results, the emotion engine provides incentives such as points or badges to the user.

[1119] Example 1

[1120] 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."

[1121] In today's social networking sites, communication between users who speak different languages ​​is difficult, and language barriers exist. It is also difficult to understand users' emotions, and misunderstandings can occur due to differences in cultural backgrounds. Furthermore, there is a lack of ways to deepen connections between users and form communities that transcend borders and cultures.

[1122] 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.

[1123] In this invention, the server includes a means for utilizing generative AI to provide a social networking service that can be used in various languages, a means for translating text posted by users into other languages, a means for analyzing emotions from user posts and reflecting the results in the user's profile, a means for understanding differences in cultural backgrounds between users, and a means for providing a gamification element in which users guess each other's origins and receive incentives if they are correct. This enables communication across language barriers, makes it easier to understand users' emotions, and reduces misunderstandings due to differences in cultural background. It also deepens connections between users and enables the formation of a community that transcends borders and cultures.

[1124] "Generative AI" is a system that uses artificial intelligence technology to generate, analyze, and translate data such as text and images.

[1125] "SNS" is an abbreviation for social networking service, a platform for users to communicate over the Internet.

[1126] "User" refers to an individual or organization that uses the SNS system.

[1127] "Text" refers to the written information posted by users on social media.

[1128] "Translation" is the process of converting text written in one language into another.

[1129] An "emotion engine" is software or an algorithm that analyzes emotions from text data and identifies those emotions.

[1130] A "profile" is a page or section on a social networking site that displays information about a user.

[1131] "Cultural background" refers to information about the culture and social environment to which the user belongs.

[1132] "Incentives" refer to rewards or benefits offered to encourage user behavior.

[1133] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[1134] This invention provides an SNS system that utilizes generation AI and allows participation in the languages ​​of various countries. A specific embodiment of this system will be described below.

[1135] First, a user accesses the SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system.

[1136] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The server inputs the received text data into a generative AI model (e.g., a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a lot of fun today!" is translated into English as "I had a great time today!"

[1137] The server then distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages.

[1138] The server then inputs the received text data into an emotion engine (e.g., a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The server then reflects this result in the user's profile, allowing other users to understand the user's emotional state.

[1139] As a concrete example, consider the case where User A posts in Japanese, "I had a great time today!". User A uses a smartphone to access a social networking application, enters text, and posts. The server receives this text and translates it into English using a generative AI model. The translated result, "I had a great time today!", is then distributed by the server to other users.

[1140] At the same time, the server uses an emotion engine to analyze the text "I had so much fun today!" and detects the emotion "joy." This result is reflected in User A's profile, allowing other users to understand User A's emotions.

[1141] An example prompt is:

[1142] "Translate user-submitted text into other languages. Original text: 'I had so much fun today!'"

[1143] "Analyze the sentiment of a user's post and return the result. Original text: 'Today was so much fun!'"

[1144] In this way, the server, terminal, and user work together to achieve communication and emotional sharing that transcends language barriers.

[1145] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1146] Step 1:

[1147] A user posts text to a social networking site.

[1148] A user accesses an SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system. The input is the text entered by the user, and the output is the text data sent to the server.

[1149] Step 2:

[1150] The server receives the posted text.

[1151] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The input is the text data sent by the user, and the output is the text data stored in the database.

[1152] Step 3:

[1153] The server uses a generative AI model to translate the text into other languages.

[1154] The server inputs the received text data into a generative AI model (for example, a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a great time today!" is translated into English as "I had a great time today!" The input is the received text data and the prompt, and the output is the translated text.

[1155] Step 4:

[1156] The server distributes the translated text to other users.

[1157] The server distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages. The input is the translated text, and the output is the text distributed to other users.

[1158] Step 5:

[1159] The server analyzes the sentiment of the text using an emotion engine.

[1160] The server inputs the received text data into an emotion engine (for example, a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The input is the received text data, and the output is the analyzed emotion data.

[1161] Step 6:

[1162] The server reflects the analysis results in the user's profile.

[1163] The server reflects the emotion results analyzed by the emotion engine in the user's profile, allowing other users to understand the user's emotional state. For example, a "joy" icon is displayed in the user's profile. The input is the analyzed emotion data, and the output is the emotion information reflected in the user's profile.

[1164] (Application example 1)

[1165] 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."

[1166] On modern social networking sites, communication between users is often limited by language barriers and differences in cultural backgrounds. It can also be difficult for other users to understand the sentiment behind a user's posts, which can lead to a decline in the quality of communication. Furthermore, the lack of real-time translation and sentiment analysis makes it difficult for users to deepen their connections.

[1167] The identification processing by the identification 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 means for utilizing a generative AI to provide a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for adding a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text posted by users in real time, means for analyzing emotions from the content posted by users and reflecting the results in the user's profile, and means for displaying the translated text and the results of the emotion analysis to other users. This enables communication across language barriers, deepens connections between users, and promotes the formation of communities that transcend borders and cultures.

[1168] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[1169] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[1170] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[1171] "Incentives" are rewards or benefits offered to encourage user behavior.

[1172] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[1173] "Real-time" refers to the instantaneous processing of data and provision of information.

[1174] "Translation" is the conversion of text written in one language into another language.

[1175] "Sentiment analysis" is a technology that identifies and classifies user emotions from text data.

[1176] A "profile" is information that compiles a user's personal information and activity history.

[1177] A "community" is a group or group of users who share common interests or goals.

[1178] A system for implementing this invention has the following configuration: First, a user accesses an SNS using a device such as a smartphone or head-mounted display. Text data posted by the user is sent to a server.

[1179] The server uses a generative AI model to translate the posted text in real time, for example using the Google Translate API, and then converts the translated text into the user's language of choice.

[1180] The server then uses a sentiment analysis engine, such as IBM Watson, to analyze the sentiment of the posted text. The results of the sentiment analysis (happiness, sadness, anger, surprise, etc.) are reflected in the user's profile.

[1181] The translated text and the results of the sentiment analysis are displayed to other users, who can see the translated text and the results of the sentiment analysis in real time.

[1182] As a concrete example, if user A posts "I had a great time today!", the server translates this text into English using the Google Translate API. The translation result is "I had a great time today!". Next, the server analyzes the sentiment of the text using IBM Watson's sentiment analysis engine. The sentiment analysis result is "joy." The translated text and the sentiment analysis result are displayed to other users.

[1183] Examples of prompts to input to a generative AI model include:

[1184] "Translate user-submitted text and analyze the sentiment. Text: 'Today was so much fun!'"

[1185] In this way, communication between users can be carried out smoothly across language barriers, making it easier to share emotions.

[1186] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1187] Step 1:

[1188] A user accesses an SNS using a device and posts text. The input is the text data entered by the user, and the output is the text data sent to the server.

[1189] Step 2:

[1190] The server inputs the received text data into the generative AI model and translates it in real time. The input is the text data posted by the user, and the output is the translated text data. Specifically, the server calls the Google Translate API to translate the text into the specified language.

[1191] Step 3:

[1192] The server inputs the translated text data into a sentiment analysis engine to perform sentiment analysis. The input is the translated text data, and the output is the result of the sentiment analysis. Specifically, the server uses IBM Watson's sentiment analysis engine to analyze the sentiment of the text.

[1193] Step 4:

[1194] The server reflects the results of the sentiment analysis in the user's profile. The input is the result of the sentiment analysis, and the output is the updated user's profile data. Specifically, the server updates the user's profile database and adds the results of the sentiment analysis.

[1195] Step 5:

[1196] The server displays the translated text and the results of sentiment analysis to other users. The input is the translated text data and the results of sentiment analysis, and the output is the information displayed on the other user's device. Specifically, the server sends the translated text and the results of sentiment analysis to the other user's device.

[1197] Step 6:

[1198] Other users use the device to check the translated text and the results of sentiment analysis. The input is the information sent from the server, and the output is the display data viewed by other users. Specifically, the device displays the information received from the server on the screen.

[1199] Example 2

[1200] 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."

[1201] In modern social networking services (SNS), misunderstandings and friction are likely to occur due to a lack of understanding of differences in cultural backgrounds and emotions between users. Communication between users with different languages ​​and cultures is also often hindered. Furthermore, there are a lack of ways for users to understand each other's cultural backgrounds and deepen empathy. This makes it difficult to form communities that transcend borders and cultures, limiting their contribution to world peace.

[1202] 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.

[1203] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be used in various languages; a means for understanding differences in cultural backgrounds among users; a means for inferring cultural backgrounds from text posted by users; a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct; and a means for analyzing emotions in communication between users and providing feedback. This allows users to deepen their understanding of each other's cultural backgrounds and emotions, enabling smooth communication between users with different languages ​​and cultures. Furthermore, by users understanding each other's cultural backgrounds and deepening empathy, the formation of communities that transcend borders and cultures can be promoted, contributing to world peace.

[1204] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[1205] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[1206] "Means for understanding differences in cultural backgrounds among users" is a function that infers a user's cultural background from the text posted by the user and their behavior, and provides that information to other users.

[1207] The "means of inferring a user's cultural background from the text posted by the user" refers to an algorithm that uses natural language processing technology to analyze the content posted by the user and infer the user's cultural background.

[1208] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a function that allows users to guess the cultural background of other users and offers rewards or points if their guess is correct.

[1209] "Means for analyzing emotions in communication between users and providing feedback" refers to a function that analyzes users' reactions and comments, determines their emotions, and provides feedback to the users.

[1210] This invention utilizes generative AI to provide a social networking system that can be used in various languages. The system understands the differences in cultural backgrounds between users and includes a gamification feature that allows users to guess each other's origins and provides incentives for those who guess correctly. It also has a function that analyzes emotions in communication between users and provides feedback.

[1211] Hardware and software used

[1212] The server receives user posts and reaction data and stores them in a database. The server provides the computational resources to run natural language processing (NLP) and sentiment analysis engines, specifically generative AI models such as Google's BERT and OpenAI's GPT-3.

[1213] A device is a device that users use to access SNS and post or react to posts. Devices include smartphones, tablets, and PCs.

[1214] Users participate in social networking sites, post text, and react to other users' posts.

[1215] Specific operation of the system

[1216] Cultural background guessing function

[1217] A user posts, "I eat rice cakes on New Year's Day."

[1218] The server receives this post and stores it in a database.

[1219] The server sends the stored text data to the NLP engine.

[1220] The NLP engine extracts keywords such as "New Year's" and "mochi."

[1221] Based on the extracted keywords, the server infers that the user is likely to have a Japanese cultural background.

[1222] The server notifies the user, "From your posts, we infer that you are likely to have a Japanese cultural background."

[1223] Emotion analysis using an emotion engine

[1224] A user "likes" another user's post.

[1225] The server receives this reaction and stores it in a database.

[1226] The server transmits the stored reaction data to the emotion engine.

[1227] The emotion engine analyzes that the reaction of "like" is based on "joy" and "empathy."

[1228] The server informs the user, "This reaction is based on joy."

[1229] Prompt Sentence Examples

[1230] Cultural background speculation

[1231] Prompt: "Infer the user's cultural background from the text they post. For example, if the user posts, 'I eat mochi on New Year's Day,' infer that they are likely from a Japanese cultural background."

[1232] Emotion analysis

[1233] Prompt: "Analyze the emotions behind users' reactions to other users' posts. For example, analyze whether the reaction 'Like' is based on 'Joy' or 'Empathy'."

[1234] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1235] Cultural background guessing function

[1236] Step 1:

[1237] A user posts text to an SNS, for example, "I eat rice cakes on New Year's Day."

[1238] Input: Text data entered by the user.

[1239] Output: The text data sent to the server.

[1240] Step 2:

[1241] The server receives the text data posted by the user and stores it in a database.

[1242] Input: Text data submitted by the user.

[1243] Output: Text data stored in a database.

[1244] Step 3:

[1245] The server sends the stored text data to a natural language processing (NLP) engine, which uses generative AI models such as Google's BERT or OpenAI's GPT-3.

[1246] Input: Text data stored in a database.

[1247] Output: The text data sent to the NLP engine.

[1248] Step 4:

[1249] The NLP engine analyzes text data and extracts specific keywords and phrases, such as "New Year's" and "rice cakes."

[1250] Input: Text data sent to the NLP engine.

[1251] Output: Extracted keywords and phrases.

[1252] Step 5:

[1253] The server runs an algorithm to infer the user's cultural background based on the extracted keywords. For example, keywords like "New Year's" and "mochi" may suggest that the user is likely to have a Japanese cultural background.

[1254] Input: Extracted keywords or phrases.

[1255] Output: Inferred cultural background information.

[1256] Step 6:

[1257] The server then provides feedback to the user based on the results of its predictions, such as displaying a message like, "Based on your posts, we have determined that you are likely to have a Japanese cultural background."

[1258] Input: Inferred cultural background information.

[1259] Output: The feedback message that is displayed to the user.

[1260] Emotion analysis using an emotion engine

[1261] Step 1:

[1262] Users react to other users' posts (e.g., "like" or comment).

[1263] Input: User reaction data.

[1264] Output: The reaction data sent to the server.

[1265] Step 2:

[1266] The server receives the reaction data from the user and stores it in a database.

[1267] Input: Reaction data sent by the user.

[1268] Output: Reaction data stored in a database.

[1269] Step 3:

[1270] The server sends the stored reaction data to an emotion engine, which also uses generative AI models such as Google's BERT or OpenAI's GPT-3.

[1271] Input: Reaction data stored in the database.

[1272] Output: Reaction data sent to the emotion engine.

[1273] Step 4:

[1274] The emotion engine analyzes the reaction data and determines what emotion the reaction is based on. For example, it analyzes that a "like" reaction is based on "joy" or "empathy."

[1275] Input: Reaction data sent to the emotion engine.

[1276] Output: Determined emotion information.

[1277] Step 5:

[1278] The server uses the analysis results of the emotion engine to provide feedback to deepen understanding of emotions between users, for example, displaying a message such as "This reaction is based on joy."

[1279] Input: Determined emotion information.

[1280] Output: The feedback message that is displayed to the user.

[1281] (Application example 2)

[1282] 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."

[1283] In modern social networking sites, it is difficult to understand the differences in cultural backgrounds and feelings between users, which often leads to misunderstandings and friction. Furthermore, there is a lack of means for users with different cultural backgrounds to communicate smoothly. Furthermore, there is a problem of a decline in the quality of communication because feedback is not provided to users to help them understand the cultural backgrounds and feelings of other users.

[1284] 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.

[1285] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries; a means for understanding differences in cultural backgrounds between users; a means for providing a game element in which users guess each other's origins and are given incentives if they are correct; a means for guessing cultural backgrounds from text posted by users; a means for analyzing emotions based on user reactions; and a means for providing feedback to deepen understanding of emotions between users based on the analysis results. This makes it possible to understand differences in cultural backgrounds and emotions between users and promote smooth communication.

[1286] "Generative AI" is an artificial intelligence technology that generates new information and content based on user input data.

[1287] An "SNS that can be accessed in various languages" is a social networking service that supports multiple languages ​​and allows users who speak different languages ​​to participate.

[1288] "Means for understanding differences in cultural backgrounds among users" refers to technology that infers a user's cultural background from their posts and behavior and provides that information to other users.

[1289] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a system in which users guess the cultural background of other users and are offered rewards or benefits if their guess is correct.

[1290] "Means for inferring cultural background from text posted by a user" refers to a technology that analyzes text data posted by a user and infers the user's cultural background.

[1291] "Means for analyzing emotions based on user reactions" refers to technology that analyzes reactions (e.g., "likes" and comments) made by users to posts by other users and identifies emotions based on those reactions.

[1292] "Means for providing feedback to deepen understanding of emotions among users based on analysis results" refers to technology that provides users with information and advice to help them understand the emotions of other users based on the results of emotion analysis.

[1293] A system for carrying out this invention has the following configuration: The server utilizes a generative AI and includes means for providing a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for providing a game element in which users guess each other's origins and are offered incentives if they are correct, means for guessing cultural backgrounds from text posted by users, means for analyzing emotions based on user reactions, and means for providing feedback to deepen understanding of emotions between users based on the analysis results.

[1294] Explaining the program's processing in natural language

[1295] The server uses software such as Python, nltk, TextBlob, and langdetect to analyze the text posted by users. Specifically, it receives the text data posted by users as input, first detects the language, then checks whether certain keywords are included, and based on the results, infers the user's cultural background.

[1296] Furthermore, the TextBlob library is used to analyze the sentiment of the text and classify it as positive, negative, or neutral, and the analysis results are provided as feedback to users to help them understand the sentiment of other users.

[1297] Specific examples

[1298] For example, if a user posts, "I'm looking forward to eating rice cakes for New Year's!", the server will process it as follows:

[1299] 1. Receive the post text.

[1300] 2. Detect the language and confirm that it is Japanese.

[1301] 3. Detect keywords such as "New Year's" and "mochi" in the text and infer the user's cultural background as "Japan."

[1302] 4. Use the TextBlob library to analyze the sentiment of the text and determine it as positive.

[1303] 5. Based on the analysis results, provide feedback to other users such as "This user has a Japanese cultural background and positive feelings."

[1304] Prompt Sentence Examples

[1305] An example of a prompt sentence to input to the generative AI model is as follows:

[1306] Analyze the cultural context and sentiment of user-submitted text. Analyze the following text:

[1307] "I'm looking forward to eating mochi on New Year's!"

[1308] This prompt can be used to ask a generative AI model to analyze the text.

[1309] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1310] Step 1:

[1311] The user enters the post text.

[1312] Users post text through a social networking application, and this text becomes input data for the system.

[1313] Step 2:

[1314] The server receives the posted text.

[1315] The server receives the text data sent by the user and prepares it for the next processing step.

[1316] Step 3:

[1317] The server detects the language of the text.

[1318] The server uses the langdetect library to detect the language of the received text, for example, to determine whether the text is Japanese or English. The input is the user-submitted text, and the output is the detected language.

[1319] Step 4:

[1320] The server detects keywords in the text.

[1321] The server extracts keywords that indicate a specific cultural background (e.g., "New Year's Day," "mochi," etc.) from the text. The input is the user's posted text, and the output is the detected keywords.

[1322] Step 5:

[1323] The server infers the user's cultural background.

[1324] The server infers the user's cultural background based on the detected language and keywords. For example, if the language is Japanese and the keywords "New Year" and "mochi" are included, the server infers the user's cultural background as "Japan." The input is the detected language and keywords, and the output is the inferred cultural background.

[1325] Step 6:

[1326] The server analyzes the sentiment of the text.

[1327] The server uses the TextBlob library to analyze the sentiment of the posted text. Sentiment is classified as positive, negative, or neutral. The input is the user's posted text, and the output is the analyzed sentiment.

[1328] Step 7:

[1329] The server generates feedback based on the analysis results.

[1330] The server generates feedback for other users based on the inferred cultural background and analyzed emotions. For example, it provides feedback such as "This user has a Japanese cultural background and positive emotions." The input is the inferred cultural background and analyzed emotions, and the output is the generated feedback.

[1331] Step 8:

[1332] The server provides feedback to the user.

[1333] The server provides the generated feedback to other users to enhance sentiment understanding between users. The input is the generated feedback, and the output is the feedback provided to the users.

[1334] Example 3

[1335] 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."

[1336] In modern social networking sites, it is difficult to understand and respect the differences in cultural backgrounds between users. Users also rarely express their feelings about other users' posts, which can lead to a lack of active communication. Furthermore, due to language barriers, communication between users who speak different languages ​​often does not proceed smoothly.

[1337] The identification process by the identification processing unit 290 of the data processing device 12 in Example 3 is realized by the following means. In this invention, the server includes a means for utilizing a generation AI to provide an SNS that can be participated in in various languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and provide incentives if they are correct, a means for analyzing the emotions in the text posted by users and providing incentives based on the results, and a means for providing incentives if a user reacts to another user's post showing joy or sympathy. This encourages users to understand and respect other cultures, activates communication on the SNS, and enables smooth communication across language barriers.

[1338] "Generative AI" is an artificial intelligence technology that uses natural language processing and machine learning to generate text and data.

[1339] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[1340] "User" refers to an individual or organization that uses SNS.

[1341] "Cultural background" refers to information related to a user's culture, such as their country of origin, ethnicity, religion, and language.

[1342] "Incentives" refer to rewards or benefits offered to encourage user behavior.

[1343] "Emotion analysis" is a technology that identifies emotions from text data and evaluates the type and intensity of those emotions.

[1344] "Reactions" refer to comments or emotional expressions made by users in response to other users' posts.

[1345] "Points" refers to numerical rewards given to users.

[1346] A "badge" is a digital insignia that denotes a user's specific actions or achievements.

[1347] A "database" refers to a system for efficiently storing, managing, and retrieving data.

[1348] "Emotion engine" refers to software or algorithms for analyzing the emotions in text data.

[1349] This invention is a gamified SNS system that allows users to guess the cultural backgrounds of other users and provides incentives for those who correctly guess. It also uses an emotion engine to analyze the emotions in users' posted text and provides incentives for those who react to other users' posts with joy or sympathy.

[1350] Hardware and software used

[1351] Server: Database management system (e.g., MySQL), sentiment analysis engine (e.g., IBM Watson Natural Language Understanding)

[1352] Device: The smartphone or PC used by the user

[1353] Generative AI models: Natural language generation models such as GPT-4

[1354] Specific operation of the system

[1355] 1. Guessing the User's Cultural Background

[1356] Users view other users' profiles

[1357] Users open the application and visit other users' profile pages, which display information such as their name, photo, and bio.

[1358] Users infer cultural background

[1359] Based on the displayed information, users can guess the cultural background of other users (e.g., country of origin, ethnicity, religion, etc.) and enter their guesses into the input form.

[1360] The device sends the guess results to the server

[1361] When the user inputs their guess and presses the send button, the device sends the data to the server, which includes the guess and the ID of the user who made the guess.

[1362] The server saves the guess results in a database and determines whether they are correct.

[1363] The server stores the received data in a database, and checks it against the correct data in the database to determine whether the guess is correct.

[1364] The server gives an incentive if the answer is correct.

[1365] If the guess is correct, the server will award the user points or badges, and the incentive information will be updated in the database and reflected in the user's account.

[1366] 2. Emotion analysis using an emotion engine

[1367] Users react to other users' posts

[1368] Users can view other users' posts and comment or react to them, which includes emotional text such as "like" or "agree."

[1369] The device sends the reaction text to the server

[1370] When a user sends a reaction, the device sends the text data to the server, which includes the text of the reaction and the ID of the user who sent the reaction.

[1371] The server uses an emotion analysis engine to analyze the emotions of the reactions.

[1372] The server sends the received reaction text to a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding), which analyzes the sentiment of the text and returns the results to the server.

[1373] The server detects emotions such as joy and empathy

[1374] The server receives the analysis results and checks whether the reactions contain emotions such as joy or empathy. If emotions such as joy or empathy are detected, the information is stored in a database.

[1375] The server provides incentives

[1376] If emotions of joy or empathy are detected, the server will award points or badges to the user, and the incentive information will be updated in the database and reflected in the user's account.

[1377] Specific examples

[1378] 1. A specific example of the cultural background guessing game

[1379] Users view other users' profiles

[1380] Example: User A opens the app and visits the profile page of User B. The profile displays "Name: Taro" and "Self-introduction: I love Japanese culture."

[1381] Users infer cultural background

[1382] Example: User A guesses that "User B is from Japan" and enters "Japan" into the input form.

[1383] The device sends the guess results to the server

[1384] Example: When User A presses the send button, the device sends "User ID: A" and "Guessed result: Japan" to the server.

[1385] The server saves the guess results in a database and determines whether they are correct.

[1386] Example: The server checks its database and verifies that User B is in fact from Japan.

[1387] The server gives an incentive if the answer is correct.

[1388] Example: The server awards 10 points to user A and updates the database.

[1389] 2. Specific examples of emotion analysis using emotion engines

[1390] Users react to other users' posts

[1391] Example: User C comments on User D's post "Today was a great day!" with "Great post!"

[1392] The device sends the reaction text to the server

[1393] Example: When user C submits a comment, the device sends "User ID: C" and "Comment: Great post!" to the server.

[1394] The server uses an emotion analysis engine to analyze the emotions of the reactions.

[1395] Example: The server sends comments to a sentiment analysis engine and receives the analysis results.

[1396] The server detects emotions such as joy and empathy

[1397] Example: A sentiment analysis engine detects the emotion "joy" in a comment and returns the result to the server.

[1398] The server provides incentives

[1399] Example: The server awards 5 points to user C and updates the database.

[1400] Example prompts for generative AI models

[1401] Prompt: "Describe a system that allows users to guess the cultural backgrounds of other users and offers incentives if they guess correctly."

[1402] In this way, a system is constructed that encourages users to understand and respect other cultures and activates communication on SNS. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1403] Step 1:

[1404] Users view other users' profiles.

[1405] What it does: A user opens the app and visits another user's profile page, which displays information such as their name, photo, and bio.

[1406] Input: A user action requesting a profile page.

[1407] Output: Display of profile page.

[1408] Step 2:

[1409] The user infers cultural background.

[1410] Specific behavior: Based on the displayed information, the user guesses the cultural background of the other user (e.g., country of origin, ethnicity, religion, etc.) and enters the guess into the input form.

[1411] Input: The information shown on your profile page.

[1412] Output: The input data for the prediction results.

[1413] Step 3:

[1414] The device sends the guess result to the server.

[1415] Specific operation: When the user inputs the guess and presses the send button, the device sends the data to the server, including the guess and the ID of the user who made the guess.

[1416] Input: Input data for the prediction result.

[1417] Output: Sending data to the server.

[1418] Step 4:

[1419] The server stores the guess results in a database and determines whether they are correct.

[1420] Specific operation: The server stores the received data in a database. The server compares it with the correct data in the database and determines whether the guess is correct.

[1421] Input: Submitted guess result data.

[1422] Output: Correct answer result.

[1423] Step 5:

[1424] If the server answers correctly, an incentive is awarded.

[1425] What happens: If the guess is correct, the server will award the user points and badges, and the incentive information will be updated in the database and reflected in the user's account.

[1426] Input: Correct answer result.

[1427] Output: Incentives awarded and database updated.

[1428] Step 6:

[1429] Users react to other users' posts.

[1430] What it does: Users view other users' posts and comment or react to them. Reactions include emotional text such as "like" or "agree."

[1431] Input: Reactions to the post.

[1432] Output: Reaction text data.

[1433] Step 7:

[1434] The device sends the reaction text to the server.

[1435] What it does: When a user posts a reaction, the device sends the text data to the server, including the text of the reaction and the ID of the user who posted the reaction.

[1436] Input: Reaction text data.

[1437] Output: Sending data to the server.

[1438] Step 8:

[1439] The server analyzes the emotions of the reactions using an emotion analysis engine.

[1440] Specific operation: The server sends the received reaction text to a sentiment analysis engine (e.g., IBM Watson Natural Language Understanding), which analyzes the sentiment of the text and returns the results to the server.

[1441] Input: Reaction text data.

[1442] Output: Emotion analysis results.

[1443] Step 9:

[1444] The server detects emotions such as joy and empathy.

[1445] Specific operation: The server receives the analysis results and checks whether the reaction contains emotions such as joy or empathy. If emotions such as joy or empathy are detected, the information is stored in a database.

[1446] Input: Sentiment analysis results.

[1447] Output: Emotion detection results stored in a database.

[1448] Step 10:

[1449] The server provides the incentives.

[1450] Specific behavior: If emotions of joy or empathy are detected, the server will award points or badges to the user. The incentive information will be updated in the database and reflected in the user's account.

[1451] Input: Emotion detection results.

[1452] Output: Incentives awarded and database updated.

[1453] (Application example 3)

[1454] 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."

[1455] On modern social media, users have limited opportunities to understand and respect other cultures. Furthermore, users' emotional expressions are not fully appreciated, which hinders communication. Furthermore, there is a lack of content for learning about other cultures, which leaves users with little motivation to learn about them.

[1456] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 3 is realized by the following means.

[1457] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for providing a game element in which users guess each other's origins and are offered incentives if they are correct, means for analyzing the sentiment of users' posted text and offering incentives if they respond positively, means for displaying rankings based on points and badges earned by users, and means for providing content for learning about different cultures. This encourages users to understand and respect other cultures, activates communication on the social networking service, and strengthens motivation to learn about different cultures.

[1458] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide appropriate responses and content based on user input.

[1459] "SNS" stands for Social Networking Service, a platform that allows users to communicate with other users online.

[1460] "Cultural background" is a general term for the culture, customs, language, history, etc. of the country or region to which the user belongs.

[1461] "Incentives" are rewards or benefits offered to encourage user behavior, such as points or badges.

[1462] "Gamification" is a characteristic of a system that allows users to participate in a fun way, providing a sense of competition and accomplishment.

[1463] "Sentiment analysis" is a technology that automatically analyzes emotions from text posted by users and determines the type and intensity of those emotions.

[1464] A "positive reaction" is a user's reaction to another user's post that shows joy or sympathy.

[1465] "Ranking" is a system that displays users' rankings based on the points and badges they have earned.

[1466] "Intercultural learning content" refers to information and educational materials provided to users to deepen their understanding of other cultures.

[1467] The following system configuration will be described as an embodiment of the present invention.

[1468] System Configuration

[1469] This system includes a server, a user device, and a generative AI model. The server provides a social networking service that can be used in various languages ​​and manages data to understand differences in cultural backgrounds among users. The user device is a device such as a smartphone or PC, and provides an interface for users to access the social networking service and post and react to messages. The generative AI model is used to analyze the text posted by users and determine their emotions.

[1470] Program processing

[1471] The server executes a program that includes the following means:

[1472] 1. SNS provision method:

[1473] The server provides a social networking site that can be used in various languages. Users can register their own cultural background and communicate with other users.

[1474] 2. Cultural background understanding tools:

[1475] The server manages the cultural background data registered by users and provides information that allows other users to guess their background.

[1476] 3. Incentive delivery methods:

[1477] The server provides incentives such as points and badges to users who correctly guess the cultural backgrounds of other users or who give positive reactions.

[1478] 4. Emotion analysis means:

[1479] The server analyzes the user's posted text using a generative AI model to determine the sentiment, and if a positive sentiment is detected, provides an incentive to the user.

[1480] 5. Ranking display method:

[1481] The server displays a ranking based on the points and badges that users have earned, allowing users to track their progress and compete with other users.

[1482] 6. Means of providing intercultural learning content:

[1483] The server provides content for users to learn about different cultures, thereby enabling users to deepen their understanding of other cultures.

[1484] Hardware and software used

[1485] Hardware: smartphones, PCs, servers

[1486] Software: Python, TextBlob (sentiment analysis library)

[1487] Specific examples

[1488] For example, if a user correctly guesses "user1"'s cultural background as "Japanese," they will receive 10 points and the "Culture Expert" badge. If "user2" posts a positive comment like "I love this app!", they will receive 5 points and the "Positive Vibes" badge.

[1489] Prompt Sentence Examples

[1490] "Create a system where users can guess the cultural backgrounds of other users and reward them with points or badges if they guess correctly. Also, add a feature that analyzes the text of users' posts and rewards them for positive reactions."

[1491] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1492] Step 1:

[1493] A user accesses an SNS using a device and creates an account.

[1494] Input: User's basic information (name, email address, password)

[1495] Output: User account creation completion notification

[1496] Specific operation: The device enters the user's basic information into an input form and sends it to the server. The server stores the received information in a database and returns a notification to the device that account creation is complete.

[1497] Step 2:

[1498] Users register their cultural background.

[1499] Input: User's cultural background information (nationality, language, cultural characteristics)

[1500] Output: Notification of completion of cultural background information registration

[1501] Specific operation: The terminal enters the user's cultural background information into an input form and sends it to the server. The server stores the received information in a database and returns a registration completion notice to the terminal.

[1502] Step 3:

[1503] Users infer the cultural background of other users.

[1504] Input: User ID to be guessed, guessed cultural background

[1505] Output: Guessing result (correct / incorrect), incentives (points, badges)

[1506] Specific operation: The device sends the user ID to be guessed and the guessed cultural background to the server. The server retrieves the actual cultural background from the database and judges the guess result. If the answer is correct, points and badges are awarded to the user and the result is returned to the device.

[1507] Step 4:

[1508] The user enters the post text and performs sentiment analysis.

[1509] Input: Post text

[1510] Output: Sentiment analysis results (positive / negative / neutral), incentives (points, badges)

[1511] Specific operation: The device sends the user's posted text to the server. The server uses a generative AI model (TextBlob) to analyze the text's sentiment and determines the result. If a positive sentiment is detected, points or badges are awarded to the user and the result is returned to the device.

[1512] Step 5:

[1513] The server displays rankings based on the points and badges that users have earned.

[1514] Input: User points and badge information

[1515] Output: Ranking list

[1516] Specific operation: The server retrieves all users' points and badge information from the database, generates a ranking, and sends the generated ranking list to the terminal for display.

[1517] Step 6:

[1518] The server provides content for learning about different cultures.

[1519] Input: User request (type of culture you want to learn)

[1520] Output: Intercultural learning content

[1521] Specific operations: The terminal sends the user's request to the server. The server retrieves the relevant intercultural learning content from the database and sends it to the terminal. The terminal displays the received content to the user.

[1522] 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.

[1523] 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.

[1524] Another example of generative AI is Gemini (internet search engine). <url: https: gemini.google.com ?hl="ja">) are mentioned.

[1525] 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.

[1526] [Third embodiment]

[1527] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1528] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1529] 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).

[1530] 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.

[1531] 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.

[1532] 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).

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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.

[1537] 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.

[1538] Next, the specific processing by the specific processing unit 290 of the data processing device 12 will be described.

[1539] "Example 1"

[1540] One embodiment of the present invention is a social networking system that utilizes generative AI. This system allows users to participate in the system in their own languages ​​and supports communication between users. The generative AI analyzes text posted by users and translates that text into other languages, enabling communication between users across language barriers.

[1541] "Example 2"

[1542] Furthermore, this system provides a means to understand differences in cultural backgrounds among users. Specifically, it has the ability to infer a user's cultural background from the text they post. For example, if a user posts, "I eat mochi on New Year's Day," the system will infer that the user is likely to have a Japanese cultural background.

[1543] "Example 3"

[1544] The system also features a gamification element, where users are asked to guess each other's origins and are given incentives if they are correct. Specifically, if a user correctly guesses another user's cultural background, they are given incentives such as points or badges. This encourages users to understand and respect other cultures.

[1545] The processing flow of each embodiment will be described below.

[1546] "Example 1"

[1547] Step 1: Users post on social media in their own language.

[1548] Step 2: The generative AI analyzes the posted text.

[1549] Step 3: The generative AI translates the analyzed text into other languages.

[1550] Step 4: The translated text will be displayed to other users and you can start communicating with them.

[1551] "Example 2"

[1552] Step 1: A user posts on social media.

[1553] Step 2: The system infers the user's cultural background from the posted text.

[1554] Step 3: The guesses are reflected in the user's profile and are visible to other users.

[1555] "Example 3"

[1556] Step 1: Users participate in a game where they have to guess the cultural backgrounds of other users.

[1557] Step 2: Users submit their guesses.

[1558] Step 3: The system determines whether the guess is correct or incorrect.

[1559] Step 4: If the guess is correct, provide the user with an incentive (e.g., points or a badge).

[1560] Example 1

[1561] 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."

[1562] In today's social networking sites, there is a language barrier when communicating between users who speak different languages, making it difficult to have smooth interactions. It is also difficult for users to understand the differences in their cultural backgrounds, making it difficult to form communities that transcend national borders and cultures. Furthermore, there is a lack of ways to make communication more fun by adding a game element to the experience, such as users guessing each other's origins.

[1563] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, a means for analyzing text posted by users and translating the text into other languages, and a means for sending the translated text to other users' devices. This enables smooth communication between users who speak different languages, promotes mutual understanding of differences in cultural backgrounds, and promotes the formation of communities that transcend borders and cultures. Furthermore, the gamification element makes interactions between users more enjoyable.

[1564] "Generative AI" is an artificial intelligence technology that analyzes text posted by users and understands its content.

[1565] "SNS" is an abbreviation for social networking service, a platform for users to communicate online.

[1566] "User" means an individual or organization that uses SNS to post text and communicate with other users.

[1567] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[1568] "Roots" refers to the user's origins, such as place of birth, ethnicity, and cultural background.

[1569] An "incentive" is a reward or benefit that encourages users to take a particular action.

[1570] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[1571] "Text" refers to the written information that users post on social media.

[1572] "Translation" is the process of converting text written in one language into another.

[1573] A "terminal" is a device (smartphone, PC, etc.) that a user uses to access an SNS.

[1574] A "server" is a computer system used to operate a social networking site and process data.

[1575] This invention utilizes generative AI to provide a social networking system that allows participation in various languages. The system aims to support communication between users and overcome language barriers. Specific embodiments of this system are described below.

[1576] Hardware and Software Configuration

[1577] server

[1578] The server is the central computer system of the SNS system. The server is built using a virtual machine from a cloud service provider. Specifically, it uses a virtual machine instance from a common cloud service provider. The server includes the following main software components:

[1579] Generative AI models: Use popular generative AI models (e.g., GPT-4) to analyze user-submitted text.

[1580] Translation API: Use a common translation API (e.g., Cloud Translation API) to translate text into other languages.

[1581] Terminal

[1582] A terminal is a device that a user uses to access an SNS. Terminals include smartphones, tablets, PCs, etc. Terminals are connected to a server via the Internet.

[1583] User

[1584] A user is an individual or an organization that uses the SNS system to post text and communicate with other users.

[1585] System Operation

[1586] User Submissions

[1587] A user posts text to an SNS using a terminal. For example, the user posts, "It's nice weather today."

[1588] Sending a text

[1589] The device sends the posted text to the server, along with metadata such as the user ID and posting time.

[1590] Text Analysis

[1591] The server sends the received text to a generative AI model, which analyzes the content of the text. For example, the text "It's a nice day today" is analyzed as a "positive comment about the weather."

[1592] Executing the translation

[1593] Based on the analysis results, the server uses a translation API to translate the text into another language, for example, translating the Japanese phrase "Kyo wa ii tenki desu ne" (It's a nice day today) into English.

[1594] Sending translation results

[1595] The server sends the translated text to the other user's device, for example, an English-speaking user's device might display "It's a nice day today."

[1596] Specific examples

[1597] User A: Posts in Japanese, "It's a nice day today."

[1598] Server: Receives posts and analyzes them with a generative AI model.

[1599] Server: Use the translation API to translate "It's a nice day today" into English.

[1600] Server: Sends the translation result "It's a nice day today" to User B, who speaks English.

[1601] User B: Receives and understands "It's a nice day today" in English.

[1602] Prompt Sentence Examples

[1603] Prompt: If a user posts "It's a nice day today," translate this into English.

[1604] In this way, by utilizing generative AI and translation APIs, smooth communication between users who speak different languages ​​is achieved.

[1605] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1606] Step 1:

[1607] A user posts text to a social networking site using a device. Specifically, the user opens the social networking site app, enters "It's a nice day today" in the text input field, and presses the post button. The input is the text data entered by the user, and the output is the text data sent from the device to the server.

[1608] Step 2:

[1609] The device sends the posted text to the server. Specifically, the device sends the text data entered by the user to the server via the Internet. The transmitted data also includes metadata such as the user ID and posting time. The input is the text data entered by the user, and the output is the text data sent to the server.

[1610] Step 3:

[1611] The server sends the received text to the generative AI model, which analyzes the text content. Specifically, the server sends the received text data to the API endpoint of the generative AI model and receives the analysis results. The input is the text data received by the server, and the output is the analysis results returned by the generative AI model.

[1612] Step 4:

[1613] The server uses the translation API to translate the text into another language based on the analysis results. Specifically, the server sends a request to the translation API to translate "It's a nice day today" based on the analysis results. The input is the analysis result from the generative AI model, and the output is the translation result returned by the translation API.

[1614] Step 5:

[1615] The server sends the translated text to the other user's device. Specifically, the server receives the translation result and sends "It's a nice day today" to the English-speaking user's device. The sent data also includes metadata such as the original text and translation time. The input is the translation result from the translation API, and the output is the translated text sent to the other user's device.

[1616] (Application example 1)

[1617] 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."

[1618] In today's global society, it is difficult for users who speak different languages ​​to communicate in real time. In particular, in social networking sites and chat applications, language barriers are a major factor hindering interaction between users. Differences in cultural backgrounds can also be an obstacle to communication. There is a need to solve these problems, deepen connections between users, and form communities that transcend borders and cultures.

[1619] 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.

[1620] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text entered by users into other languages ​​in real time, means for displaying the translated text to other users, and means for supporting real-time communication between users who speak different languages. This enables smooth communication that transcends language barriers, deepens connections between users, and enables the formation of a community that transcends borders and cultures.

[1621] "Generative AI" is an artificial intelligence technology that analyzes text entered by a user and translates it into other languages.

[1622] "SNS" stands for Social Networking Service, a platform for users to communicate online.

[1623] "Cultural background" is a general term for the culture, customs, values, etc. of the country or region to which the user belongs.

[1624] "Roots" refers to the user's place of origin or ethnic background.

[1625] An "incentive" is a reward or benefit that encourages users to take a particular action.

[1626] "Real-time" means that information entered by a user is processed immediately and displayed to other users almost simultaneously.

[1627] "Translation" is the process of converting text written in one language into another.

[1628] "Communication" is the act of exchanging information and emotions between users.

[1629] "Connection" refers to the relationships and bonds between users.

[1630] A "community" is a group or group of users who share common interests or goals.

[1631] As an embodiment of the present invention, the following system can be constructed.

[1632] First, the server uses generative AI to provide a social networking site that can be used in various languages. Users access the site from their smartphones, computers, or other devices and enter text. The entered text is sent to the server, where it is translated into other languages ​​in real time by the generative AI model. The translated text is then displayed to other users.

[1633] The system uses the following hardware and software:

[1634] Server: A cloud server, such as an AWS EC2 instance

[1635] Client: Smartphone (iOS or Android), PC

[1636] Software: Flask (a lightweight Python web framework), Transformers (a library of generative AI models provided by Hugging Face), MarianMTModel (Hugging Face's translation model)

[1637] The server receives the text entered by the user and translates it using a generative AI model. Specifically, the text entered by the user is tokenized and input into the generative AI model. The model translates the text and detokenizes the result back into text. The translated text is then displayed to other users.

[1638] For example, if User A (an English speaker) types "Hello, how are you?", the app sends this text to the server, where the generative AI model translates it to "Hello, how are you?" The translated text is then displayed to User B (a German speaker).

[1639] An example of a prompt is as follows:

[1640] "Translate the following text from English to German: "Hello, how are you?"

[1641] In this way, a system can be realized that allows users who speak different languages ​​to communicate smoothly in real time.

[1642] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1643] Step 1:

[1644] A user accesses an SNS application from a device and inputs text. The input text is sent to a server along with the user's language. The input data consists of the text and language input by the user.

[1645] Step 2:

[1646] The server analyzes the received text data and tokenizes it for input to the generative AI model. Specifically, it breaks the text down into words and phrases and converts them into a format that the model can understand. The input data is the text entered by the user, and the output data is the tokenized text.

[1647] Step 3:

[1648] The server inputs the tokenized text into the generative AI model for translation. The generative AI model translates the input text into the specified target language. The input data is the tokenized text, and the output data is the translated tokenized text.

[1649] Step 4:

[1650] The server detokenizes the translated tokenized text and returns it to its original text form. Specifically, it combines words and phrases to reconstruct natural-sounding sentences. The input data is the translated tokenized text, and the output data is the translated text.

[1651] Step 5:

[1652] The server sends the translated text to the original user and the target user. The target user receives and displays the translated text on their device. The input data is the translated text, and the output data is the text displayed on the target user's device.

[1653] Step 6:

[1654] The target user checks the translated text and replies if necessary. The returned text is sent back to the server, where it goes through the same processing steps and is then displayed to the original user. The input data is the text entered by the target user, and the output data is the text displayed on the original user's device.

[1655] In this way, a system is realized that allows users who speak different languages ​​to communicate smoothly in real time.

[1656] Example 2

[1657] 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."

[1658] In today's global society, it is important to facilitate communication between users with different cultural backgrounds. However, language barriers and cultural differences can be obstacles that make it difficult to deepen understanding and connections between users. Furthermore, a lack of tools for users to understand each other's cultural backgrounds can lead to misunderstandings and friction. Furthermore, there is a need for a game-like feature that allows users to guess each other's origins, allowing them to deepen cultural understanding while having fun.

[1659] 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.

[1660] In this invention, the server includes: a means for utilizing generative AI to provide a social networking service that can be used in the languages ​​of various countries; a means for understanding differences in cultural backgrounds between users; a means for providing a game element in which users guess each other's origins and are given incentives if they are correct; a means for analyzing text posted by users and guessing their cultural background; and a means for guessing the user's cultural background based on the analysis results and returning the result to the user. This makes it possible to facilitate communication between users with different cultural backgrounds and deepen cultural understanding.

[1661] "Generative AI" is a system that uses artificial intelligence technology to generate data such as text and images.

[1662] "SNS" is an abbreviation for Social Networking Service, an online platform for users to communicate over the Internet.

[1663] "Cultural background" refers to the totality of customs, values, traditions, etc. of the culture or society to which the user belongs.

[1664] "Analyzing text" means analyzing text data posted by users using natural language processing technology to understand its content.

[1665] An "incentive" is a reward or benefit offered to encourage users to take a particular action.

[1666] "Gameplay" refers to elements and mechanisms designed to allow users to participate in a fun way.

[1667] A "prompt" is an instruction given to a generative AI model, and includes instructions for specific analysis or generation.

[1668] "Analysis results" refers to the information or data obtained after a generative AI model analyzes text.

[1669] This invention is a system for understanding differences in cultural backgrounds between users and facilitating smooth communication. A specific embodiment of this system will be described below.

[1670] First, a user posts text to the system using their own device (smartphone, PC, etc.). For example, the user might type, "I eat rice cakes on New Year's Day."

[1671] The device then transmits the text entered by the user to the server, using an Internet connection to transmit the data, which is received by the server and stored in a database.

[1672] The server uses natural language processing (NLP) techniques to analyze the received text data. Specifically, it uses a generative AI model (e.g., GPT-4) to understand the content of the text. For analysis, it uses prompts such as:

[1673] "Infer the user's cultural background from this text"

[1674] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" and "rice cakes"). For example, if a user posts "I eat rice cakes on New Year's Day," the server detects keywords such as "New Year's Day" and "rice cakes" and determines that this is related to Japanese culture.

[1675] Finally, the server returns the prediction results to the user. The prediction results are packaged in JSON format and sent to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background."

[1676] This system will facilitate communication between users with different cultural backgrounds and deepen cultural understanding. In addition, by adding a game element to the system, users can guess each other's origins, promoting cultural understanding while having fun.

[1677] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1678] Step 1:

[1679] User posts text

[1680] A user uses their own device (smartphone, PC, etc.) to enter text into the system and click the send button. For example, they might enter "I eat rice cakes on New Year's Day." The input data is in text format.

[1681] Step 2:

[1682] The device sends the text to the server

[1683] The terminal packages the text entered by the user in JSON format and sends it to the server using an HTTP POST request: the input is the user's text data, and the output is an HTTP request to the server.

[1684] Step 3:

[1685] The server receives the text

[1686] The server receives the HTTP request sent from the terminal, extracts the text data, and stores it in a database. The input is the HTTP request, and the output is the text data stored in the database.

[1687] Step 4:

[1688] The server parses the text

[1689] The server inputs the saved text data into a generative AI model (GPT-4) and analyzes the content of the text. Specifically, it uses the following prompt: "Please infer the user's cultural background from this text." The input is text data obtained from the database, and the output is the analysis result from the generative AI model.

[1690] Step 5:

[1691] The server guesses the cultural background

[1692] The server receives the analysis results from the generative AI model and infers the user's cultural background based on specific keywords (such as "New Year's Day" or "rice cakes"). The input is the analysis results from the generative AI model, and the output is information about the inferred cultural background.

[1693] Step 6:

[1694] The server returns the guess to the user

[1695] To return the prediction results to the user, the server packages the results in JSON format and sends them to the device as an HTTP response. The device then displays the received results on the screen. For example, it might say, "Based on your post, we infer that you are likely to have a Japanese cultural background." The input is the prediction result information, and the output is the message displayed on the user's device.

[1696] (Application example 2)

[1697] 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."

[1698] In today's social networking sites, it is difficult to understand the differences in cultural backgrounds between users and provide appropriate content. There are also insufficient means for users to deepen communication by guessing each other's cultural backgrounds. Furthermore, there are insufficient means to support communication between users across language barriers. To solve these issues, a system is needed that can accurately guess a user's cultural background and provide personalized content based on that.

[1699] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a means for utilizing a generative AI to provide a social networking service that can be used in various languages, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, and a means for inferring cultural backgrounds from the content posted by users and recommending appropriate content based on that guess. This makes it possible to understand the cultural backgrounds of users and provide appropriate content. Furthermore, by users guessing each other's cultural backgrounds, communication can be deepened and communication support that overcomes language barriers can be realized.

[1700] "Generative AI" is an artificial intelligence technology that analyzes user input data and performs natural language processing.

[1701] An "SNS that can be accessed in various languages" is a social networking service that supports multiple languages ​​and allows users who speak different languages ​​to participate.

[1702] "Means for understanding differences in cultural backgrounds among users" refers to technology that analyzes text posted by users and behavioral data to infer their cultural backgrounds.

[1703] "A means of gamifying the system by allowing users to guess each other's origins and offering incentives if they are correct" is a system in which users guess the cultural backgrounds of other users and are offered rewards or benefits if they are correct.

[1704] "Means of inferring cultural background from user posts and recommending appropriate content based on that" refers to technology that analyzes text posted by users and provides relevant content based on that cultural background.

[1705] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for providing a gamification element in which users guess each other's origins and are provided with incentives if they are correct, and a means for inferring cultural backgrounds from user posts and recommending appropriate content based on that guess.

[1706] The server uses a generative AI model to analyze the user's posts and infer their cultural background. Specifically, it receives the text posted by the user as input and analyzes it using the generative AI model. For example, if a user posts "I eat rice cakes on New Year's Day," the server infers that the user has a Japanese cultural background. This analysis uses the OpenAI API and the text-davinci-003 engine.

[1707] The server then recommends content appropriate to the user based on the inferred cultural background. For example, if a user is inferred to have a Japanese cultural background, it will recommend movies, music, articles, etc. related to Japan. This recommendation system is realized by referencing a pre-defined list of correspondences between cultural backgrounds and content.

[1708] To understand the differences in cultural backgrounds between users, the server uses technology to analyze the text and behavioral data posted by users and infer their cultural background, making it easier for users to understand each other's cultural backgrounds.

[1709] It also includes a gamification mechanism where users can guess each other's origins and receive incentives if they guess correctly. This system allows users to guess the cultural background of other users and provides rewards and benefits if they guess correctly. This promotes communication between users.

[1710] For example, if a user posts "I eat mochi on New Year's Day," the server will input the following prompt into the generative AI model:

[1711] User-submitted text: Eat rice cakes on New Year's Day

[1712] Guess this user's cultural background.

[1713] Based on this prompt, the generative AI model infers the user's cultural background and returns the results to the server, which then recommends appropriate content to the user.

[1714] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[1715] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1716] Step 1:

[1717] The user enters the text to post on the SNS. The user posts, "I eat rice cakes on New Year's Day."

[1718] Step 2:

[1719] The server receives the user's posted text, which is "I eat rice cakes on New Year's Day."

[1720] Step 3:

[1721] The server generates a prompt for the generative AI model. The generated prompt is "Text posted by the user: I eat rice cakes on New Year's Day. Please guess this user's cultural background."

[1722] Step 4:

[1723] The server sends a prompt to the generative AI model and requests it to analyze it. The generative AI model analyzes the prompt and infers the user's cultural background.

[1724] Step 5:

[1725] The generative AI model returns the analysis result to the server, which is "has a Japanese cultural background."

[1726] Step 6:

[1727] The server then uses the analysis results to recommend content appropriate to the user. For example, a user with a Japanese cultural background would be recommended movies, music, articles, etc. related to Japan.

[1728] Step 7:

[1729] The server sends the recommended content to the user's device, which then displays the recommended content.

[1730] Step 8:

[1731] Users can browse and enjoy recommended content, including movies, music, and articles related to Japan.

[1732] Step 9:

[1733] Users participate in a game where they must guess the cultural backgrounds of other users. Users look at the posts of other users and guess their cultural backgrounds.

[1734] Step 10:

[1735] The server evaluates the user's guess and provides incentives if the user guesses correctly. If the user guesses correctly, they are given rewards or benefits.

[1736] In this way, it becomes possible to understand the user's cultural background and provide appropriate content. Furthermore, by allowing users to guess each other's cultural backgrounds, communication can be deepened, and communication support that transcends language barriers can be realized.

[1737] Example 3

[1738] 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."

[1739] On modern social media, there are few opportunities for users to understand and respect the differences in cultural backgrounds between them. It is also difficult to deepen communication across cultural and language barriers. Furthermore, there is no incentive for users to guess each other's origins and get it right, which makes it difficult to promote understanding and respect for other cultures.

[1740] 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.

[1741] In this invention, the server includes means for utilizing generative AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for users to log in to the system, means for displaying user profile information, means for users to view other users' profile information, means for users to guess other users' cultural backgrounds, means for evaluating the accuracy of guesses using a generative AI model, means for notifying users of the results of their guesses, and means for offering incentives for correct guesses, thereby enabling users to understand and respect other cultures.

[1742] "Generative AI" is a system that uses artificial intelligence technology to automatically generate content such as text and images.

[1743] "SNS" stands for Social Networking Service, a platform where users can interact and share information with other users online.

[1744] "User" means any individual or entity that accesses and uses the System.

[1745] "Profile information" is basic information about a user, including name, hometown, hobbies, etc.

[1746] "Cultural background" refers to the cultural and social background to which a user belongs, including place of origin, language, customs, values, etc.

[1747] "Incentives" are rewards or benefits offered to encourage user behavior, such as points or badges.

[1748] "Login" means the authentication process required for a user to access a system, including entering a username and password.

[1749] A "generative AI model" is a specific implementation of generative AI, an artificial intelligence model optimized for a specific task.

[1750] "Guessing" refers to the act of a user predicting the cultural background of another user.

[1751] "Evaluation" is the process of using a generative AI model to determine the accuracy of a user's guess.

[1752] "Notification" refers to the act of the server conveying information to the user, including displaying information on the screen and sending messages.

[1753] This invention is a social networking service system with a gamified element in which users guess each other's origins and receive incentives if they are correct. A specific embodiment of this system will be described below.

[1754] Hardware and software used

[1755] Hardware

[1756] Server: Responsible for database management and execution of generative AI models.

[1757] Terminal: The device (PC, smartphone, etc.) through which a user accesses the system.

[1758] software

[1759] Database: Manages user profile information and incentive information (e.g., MySQL).

[1760] Generative AI models: Used to evaluate the accuracy of user guesses (e.g., OpenAI GPT-4).

[1761] Web application: Provides the user interface and accepts user interactions.

[1762] Explanation of program processing

[1763] 1. A user logs into the system

[1764] The user opens a browser on their device and accesses the system's login page, enters their username and password, and clicks the "Login" button. The server checks the entered information against the database, and if authentication is successful, redirects the user to the home page.

[1765] 2. The server displays the user's profile information

[1766] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Taro Tanaka," "Birthplace: Tokyo," and "Hobbies: Tea ceremony, anime" may be displayed.

[1767] 3. Users view other users' profile information

[1768] Users use the search function on the homepage to search for other users. The server retrieves the search results from the database and displays them on the device. Users can click on the displayed profile information to view details.

[1769] 4. Users infer other users' cultural backgrounds

[1770] Users can infer the cultural background of other users based on the profile information they have viewed. For example, they can infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" in the input form, and press the submit button.

[1771] 5. The server receives the guesses and evaluates them using a generative AI model

[1772] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate the accuracy of this guess." is input into the generative AI model.

[1773] 6. The server notifies the user of the result of the guess.

[1774] The server receives the evaluation results from the generative AI model and notifies the user of the results, for example, by displaying a message on the device screen such as "Your guess was correct. Points have been awarded."

[1775] 7. The server provides incentives for correct guesses

[1776] If the guess is correct, the server adds points or badges to the user's account, e.g., "10 points awarded." This information is stored in the database and reflected in the user's profile.

[1777] Examples of concrete examples and prompts

[1778] As a concrete example, consider a scenario in which User A wants to guess User B's cultural background. User A views User B's profile information (e.g., name, hometown, hobbies, etc.) and guesses that User B's cultural background is "Japan." After User A enters their guess, the server uses a generative AI model to evaluate the accuracy of their guess. If the guess is correct, User A is awarded points.

[1779] Example prompt sentence:

[1780] User A infers the cultural background of User B. User B's profile information is as follows:

[1781] Name: Tanaka Taro

[1782] Hometown: Tokyo

[1783] Hobbies: Tea ceremony, anime

[1784] User A guesses: "Japan"

[1785] Evaluate whether this assumption is accurate.

[1786] In this way, the user can understand and respect other cultures. The flow of the identification process in the third embodiment will be described with reference to FIG.

[1787] Step 1:

[1788] A user logs into the system

[1789] The user opens a browser on the terminal and accesses the system's login page. He enters his username and password and clicks the "Login" button. The server checks the entered username and password against the database, and if authentication is successful, redirects the user to the home page. The input is the username and password, and the output is the authentication result. To check the database, the server executes an SQL query.

[1790] Step 2:

[1791] The server displays the user's profile information

[1792] The server retrieves the user's profile information from the database and displays it on the homepage. For example, information such as "Name: Tanaka Taro," "Hometown: Tokyo," and "Hobbies: Tea ceremony, anime" is displayed. The input is the user ID, and the output is the profile information. To retrieve the information from the database, the server executes an SQL query.

[1793] Step 3:

[1794] Users view other users' profile information

[1795] A user searches for other users using the search function on their homepage. The server retrieves the search results from the database and displays them on their device. The user clicks on the displayed profile information to view details. The input is the search query and the output is the search results. To retrieve information from the database, the server executes an SQL query.

[1796] Step 4:

[1797] Users infer the cultural background of other users

[1798] A user infers the cultural background of other users based on the profile information they have viewed. For example, they may infer that "this user is knowledgeable about Japanese culture, so I think they are from Japan," enter "Japan" into the input form, and press the submit button. The input is the inference, and the output is the transmission of the inference result.

[1799] Step 5:

[1800] The server receives the guesses and evaluates them using a generative AI model

[1801] The server receives the guess sent by the user and inputs a prompt sentence into the generative AI model. For example, a prompt sentence such as "User A guesses the cultural background of User B. User B's profile information is as follows: Name: Tanaka Taro, Hometown: Tokyo, Hobbies: Tea ceremony, Anime. User A's guess: 'Japan'. Please rate whether this guess is accurate." is input into the generative AI model. The input is the prompt sentence, and the output is the evaluation result. To execute the generative AI model, the server sends an API request.

[1802] Step 6:

[1803] The server notifies the user of the result of the guess.

[1804] The server receives the evaluation results from the generative AI model and notifies the user of the results. For example, a message such as "Your guess is correct. Points have been awarded." is displayed on the device screen. The input is the evaluation result, and the output is the notification message.

[1805] Step 7:

[1806] The server provides incentives for correct guesses

[1807] If the guess is correct, the server adds points or badges to the user's account. For example, "10 points awarded." This information is saved in the database and reflected in the user's profile. The input is the evaluation result, and the output is the awarded incentive. To update the database, the server executes an SQL query.

[1808] (Application example 3)

[1809] 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."

[1810] In modern society, it is important for people with different cultural backgrounds to understand each other. However, language barriers and cultural differences often become obstacles, making mutual understanding difficult. In addition, users lack motivation to learn about and respect other cultures. For this reason, there is a need for a system that promotes intercultural understanding and allows users to learn in a fun way.

[1811] The identification process by the identification 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 means for utilizing a generation AI to provide a social networking service that can be participated in in the languages ​​of various countries, means for understanding differences in cultural backgrounds between users, means for providing a gamification element in which users guess each other's origins and are given incentives if they are correct, means for determining whether users' guesses are correct and for awarding points and badges, and means for managing users' points and badges. This enables users to enjoy learning about other cultures and deepen mutual understanding.

[1812] "Generative AI" is a system that uses artificial intelligence technology to generate data and provide appropriate responses and content based on user input.

[1813] "SNS" stands for Social Networking Service, a platform for users to communicate with other users online.

[1814] "Cultural background" refers to the culture, social environment, history, customs, values, etc. to which an individual belongs.

[1815] "Incentives" are rewards or benefits offered to encourage user behavior.

[1816] "Gamification" refers to elements of competition or challenge designed to allow users to participate in a fun way.

[1817] "Points" are a numerical evaluation given to users based on their activities and achievements.

[1818] A "badge" is a digital title or insignia that users can earn when they meet certain conditions.

[1819] "Guessing" is the act of predicting the cultural background of other users based on limited information.

[1820] "Verification" is the process of evaluating whether the user's guess is correct.

[1821] "Management" refers to tracking the status of a user's points and badges and updating them appropriately.

[1822] A system for implementing this invention includes a means for utilizing generative AI to provide a social networking site that can be accessed in the languages ​​of various countries, a means for understanding differences in cultural backgrounds between users, a means for gamifying users' guesses about each other's origins and providing incentives if they are correct, a means for determining whether users' guesses are correct and for awarding points and badges, and a means for managing users' points and badges.

[1823] Program processing explanation

[1824] Hardware

[1825] Smartphone (iOS or Android)

[1826] software

[1827] Python

[1828] Mobile app development frameworks (React Native, Flutter, etc.)

[1829] Data processing and calculation

[1830] 1. User information management: The server creates a class to manage the user's name, cultural background, points, and badges, allowing it to track the user's basic information and game progress.

[1831] 2. Judgment of guesses: The server has a function that allows users to guess the cultural backgrounds of other users and judge whether their guesses are correct. This allows points to be added if the user's guess is correct.

[1832] 3. Points and badge management: The server adds points when users answer correctly and awards badges when they reach a certain number of points, allowing the server to evaluate users' activities and provide incentives.

[1833] Specific examples

[1834] For example, if user "A" correctly guesses user "B"'s cultural background as "American," user "A" will receive 10 points. When user "A" reaches 100 points, he or she will be awarded the badge "Cultural Expert."

[1835] Prompt Sentence Examples

[1836] I want to develop a game app where users guess the cultural backgrounds of other users and are awarded points and badges if they guess correctly. Please create a class that manages the user's name, cultural background, points, and badges, and a program that has a function to determine whether the guess is correct.

[1837] In this way, users can learn about other cultures in a fun way and deepen mutual understanding.

[1838] The flow of the specific processing in Application Example 3 will be described with reference to FIG.

[1839] Step 1:

[1840] When a user logs in to the application, the server retrieves the user's basic information (name, cultural background, points, badges) from the database. The input is the user's login information, and the output is the user's basic information. Specifically, the server executes a database query to retrieve the user information.

[1841] Step 2:

[1842] Users view other users' profiles and posts to infer their cultural backgrounds. The input is the profile information of other users, and the output is the user's inference. Specifically, users check other users' information through the application interface.

[1843] Step 3:

[1844] The user inputs their guess about the cultural background into the application. The input is the user's guess, and the output is sending the guess data to the server. Specifically, the user enters their guess in the text box and presses the submit button.

[1845] Step 4:

[1846] The server determines whether the user's guess is correct. The input is the user's guess data and the target user's cultural background, and the output is the judgment result. Specifically, the server compares the guess data with the target user's cultural background to see if they match.

[1847] Step 5:

[1848] The server adds points if the user guesses correctly and awards a badge when a certain number of points is reached. The input is the judgment result, and the output is the updated user points and badge. Specifically, the server adds points and awards a badge when the conditions are met.

[1849] Step 6:

[1850] The server saves the updated user points and badges to the database. The input is the updated user points and badges, and the output is the result saved in the database. Specifically, the server executes a database query and updates the user information.

[1851] Step 7:

[1852] The server notifies the user of the updated points and badges. The input is the updated user points and badges, and the output is a notification message. Specifically, the server generates a notification message and sends it to the user's device.

[1853] Step 8:

[1854] The user receives a notification and checks the update of points and badges in the application. The input is the notification message and the output is the user's confirmation. In concrete terms, the user opens the application notification and checks the update of points and badges.

[1855] 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.

[1856] "Example 1"

[1857] As one embodiment of the present invention, a system incorporating an emotion engine is provided. This system analyzes emotions from the content posted by users and reflects the results in the user's profile. Specifically, the emotion engine analyzes text data posted by users on social networking sites and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise. The emotion results are then reflected in the user's profile, allowing other users to understand those emotions.

[1858] "Example 2"

[1859] The emotion engine also deepens understanding of emotions in communication between users, enabling richer communication. Specifically, when a user reacts to another user's post (e.g., "like" or comment), the emotion engine analyzes the emotion behind that reaction. Based on the results of this analysis, it provides feedback to deepen understanding of emotions between users.

[1860] "Example 3"

[1861] Furthermore, the emotion engine analyzes the emotions in the text posted by users and provides incentives to users based on the results. Specifically, if a user reacts to another user's post with joy or sympathy, that user is given incentives such as points or badges. This encourages users to express their emotions more proactively, stimulating communication on social media.

[1862] The processing flow of each embodiment will be described below.

[1863] "Example 1"

[1864] Step 1: A user posts text data on a social networking site.

[1865] Step 2: The emotion engine analyzes the posted text data and determines whether the text expresses an emotion such as joy, sadness, anger, or surprise.

[1866] Step 3: The emotion engine applies the analysis results to the user's profile.

[1867] "Example 2"

[1868] Step 1: A user reacts to another user's post (e.g., "likes" or comments).

[1869] Step 2: The emotion engine analyzes the emotion behind the reaction.

[1870] Step 3: The emotion engine uses the analysis results to provide feedback to deepen understanding of emotions between users.

[1871] "Example 3"

[1872] Step 1: Users react to other users' posts expressing joy or empathy.

[1873] Step 2: The emotion engine analyzes the emotion behind the reaction.

[1874] Step 3: Based on the analysis results, the emotion engine provides incentives such as points or badges to the user.

[1875] Example 1

[1876] 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."

[1877] In today's social networking sites, communication between users who speak different languages ​​is difficult, and language barriers exist. It is also difficult to understand users' emotions, and misunderstandings can occur due to differences in cultural backgrounds. Furthermore, there is a lack of ways to deepen connections between users and form communities that transcend borders and cultures.

[1878] 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.

[1879] In this invention, the server includes a means for utilizing generative AI to provide a social networking service that can be used in various languages, a means for translating text posted by users into other languages, a means for analyzing emotions from user posts and reflecting the results in the user's profile, a means for understanding differences in cultural backgrounds between users, and a means for providing a gamification element in which users guess each other's origins and receive incentives if they are correct. This enables communication across language barriers, makes it easier to understand users' emotions, and reduces misunderstandings due to differences in cultural background. It also deepens connections between users and enables the formation of a community that transcends borders and cultures.

[1880] "Generative AI" is a system that uses artificial intelligence technology to generate, analyze, and translate data such as text and images.

[1881] "SNS" is an abbreviation for social networking service, a platform for users to communicate over the Internet.

[1882] "User" refers to an individual or organization that uses the SNS system.

[1883] "Text" refers to the written information posted by users on social media.

[1884] "Translation" is the process of converting text written in one language into another.

[1885] An "emotion engine" is software or an algorithm that analyzes emotions from text data and identifies those emotions.

[1886] A "profile" is a page or section on a social networking site that displays information about a user.

[1887] "Cultural background" refers to information about the culture and social environment to which the user belongs.

[1888] "Incentives" refer to rewards or benefits offered to encourage user behavior.

[1889] "Game-like" refers to elements and mechanisms that allow users to participate in a fun way.

[1890] This invention provides an SNS system that utilizes generation AI and allows participation in the languages ​​of various countries. A specific embodiment of this system will be described below.

[1891] First, a user accesses the SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system.

[1892] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The server inputs the received text data into a generative AI model (e.g., a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a lot of fun today!" is translated into English as "I had a great time today!"

[1893] The server then distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages.

[1894] The server then inputs the received text data into an emotion engine (e.g., a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The server then reflects this result in the user's profile, allowing other users to understand the user's emotional state.

[1895] As a concrete example, consider the case where User A posts in Japanese, "I had a great time today!". User A uses a smartphone to access a social networking application, enters text, and posts. The server receives this text and translates it into English using a generative AI model. The translated result, "I had a great time today!", is then distributed by the server to other users.

[1896] At the same time, the server uses an emotion engine to analyze the text "I had so much fun today!" and detects the emotion "joy." This result is reflected in User A's profile, allowing other users to understand User A's emotions.

[1897] An example prompt is:

[1898] "Translate user-submitted text into other languages. Original text: 'I had so much fun today!'"

[1899] "Analyze the sentiment of a user's post and return the result. Original text: 'Today was so much fun!'"

[1900] In this way, the server, terminal, and user work together to achieve communication and emotional sharing that transcends language barriers.

[1901] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1902] Step 1:

[1903] A user posts text to a social networking site.

[1904] A user accesses an SNS application using their own device (such as a smartphone or PC). The user enters text into the posting form and clicks the "Post" button. This operation sends the text data to the SNS system. The input is the text entered by the user, and the output is the text data sent to the server.

[1905] Step 2:

[1906] The server receives the posted text.

[1907] The server receives text data sent by the user. The received data is stored in a database and passed to the next processing step. The input is the text data sent by the user, and the output is the text data stored in the database.

[1908] Step 3:

[1909] The server uses a generative AI model to translate the text into other languages.

[1910] The server inputs the received text data into a generative AI model (for example, a general generative AI model). The generative AI model translates the text into another language based on the specified prompt. For example, the Japanese text "I had a great time today!" is translated into English as "I had a great time today!" The input is the received text data and the prompt, and the output is the translated text.

[1911] Step 4:

[1912] The server distributes the translated text to other users.

[1913] The server distributes the translated text to other users, who can communicate through the translated text, even if they speak different languages. The input is the translated text, and the output is the text distributed to other users.

[1914] Step 5:

[1915] The server analyzes the sentiment of the text using an emotion engine.

[1916] The server inputs the received text data into an emotion engine (for example, a general emotion analysis engine). The emotion engine analyzes whether the text expresses any emotion, such as joy, sadness, anger, or surprise. For example, the text "I had so much fun today!" is determined to express the emotion of "joy." The input is the received text data, and the output is the analyzed emotion data.

[1917] Step 6:

[1918] The server reflects the analysis results in the user's profile.

[1919] The server reflects the emotion results analyzed by the emotion engine in the user's profile, allowing other users to understand the user's emotional state. For example, a "joy" icon is displayed in the user's profile. The input is the analyzed emotion data, and the output is the emotion information reflected in the user's profile.

[1920] (Application example 1)

[1921] 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."

[1922] On modern social networking sites, communication between users is often limited by language barriers and differences in cultural backgrounds. It can also be difficult for other users to understand the sentiment behind a user's posts, which can lead to a decline in the quality of communication. Furthermore, the lack of real-time translation and sentiment analysis makes it difficult for users to deepen their connections.

[1923] The identification processing by the identification 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 means for utilizing a generative AI to provide a social networking service that can be used in various languages, means for understanding differences in cultural backgrounds between users, means for adding a gamification element in which users guess each other's origins and are offered incentives if they are correct, means for translating text posted by users in real time, means for analyzing emotions from the content posted by users and reflecting the results in the user's profile, and means for displaying the translated text and the results of the emotion analysis to other users. This enables communication across language barriers, deepens connections between users, and promotes the for...

Claims

1. Using generative AI, we can provide a social networking site that can be used in multiple languages. A means of understanding the differences in cultural backgrounds between multiple users, A means for providing a game element in which a plurality of users guess each other's origins and provide an incentive when the guess of the origin is correct; A means for translating said user's postings into other languages; and means for transmitting the translated posted content to a terminal of another user.

2. Using generative AI, we can provide a social networking site that can be used in multiple languages. A means of understanding the differences in cultural backgrounds between multiple users, A means for providing a game element in which a plurality of users guess each other's origins and provide an incentive when the guess of the origin is correct; and means for inferring the cultural background from the content posted by the user and recommending content based on the inference of the cultural background.

3. Using generative AI, we can provide a social networking site that can be used in multiple languages. A means of understanding the differences in cultural backgrounds between multiple users, A means for providing a game element in which a plurality of users guess each other's origins and provide an incentive when the guess of the origin is correct; A means for inferring the cultural background from the content posted by the user; A means for analyzing emotions based on the user's reactions; means for providing feedback based on the results of the analysis to improve understanding of the sentiment among the users; A system including:

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