system

Abstract

We provide the system. [Solution] To support intercultural communication, A method for analyzing user communication requests using generative AI models and natural language processing models, and proposing culture-specific business manners and language usage, A means of generating personalized advice based on user profiles and historical data, A means of sending generated suggestions and advice to the user's device and presenting them in real time, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In international projects and multinational teams, there is a problem that there are risks of misunderstandings and harassment due to cross-cultural communication. This has made communication not proceed smoothly and has become a factor hindering the success of the project. In particular, it is pointed out that there is a problem that business manners and appropriate wordings specific to a culture are not understood and communication respecting diversity has not been realized. As a result, discord within and outside the team and a decrease in productivity may occur.

Means for Solving the Problems

[0005] This invention provides a means to analyze users' communication requests and suggest culture-specific business manners and language usage in real time, using generative AI models and natural language processing models. Furthermore, it generates personalized advice based on user profiles and historical data, and sends the generated suggestions to the user's device for presentation. This enables users to communicate effectively across cultures and reduce the risk of misunderstandings and harassment. In addition, it provides an environment that deepens multicultural understanding by automatically generating and providing training and workshop content to users. Furthermore, it aims for continuous system improvement by collecting user feedback and analyzing the data to improve the quality of the suggestions.

[0006] A "generative AI model" is an artificial intelligence technology model that generates suggestions for cultural backgrounds and business etiquette from user input data.

[0007] A "natural language processing model" is a model equipped with the technology to analyze natural language and interpret its meaning and intent.

[0008] A "user profile" is a dataset that aggregates information about individual users and is used to provide personalized services.

[0009] "Personalized advice" refers to providing optimized advice and suggestions based on each user's characteristics and past history.

[0010] A "terminal" is a hardware device that a user uses to connect to a system and display or input information.

[0011] "Training and workshop content" refers to a collection of educational information and activities provided to users with the aim of improving multicultural understanding and communication skills.

[0012] "Feedback" refers to information that users provide to the system, including their experiences and impressions, which is used to improve the quality and content of the service. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0016] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

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

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] This invention is implemented as an AI agent system that solves problems in intercultural communication. The system mainly consists of three elements: a server, a terminal, and a user, and operates as follows.

[0035] The server hosts generative AI models and natural language processing models, generating culture-specific communication suggestions based on user requests. First, the server receives a user request and retrieves information on appropriate business etiquette and language usage from a cultural database. Next, it analyzes the request using the natural language processing model and creates suggestions. These suggestions are then personalized, taking into account the user's past profile information and history.

[0036] The terminal functions as an interface through which the user accesses the AI ​​agent system and sends requests to the server. Requests sent by the user via the terminal are passed to the server, and the generated suggestions are returned to the terminal. The terminal clearly displays the suggested communication style and business etiquette to the user. Users can also send feedback through the terminal.

[0037] Users can utilize the system to improve their communication skills during cross-cultural interactions. For example, during meetings with business partners from diverse cultural backgrounds, users can receive real-time advice from an AI agent to make appropriate statements. Users can also participate in automatically generated training and workshop content to promote multicultural understanding.

[0038] As a concrete example, when a user sends an email to a client in Germany, the server generates suggestions that include business etiquette specific to that culture and presents them to the user via their device. Based on these suggestions, the user can adjust the email content to ensure that their intended message is correctly conveyed. This entire process helps prevent misunderstandings in cross-cultural communication and contributes to the success of the project.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches the AI ​​agent application using their device and enters a request for communication support related to a specific cultural context. For example, the user might enter, "I would like suggestions for an email to send to a business partner in Germany."

[0042] Step 2:

[0043] The terminal sends the user's request data to the server. The request includes the user's request details and profile information.

[0044] Step 3:

[0045] The server analyzes the received request and searches for and extracts appropriate business etiquette and language-specific information from relevant cultural databases.

[0046] Step 4:

[0047] The server uses a natural language processing model to analyze user requests in detail and generates personalized communication suggestions based on the user's cultural background and past history.

[0048] Step 5:

[0049] The server sends the generated suggestions to the terminal. The suggestions include specific wording and example sentences that correspond to the user's request.

[0050] Step 6:

[0051] The device receives suggestions from the server and displays them to the user. The user then adjusts the content of their communication based on these suggestions.

[0052] Step 7:

[0053] The user reviews the generated suggestions and incorporates them into communications as needed. For example, the user prepares to send an email to a client with the suggested content.

[0054] Step 8:

[0055] Users evaluate the quality and suitability of the suggestions provided by entering feedback into their device after communication and sending it to the server.

[0056] Step 9:

[0057] The server analyzes user feedback data and updates the database and models to improve the quality of future suggestions.

[0058] (Example 1)

[0059] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0060] In intercultural communication, misunderstandings and friction arising from differences in culturally specific behavioral norms and language use make efficient and effective interaction difficult. Furthermore, conventional methods struggle to provide timely and appropriate advice, creating a need for immediate communication support. This invention aims to address these challenges by providing a system that proposes the optimal communication style tailored to the individual circumstances of users with diverse cultural backgrounds.

[0061] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0062] In this invention, the server includes means for analyzing the user's communication requests using generative models and language processing models and proposing culture-specific behavioral norms and language use; means for generating personalized guidance based on user characteristics and past recorded data; and means for transmitting the generated suggestions and guidance to the user's information terminal for immediate presentation. This enables the real-time presentation of appropriate communication styles for diverse cultural backgrounds.

[0063] Intercultural communication is the process by which individuals and groups with different cultural backgrounds communicate with each other.

[0064] A "generative model" is a general term for algorithms and systems that automatically generate new information or suggestions based on data.

[0065] A "language processing model" is a computer program designed to understand, analyze, and generate human language.

[0066] A "code of conduct" refers to standards or rules regarding appropriate behavior and language use within a particular culture or society.

[0067] "User characteristics" refer to the attributes and features of individual users who use the system.

[0068] "Past record data" refers to historical information saved based on a user's previous activities and interactions.

[0069] "Personalized instruction" means providing suggestions and advice that are optimized according to the user's characteristics and history.

[0070] An "information terminal" is an electronic device used by a user to access the functions of a system.

[0071] This invention is realized by using generative models and language processing models as a system to support intercultural communication. Specific embodiments are described below.

[0072] The server is responsible for hosting generative AI models (e.g., GPT) and natural language processing models (e.g., BERT). This allows it to analyze prompt sentences sent by users and generate suggestions regarding the behavioral norms and language use of the relevant culture. The server first receives prompt sentences from users via a terminal. For example, a possible prompt might be, "Please tell me the appropriate way to greet someone in a Japanese business meeting." Upon receiving this prompt, the server retrieves data related to Japanese business culture from its cultural database.

[0073] The terminal functions as an interface for users to send prompt messages to the server. By using the terminal to input prompt messages and sending them to the server, users can receive advice on culture-specific communication styles and language use. The terminal displays the received suggestions to the user in an easy-to-understand manner. For example, in Japanese business culture, specific advice such as "It is important to start with a bow and make eye contact when introducing yourself" might be displayed.

[0074] This system allows users to deepen their understanding of different cultures and communicate effectively. Furthermore, through personalized guidance provided by the system, users can improve their business skills and cross-cultural understanding.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user enters a prompt message into the system via a terminal. This prompt message is intended to provide specific advice on intercultural communication and may include, for example, "Please tell me how to greet people in a Japanese business meeting." The entered prompt message is then sent to the server.

[0078] Step 2:

[0079] The server inputs the prompt text received from the terminal into a generation AI model and a natural language processing model. These models analyze the content of the prompt text and determine which cultural information is needed. Data processing involves extracting keywords and important phrases from the prompt text and identifying relevant cultural information based on these. As a result of the analysis, the system is ready to acquire appropriate cultural data.

[0080] Step 3:

[0081] Based on the analysis results, the server retrieves information about the behavioral norms and language of the relevant culture from the cultural database. Here, queries are executed on the database to extract business etiquette and communication styles related to the specific culture. As a result, the necessary cultural background information is returned to the server.

[0082] Step 4:

[0083] The server uses a generative AI model based on acquired cultural information to generate optimal communication suggestions for the user. This process also considers previous user profile data and history to personalize the suggestions. The generated suggestions include specific actions the user should take. For example, "It's important to first bow, and then make eye contact when introducing yourself."

[0084] Step 5:

[0085] The server sends the generated suggestions to the terminal. The terminal displays these suggestions in an easy-to-understand visual format for the user. This output is designed to help the user concretely visualize their next action. The user can review the suggestions and prepare to engage in actual communication based on them.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In intercultural communication, misunderstandings and inappropriate expressions are common due to differences in cultural backgrounds. Furthermore, a lack of real-time communication support makes effective information exchange between participants from different cultures difficult. To address these challenges, a system is needed that smoothly supports communication tailored to the cultural characteristics of the user.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes means for analyzing the user's communication requests using a generative AI model and a natural language processing model and suggesting culture-specific business manners and language; means for generating personalized advice based on the user profile and past history data; means for transmitting the generated suggestions and advice to the user's information terminal and presenting them in real time; and means for suggesting cultural contexts and appropriate expressions based on voice input to facilitate communication between participants from different cultures. This makes it possible to minimize misunderstandings between cultures and achieve smooth communication.

[0091] "Intercultural communication" is the process of information exchange and dialogue that takes place between individuals or groups with different cultural backgrounds.

[0092] A "generative AI model" is a mathematical and algorithmic structure that uses artificial intelligence technology to automatically generate new suggestions and ideas from specific data.

[0093] A "natural language processing model" is a technology that enables the understanding, generation, and analysis of human language, and is designed to process text and audio data.

[0094] A "user profile" is a dataset that compiles information about an individual user, including their behavior, preferences, and past history.

[0095] "Real-time presentation" means a process that provides users with the information and advice they need immediately, enabling a response that is relevant to the current situation without delay.

[0096] "Voice input" is a method of capturing instructions and information provided in a human voice in a digital format, and it utilizes speech recognition technology.

[0097] The system implementing this invention mainly consists of a server, a terminal, and a user. The server hosts a generative AI model and a natural language processing model, and generates advice and suggestions aimed at improving intercultural communication based on the user's request. The server runs on Flask using Python, uses TENSORFLOW® for natural language processing, and utilizes Google® Speech-to-Text API for speech data analysis.

[0098] The terminal acts as an interface that sends user-inputted requests to the server and presents the user with suggestions and advice received from the server. Users can input requests via voice or text using smartphones or tablets. The interface employs an intuitive design to provide a user-friendly experience.

[0099] Users can use this system to receive advice on cross-cultural communication. For example, a foreign client unfamiliar with Japanese business etiquette can use this application to learn and apply specific manners and expressions to use during meetings in a timely manner. In this way, misunderstandings between cultures are reduced, and effective communication is achieved.

[0100] For example, a user can enter a question such as, "What are some cultural points I should pay particular attention to in my next presentation to my German partner?" Based on this prompt, the server will generate appropriate cultural advice.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The user uses a device to input requests via voice or text. These inputs are questions or clarifications related to cross-cultural communication the user is facing. The device converts these inputs into text data and sends it to the server.

[0104] Step 2:

[0105] The server inputs the received request into a generating AI model. This model uses natural language processing techniques to analyze the request and retrieve necessary cultural background and business etiquette information from a cultural database. As a result of data processing, a set of appropriate advice and suggestions related to the specific culture is generated.

[0106] Step 3:

[0107] The generated advice is further personalized by the server based on the user profile and historical data. The server optimizes the generated suggestions for each user, adjusting them to be optimal for their specific situation and background. The output here is a personalized set of advice.

[0108] Step 4:

[0109] The server sends personalized advice to the terminal. The terminal displays this information to the user in real time, providing necessary cultural context and expressions in audio or text format. The output is an interface display that makes it easy for the user to understand the presented information.

[0110] Step 5:

[0111] Users communicate based on the advice provided. They can input feedback on the results of applying the advice through their device. This feedback is sent to the server for data analysis so that it can be used to improve future advice.

[0112] Step 6:

[0113] The server analyzes the received feedback and uses it as data to improve the accuracy of its generative AI models and natural language processing models. This continuously improves the quality of future suggestions and advice, resulting in a better user experience on subsequent visits.

[0114] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0115] This invention is implemented as an intercultural communication support system that incorporates an emotion engine. This system is based on three elements: a server, a terminal, and a user, and utilizes the emotion engine to provide communication support that is tailored to the user's emotional state.

[0116] The server hosts a sentiment engine in addition to generative AI models and natural language processing models. First, the server receives requests from users and generates suggestions for culture-specific business etiquette and language use. The sentiment engine analyzes user input data (text, voice, etc.) and recognizes the user's emotional state in real time. Based on this, the server adjusts the suggestions to match the user's emotions, providing the optimal communication method.

[0117] The terminal functions as an interface for the user to interact with the system. When the user launches the AI ​​agent application on the terminal and enters a request for communication support, data including the user's sentiment information is sent to the server. Suggestions received from the server are displayed to the user on the terminal.

[0118] Users can refer to submitted suggestions and utilize them in their communication to reduce culture-specific misunderstandings and enable flexible responses tailored to their emotions. For example, if a user is feeling tense during a meeting, the emotion engine can detect this and suggest a more relaxing communication style. Training and workshop content are also provided with the user's emotional patterns in mind, contributing to a deeper understanding of multiculturalism.

[0119] For example, when a user is feeling stressed and sends an email to a German business partner, the emotion engine recognizes that emotion and recommends using stress-reducing language and positive messages. This allows the user to communicate more effectively and prevent cross-cultural misunderstandings. By providing communication support tailored to individual cultural backgrounds and emotional states, this system contributes to project success and improved work environments.

[0120] The following describes the processing flow.

[0121] Step 1:

[0122] The user opens an AI agent application via their device and inputs their emotional state and the cultural context in which they seek communication support. At this point, the user's voice and text data may also be collected.

[0123] Step 2:

[0124] The device sends data collected from the user to the server. This data includes input (voice, text) to detect the user's emotional state, the requested cultural context, and the user's profile information.

[0125] Step 3:

[0126] The server uses an emotion engine to analyze user input data and recognize the user's emotional state in real time. Emotional states are classified as joy, sadness, anxiety, stress, etc.

[0127] Step 4:

[0128] The server utilizes generative AI models and natural language processing models to generate personalized suggestions for culture-specific business etiquette and language use, based on user requests and recognized emotional states.

[0129] Step 5:

[0130] The server sends the generated suggestions to the terminal. These suggestions include language and communication tone that best suit the user's current emotional state.

[0131] Step 6:

[0132] The device presents the received suggestions to the user. The user then adjusts their communication based on these suggestions.

[0133] Step 7:

[0134] Users communicate with individuals from different cultural backgrounds based on the suggested content. This reduces the risk of misunderstandings and facilitates effective communication of intentions.

[0135] Step 8:

[0136] Users input feedback on the communication results and the usefulness of the suggestions into their devices and send it to the server. This feedback is used to improve the system in the future.

[0137] Step 9:

[0138] The server analyzes user feedback and updates the sentiment engine and suggestion generation models. The data collected here is used to improve the overall accuracy of the system and create a better user experience.

[0139] (Example 2)

[0140] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0141] In intercultural communication, misunderstandings frequently occur due to differences in cultural background and language, hindering smooth dialogue. Furthermore, depending on the user's emotional state, they may be unable to select an appropriate communication method, potentially leading to further misunderstandings. Conventional technologies lack sufficient flexible and immediate support to address these issues, highlighting the need for a system that enables users to express their emotions appropriately while reducing cultural misunderstandings.

[0142] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0143] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific manners and language; a server that recognizes the user's emotional state in real time using an emotion analysis engine and adjusts the content of the communication proposals to suit the emotions; and a server that transmits the generated proposals and advice to the user's terminal for visual presentation. This enables the user to reduce cross-cultural misunderstandings and engage in effective communication that is adapted to their emotions.

[0144] A "generative AI model" refers to an algorithm that learns specific patterns and rules from multiple data inputs and generates creative results for new data.

[0145] A "natural language processing model" refers to a collection of algorithms and technologies that enable computers to understand and process the language that humans use in everyday life.

[0146] An "emotion analysis engine" refers to a technology that identifies a user's emotional state from text and audio data and performs quantitative evaluations based on that.

[0147] "User communication requests" refer to information that users input to seek assistance in communicating in a specific context or situation.

[0148] "Culture-specific manners" refer to the general etiquette and behaviors within a particular culture.

[0149] "Adjusting to suit emotions" means taking into account the user's current emotional state and appropriately modifying the suggested communication style.

[0150] "Visual presentation" refers to making information easily viewable on the user's device through screen displays and graphic elements.

[0151] This invention is a system that supports intercultural communication and provides a communication method adapted to the user's culture-specific needs and emotional state. This system consists of three main elements: a server, a terminal, and a user, and is implemented using a variety of technologies.

[0152] The server hosts generative AI models, natural language processing models, and sentiment analysis engines, which are used to analyze user input. Generative AI models have the ability to learn from large amounts of data and generate new suggestions, while natural language processing models analyze input language data to enable natural dialogue. The sentiment analysis engine is used to recognize the user's emotional state in real time from text and speech.

[0153] The terminal is a device that the user uses as an interface. The user requests communication assistance through applications provided on the terminal, and the resulting text and voice data are sent to the server. Based on this data, the server generates suggestions that take into account the user's cultural background and current emotional state, and presents them visually to the user by returning them to the terminal.

[0154] As a concrete example, if a user is feeling nervous when trying to send an email to a business partner in a certain country, the sentiment analysis engine will detect this emotion. The server will then generate suggestions adapted to this emotional state, showing the user relaxing expressions and messages. An example of a prompt might be, "Please suggest relaxing expressions that can be used in a business setting in that country, which would be helpful if the user is feeling nervous."

[0155] In this way, the entire system works in coordination, enabling users to communicate effectively across cultures and respond appropriately, taking into account the influence of emotions.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The user launches an application on their device and enters a request for cross-cultural communication support. This input can be in text or voice format, and the device sends it to the server. The device formats the request data as a packet and transfers it to the server using a secure communication protocol.

[0159] Step 2:

[0160] The server processes data received from the terminal using an emotion analysis engine. Input includes user text and voice data, which the emotion analysis engine analyzes to output the emotional state. Specifically, it extracts emotional indicators using text analysis algorithms and voice processing technologies, and provides the results to the next step.

[0161] Step 3:

[0162] The server utilizes generative AI models and natural language processing models to create communication suggestions related to the user's emotional state and culture. The input consists of the emotional state obtained in step 2 and the user's communication request. The generative AI model performs data calculations based on these inputs and outputs suggestions that include culture-specific manners and language. A prompt such as "Generate an appropriate business email example based on the user's emotional state" might be used.

[0163] Step 4:

[0164] The server sends the generated communication proposal to the terminal. The output is the proposal content in text format, and the server uses a communication protocol to send it to the terminal. Upon receiving this information, the terminal visually presents it to the user. Specifically, the proposal content is displayed on the screen as a pop-up or notification.

[0165] Step 5:

[0166] Users review the suggestions displayed on the screen and use them as a basis for cross-cultural communication. The output is a more appropriate communication strategy, which users can then use to proceed with actual emails and conversations. Specific actions include preparing to send emails and simulating conversations.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0169] In intercultural communication, the challenge lies in mitigating misunderstandings and anxieties arising from linguistic and cultural differences, and enabling users to communicate with confidence. In particular, when emotional states can cause misunderstandings, real-time support that takes these states into account is necessary. Furthermore, in situations involving direct communication, such as electronic payments, it is essential to provide an environment where users can comfortably engage in dialogue.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific business manners and language; a server that analyzes the user's emotional state using emotion recognition technology and adjusts the communication style in real time based on the emotional state; and a server that evaluates the effectiveness of the suggestions based on emotional information and forms a feedback loop for future system improvements. This enables users to feel emotionally secure in cross-cultural communication and to engage in appropriate dialogue in real time with confidence.

[0172] "Intercultural communication" is the process by which individuals and groups from different cultural backgrounds communicate and understand each other, overcoming differences in language, customs, and values.

[0173] A "generative AI model" is a type of artificial intelligence that performs language generation and task execution based on large amounts of data, and its ability to generate natural language expressions is a key feature of this technology.

[0174] A "natural language processing model" is an algorithm or model that allows computers to understand and process human language, possessing the ability to analyze human language and extract necessary information.

[0175] "Emotion recognition technology" is a technology that analyzes and recognizes a user's emotional state from their voice and text data, enabling appropriate responses based on individual emotions.

[0176] "Communication style" refers to the manner of interaction during a conversation, including the methods of information transmission and the characteristics of language use, which vary depending on the specific culture and individual traits.

[0177] A "feedback loop" is a process in which a system continuously improves itself based on the information it receives, and it is a method for making suggestions and responses function more effectively.

[0178] "Real-time" refers to a temporal concept where information processing, communication, and responses occur almost instantly with virtually no delay, indicating a state where users can obtain the information they need immediately.

[0179] This invention constructs a system that supports intercultural communication through the coordinated efforts of a server, terminal, and user. The server hosts generative AI models and natural language processing models, and further analyzes the user's emotional state using emotion recognition technology. Based on data input by the user (e.g., voice data and text data), this system proposes a communication style specific to the user's culture.

[0180] Specifically, the server receives the user's voice or text input and uses emotion recognition technology to determine their emotions at that time. Based on this, a generative AI model devises the optimal communication method and sends suggestions to the user in real time. In this process, the user's emotional state is a key indicator, and the server adjusts its communication style based on the analysis results.

[0181] The terminal acts as an interface for the user to interact with this system and displays suggestions generated by the server. The user can refer to these suggestions to receive assistance in effectively communicating across cultures.

[0182] For example, if a user traveling feels anxious when making an electronic payment, emotion recognition can detect this and suggest "expressions that create a calm atmosphere." This suggestion is displayed on the terminal and helps the user to conduct transactions with peace of mind.

[0183] An example of a prompt might be, "Please suggest a communication method that will help the user feel emotionally comfortable in a cross-cultural environment." In this way, it becomes possible to provide dialogue support that is adapted to the user's emotional state and specific to the local culture.

[0184] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0185] Step 1:

[0186] The terminal receives user input. The user enters communication assistance requests in voice or text format. This input data is sent by the terminal to the server. The input data contains important information for inferring the user's real-time emotional state.

[0187] Step 2:

[0188] The server analyzes the user input it receives. Emotion recognition technology, hosted on the server, uses this data to analyze the user's emotional state. It extracts features from the voice and text data and estimates the current emotional state in real time. As a result of the analysis, data about the user's emotional state is obtained.

[0189] Step 3:

[0190] The server uses a generative AI model to create communication suggestions. The server utilizes the generative AI model, taking analyzed emotional state data and prompt sentences as input, to generate linguistic expressions that suggest culture-specific communication methods appropriate to the user's situation. The output is suggestions tailored to the user's emotional state.

[0191] Step 4:

[0192] The server sends the generated suggestions to the terminal. The terminal then presents the received suggestions to the user, providing support to help the user communicate effectively across cultures. The suggestions are displayed on the user's screen and can be referenced immediately to aid communication.

[0193] Step 5:

[0194] Users will use the suggestions as a reference for their communication. By utilizing these suggestions, they can improve communication in ongoing conversations and situations such as electronic payments. User experiences may also be fed back into the system as feedback, contributing to further system improvements.

[0195] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0196] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0197] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0198] [Second Embodiment]

[0199] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0200] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0201] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0202] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0203] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0204] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0205] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0206] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0207] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0208] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0209] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0210] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0211] This invention is implemented as an AI agent system that solves problems in intercultural communication. The system mainly consists of three elements: a server, a terminal, and a user, and operates as follows.

[0212] The server hosts generative AI models and natural language processing models, generating culture-specific communication suggestions based on user requests. First, the server receives a user request and retrieves information on appropriate business etiquette and language usage from a cultural database. Next, it analyzes the request using the natural language processing model and creates suggestions. These suggestions are then personalized, taking into account the user's past profile information and history.

[0213] The terminal functions as an interface through which the user accesses the AI ​​agent system and sends requests to the server. Requests sent by the user via the terminal are passed to the server, and the generated suggestions are returned to the terminal. The terminal clearly displays the suggested communication style and business etiquette to the user. Users can also send feedback through the terminal.

[0214] Users can utilize the system to improve their communication skills during cross-cultural interactions. For example, during meetings with business partners from diverse cultural backgrounds, users can receive real-time advice from an AI agent to make appropriate statements. Users can also participate in automatically generated training and workshop content to promote multicultural understanding.

[0215] As a concrete example, when a user sends an email to a client in Germany, the server generates suggestions that include business etiquette specific to that culture and presents them to the user via their device. Based on these suggestions, the user can adjust the email content to ensure that their intended message is correctly conveyed. This entire process helps prevent misunderstandings in cross-cultural communication and contributes to the success of the project.

[0216] The following describes the processing flow.

[0217] Step 1:

[0218] The user launches the AI ​​agent application using their device and enters a request for communication support related to a specific cultural context. For example, the user might enter, "I would like suggestions for an email to send to a business partner in Germany."

[0219] Step 2:

[0220] The terminal sends the user's request data to the server. The request includes the user's request details and profile information.

[0221] Step 3:

[0222] The server analyzes the received request and searches for and extracts appropriate business etiquette and language-specific information from relevant cultural databases.

[0223] Step 4:

[0224] The server uses a natural language processing model to analyze user requests in detail and generates personalized communication suggestions based on the user's cultural background and past history.

[0225] Step 5:

[0226] The server sends the generated suggestions to the terminal. The suggestions include specific wording and example sentences that correspond to the user's request.

[0227] Step 6:

[0228] The device receives suggestions from the server and displays them to the user. The user then adjusts the content of their communication based on these suggestions.

[0229] Step 7:

[0230] The user reviews the generated suggestions and incorporates them into communications as needed. For example, the user prepares to send an email to a client with the suggested content.

[0231] Step 8:

[0232] Users evaluate the quality and suitability of the suggestions provided by entering feedback into their device after communication and sending it to the server.

[0233] Step 9:

[0234] The server analyzes user feedback data and updates the database and models to improve the quality of future suggestions.

[0235] (Example 1)

[0236] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0237] In intercultural communication, misunderstandings and friction arising from differences in culturally specific behavioral norms and language use make efficient and effective interaction difficult. Furthermore, conventional methods struggle to provide timely and appropriate advice, creating a need for immediate communication support. This invention aims to address these challenges by providing a system that proposes the optimal communication style tailored to the individual circumstances of users with diverse cultural backgrounds.

[0238] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0239] In this invention, the server includes means for analyzing the user's communication requests using generative models and language processing models and proposing culture-specific behavioral norms and language use; means for generating personalized guidance based on user characteristics and past recorded data; and means for transmitting the generated suggestions and guidance to the user's information terminal for immediate presentation. This enables the real-time presentation of appropriate communication styles for diverse cultural backgrounds.

[0240] Intercultural communication is the process by which individuals and groups with different cultural backgrounds communicate with each other.

[0241] A "generative model" is a general term for algorithms and systems that automatically generate new information or suggestions based on data.

[0242] A "language processing model" is a computer program designed to understand, analyze, and generate human language.

[0243] A "code of conduct" refers to standards or rules regarding appropriate behavior and language use within a particular culture or society.

[0244] "User characteristics" refer to the attributes and features of individual users who use the system.

[0245] "Past record data" refers to historical information saved based on a user's previous activities and interactions.

[0246] "Personalized instruction" means providing suggestions and advice that are optimized according to the user's characteristics and history.

[0247] An "information terminal" is an electronic device used by a user to access the functions of a system.

[0248] This invention is realized by using generative models and language processing models as a system to support intercultural communication. Specific embodiments are described below.

[0249] The server is responsible for hosting generative AI models (e.g., GPT) and natural language processing models (e.g., BERT). This allows it to analyze prompt sentences sent by users and generate suggestions regarding the behavioral norms and language use of the relevant culture. The server first receives prompt sentences from users via a terminal. For example, a possible prompt might be, "Please tell me the appropriate way to greet someone in a Japanese business meeting." Upon receiving this prompt, the server retrieves data related to Japanese business culture from its cultural database.

[0250] The terminal functions as an interface for users to send prompt messages to the server. By using the terminal to input prompt messages and sending them to the server, users can receive advice on culture-specific communication styles and language use. The terminal displays the received suggestions to the user in an easy-to-understand manner. For example, in Japanese business culture, specific advice such as "It is important to start with a bow and make eye contact when introducing yourself" might be displayed.

[0251] This system allows users to deepen their understanding of different cultures and communicate effectively. Furthermore, through personalized guidance provided by the system, users can improve their business skills and cross-cultural understanding.

[0252] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0253] Step 1:

[0254] The user enters a prompt message into the system via a terminal. This prompt message is intended to provide specific advice on intercultural communication and may include, for example, "Please tell me how to greet people in a Japanese business meeting." The entered prompt message is then sent to the server.

[0255] Step 2:

[0256] The server inputs the prompt text received from the terminal into a generation AI model and a natural language processing model. These models analyze the content of the prompt text and determine which cultural information is needed. Data processing involves extracting keywords and important phrases from the prompt text and identifying relevant cultural information based on these. As a result of the analysis, the system is ready to acquire appropriate cultural data.

[0257] Step 3:

[0258] Based on the analysis results, the server retrieves information about the behavioral norms and language of the relevant culture from the cultural database. Here, queries are executed on the database to extract business etiquette and communication styles related to the specific culture. As a result, the necessary cultural background information is returned to the server.

[0259] Step 4:

[0260] The server uses a generative AI model based on acquired cultural information to generate optimal communication suggestions for the user. This process also considers previous user profile data and history to personalize the suggestions. The generated suggestions include specific actions the user should take. For example, "It's important to first bow, and then make eye contact when introducing yourself."

[0261] Step 5:

[0262] The server sends the generated suggestions to the terminal. The terminal displays these suggestions in an easy-to-understand visual format for the user. This output is designed to help the user concretely visualize their next action. The user can review the suggestions and prepare to engage in actual communication based on them.

[0263] (Application Example 1)

[0264] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0265] In intercultural communication, misunderstandings and inappropriate expressions are common due to differences in cultural backgrounds. Furthermore, a lack of real-time communication support makes effective information exchange between participants from different cultures difficult. To address these challenges, a system is needed that smoothly supports communication tailored to the cultural characteristics of the user.

[0266] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0267] In this invention, the server includes means for analyzing the user's communication requests using a generative AI model and a natural language processing model and suggesting culture-specific business manners and language; means for generating personalized advice based on the user profile and past history data; means for transmitting the generated suggestions and advice to the user's information terminal and presenting them in real time; and means for suggesting cultural contexts and appropriate expressions based on voice input to facilitate communication between participants from different cultures. This makes it possible to minimize misunderstandings between cultures and achieve smooth communication.

[0268] "Intercultural communication" is the process of information exchange and dialogue that takes place between individuals or groups with different cultural backgrounds.

[0269] A "generative AI model" is a mathematical and algorithmic structure that uses artificial intelligence technology to automatically generate new suggestions and ideas from specific data.

[0270] A "natural language processing model" is a technology that enables the understanding, generation, and analysis of human language, and is designed to process text and audio data.

[0271] A "user profile" is a dataset that compiles information about an individual user, including their behavior, preferences, and past history.

[0272] "Real-time presentation" means a process that provides users with the information and advice they need immediately, enabling a response that is relevant to the current situation without delay.

[0273] "Voice input" is a method of capturing instructions and information provided in a human voice in a digital format, and it utilizes speech recognition technology.

[0274] The system implementing this invention mainly consists of a server, a terminal, and a user. The server hosts a generative AI model and a natural language processing model, and generates advice and suggestions aimed at improving intercultural communication based on the user's request. The server runs on Flask using Python, uses TensorFlow for natural language processing, and utilizes the Google Speech-to-Text API for speech data analysis.

[0275] The terminal acts as an interface that sends user-inputted requests to the server and presents the user with suggestions and advice received from the server. Users can input requests via voice or text using smartphones or tablets. The interface employs an intuitive design to provide a user-friendly experience.

[0276] Users can use this system to receive advice on cross-cultural communication. For example, a foreign client unfamiliar with Japanese business etiquette can use this application to learn and apply specific manners and expressions to use during meetings in a timely manner. In this way, misunderstandings between cultures are reduced, and effective communication is achieved.

[0277] For example, a user can enter a question such as, "What are some cultural points I should pay particular attention to in my next presentation to my German partner?" Based on this prompt, the server will generate appropriate cultural advice.

[0278] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0279] Step 1:

[0280] The user uses a device to input requests via voice or text. These inputs are questions or clarifications related to cross-cultural communication the user is facing. The device converts these inputs into text data and sends it to the server.

[0281] Step 2:

[0282] The server inputs the received request into a generating AI model. This model uses natural language processing techniques to analyze the request and retrieve necessary cultural background and business etiquette information from a cultural database. As a result of data processing, a set of appropriate advice and suggestions related to the specific culture is generated.

[0283] Step 3:

[0284] The generated advice is further personalized by the server based on the user profile and past history data. The server optimizes the generated proposals for each user and adjusts them to the optimal content according to specific situations and backgrounds. What is output here is a set of individualized advice.

[0285] Step 4:

[0286] The server sends the personalized advice to the terminal. The terminal displays this information to the user in real time and provides the necessary cultural context and expression methods in voice or text form. As output, an interface display is made to make it easier for the user to obtain the information presented.

[0287] Step 5:

[0288] The user communicates based on the presented advice. Feedback on the applied results can be input through the terminal. This feedback is sent to the server for data analysis as it is reflected in the improvement of advice in subsequent times.

[0289] Step 6:

[0290] The server analyzes the received feedback and utilizes it as data for improving the accuracy of the generated AI model and natural language processing model. As a result, the quality of subsequent proposals and advice is continuously improved, and the user experience in subsequent times is improved.

[0291] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0292] This invention is implemented as an intercultural communication support system that incorporates an emotion engine. This system is based on three elements: a server, a terminal, and a user, and utilizes the emotion engine to provide communication support that is tailored to the user's emotional state.

[0293] The server hosts a sentiment engine in addition to generative AI models and natural language processing models. First, the server receives requests from users and generates suggestions for culture-specific business etiquette and language use. The sentiment engine analyzes user input data (text, voice, etc.) and recognizes the user's emotional state in real time. Based on this, the server adjusts the suggestions to match the user's emotions, providing the optimal communication method.

[0294] The terminal functions as an interface for the user to interact with the system. When the user launches the AI ​​agent application on the terminal and enters a request for communication support, data including the user's sentiment information is sent to the server. Suggestions received from the server are displayed to the user on the terminal.

[0295] Users can refer to submitted suggestions and utilize them in their communication to reduce culture-specific misunderstandings and enable flexible responses tailored to their emotions. For example, if a user is feeling tense during a meeting, the emotion engine can detect this and suggest a more relaxing communication style. Training and workshop content are also provided with the user's emotional patterns in mind, contributing to a deeper understanding of multiculturalism.

[0296] For example, when a user is feeling stressed and sends an email to a German business partner, the emotion engine recognizes that emotion and recommends using stress-reducing language and positive messages. This allows the user to communicate more effectively and prevent cross-cultural misunderstandings. By providing communication support tailored to individual cultural backgrounds and emotional states, this system contributes to project success and improved work environments.

[0297] The following describes the processing flow.

[0298] Step 1:

[0299] The user opens an AI agent application via their device and inputs their emotional state and the cultural context in which they seek communication support. At this point, the user's voice and text data may also be collected.

[0300] Step 2:

[0301] The device sends data collected from the user to the server. This data includes input (voice, text) to detect the user's emotional state, the requested cultural context, and the user's profile information.

[0302] Step 3:

[0303] The server uses an emotion engine to analyze user input data and recognize the user's emotional state in real time. Emotional states are classified as joy, sadness, anxiety, stress, etc.

[0304] Step 4:

[0305] The server utilizes generative AI models and natural language processing models to generate personalized suggestions for culture-specific business etiquette and language use, based on user requests and recognized emotional states.

[0306] Step 5:

[0307] The server sends the generated proposal to the terminal. This proposal includes the word usage and communication tone most suitable for the user's current emotional state.

[0308] Step 6:

[0309] The terminal presents the received proposal to the user. Based on this proposal, the user adjusts their communication.

[0310] Step 7:

[0311] The user communicates with a counterpart with a different cultural background based on the proposed content. This can reduce the risk of misunderstanding and achieve effective transmission of intentions.

[0312] Step 8:

[0313] The user inputs feedback on the communication result and the usefulness of the proposal to the terminal and sends it to the server. This feedback can be used for future improvement.

[0314] Step 9:

[0315] The server analyzes the feedback from the user and updates the emotion engine and the model for proposal generation. The data accumulated here is used to improve the accuracy of the entire system and provide a better user experience.

[0316] (Example 2)

[0317] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0318] In intercultural communication, misunderstandings frequently occur due to differences in cultural background and language, hindering smooth dialogue. Furthermore, depending on the user's emotional state, they may be unable to select an appropriate communication method, potentially leading to further misunderstandings. Conventional technologies lack sufficient flexible and immediate support to address these issues, highlighting the need for a system that enables users to express their emotions appropriately while reducing cultural misunderstandings.

[0319] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0320] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific manners and language; a server that recognizes the user's emotional state in real time using an emotion analysis engine and adjusts the content of the communication proposals to suit the emotions; and a server that transmits the generated proposals and advice to the user's terminal for visual presentation. This enables the user to reduce cross-cultural misunderstandings and engage in effective communication that is adapted to their emotions.

[0321] A "generative AI model" refers to an algorithm that learns specific patterns and rules from multiple data inputs and generates creative results for new data.

[0322] A "natural language processing model" refers to a collection of algorithms and technologies that enable computers to understand and process the language that humans use in everyday life.

[0323] An "emotion analysis engine" refers to a technology that identifies a user's emotional state from text and audio data and performs quantitative evaluations based on that.

[0324] "User communication requests" refer to information that users input to seek assistance in communicating in a specific context or situation.

[0325] "Culture-specific manners" refer to the general etiquette and behaviors within a particular culture.

[0326] "Adjusting to suit emotions" means taking into account the user's current emotional state and appropriately modifying the suggested communication style.

[0327] "Visual presentation" refers to making information easily viewable on the user's device through screen displays and graphic elements.

[0328] This invention is a system that supports intercultural communication and provides a communication method adapted to the user's culture-specific needs and emotional state. This system consists of three main elements: a server, a terminal, and a user, and is implemented using a variety of technologies.

[0329] The server hosts generative AI models, natural language processing models, and sentiment analysis engines, which are used to analyze user input. Generative AI models have the ability to learn from large amounts of data and generate new suggestions, while natural language processing models analyze input language data to enable natural dialogue. The sentiment analysis engine is used to recognize the user's emotional state in real time from text and speech.

[0330] The terminal is a device that the user uses as an interface. The user requests communication assistance through applications provided on the terminal, and the resulting text and voice data are sent to the server. Based on this data, the server generates suggestions that take into account the user's cultural background and current emotional state, and presents them visually to the user by returning them to the terminal.

[0331] As a concrete example, if a user is feeling nervous when trying to send an email to a business partner in a certain country, the sentiment analysis engine will detect this emotion. The server will then generate suggestions adapted to this emotional state, showing the user relaxing expressions and messages. An example of a prompt might be, "Please suggest relaxing expressions that can be used in a business setting in that country, which would be helpful if the user is feeling nervous."

[0332] In this way, the entire system works in coordination, enabling users to communicate effectively across cultures and respond appropriately, taking into account the influence of emotions.

[0333] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0334] Step 1:

[0335] The user launches an application on their device and enters a request for cross-cultural communication support. This input can be in text or voice format, and the device sends it to the server. The device formats the request data as a packet and transfers it to the server using a secure communication protocol.

[0336] Step 2:

[0337] The server processes data received from the terminal using an emotion analysis engine. Input includes user text and voice data, which the emotion analysis engine analyzes to output the emotional state. Specifically, it extracts emotional indicators using text analysis algorithms and voice processing technologies, and provides the results to the next step.

[0338] Step 3:

[0339] The server utilizes generative AI models and natural language processing models to create communication suggestions related to the user's emotional state and culture. The input consists of the emotional state obtained in step 2 and the user's communication request. The generative AI model performs data calculations based on these inputs and outputs suggestions that include culture-specific manners and language. A prompt such as "Generate an appropriate business email example based on the user's emotional state" might be used.

[0340] Step 4:

[0341] The server sends the generated communication proposal to the terminal. The output is the proposal content in text format, and the server uses a communication protocol to send it to the terminal. Upon receiving this information, the terminal visually presents it to the user. Specifically, the proposal content is displayed on the screen as a pop-up or notification.

[0342] Step 5:

[0343] Users review the suggestions displayed on the screen and use them as a basis for cross-cultural communication. The output is a more appropriate communication strategy, which users can then use to proceed with actual emails and conversations. Specific actions include preparing to send emails and simulating conversations.

[0344] (Application Example 2)

[0345] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0346] In intercultural communication, the challenge lies in mitigating misunderstandings and anxieties arising from linguistic and cultural differences, and enabling users to communicate with confidence. In particular, when emotional states can cause misunderstandings, real-time support that takes these states into account is necessary. Furthermore, in situations involving direct communication, such as electronic payments, it is essential to provide an environment where users can comfortably engage in dialogue.

[0347] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0348] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific business manners and language; a server that analyzes the user's emotional state using emotion recognition technology and adjusts the communication style in real time based on the emotional state; and a server that evaluates the effectiveness of the suggestions based on emotional information and forms a feedback loop for future system improvements. This enables users to feel emotionally secure in cross-cultural communication and to engage in appropriate dialogue in real time with confidence.

[0349] "Intercultural communication" is the process by which individuals and groups from different cultural backgrounds communicate and understand each other, overcoming differences in language, customs, and values.

[0350] A "generative AI model" is a type of artificial intelligence that performs language generation and task execution based on large amounts of data, and its ability to generate natural language expressions is a key feature of this technology.

[0351] A "natural language processing model" is an algorithm or model that allows computers to understand and process human language, possessing the ability to analyze human language and extract necessary information.

[0352] "Emotion recognition technology" is a technology that analyzes and recognizes a user's emotional state from their voice and text data, enabling appropriate responses based on individual emotions.

[0353] "Communication style" refers to the manner of interaction during a conversation, including the methods of information transmission and the characteristics of language use, which vary depending on the specific culture and individual traits.

[0354] A "feedback loop" is a process in which a system continuously improves itself based on the information it receives, and it is a method for making suggestions and responses function more effectively.

[0355] "Real-time" refers to a temporal concept where information processing, communication, and responses occur almost instantly with virtually no delay, indicating a state where users can obtain the information they need immediately.

[0356] This invention constructs a system that supports intercultural communication through the coordinated efforts of a server, terminal, and user. The server hosts generative AI models and natural language processing models, and further analyzes the user's emotional state using emotion recognition technology. Based on data input by the user (e.g., voice data and text data), this system proposes a communication style specific to the user's culture.

[0357] Specifically, the server receives the user's voice or text input and uses emotion recognition technology to determine their emotions at that time. Based on this, a generative AI model devises the optimal communication method and sends suggestions to the user in real time. In this process, the user's emotional state is a key indicator, and the server adjusts its communication style based on the analysis results.

[0358] The terminal acts as an interface for the user to interact with this system and displays suggestions generated by the server. The user can refer to these suggestions to receive assistance in effectively communicating across cultures.

[0359] For example, if a user traveling feels anxious when making an electronic payment, emotion recognition can detect this and suggest "expressions that create a calm atmosphere." This suggestion is displayed on the terminal and helps the user to conduct transactions with peace of mind.

[0360] An example of a prompt might be, "Please suggest a communication method that will help the user feel emotionally comfortable in a cross-cultural environment." In this way, it becomes possible to provide dialogue support that is adapted to the user's emotional state and specific to the local culture.

[0361] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0362] Step 1:

[0363] The terminal receives user input. The user enters communication assistance requests in voice or text format. This input data is sent by the terminal to the server. The input data contains important information for inferring the user's real-time emotional state.

[0364] Step 2:

[0365] The server analyzes the user input it receives. Emotion recognition technology, hosted on the server, uses this data to analyze the user's emotional state. It extracts features from the voice and text data and estimates the current emotional state in real time. As a result of the analysis, data about the user's emotional state is obtained.

[0366] Step 3:

[0367] The server uses a generative AI model to create communication suggestions. The server utilizes the generative AI model, taking analyzed emotional state data and prompt sentences as input, to generate linguistic expressions that suggest culture-specific communication methods appropriate to the user's situation. The output is suggestions tailored to the user's emotional state.

[0368] Step 4:

[0369] The server sends the generated suggestions to the terminal. The terminal then presents the received suggestions to the user, providing support to help the user communicate effectively across cultures. The suggestions are displayed on the user's screen and can be referenced immediately to aid communication.

[0370] Step 5:

[0371] Users will use the suggestions as a reference for their communication. By utilizing these suggestions, they can improve communication in ongoing conversations and situations such as electronic payments. User experiences may also be fed back into the system as feedback, contributing to further system improvements.

[0372] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0373] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0374] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0375] [Third Embodiment]

[0376] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0377] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0378] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0379] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0380] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0381] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0382] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0383] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0384] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0385] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0386] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0387] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0388] This invention is implemented as an AI agent system that solves problems in intercultural communication. The system mainly consists of three elements: a server, a terminal, and a user, and operates as follows.

[0389] The server hosts generative AI models and natural language processing models, generating culture-specific communication suggestions based on user requests. First, the server receives a user request and retrieves information on appropriate business etiquette and language usage from a cultural database. Next, it analyzes the request using the natural language processing model and creates suggestions. These suggestions are then personalized, taking into account the user's past profile information and history.

[0390] The terminal functions as an interface through which the user accesses the AI ​​agent system and sends requests to the server. Requests sent by the user via the terminal are passed to the server, and the generated suggestions are returned to the terminal. The terminal clearly displays the suggested communication style and business etiquette to the user. Users can also send feedback through the terminal.

[0391] Users can utilize the system to improve their communication skills during cross-cultural interactions. For example, during meetings with business partners from diverse cultural backgrounds, users can receive real-time advice from an AI agent to make appropriate statements. Users can also participate in automatically generated training and workshop content to promote multicultural understanding.

[0392] As a concrete example, when a user sends an email to a client in Germany, the server generates suggestions that include business etiquette specific to that culture and presents them to the user via their device. Based on these suggestions, the user can adjust the email content to ensure that their intended message is correctly conveyed. This entire process helps prevent misunderstandings in cross-cultural communication and contributes to the success of the project.

[0393] The following describes the processing flow.

[0394] Step 1:

[0395] The user launches the AI ​​agent application using their device and enters a request for communication support related to a specific cultural context. For example, the user might enter, "I would like suggestions for an email to send to a business partner in Germany."

[0396] Step 2:

[0397] The terminal sends the user's request data to the server. The request includes the user's request details and profile information.

[0398] Step 3:

[0399] The server analyzes the received request and searches for and extracts appropriate business etiquette and language-specific information from relevant cultural databases.

[0400] Step 4:

[0401] The server uses a natural language processing model to analyze user requests in detail and generates personalized communication suggestions based on the user's cultural background and past history.

[0402] Step 5:

[0403] The server sends the generated suggestions to the terminal. The suggestions include specific wording and example sentences that correspond to the user's request.

[0404] Step 6:

[0405] The device receives suggestions from the server and displays them to the user. The user then adjusts the content of their communication based on these suggestions.

[0406] Step 7:

[0407] The user reviews the generated suggestions and incorporates them into communications as needed. For example, the user prepares to send an email to a client with the suggested content.

[0408] Step 8:

[0409] Users evaluate the quality and suitability of the suggestions provided by entering feedback into their device after communication and sending it to the server.

[0410] Step 9:

[0411] The server analyzes user feedback data and updates the database and models to improve the quality of future suggestions.

[0412] (Example 1)

[0413] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0414] In intercultural communication, misunderstandings and friction arising from differences in culturally specific behavioral norms and language use make efficient and effective interaction difficult. Furthermore, conventional methods struggle to provide timely and appropriate advice, creating a need for immediate communication support. This invention aims to address these challenges by providing a system that proposes the optimal communication style tailored to the individual circumstances of users with diverse cultural backgrounds.

[0415] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0416] In this invention, the server includes means for analyzing the user's communication requests using generative models and language processing models and proposing culture-specific behavioral norms and language use; means for generating personalized guidance based on user characteristics and past recorded data; and means for transmitting the generated suggestions and guidance to the user's information terminal for immediate presentation. This enables the real-time presentation of appropriate communication styles for diverse cultural backgrounds.

[0417] Intercultural communication is the process by which individuals and groups with different cultural backgrounds communicate with each other.

[0418] A "generative model" is a general term for algorithms and systems that automatically generate new information or suggestions based on data.

[0419] A "language processing model" is a computer program designed to understand, analyze, and generate human language.

[0420] A "code of conduct" refers to standards or rules regarding appropriate behavior and language use within a particular culture or society.

[0421] "User characteristics" refer to the attributes and features of individual users who use the system.

[0422] "Past record data" refers to historical information saved based on a user's previous activities and interactions.

[0423] "Personalized instruction" means providing suggestions and advice that are optimized according to the user's characteristics and history.

[0424] An "information terminal" is an electronic device used by a user to access the functions of a system.

[0425] This invention is realized by using generative models and language processing models as a system to support intercultural communication. Specific embodiments are described below.

[0426] The server is responsible for hosting generative AI models (e.g., GPT) and natural language processing models (e.g., BERT). This allows it to analyze prompt sentences sent by users and generate suggestions regarding the behavioral norms and language use of the relevant culture. The server first receives prompt sentences from users via a terminal. For example, a possible prompt might be, "Please tell me the appropriate way to greet someone in a Japanese business meeting." Upon receiving this prompt, the server retrieves data related to Japanese business culture from its cultural database.

[0427] The terminal functions as an interface for users to send prompt messages to the server. By using the terminal to input prompt messages and sending them to the server, users can receive advice on culture-specific communication styles and language use. The terminal displays the received suggestions to the user in an easy-to-understand manner. For example, in Japanese business culture, specific advice such as "It is important to start with a bow and make eye contact when introducing yourself" might be displayed.

[0428] This system allows users to deepen their understanding of different cultures and communicate effectively. Furthermore, through personalized guidance provided by the system, users can improve their business skills and cross-cultural understanding.

[0429] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0430] Step 1:

[0431] The user enters a prompt message into the system via a terminal. This prompt message is intended to provide specific advice on intercultural communication and may include, for example, "Please tell me how to greet people in a Japanese business meeting." The entered prompt message is then sent to the server.

[0432] Step 2:

[0433] The server inputs the prompt text received from the terminal into a generation AI model and a natural language processing model. These models analyze the content of the prompt text and determine which cultural information is needed. Data processing involves extracting keywords and important phrases from the prompt text and identifying relevant cultural information based on these. As a result of the analysis, the system is ready to acquire appropriate cultural data.

[0434] Step 3:

[0435] Based on the analysis results, the server retrieves information about the behavioral norms and language of the relevant culture from the cultural database. Here, queries are executed on the database to extract business etiquette and communication styles related to the specific culture. As a result, the necessary cultural background information is returned to the server.

[0436] Step 4:

[0437] The server uses a generative AI model based on acquired cultural information to generate optimal communication suggestions for the user. This process also considers previous user profile data and history to personalize the suggestions. The generated suggestions include specific actions the user should take. For example, "It's important to first bow, and then make eye contact when introducing yourself."

[0438] Step 5:

[0439] The server sends the generated suggestions to the terminal. The terminal displays these suggestions in an easy-to-understand visual format for the user. This output is designed to help the user concretely visualize their next action. The user can review the suggestions and prepare to engage in actual communication based on them.

[0440] (Application Example 1)

[0441] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0442] In intercultural communication, misunderstandings and inappropriate expressions are common due to differences in cultural backgrounds. Furthermore, a lack of real-time communication support makes effective information exchange between participants from different cultures difficult. To address these challenges, a system is needed that smoothly supports communication tailored to the cultural characteristics of the user.

[0443] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0444] In this invention, the server includes means for analyzing the user's communication requests using a generative AI model and a natural language processing model and suggesting culture-specific business manners and language; means for generating personalized advice based on the user profile and past history data; means for transmitting the generated suggestions and advice to the user's information terminal and presenting them in real time; and means for suggesting cultural contexts and appropriate expressions based on voice input to facilitate communication between participants from different cultures. This makes it possible to minimize misunderstandings between cultures and achieve smooth communication.

[0445] "Intercultural communication" is the process of information exchange and dialogue that takes place between individuals or groups with different cultural backgrounds.

[0446] A "generative AI model" is a mathematical and algorithmic structure that uses artificial intelligence technology to automatically generate new suggestions and ideas from specific data.

[0447] A "natural language processing model" is a technology that enables the understanding, generation, and analysis of human language, and is designed to process text and audio data.

[0448] A "user profile" is a dataset that compiles information about an individual user, including their behavior, preferences, and past history.

[0449] "Real-time presentation" means a process that provides users with the information and advice they need immediately, enabling a response that is relevant to the current situation without delay.

[0450] "Voice input" is a method of capturing instructions and information provided in a human voice in a digital format, and it utilizes speech recognition technology.

[0451] The system implementing this invention mainly consists of a server, a terminal, and a user. The server hosts a generative AI model and a natural language processing model, and generates advice and suggestions aimed at improving intercultural communication based on the user's request. The server runs on Flask using Python, uses TensorFlow for natural language processing, and utilizes the Google Speech-to-Text API for speech data analysis.

[0452] The terminal acts as an interface that sends user-inputted requests to the server and presents the user with suggestions and advice received from the server. Users can input requests via voice or text using smartphones or tablets. The interface employs an intuitive design to provide a user-friendly experience.

[0453] Users can use this system to receive advice on cross-cultural communication. For example, a foreign client unfamiliar with Japanese business etiquette can use this application to learn and apply specific manners and expressions to use during meetings in a timely manner. In this way, misunderstandings between cultures are reduced, and effective communication is achieved.

[0454] For example, a user can enter a question such as, "What are some cultural points I should pay particular attention to in my next presentation to my German partner?" Based on this prompt, the server will generate appropriate cultural advice.

[0455] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0456] Step 1:

[0457] The user uses a device to input requests via voice or text. These inputs are questions or clarifications related to cross-cultural communication the user is facing. The device converts these inputs into text data and sends it to the server.

[0458] Step 2:

[0459] The server inputs the received request into a generating AI model. This model uses natural language processing techniques to analyze the request and retrieve necessary cultural background and business etiquette information from a cultural database. As a result of data processing, a set of appropriate advice and suggestions related to the specific culture is generated.

[0460] Step 3:

[0461] The generated advice is further personalized by the server based on the user profile and historical data. The server optimizes the generated suggestions for each user, adjusting them to be optimal for their specific situation and background. The output here is a personalized set of advice.

[0462] Step 4:

[0463] The server sends personalized advice to the terminal. The terminal displays this information to the user in real time, providing necessary cultural context and expressions in audio or text format. The output is an interface display that makes it easy for the user to understand the presented information.

[0464] Step 5:

[0465] Users communicate based on the advice provided. They can input feedback on the results of applying the advice through their device. This feedback is sent to the server for data analysis so that it can be used to improve future advice.

[0466] Step 6:

[0467] The server analyzes the received feedback and uses it as data to improve the accuracy of its generative AI models and natural language processing models. This continuously improves the quality of future suggestions and advice, resulting in a better user experience on subsequent visits.

[0468] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0469] This invention is implemented as an intercultural communication support system that incorporates an emotion engine. This system is based on three elements: a server, a terminal, and a user, and utilizes the emotion engine to provide communication support that is tailored to the user's emotional state.

[0470] The server hosts a sentiment engine in addition to generative AI models and natural language processing models. First, the server receives requests from users and generates suggestions for culture-specific business etiquette and language use. The sentiment engine analyzes user input data (text, voice, etc.) and recognizes the user's emotional state in real time. Based on this, the server adjusts the suggestions to match the user's emotions, providing the optimal communication method.

[0471] The terminal functions as an interface for the user to interact with the system. When the user launches the AI ​​agent application on the terminal and enters a request for communication support, data including the user's sentiment information is sent to the server. Suggestions received from the server are displayed to the user on the terminal.

[0472] Users can refer to submitted suggestions and utilize them in their communication to reduce culture-specific misunderstandings and enable flexible responses tailored to their emotions. For example, if a user is feeling tense during a meeting, the emotion engine can detect this and suggest a more relaxing communication style. Training and workshop content are also provided with the user's emotional patterns in mind, contributing to a deeper understanding of multiculturalism.

[0473] For example, when a user is feeling stressed and sends an email to a German business partner, the emotion engine recognizes that emotion and recommends using stress-reducing language and positive messages. This allows the user to communicate more effectively and prevent cross-cultural misunderstandings. By providing communication support tailored to individual cultural backgrounds and emotional states, this system contributes to project success and improved work environments.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The user opens an AI agent application via their device and inputs their emotional state and the cultural context in which they seek communication support. At this point, the user's voice and text data may also be collected.

[0477] Step 2:

[0478] The device sends data collected from the user to the server. This data includes input (voice, text) to detect the user's emotional state, the requested cultural context, and the user's profile information.

[0479] Step 3:

[0480] The server uses an emotion engine to analyze user input data and recognize the user's emotional state in real time. Emotional states are classified as joy, sadness, anxiety, stress, etc.

[0481] Step 4:

[0482] The server utilizes generative AI models and natural language processing models to generate personalized suggestions for culture-specific business etiquette and language use, based on user requests and recognized emotional states.

[0483] Step 5:

[0484] The server sends the generated suggestions to the terminal. These suggestions include language and communication tone that best suit the user's current emotional state.

[0485] Step 6:

[0486] The device presents the received suggestions to the user. The user then adjusts their communication based on these suggestions.

[0487] Step 7:

[0488] Users communicate with individuals from different cultural backgrounds based on the suggested content. This reduces the risk of misunderstandings and facilitates effective communication of intentions.

[0489] Step 8:

[0490] Users input feedback on the communication results and the usefulness of the suggestions into their devices and send it to the server. This feedback is used to improve the system in the future.

[0491] Step 9:

[0492] The server analyzes user feedback and updates the sentiment engine and suggestion generation models. The data collected here is used to improve the overall accuracy of the system and create a better user experience.

[0493] (Example 2)

[0494] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0495] In intercultural communication, misunderstandings frequently occur due to differences in cultural background and language, hindering smooth dialogue. Furthermore, depending on the user's emotional state, they may be unable to select an appropriate communication method, potentially leading to further misunderstandings. Conventional technologies lack sufficient flexible and immediate support to address these issues, highlighting the need for a system that enables users to express their emotions appropriately while reducing cultural misunderstandings.

[0496] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0497] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific manners and language; a server that recognizes the user's emotional state in real time using an emotion analysis engine and adjusts the content of the communication proposals to suit the emotions; and a server that transmits the generated proposals and advice to the user's terminal for visual presentation. This enables the user to reduce cross-cultural misunderstandings and engage in effective communication that is adapted to their emotions.

[0498] A "generative AI model" refers to an algorithm that learns specific patterns and rules from multiple data inputs and generates creative results for new data.

[0499] A "natural language processing model" refers to a collection of algorithms and technologies that enable computers to understand and process the language that humans use in everyday life.

[0500] An "emotion analysis engine" refers to a technology that identifies a user's emotional state from text and audio data and performs quantitative evaluations based on that.

[0501] "User communication requests" refer to information that users input to seek assistance in communicating in a specific context or situation.

[0502] "Culture-specific manners" refer to the general etiquette and behaviors within a particular culture.

[0503] "Adjusting to suit emotions" means taking into account the user's current emotional state and appropriately modifying the suggested communication style.

[0504] "Visual presentation" refers to making information easily viewable on the user's device through screen displays and graphic elements.

[0505] This invention is a system that supports intercultural communication and provides a communication method adapted to the user's culture-specific needs and emotional state. This system consists of three main elements: a server, a terminal, and a user, and is implemented using a variety of technologies.

[0506] The server hosts generative AI models, natural language processing models, and sentiment analysis engines, which are used to analyze user input. Generative AI models have the ability to learn from large amounts of data and generate new suggestions, while natural language processing models analyze input language data to enable natural dialogue. The sentiment analysis engine is used to recognize the user's emotional state in real time from text and speech.

[0507] The terminal is a device that the user uses as an interface. The user requests communication assistance through applications provided on the terminal, and the resulting text and voice data are sent to the server. Based on this data, the server generates suggestions that take into account the user's cultural background and current emotional state, and presents them visually to the user by returning them to the terminal.

[0508] As a concrete example, if a user is feeling nervous when trying to send an email to a business partner in a certain country, the sentiment analysis engine will detect this emotion. The server will then generate suggestions adapted to this emotional state, showing the user relaxing expressions and messages. An example of a prompt might be, "Please suggest relaxing expressions that can be used in a business setting in that country, which would be helpful if the user is feeling nervous."

[0509] In this way, the entire system works in coordination, enabling users to communicate effectively across cultures and respond appropriately, taking into account the influence of emotions.

[0510] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0511] Step 1:

[0512] The user launches an application on their device and enters a request for cross-cultural communication support. This input can be in text or voice format, and the device sends it to the server. The device formats the request data as a packet and transfers it to the server using a secure communication protocol.

[0513] Step 2:

[0514] The server processes data received from the terminal using an emotion analysis engine. Input includes user text and voice data, which the emotion analysis engine analyzes to output the emotional state. Specifically, it extracts emotional indicators using text analysis algorithms and voice processing technologies, and provides the results to the next step.

[0515] Step 3:

[0516] The server utilizes generative AI models and natural language processing models to create communication suggestions related to the user's emotional state and culture. The input consists of the emotional state obtained in step 2 and the user's communication request. The generative AI model performs data calculations based on these inputs and outputs suggestions that include culture-specific manners and language. A prompt such as "Generate an appropriate business email example based on the user's emotional state" might be used.

[0517] Step 4:

[0518] The server sends the generated communication proposal to the terminal. The output is the proposal content in text format, and the server uses a communication protocol to send it to the terminal. Upon receiving this information, the terminal visually presents it to the user. Specifically, the proposal content is displayed on the screen as a pop-up or notification.

[0519] Step 5:

[0520] Users review the suggestions displayed on the screen and use them as a basis for cross-cultural communication. The output is a more appropriate communication strategy, which users can then use to proceed with actual emails and conversations. Specific actions include preparing to send emails and simulating conversations.

[0521] (Application Example 2)

[0522] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0523] In intercultural communication, the challenge lies in mitigating misunderstandings and anxieties arising from linguistic and cultural differences, and enabling users to communicate with confidence. In particular, when emotional states can cause misunderstandings, real-time support that takes these states into account is necessary. Furthermore, in situations involving direct communication, such as electronic payments, it is essential to provide an environment where users can comfortably engage in dialogue.

[0524] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0525] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific business manners and language; a server that analyzes the user's emotional state using emotion recognition technology and adjusts the communication style in real time based on the emotional state; and a server that evaluates the effectiveness of the suggestions based on emotional information and forms a feedback loop for future system improvements. This enables users to feel emotionally secure in cross-cultural communication and to engage in appropriate dialogue in real time with confidence.

[0526] "Intercultural communication" is the process by which individuals and groups from different cultural backgrounds communicate and understand each other, overcoming differences in language, customs, and values.

[0527] A "generative AI model" is a type of artificial intelligence that performs language generation and task execution based on large amounts of data, and its ability to generate natural language expressions is a key feature of this technology.

[0528] A "natural language processing model" is an algorithm or model that allows computers to understand and process human language, possessing the ability to analyze human language and extract necessary information.

[0529] "Emotion recognition technology" is a technology that analyzes and recognizes a user's emotional state from their voice and text data, enabling appropriate responses based on individual emotions.

[0530] "Communication style" refers to the manner of interaction during a conversation, including the methods of information transmission and the characteristics of language use, which vary depending on the specific culture and individual traits.

[0531] A "feedback loop" is a process in which a system continuously improves itself based on the information it receives, and it is a method for making suggestions and responses function more effectively.

[0532] "Real-time" refers to a temporal concept where information processing, communication, and responses occur almost instantly with virtually no delay, indicating a state where users can obtain the information they need immediately.

[0533] This invention constructs a system that supports intercultural communication through the coordinated efforts of a server, terminal, and user. The server hosts generative AI models and natural language processing models, and further analyzes the user's emotional state using emotion recognition technology. Based on data input by the user (e.g., voice data and text data), this system proposes a communication style specific to the user's culture.

[0534] Specifically, the server receives the user's voice or text input and uses emotion recognition technology to determine their emotions at that time. Based on this, a generative AI model devises the optimal communication method and sends suggestions to the user in real time. In this process, the user's emotional state is a key indicator, and the server adjusts its communication style based on the analysis results.

[0535] The terminal acts as an interface for the user to interact with this system and displays suggestions generated by the server. The user can refer to these suggestions to receive assistance in effectively communicating across cultures.

[0536] For example, if a user traveling feels anxious when making an electronic payment, emotion recognition can detect this and suggest "expressions that create a calm atmosphere." This suggestion is displayed on the terminal and helps the user to conduct transactions with peace of mind.

[0537] An example of a prompt might be, "Please suggest a communication method that will help the user feel emotionally comfortable in a cross-cultural environment." In this way, it becomes possible to provide dialogue support that is adapted to the user's emotional state and specific to the local culture.

[0538] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0539] Step 1:

[0540] The terminal receives user input. The user enters communication assistance requests in voice or text format. This input data is sent by the terminal to the server. The input data contains important information for inferring the user's real-time emotional state.

[0541] Step 2:

[0542] The server analyzes the user input it receives. Emotion recognition technology, hosted on the server, uses this data to analyze the user's emotional state. It extracts features from the voice and text data and estimates the current emotional state in real time. As a result of the analysis, data about the user's emotional state is obtained.

[0543] Step 3:

[0544] The server uses a generative AI model to create communication suggestions. The server utilizes the generative AI model, taking analyzed emotional state data and prompt sentences as input, to generate linguistic expressions that suggest culture-specific communication methods appropriate to the user's situation. The output is suggestions tailored to the user's emotional state.

[0545] Step 4:

[0546] The server sends the generated suggestions to the terminal. The terminal then presents the received suggestions to the user, providing support to help the user communicate effectively across cultures. The suggestions are displayed on the user's screen and can be referenced immediately to aid communication.

[0547] Step 5:

[0548] Users will use the suggestions as a reference for their communication. By utilizing these suggestions, they can improve communication in ongoing conversations and situations such as electronic payments. User experiences may also be fed back into the system as feedback, contributing to further system improvements.

[0549] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0550] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0551] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0552] [Fourth Embodiment]

[0553] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0554] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0555] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0556] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0557] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0558] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0559] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0560] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0561] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0562] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0563] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0564] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0565] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0566] This invention is implemented as an AI agent system that solves problems in intercultural communication. The system mainly consists of three elements: a server, a terminal, and a user, and operates as follows.

[0567] The server hosts generative AI models and natural language processing models, generating culture-specific communication suggestions based on user requests. First, the server receives a user request and retrieves information on appropriate business etiquette and language usage from a cultural database. Next, it analyzes the request using the natural language processing model and creates suggestions. These suggestions are then personalized, taking into account the user's past profile information and history.

[0568] The terminal functions as an interface through which the user accesses the AI ​​agent system and sends requests to the server. Requests sent by the user via the terminal are passed to the server, and the generated suggestions are returned to the terminal. The terminal clearly displays the suggested communication style and business etiquette to the user. Users can also send feedback through the terminal.

[0569] Users can utilize the system to improve their communication skills during cross-cultural interactions. For example, during meetings with business partners from diverse cultural backgrounds, users can receive real-time advice from an AI agent to make appropriate statements. Users can also participate in automatically generated training and workshop content to promote multicultural understanding.

[0570] As a concrete example, when a user sends an email to a client in Germany, the server generates suggestions that include business etiquette specific to that culture and presents them to the user via their device. Based on these suggestions, the user can adjust the email content to ensure that their intended message is correctly conveyed. This entire process helps prevent misunderstandings in cross-cultural communication and contributes to the success of the project.

[0571] The following describes the processing flow.

[0572] Step 1:

[0573] The user launches the AI ​​agent application using their device and enters a request for communication support related to a specific cultural context. For example, the user might enter, "I would like suggestions for an email to send to a business partner in Germany."

[0574] Step 2:

[0575] The terminal sends the user's request data to the server. The request includes the user's request details and profile information.

[0576] Step 3:

[0577] The server analyzes the received request and searches for and extracts appropriate business etiquette and language-specific information from relevant cultural databases.

[0578] Step 4:

[0579] The server uses a natural language processing model to analyze user requests in detail and generates personalized communication suggestions based on the user's cultural background and past history.

[0580] Step 5:

[0581] The server sends the generated suggestions to the terminal. The suggestions include specific wording and example sentences that correspond to the user's request.

[0582] Step 6:

[0583] The device receives suggestions from the server and displays them to the user. The user then adjusts the content of their communication based on these suggestions.

[0584] Step 7:

[0585] The user reviews the generated suggestions and incorporates them into communications as needed. For example, the user prepares to send an email to a client with the suggested content.

[0586] Step 8:

[0587] Users evaluate the quality and suitability of the suggestions provided by entering feedback into their device after communication and sending it to the server.

[0588] Step 9:

[0589] The server analyzes user feedback data and updates the database and models to improve the quality of future suggestions.

[0590] (Example 1)

[0591] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0592] In intercultural communication, misunderstandings and friction arising from differences in culturally specific behavioral norms and language use make efficient and effective interaction difficult. Furthermore, conventional methods struggle to provide timely and appropriate advice, creating a need for immediate communication support. This invention aims to address these challenges by providing a system that proposes the optimal communication style tailored to the individual circumstances of users with diverse cultural backgrounds.

[0593] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0594] In this invention, the server includes means for analyzing the user's communication requests using generative models and language processing models and proposing culture-specific behavioral norms and language use; means for generating personalized guidance based on user characteristics and past recorded data; and means for transmitting the generated suggestions and guidance to the user's information terminal for immediate presentation. This enables the real-time presentation of appropriate communication styles for diverse cultural backgrounds.

[0595] Intercultural communication is the process by which individuals and groups with different cultural backgrounds communicate with each other.

[0596] A "generative model" is a general term for algorithms and systems that automatically generate new information or suggestions based on data.

[0597] A "language processing model" is a computer program designed to understand, analyze, and generate human language.

[0598] A "code of conduct" refers to standards or rules regarding appropriate behavior and language use within a particular culture or society.

[0599] "User characteristics" refer to the attributes and features of individual users who use the system.

[0600] "Past record data" refers to historical information saved based on a user's previous activities and interactions.

[0601] "Personalized instruction" means providing suggestions and advice that are optimized according to the user's characteristics and history.

[0602] An "information terminal" is an electronic device used by a user to access the functions of a system.

[0603] This invention is realized by using generative models and language processing models as a system to support intercultural communication. Specific embodiments are described below.

[0604] The server is responsible for hosting generative AI models (e.g., GPT) and natural language processing models (e.g., BERT). This allows it to analyze prompt sentences sent by users and generate suggestions regarding the behavioral norms and language use of the relevant culture. The server first receives prompt sentences from users via a terminal. For example, a possible prompt might be, "Please tell me the appropriate way to greet someone in a Japanese business meeting." Upon receiving this prompt, the server retrieves data related to Japanese business culture from its cultural database.

[0605] The terminal functions as an interface for users to send prompt messages to the server. By using the terminal to input prompt messages and sending them to the server, users can receive advice on culture-specific communication styles and language use. The terminal displays the received suggestions to the user in an easy-to-understand manner. For example, in Japanese business culture, specific advice such as "It is important to start with a bow and make eye contact when introducing yourself" might be displayed.

[0606] This system allows users to deepen their understanding of different cultures and communicate effectively. Furthermore, through personalized guidance provided by the system, users can improve their business skills and cross-cultural understanding.

[0607] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0608] Step 1:

[0609] The user enters a prompt message into the system via a terminal. This prompt message is intended to provide specific advice on intercultural communication and may include, for example, "Please tell me how to greet people in a Japanese business meeting." The entered prompt message is then sent to the server.

[0610] Step 2:

[0611] The server inputs the prompt text received from the terminal into a generation AI model and a natural language processing model. These models analyze the content of the prompt text and determine which cultural information is needed. Data processing involves extracting keywords and important phrases from the prompt text and identifying relevant cultural information based on these. As a result of the analysis, the system is ready to acquire appropriate cultural data.

[0612] Step 3:

[0613] Based on the analysis results, the server retrieves information about the behavioral norms and language of the relevant culture from the cultural database. Here, queries are executed on the database to extract business etiquette and communication styles related to the specific culture. As a result, the necessary cultural background information is returned to the server.

[0614] Step 4:

[0615] The server uses a generative AI model based on acquired cultural information to generate optimal communication suggestions for the user. This process also considers previous user profile data and history to personalize the suggestions. The generated suggestions include specific actions the user should take. For example, "It's important to first bow, and then make eye contact when introducing yourself."

[0616] Step 5:

[0617] The server sends the generated suggestions to the terminal. The terminal displays these suggestions in an easy-to-understand visual format for the user. This output is designed to help the user concretely visualize their next action. The user can review the suggestions and prepare to engage in actual communication based on them.

[0618] (Application Example 1)

[0619] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0620] In intercultural communication, misunderstandings and inappropriate expressions are common due to differences in cultural backgrounds. Furthermore, a lack of real-time communication support makes effective information exchange between participants from different cultures difficult. To address these challenges, a system is needed that smoothly supports communication tailored to the cultural characteristics of the user.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0622] In this invention, the server includes means for analyzing the user's communication requests using a generative AI model and a natural language processing model and suggesting culture-specific business manners and language; means for generating personalized advice based on the user profile and past history data; means for transmitting the generated suggestions and advice to the user's information terminal and presenting them in real time; and means for suggesting cultural contexts and appropriate expressions based on voice input to facilitate communication between participants from different cultures. This makes it possible to minimize misunderstandings between cultures and achieve smooth communication.

[0623] "Intercultural communication" is the process of information exchange and dialogue that takes place between individuals or groups with different cultural backgrounds.

[0624] A "generative AI model" is a mathematical and algorithmic structure that uses artificial intelligence technology to automatically generate new suggestions and ideas from specific data.

[0625] A "natural language processing model" is a technology that enables the understanding, generation, and analysis of human language, and is designed to process text and audio data.

[0626] A "user profile" is a dataset that compiles information about an individual user, including their behavior, preferences, and past history.

[0627] "Real-time presentation" means a process that provides users with the information and advice they need immediately, enabling a response that is relevant to the current situation without delay.

[0628] "Voice input" is a method of capturing instructions and information provided in a human voice in a digital format, and it utilizes speech recognition technology.

[0629] The system implementing this invention mainly consists of a server, a terminal, and a user. The server hosts a generative AI model and a natural language processing model, and generates advice and suggestions aimed at improving intercultural communication based on the user's request. The server runs on Flask using Python, uses TensorFlow for natural language processing, and utilizes the Google Speech-to-Text API for speech data analysis.

[0630] The terminal acts as an interface that sends user-inputted requests to the server and presents the user with suggestions and advice received from the server. Users can input requests via voice or text using smartphones or tablets. The interface employs an intuitive design to provide a user-friendly experience.

[0631] Users can use this system to receive advice on cross-cultural communication. For example, a foreign client unfamiliar with Japanese business etiquette can use this application to learn and apply specific manners and expressions to use during meetings in a timely manner. In this way, misunderstandings between cultures are reduced, and effective communication is achieved.

[0632] For example, a user can enter a question such as, "What are some cultural points I should pay particular attention to in my next presentation to my German partner?" Based on this prompt, the server will generate appropriate cultural advice.

[0633] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0634] Step 1:

[0635] The user uses a device to input requests via voice or text. These inputs are questions or clarifications related to cross-cultural communication the user is facing. The device converts these inputs into text data and sends it to the server.

[0636] Step 2:

[0637] The server inputs the received request into a generating AI model. This model uses natural language processing techniques to analyze the request and retrieve necessary cultural background and business etiquette information from a cultural database. As a result of data processing, a set of appropriate advice and suggestions related to the specific culture is generated.

[0638] Step 3:

[0639] The generated advice is further personalized by the server based on the user profile and historical data. The server optimizes the generated suggestions for each user, adjusting them to be optimal for their specific situation and background. The output here is a personalized set of advice.

[0640] Step 4:

[0641] The server sends personalized advice to the terminal. The terminal displays this information to the user in real time, providing necessary cultural context and expressions in audio or text format. The output is an interface display that makes it easy for the user to understand the presented information.

[0642] Step 5:

[0643] Users communicate based on the advice provided. They can input feedback on the results of applying the advice through their device. This feedback is sent to the server for data analysis so that it can be used to improve future advice.

[0644] Step 6:

[0645] The server analyzes the received feedback and uses it as data to improve the accuracy of its generative AI models and natural language processing models. This continuously improves the quality of future suggestions and advice, resulting in a better user experience on subsequent visits.

[0646] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0647] This invention is implemented as an intercultural communication support system that incorporates an emotion engine. This system is based on three elements: a server, a terminal, and a user, and utilizes the emotion engine to provide communication support that is tailored to the user's emotional state.

[0648] The server hosts a sentiment engine in addition to generative AI models and natural language processing models. First, the server receives requests from users and generates suggestions for culture-specific business etiquette and language use. The sentiment engine analyzes user input data (text, voice, etc.) and recognizes the user's emotional state in real time. Based on this, the server adjusts the suggestions to match the user's emotions, providing the optimal communication method.

[0649] The terminal functions as an interface for the user to interact with the system. When the user launches the AI ​​agent application on the terminal and enters a request for communication support, data including the user's sentiment information is sent to the server. Suggestions received from the server are displayed to the user on the terminal.

[0650] Users can refer to submitted suggestions and utilize them in their communication to reduce culture-specific misunderstandings and enable flexible responses tailored to their emotions. For example, if a user is feeling tense during a meeting, the emotion engine can detect this and suggest a more relaxing communication style. Training and workshop content are also provided with the user's emotional patterns in mind, contributing to a deeper understanding of multiculturalism.

[0651] For example, when a user is feeling stressed and sends an email to a German business partner, the emotion engine recognizes that emotion and recommends using stress-reducing language and positive messages. This allows the user to communicate more effectively and prevent cross-cultural misunderstandings. By providing communication support tailored to individual cultural backgrounds and emotional states, this system contributes to project success and improved work environments.

[0652] The following describes the processing flow.

[0653] Step 1:

[0654] The user opens an AI agent application via their device and inputs their emotional state and the cultural context in which they seek communication support. At this point, the user's voice and text data may also be collected.

[0655] Step 2:

[0656] The device sends data collected from the user to the server. This data includes input (voice, text) to detect the user's emotional state, the requested cultural context, and the user's profile information.

[0657] Step 3:

[0658] The server uses an emotion engine to analyze user input data and recognize the user's emotional state in real time. Emotional states are classified as joy, sadness, anxiety, stress, etc.

[0659] Step 4:

[0660] The server utilizes generative AI models and natural language processing models to generate personalized suggestions for culture-specific business etiquette and language use, based on user requests and recognized emotional states.

[0661] Step 5:

[0662] The server sends the generated suggestions to the terminal. These suggestions include language and communication tone that best suit the user's current emotional state.

[0663] Step 6:

[0664] The device presents the received suggestions to the user. The user then adjusts their communication based on these suggestions.

[0665] Step 7:

[0666] Users communicate with individuals from different cultural backgrounds based on the suggested content. This reduces the risk of misunderstandings and facilitates effective communication of intentions.

[0667] Step 8:

[0668] Users input feedback on the communication results and the usefulness of the suggestions into their devices and send it to the server. This feedback is used to improve the system in the future.

[0669] Step 9:

[0670] The server analyzes user feedback and updates the sentiment engine and suggestion generation models. The data collected here is used to improve the overall accuracy of the system and create a better user experience.

[0671] (Example 2)

[0672] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0673] In intercultural communication, misunderstandings frequently occur due to differences in cultural background and language, hindering smooth dialogue. Furthermore, depending on the user's emotional state, they may be unable to select an appropriate communication method, potentially leading to further misunderstandings. Conventional technologies lack sufficient flexible and immediate support to address these issues, highlighting the need for a system that enables users to express their emotions appropriately while reducing cultural misunderstandings.

[0674] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0675] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific manners and language; a server that recognizes the user's emotional state in real time using an emotion analysis engine and adjusts the content of the communication proposals to suit the emotions; and a server that transmits the generated proposals and advice to the user's terminal for visual presentation. This enables the user to reduce cross-cultural misunderstandings and engage in effective communication that is adapted to their emotions.

[0676] A "generative AI model" refers to an algorithm that learns specific patterns and rules from multiple data inputs and generates creative results for new data.

[0677] A "natural language processing model" refers to a collection of algorithms and technologies that enable computers to understand and process the language that humans use in everyday life.

[0678] An "emotion analysis engine" refers to a technology that identifies a user's emotional state from text and audio data and performs quantitative evaluations based on that.

[0679] "User communication requests" refer to information that users input to seek assistance in communicating in a specific context or situation.

[0680] "Culture-specific manners" refer to the general etiquette and behaviors within a particular culture.

[0681] "Adjusting to suit emotions" means taking into account the user's current emotional state and appropriately modifying the suggested communication style.

[0682] "Visual presentation" refers to making information easily viewable on the user's device through screen displays and graphic elements.

[0683] This invention is a system that supports intercultural communication and provides a communication method adapted to the user's culture-specific needs and emotional state. This system consists of three main elements: a server, a terminal, and a user, and is implemented using a variety of technologies.

[0684] The server hosts generative AI models, natural language processing models, and sentiment analysis engines, which are used to analyze user input. Generative AI models have the ability to learn from large amounts of data and generate new suggestions, while natural language processing models analyze input language data to enable natural dialogue. The sentiment analysis engine is used to recognize the user's emotional state in real time from text and speech.

[0685] The terminal is a device that the user uses as an interface. The user requests communication assistance through applications provided on the terminal, and the resulting text and voice data are sent to the server. Based on this data, the server generates suggestions that take into account the user's cultural background and current emotional state, and presents them visually to the user by returning them to the terminal.

[0686] As a concrete example, if a user is feeling nervous when trying to send an email to a business partner in a certain country, the sentiment analysis engine will detect this emotion. The server will then generate suggestions adapted to this emotional state, showing the user relaxing expressions and messages. An example of a prompt might be, "Please suggest relaxing expressions that can be used in a business setting in that country, which would be helpful if the user is feeling nervous."

[0687] In this way, the entire system works in coordination, enabling users to communicate effectively across cultures and respond appropriately, taking into account the influence of emotions.

[0688] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0689] Step 1:

[0690] The user launches an application on their device and enters a request for cross-cultural communication support. This input can be in text or voice format, and the device sends it to the server. The device formats the request data as a packet and transfers it to the server using a secure communication protocol.

[0691] Step 2:

[0692] The server processes data received from the terminal using an emotion analysis engine. Input includes user text and voice data, which the emotion analysis engine analyzes to output the emotional state. Specifically, it extracts emotional indicators using text analysis algorithms and voice processing technologies, and provides the results to the next step.

[0693] Step 3:

[0694] The server utilizes generative AI models and natural language processing models to create communication suggestions related to the user's emotional state and culture. The input consists of the emotional state obtained in step 2 and the user's communication request. The generative AI model performs data calculations based on these inputs and outputs suggestions that include culture-specific manners and language. A prompt such as "Generate an appropriate business email example based on the user's emotional state" might be used.

[0695] Step 4:

[0696] The server sends the generated communication proposal to the terminal. The output is the proposal content in text format, and the server uses a communication protocol to send it to the terminal. Upon receiving this information, the terminal visually presents it to the user. Specifically, the proposal content is displayed on the screen as a pop-up or notification.

[0697] Step 5:

[0698] Users review the suggestions displayed on the screen and use them as a basis for cross-cultural communication. The output is a more appropriate communication strategy, which users can then use to proceed with actual emails and conversations. Specific actions include preparing to send emails and simulating conversations.

[0699] (Application Example 2)

[0700] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0701] In intercultural communication, the challenge lies in mitigating misunderstandings and anxieties arising from linguistic and cultural differences, and enabling users to communicate with confidence. In particular, when emotional states can cause misunderstandings, real-time support that takes these states into account is necessary. Furthermore, in situations involving direct communication, such as electronic payments, it is essential to provide an environment where users can comfortably engage in dialogue.

[0702] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0703] This invention includes a server that analyzes the user's communication requests using a generative AI model and a natural language processing model and proposes culture-specific business manners and language; a server that analyzes the user's emotional state using emotion recognition technology and adjusts the communication style in real time based on the emotional state; and a server that evaluates the effectiveness of the suggestions based on emotional information and forms a feedback loop for future system improvements. This enables users to feel emotionally secure in cross-cultural communication and to engage in appropriate dialogue in real time with confidence.

[0704] "Intercultural communication" is the process by which individuals and groups from different cultural backgrounds communicate and understand each other, overcoming differences in language, customs, and values.

[0705] A "generative AI model" is a type of artificial intelligence that performs language generation and task execution based on large amounts of data, and its ability to generate natural language expressions is a key feature of this technology.

[0706] A "natural language processing model" is an algorithm or model that allows computers to understand and process human language, possessing the ability to analyze human language and extract necessary information.

[0707] "Emotion recognition technology" is a technology that analyzes and recognizes a user's emotional state from their voice and text data, enabling appropriate responses based on individual emotions.

[0708] "Communication style" refers to the manner of interaction during a conversation, including the methods of information transmission and the characteristics of language use, which vary depending on the specific culture and individual traits.

[0709] A "feedback loop" is a process in which a system continuously improves itself based on the information it receives, and it is a method for making suggestions and responses function more effectively.

[0710] "Real-time" refers to a temporal concept where information processing, communication, and responses occur almost instantly with virtually no delay, indicating a state where users can obtain the information they need immediately.

[0711] This invention constructs a system that supports intercultural communication through the coordinated efforts of a server, terminal, and user. The server hosts generative AI models and natural language processing models, and further analyzes the user's emotional state using emotion recognition technology. Based on data input by the user (e.g., voice data and text data), this system proposes a communication style specific to the user's culture.

[0712] Specifically, the server receives the user's voice or text input and uses emotion recognition technology to determine their emotions at that time. Based on this, a generative AI model devises the optimal communication method and sends suggestions to the user in real time. In this process, the user's emotional state is a key indicator, and the server adjusts its communication style based on the analysis results.

[0713] The terminal acts as an interface for the user to interact with this system and displays suggestions generated by the server. The user can refer to these suggestions to receive assistance in effectively communicating across cultures.

[0714] For example, if a user traveling feels anxious when making an electronic payment, emotion recognition can detect this and suggest "expressions that create a calm atmosphere." This suggestion is displayed on the terminal and helps the user to conduct transactions with peace of mind.

[0715] An example of a prompt might be, "Please suggest a communication method that will help the user feel emotionally comfortable in a cross-cultural environment." In this way, it becomes possible to provide dialogue support that is adapted to the user's emotional state and specific to the local culture.

[0716] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0717] Step 1:

[0718] The terminal receives user input. The user enters communication assistance requests in voice or text format. This input data is sent by the terminal to the server. The input data contains important information for inferring the user's real-time emotional state.

[0719] Step 2:

[0720] The server analyzes the user input it receives. Emotion recognition technology, hosted on the server, uses this data to analyze the user's emotional state. It extracts features from the voice and text data and estimates the current emotional state in real time. As a result of the analysis, data about the user's emotional state is obtained.

[0721] Step 3:

[0722] The server uses a generative AI model to create communication suggestions. The server utilizes the generative AI model, taking analyzed emotional state data and prompt sentences as input, to generate linguistic expressions that suggest culture-specific communication methods appropriate to the user's situation. The output is suggestions tailored to the user's emotional state.

[0723] Step 4:

[0724] The server sends the generated suggestions to the terminal. The terminal then presents the received suggestions to the user, providing support to help the user communicate effectively across cultures. The suggestions are displayed on the user's screen and can be referenced immediately to aid communication.

[0725] Step 5:

[0726] Users will use the suggestions as a reference for their communication. By utilizing these suggestions, they can improve communication in ongoing conversations and situations such as electronic payments. User experiences may also be fed back into the system as feedback, contributing to further system improvements.

[0727] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0728] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0730] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0731] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0732] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0733] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0734] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0735] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0736] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0737] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0738] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0739] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0740] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0741] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0742] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0743] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0744] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0745] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0746] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0747] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0748] The following is further disclosed regarding the embodiments described above.

[0749] (Claim 1)

[0750] To support intercultural communication,

[0751] A method for analyzing user communication requests using generative AI models and natural language processing models, and proposing culture-specific business manners and language usage,

[0752] A means of generating personalized advice based on user profiles and historical data,

[0753] A means of sending generated suggestions and advice to the user's device and presenting them in real time,

[0754] A system that includes this.

[0755] (Claim 2)

[0756] The system according to claim 1, further comprising means for automatically generating and providing to users cross-cultural training and workshop content.

[0757] (Claim 3)

[0758] The system according to claim 1, further comprising means for receiving user feedback and analyzing data to improve the quality of the proposed content.

[0759] "Example 1"

[0760] (Claim 1)

[0761] To support intercultural communication,

[0762] A means of analyzing users' communication requests using generative models and language processing models, and proposing culture-specific behavioral norms and language usage,

[0763] A means of generating personalized instruction based on user characteristics and past record data,

[0764] A means of sending generated suggestions and guidance to the user's information terminal for immediate presentation,

[0765] A means of obtaining culture-related information from a database based on user requests,

[0766] A means for optimizing suggestions generated based on acquired cultural information according to the user's history,

[0767] A system that includes this.

[0768] (Claim 2)

[0769] The system according to claim 1, further comprising means for automatically generating and providing to users cross-cultural training and educational content.

[0770] (Claim 3)

[0771] The system according to claim 1, further comprising means for receiving feedback from users and analyzing the information in order to improve the quality of the proposed content.

[0772] "Application Example 1"

[0773] (Claim 1)

[0774] To support intercultural communication,

[0775] A method for analyzing user communication requests using generative AI models and natural language processing models, and proposing culture-specific business manners and language usage,

[0776] A means of generating personalized advice based on user profiles and historical data,

[0777] A means of sending generated suggestions and advice to the user's information terminal and presenting them in real time,

[0778] A means of suggesting cultural context and appropriate expression methods based on voice input to facilitate communication among participants from different cultures,

[0779] A system that includes this.

[0780] (Claim 2)

[0781] The system according to claim 1, further comprising means for automatically generating and providing to users cross-cultural training and workshop content.

[0782] (Claim 3)

[0783] The system according to claim 1, further comprising means for receiving user feedback and analyzing data to improve the quality of the proposed content.

[0784] "Example 2 of combining an emotion engine"

[0785] (Claim 1)

[0786] To support intercultural communication,

[0787] A method for analyzing user communication requests using generative AI models and natural language processing models, and for suggesting culture-specific manners and language usage,

[0788] A means of recognizing the user's emotional state in real time using an emotion analysis engine and adjusting the content of communication suggestions to match those emotions,

[0789] A means of sending generated suggestions and advice to the user's device and presenting them visually,

[0790] A system that includes this.

[0791] (Claim 2)

[0792] The system according to claim 1, further comprising means for automatically generating cross-cultural training and workshop content and providing it to users in a manner that takes into account their emotional patterns.

[0793] (Claim 3)

[0794] The system according to claim 1, further comprising means for collecting user feedback and analyzing the data to improve the quality of the suggestions and the effectiveness of the communication methods.

[0795] "Application example 2 when combining with an emotional engine"

[0796] (Claim 1)

[0797] To support intercultural communication,

[0798] A method for analyzing user communication requests using generative AI models and natural language processing models, and proposing culture-specific business manners and language usage,

[0799] A means of generating personalized advice based on user profiles and historical data,

[0800] A means of sending generated suggestions and advice to the user's device and presenting them in real time,

[0801] A means of analyzing a user's emotional state using emotion recognition technology and adjusting the communication style in real time based on that emotional state,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] A means of automatically generating and providing cross-cultural training and workshop content to users,

[0805] The system according to claim 1, further comprising means for the user to propose language expressions that take into account the emotional state of the user in communication with a business partner.

[0806] (Claim 3)

[0807] A means of receiving user feedback and analyzing data to improve the quality of suggestions,

[0808] The system according to claim 1, further comprising means for evaluating the effectiveness of proposals based on emotional information and for forming a feedback loop for future system improvements. [Explanation of Symbols]

[0809] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. To support intercultural communication, A method for analyzing user communication requests using generative AI models and natural language processing models, and proposing culture-specific business manners and language usage, A means of generating personalized advice based on user profiles and historical data, A means of sending generated suggestions and advice to the user's device and presenting them in real time, A system that includes this.

2. The system according to claim 1, further comprising means for automatically generating and providing to users cross-cultural training and workshop content.

3. The system according to claim 1, further comprising means for receiving user feedback and analyzing data to improve the quality of the proposed content.

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