system

Abstract

We provide the system. [Solution] To support communication between multiple individuals with different cultural backgrounds, a means of using generative artificial intelligence that collects and learns cultural information, A means of analyzing user input to identify the appropriate cultural context, A means for generating cultural expressions according to the user's situation using the aforementioned artificial intelligence for generation, A means of presenting the generated cultural expression to the user, A means for collecting user feedback and retraining the model of the generative artificial intelligence, 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: 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 in response 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 recent years, with the progress of globalization, the opportunities for individuals with different cultural backgrounds to work together have increased. However, communication problems caused by cultural differences still exist, causing misunderstandings and frictions. Such problems are particularly prominent between foreign workers and local employees, becoming obstacles in smooth business operations and the establishment of interpersonal relationships. Therefore, there is a need to provide an effective method to support communication between individuals with different cultural backgrounds and remove cultural barriers.

Means for Solving the Problems

[0005] This invention solves the above problems by providing a means for collecting and learning cultural information using generative artificial intelligence technology. Specifically, it constructs a system that can analyze information input by a user, identify an appropriate cultural context, and present generated cultural expressions. Furthermore, it enhances support for deepening cultural understanding by collecting user feedback and retraining the artificial intelligence model. It also includes means for providing personalized learning content based on usage history and generating detailed information guides on nonverbal communication and cultural events. In this way, the present invention aims to prevent cultural misunderstandings and friction and promote smooth communication.

[0006] "Different cultural backgrounds" refers to the collective term for the unique cultural values ​​and behavioral patterns that each individual possesses, shaped by geographical, historical, social, or religious factors.

[0007] "Communication between individuals" is the act of mutually transmitting information and emotions through verbal and nonverbal means.

[0008] "Generative artificial intelligence" is a technology that automatically generates new information and content by learning knowledge from large datasets and performing natural language generation and contextual analysis.

[0009] "Cultural information" refers to knowledge and data concerning lifestyles, customs, values, language, and nonverbal expressions in a particular society or region.

[0010] "Cultural context" is a concept that refers to the meaning and significance of events and actions within a particular cultural sphere, as well as the circumstances and background in which they occur.

[0011] "Cultural expression" refers to linguistic or non-linguistic methods and techniques for conveying cultural values ​​or messages to others.

[0012] "Personalized learning content" refers to educational materials and information optimized based on the individual user's needs and usage history.

[0013] "Nonverbal communication" refers to the act of conveying information through means other than language, and includes gestures, facial expressions, body language, and eye contact.

[0014] "Feedback" refers to information provided as a reaction or evaluation to the output of a particular action or system, and is used to derive further improvements.

[0015] A "guide" is a manual or instruction book that presents specific knowledge or information in an easy-to-understand manner, aiding in learning and understanding. [Brief explanation of the drawing]

[0016] [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] It shows an emotion map on which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Embodiment 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] 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.

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

[0019] In the following embodiments, a processor with a reference numeral (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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.

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

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

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a system that supports communication between individuals with different cultural backgrounds. This system collects vast amounts of cultural information and, based on that information, uses artificial intelligence to provide users with appropriate cultural context and expression. Specifically, it is implemented in the following forms.

[0038] The server periodically collects various cultural information from a cultural database and uses this to train a generative artificial intelligence. The main purpose of the training is to deepen understanding of cultural backgrounds and customs, and to grasp cultural contexts more accurately.

[0039] Users access the system using mobile devices or computers and input specific communication challenges or situations they face through the interface. The device analyzes this input and sets up a cultural context appropriate to the user.

[0040] The server uses artificial intelligence technology to generate the most appropriate cultural representations based on user input. This generated information is provided as concrete scenarios and conversation examples to facilitate understanding of cultural differences.

[0041] For example, consider a scenario where a foreign worker wants to express their opinion to their supervisor in a Japanese workplace. This system provides specific advice on how to show respect and make indirect suggestions within Japanese business culture. It then guides the user with concrete examples of how to express themselves in actual communication situations.

[0042] Furthermore, user feedback is used to readjust the AI ​​model generated by the server. This allows the system to provide more accurate cultural understanding support over time.

[0043] Furthermore, this system provides individually customized learning content based on the user's past usage and needs. This content includes detailed information on nonverbal communication and specific cultural events, helping users navigate diverse cultural situations.

[0044] In this way, the present invention is an effective means of preventing cultural misunderstandings and friction, and of facilitating smooth communication between individuals with different cultural backgrounds.

[0045] The following describes the processing flow.

[0046] Step 1:

[0047] The server collects cultural information from publicly available databases on the internet and from partner sources. This includes articles, papers, books, and multimedia data such as audio and video. This data is then organized and prepared as a training dataset for generative AI.

[0048] Step 2:

[0049] The server uses the collected cultural information to train a generative AI model. During training, it learns to understand intercultural communication patterns and specific cultural customs, and to provide appropriate responses and behaviors in specific situations.

[0050] Step 3:

[0051] Users access the system using their devices and input specific details about the communication problems and situations they are facing. For example, they might clearly describe a challenge such as "how to make improvement suggestions to my boss."

[0052] Step 4:

[0053] The terminal analyzes user input and identifies the context of the problem that needs to be solved. Based on the analysis results, it requests relevant cultural data from the server.

[0054] Step 5:

[0055] The server uses generative AI to generate appropriate cultural expressions and solutions based on the context received from the terminal. For example, it can create information that includes advice on appropriate respectful language and nonverbal cues in Japanese companies.

[0056] Step 6:

[0057] The device provides users with generated cultural representations and solutions. These are presented in the form of specific conversation examples and scenarios, and are structured in visual or text format to ensure easy understanding for the user.

[0058] Step 7:

[0059] Users respond to real-world situations based on the information provided and send feedback to the system regarding its usefulness and effectiveness.

[0060] Step 8:

[0061] The server analyzes user feedback and uses it to improve the generated AI model. This feedback helps improve the accuracy of cultural understanding and is reflected in future use.

[0062] Step 9:

[0063] The server generates customized learning content and cultural event information based on the user's past activities and interests, and provides it to the user via their device. This information serves as a supplementary tool to deepen the user's cultural understanding.

[0064] (Example 1)

[0065] 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."

[0066] Misunderstandings and friction often arise in communication between individuals from different cultural backgrounds. These problems stem from differences in cultural backgrounds and values, hindering smooth communication. Traditional methods require considerable effort and time to acquire individual cultural knowledge, making real-time responses difficult. This invention solves these problems and provides a means to facilitate smooth intercultural communication.

[0067] 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.

[0068] In this invention, the server includes means for using an intelligent processing device to collect information about culture and train an analytical model, means for interpreting situational information input by the user to identify appropriate cultural background information, and means for constructing a cultural expression according to the user's conditions using the generative model. This reduces misunderstandings and friction in communication between individuals with different cultural backgrounds and enables smooth information transmission.

[0069] "Cultural information" refers to data that helps in understanding and interpreting customs, business etiquette, and nonverbal communication based on different cultural backgrounds.

[0070] An "analysis model" refers to a program or algorithm that utilizes artificial intelligence technology to analyze and interpret input information.

[0071] A "user" refers to an individual who uses the system to receive advice on cultural context and expression.

[0072] "Status information" refers to specific information related to the current communication situation or problems that users input into the system.

[0073] "Cultural background information" refers to information that provides relevant cultural elements and context in a particular situation.

[0074] A "generative model" refers to AI technology used to construct appropriate cultural representations based on user input.

[0075] "Cultural expression" refers to linguistic expressions and behavioral guidelines that are appropriate for facilitating communication within a specific cultural context.

[0076] This invention is a system for supporting communication between individuals with different cultural backgrounds. This system primarily consists of three elements: a server, a terminal, and a user.

[0077] server:

[0078] The server collects diverse cultural information from cultural databases and publicly available materials on the internet. This information includes detailed data on cultural customs and nonverbal communication to aid in intercultural understanding. The server also uses this cultural information to train a generative AI model. This model analyzes the collected data and improves its ability to understand different cultural contexts. For example, machine learning frameworks such as TENSORFLOW® or PyTorch could be used.

[0079] Terminal:

[0080] The user accesses the system through a terminal. The terminal receives input from the user using a mobile device or computer. This input includes specific communication challenges and situations the user is facing. The terminal analyzes this input and generates a prompt. This prompt is sent to the server and used to generate the most appropriate cultural expression.

[0081] User:

[0082] Users can use the system to receive specific advice on communication challenges. For example, consider a user who inputs into the system, "I want to propose a new project to my boss, but how should I say it?" Based on this information, the server generates appropriate cultural context and expressions and presents them to the user. The information generated in this process serves as a guide in actual communication situations. Users can also provide feedback on the information provided. This feedback is used by the server to adjust the generated AI model and improve the accuracy of the system.

[0083] Examples of prompts include, "Please advise me on how to make a suitable proposal to my boss." By analyzing these prompts and generating expressions that include appropriate cultural context, users can communicate smoothly.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server collects cultural information.

[0087] The server collects information from cultural databases and reliable sources on the internet. The program automatically searches using cultural keywords to extract the necessary data. The input consists of data collection conditions and targets, and the output is a dataset of cultural information.

[0088] Step 2:

[0089] The server trains the generative AI model.

[0090] The server trains a generative AI model using a dataset of collected cultural information. This dataset includes text data and data converted into vector formats, which are used to train a model for deepening intercultural understanding. The input is the collected data, and the output is the trained generative AI model.

[0091] Step 3:

[0092] Users input communication tasks through their devices.

[0093] The user inputs specific situations and challenges they are facing into the terminal. This input information includes details about the person they are communicating with and the situation. The input is the user's text data, and the output is a prompt sentence for parsing that information.

[0094] Step 4:

[0095] The terminal generates a prompt message and sends it to the server.

[0096] The terminal analyzes the text information received from the user and generates an appropriate prompt. This prompt is used by the server to create the most appropriate cultural expression for the user. The input is the user's input information, and the output is the generated prompt.

[0097] Step 5:

[0098] The server generates cultural expressions using a generative AI model.

[0099] The server uses a generative AI model to generate the most appropriate cultural expression based on the received prompt text. This process constructs appropriate expressions that take cultural differences into account, helping users communicate smoothly. The input is the prompt text, and the output is the cultural expression.

[0100] Step 6:

[0101] The device presents the generated cultural representation to the user.

[0102] The terminal displays cultural expressions sent from the server to the user. The user can use this as a reference to improve their own communication. The input is the generated cultural expression, and the output is the displayed content that the user sees.

[0103] Step 7:

[0104] Users provide feedback to the system.

[0105] The user inputs feedback on the presented cultural representations into the terminal. This feedback is used to improve the system's accuracy. The input is user feedback, and the output is evaluation data analyzed by the server.

[0106] (Application Example 1)

[0107] 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."

[0108] This solution addresses the challenges of misunderstandings and friction in communication between individuals with different cultural backgrounds, particularly the difficulty of presenting users with appropriate cultural context and product descriptions in virtual spaces.

[0109] 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.

[0110] In this invention, the server includes means for using generative artificial intelligence to collect and learn cultural information, means for analyzing user input to identify an appropriate cultural context, and means for generating and presenting product descriptions in a virtual space that are appropriate to the customer's cultural background. This enables culturally appropriate communication and product descriptions that are free from misunderstandings for users with different cultural backgrounds.

[0111] "Different cultural backgrounds" refers to the differences in values, beliefs, customs, languages, and other characteristics unique to individual regions, countries, societies, or individuals.

[0112] "Communication" refers to the process of exchanging information, thoughts, and feelings, and is carried out through verbal and nonverbal means.

[0113] "Cultural information" refers to knowledge, data, and examples related to a particular culture, including history, customs, values, and social norms of conduct.

[0114] "Generative artificial intelligence" refers to artificial intelligence technology that can learn patterns from data and generate human-like responses.

[0115] "Cultural context" refers to the background and assumptions behind information and behaviors within a particular culture, and is an important element for deepening understanding in communication.

[0116] "Product description" refers to an explanation provided to customers that includes information about a product, such as its characteristics, usage, and benefits.

[0117] A "virtual space" refers to a non-physical environment that is artificially created using computer technology.

[0118] "Feedback" refers to the opinions and evaluations that users provide regarding the system's output, and is information used to improve the system.

[0119] The system program for realizing this invention consists of the following elements. First, the server collects cultural information and uses it to train a generative AI model. The model learns patterns based on a vast amount of cultural background data and generates appropriate cultural contexts and expressions in communication between different cultures. Specifically, the generative AI model used is GPT-4 (registered trademark).

[0120] Users access the system via devices such as smartphones or head-mounted displays. These devices analyze user input and use a generative AI model to generate responses and product descriptions that are relevant to the cultural context. Standard smart devices can be used as hardware.

[0121] Based on input from the terminal, the server is responsible for generating appropriate cultural representations. In this process, it calculates data to provide product descriptions in a virtual space that are tailored to the user's cultural background. For example, when guiding a tourist from the United States about a Japanese matcha set, it generates information about the history of matcha and its popularity in the United States.

[0122] As a concrete example, the following prompt sentence is input to the AI ​​generation model: "Provide information about the popularity of matcha in American culture and generate cultural expressions that encourage American tourists to purchase a matcha set." This enables product descriptions that are suitable for customers with different cultural backgrounds.

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] Users access the system via a terminal. Users input basic information and questions into the terminal when they want to communicate with individuals from different cultural backgrounds or understand product information. This input may be in text or voice format.

[0126] Step 2:

[0127] Upon receiving input from the user, the terminal converts the input data into text format and begins analysis. The analysis uses natural language processing techniques to identify the cultural context required for the input information. The output is the analyzed context data.

[0128] Step 3:

[0129] The server receives the analyzed contextual data and utilizes a generative AI model to generate appropriate expressions based on the cultural background. This generative AI model (specifically using GPT-4) generates culturally appropriate expressions based on pre-trained cultural information. The input to the generative AI model is structured as prompt sentences.

[0130] Step 4:

[0131] The generated cultural expressions and product descriptions are sent from the server to the user's terminal. The terminal presents these expressions to the user, providing cultural understanding in the virtual space and appropriate context about the product. The output is displayed in a format that is easy for the user to understand.

[0132] Step 5:

[0133] Users send feedback to the system regarding the information presented. This feedback is recorded to indicate how helpful it was or where improvements are needed. The server uses this feedback to retrain the generating AI model, thereby improving its accuracy over time.

[0134] 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.

[0135] This invention is a system designed to support communication between individuals with different cultural backgrounds. This system combines generative artificial intelligence and an emotion engine to provide appropriate cultural context and expression in response to the user's emotions. Specific embodiments are described below.

[0136] The server collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. The collected data includes information about cultural customs and communication styles, and is used to improve the accuracy of the model.

[0137] Users access the system through their device and input specific communication challenges or situations. During this process, the device uses a built-in emotion engine to recognize the user's emotions in real time. The emotion engine analyzes voice tone, facial expressions, and emotional indicators extracted from text to determine the user's emotional state.

[0138] The server generates appropriate cultural context based on user input and data from the emotion engine, and the generative AI produces the most suitable cultural expressions for the user. For example, if the user is feeling surprised or nervous, suggestions and approaches that take those emotions into consideration will be taken into account.

[0139] The device presents the generated cultural expressions to the user and provides concrete conversation examples and scenarios to help the user understand how to apply them in real-world situations. For example, if a user is feeling anxious during a workplace meeting, the device will offer emotionally-based advice, such as conversational techniques to help them relax and appropriate gestures.

[0140] User feedback is collected by the server, used to retrain the generative AI model, and utilized for future improvements. This allows the system, which combines an emotion engine with generative artificial intelligence, to improve the quality of its cultural understanding with each use.

[0141] Furthermore, the device provides individually customized learning content based on the user's past usage history and sentiment analysis results. This content serves as supplementary material to deepen the user's knowledge and skills in adapting to diverse cultural backgrounds.

[0142] Thus, the present invention combines cultural information and emotion recognition technology to provide an effective solution for facilitating communication between individuals with different cultural backgrounds.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The server regularly collects diverse cultural information from open-source databases and online resources. This cultural information includes greetings, business customs, and nonverbal communication patterns in different countries. This data is then organized into a dataset necessary for training generative AI.

[0146] Step 2:

[0147] The server uses the collected data to train a generative AI model. During training, it learns about intercultural communication methods and typical response patterns, and adjusts the model to understand appropriate cultural expressions in each situation.

[0148] Step 3:

[0149] Users access the system using a terminal and input the specific problems or situations they are encountering. At this time, specific examples can be provided, such as "How to greet someone for the first time at a new workplace."

[0150] Step 4:

[0151] The device recognizes the user's emotions using a built-in emotion engine. The emotion engine automatically analyzes the emotional state from facial expressions, voice tone, and the user's text to identify the type and intensity of the emotion.

[0152] Step 5:

[0153] The device sends user input and the results of the emotion engine's analysis to the server. Based on this data, the server identifies the optimal cultural context and uses generative AI to construct a cultural representation best suited to the user.

[0154] Step 6:

[0155] The server sends the generated cultural expressions and suggestions to the terminal, which then presents them to the user. The presentation includes scenarios that consider appropriate expressions, actions, and emotions, and is provided to the user in visual or text format.

[0156] Step 7:

[0157] Users use the information presented to facilitate actual communication. They then provide feedback to the system regarding the advice and system functionality provided.

[0158] Step 8:

[0159] The server analyzes the feedback it receives and uses it to improve the accuracy of the generated AI model. Based on the feedback, it readjusts the model to provide more appropriate support to future users.

[0160] Step 9:

[0161] The server creates personalized learning content based on the user's usage history and sentiment data, and delivers it to the user via their device. This learning content is used as supplementary material to deepen the user's cultural understanding.

[0162] (Example 2)

[0163] 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".

[0164] It is necessary to reduce misunderstandings and frictions that frequently occur in communication between individuals from different cultural backgrounds and to promote smooth mutual understanding. Communication barriers arising from cultural differences are a significant issue in today's increasingly globalized society. Furthermore, it is necessary to consider the emotional state of each individual to realize more effective communication support.

[0165] 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.

[0166] In this invention, the server includes means for using an algorithm to collect and learn cultural information, means for analyzing user input information and emotions to identify an appropriate cultural context, and means for using the algorithm to generate cultural expressions corresponding to the user's emotional state. This enables appropriate communication that takes emotions into consideration, even between individuals from different cultures.

[0167] An "algorithm" is a methodology for collecting and learning cultural information in order to support communication between individuals with different cultural backgrounds.

[0168] "Emotional state" refers to the user's internal state, identified by analyzing emotional indicators extracted from the user's voice, facial expressions, and text.

[0169] "Cultural context" refers to information that describes customs and communication styles within a particular culture, serving as background information necessary for smooth communication.

[0170] "Cultural expression" refers to appropriate forms of dialogue and behavior that are culturally conscious and generated in response to the user's emotional state and circumstances.

[0171] "Feedback" refers to the opinions and reactions that users provide to a system, and is data used to improve the system and optimize its algorithms.

[0172] This invention is a system for supporting communication between individuals with different cultural backgrounds. The system combines a generative AI model and an emotion engine to provide appropriate cultural context and expression that responds to the user's emotions. This embodiment is described in detail below.

[0173] The server collects diverse cultural information via the internet and other data sources. This includes customs and communication styles necessary to promote intercultural understanding, which are used to train a generative AI model. The generative AI model is built using advanced machine learning algorithms and has the ability to generate user-appropriate context based on the cultural information.

[0174] Users access the system through their terminal and input their communication challenges and situations. This input includes specific communication scenarios, such as workplace meetings or conversations with friends. For example, a prompt message like, "I want to convey a congratulatory message to a close friend in a more culturally appropriate way," conveys the user's intentions to the system.

[0175] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expressions, and emotional indicators from text, identifying the user's emotional state in real time. Based on this emotional data, the server generates a cultural context appropriate to the user's situation and provides appropriate cultural expressions through a generative AI model.

[0176] Ultimately, the device displays the generated cultural expressions to the user, showing concrete conversation examples and scenarios. This makes it easier for users to apply them to real-life conversations. Furthermore, user feedback is stored on the server, allowing the generating AI model to be continuously retrained and its cultural understanding to evolve with each use.

[0177] This system can support culturally sensitive communication, especially in international business environments and situations requiring cross-cultural communication.

[0178] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0179] Step 1:

[0180] The server collects cultural information using the internet and other data sources. This collected data includes customs and communication styles related to different cultures. Various cultural information and related documents are provided as input. The server stores this information in a database and uses it as training data for a generative AI model. The output is the cultural dataset necessary for training.

[0181] Step 2:

[0182] Users access the system via a terminal and input their communication challenges or situations. These prompts are entered as linguistic expressions, such as "Please tell me the best way to phrase things when negotiating at work." The input is a text-based prompt, and the output is the user's request, ready for analysis.

[0183] Step 3:

[0184] The device receives input prompts from the user and activates an emotion engine to analyze them. The input consists of the user's voice, facial expressions, and text. The emotion engine analyzes this data to determine the user's emotional state in real time. The output is numerical data representing the user's emotional state.

[0185] Step 4:

[0186] The server combines user prompts with analyzed sentiment data to identify the appropriate cultural context. The input consists of user requests and sentiment data. The server uses advanced data processing techniques and algorithms to infer the most appropriate cultural background. The output is a dataset containing the cultural context.

[0187] Step 5:

[0188] The generative AI model generates cultural expressions that best match the user's emotional state, based on the cultural context created by the server. The input consists of the cultural context and the user's emotional state. The model uses machine learning algorithms to generate expressions appropriate to the selected culture. The output is text data containing specific cultural expressions.

[0189] Step 6:

[0190] The device presents the generated cultural representation to the user. It displays or plays conversational examples and scenarios to illustrate this result. The input is text data provided by the generative AI model. The output is a visualization of the cultural representation and reference materials displayed to the user.

[0191] Step 7:

[0192] Users provide feedback on proposed cultural representations. This feedback is sent back to the server as input. The server collects this feedback and uses it to optimize the generative AI model. The output is data that will be used to improve future generation accuracy.

[0193] (Application Example 2)

[0194] 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 device 14 will be referred to as the "terminal."

[0195] In communication between individuals with different cultural backgrounds, cultural differences can sometimes hinder smooth communication. Furthermore, in online purchasing activities, there is a growing demand for personalized experiences tailored to customers' cultural backgrounds and emotions. Addressing these challenges and improving the customer experience is essential.

[0196] 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.

[0197] In this invention, the server includes functions for using artificial intelligence to collect and learn cultural information, analyzing the user's emotions in real time and providing appropriate suggestions to the user based on the results, and generating personalized information based on emotions and cultural background to improve the user's purchasing experience. This makes it possible to facilitate cross-cultural communication and provide a culturally appropriate experience even in online purchasing activities.

[0198] "Artificial intelligence" refers to software that has the ability to make judgments and learn in a way that humans do.

[0199] "Emotional analysis" is the process of recognizing and identifying a user's emotional state from their voice, facial expressions, and text.

[0200] "Cultural context" refers to information that encompasses customs, values, and communication styles based on a particular culture.

[0201] "User feedback" refers to opinions and reactions from users regarding their use of the system provided.

[0202] "Personalized information" refers to content that is customized based on the individual user's cultural background and emotions.

[0203] "Purchase experience" is a concept that refers to the overall experience a user has during the process of selecting and purchasing a product.

[0204] "Nonverbal communication" is a form of communication that does not involve the use of words, and includes, for example, gestures and facial expressions.

[0205] The system for carrying out the present invention is built around artificial intelligence technology for emotion analysis and cultural adaptation. The main components of this system are a server, a terminal, and a user.

[0206] The server first collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. This AI model includes information about cultural customs and communication styles. For this purpose, it utilizes generative AI technologies such as "GPT" and "BERT". The server also collects user feedback and retrains the AI ​​model as needed to achieve a more accurate cultural understanding and sentiment recognition.

[0207] The device analyzes user emotions in real time through voice and text. This analysis utilizes libraries such as "DeepFace" and "NLP," built in Python. The analyzed emotion data is sent to a server and used to generate appropriate cultural context. The device also presents personalized information based on the user's past usage history, facilitating communication in multicultural environments.

[0208] Through this system, users can obtain appropriate cultural representations in various scenarios. For example, if a user is feeling stressed while browsing products in a virtual store, they will be offered relaxation suggestions and customized product information tailored to their emotions.

[0209] As a concrete example, consider a scenario where a Japanese user in their 30s is looking for Japanese tableware. The device recognizes that this user is impressed by the beauty of the products, and the server generates appropriate cultural background information and suggestions.

[0210] Examples of prompt statements include the following:

[0211] "A Japanese user in her 30s is looking for Japanese tableware. She is impressed by the beauty of the products. Please generate a product description suitable for her."

[0212] This system allows users to receive information that aligns with their cultural background and emotions, leading to a more fulfilling shopping experience.

[0213] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0214] Step 1:

[0215] The device accepts user input, including voice, text, and images. This input data is passed to an emotion analysis engine. The device uses Python's "DeepFace" and "NLP" libraries to analyze the user's emotions from their voice tone and facial expressions, and generates an output representing their emotional state.

[0216] Step 2:

[0217] The device sends emotion analysis results and data on the user's cultural background to the server. The server receives this data and passes it as input to a generative AI model. This generative AI model uses the pre-processed input data to calculate a cultural context appropriate for the user and generates the result.

[0218] Step 3:

[0219] The server retrieves suggestions and cultural context as output from the generative AI model. This includes, for example, cultural descriptions related to a specific product or suggestions for relaxation. The server organizes this information and sends it back to the terminal.

[0220] Step 4:

[0221] The device displays suggestions and cultural information received from the server to the user. Customized information based on analyzed emotions is presented on the user's display, providing a more personalized purchasing experience.

[0222] Step 5:

[0223] Users provide feedback on the information presented. The device collects this feedback and sends it to the server. The server uses the feedback to retrain the generative AI model, helping to improve the model's accuracy. This further enhances the quality of future communication.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] This invention is a system that supports communication between individuals with different cultural backgrounds. This system collects vast amounts of cultural information and, based on that information, uses artificial intelligence to provide users with appropriate cultural context and expression. Specifically, it is implemented in the following forms.

[0241] The server periodically collects various cultural information from a cultural database and uses this to train a generative artificial intelligence. The main purpose of the training is to deepen understanding of cultural backgrounds and customs, and to grasp cultural contexts more accurately.

[0242] Users access the system using mobile devices or computers and input specific communication challenges or situations they face through the interface. The device analyzes this input and sets up a cultural context appropriate to the user.

[0243] The server uses artificial intelligence technology to generate the most appropriate cultural representations based on user input. This generated information is provided as concrete scenarios and conversation examples to facilitate understanding of cultural differences.

[0244] For example, consider a scenario where a foreign worker wants to express their opinion to their supervisor in a Japanese workplace. This system provides specific advice on how to show respect and make indirect suggestions within Japanese business culture. It then guides the user with concrete examples of how to express themselves in actual communication situations.

[0245] Furthermore, user feedback is used to readjust the AI ​​model generated by the server. This allows the system to provide more accurate cultural understanding support over time.

[0246] Furthermore, this system provides individually customized learning content based on the user's past usage and needs. This content includes detailed information on nonverbal communication and specific cultural events, helping users navigate diverse cultural situations.

[0247] In this way, the present invention is an effective means of preventing cultural misunderstandings and friction, and of facilitating smooth communication between individuals with different cultural backgrounds.

[0248] The following describes the processing flow.

[0249] Step 1:

[0250] The server collects cultural information from publicly available databases on the internet and from partner sources. This includes articles, papers, books, and multimedia data such as audio and video. This data is then organized and prepared as a training dataset for generative AI.

[0251] Step 2:

[0252] The server uses the collected cultural information to train a generative AI model. During training, it learns to understand intercultural communication patterns and specific cultural customs, and to provide appropriate responses and behaviors in specific situations.

[0253] Step 3:

[0254] Users access the system using their devices and input specific details about the communication problems and situations they are facing. For example, they might clearly describe a challenge such as "how to make improvement suggestions to my boss."

[0255] Step 4:

[0256] The terminal analyzes user input and identifies the context of the problem that needs to be solved. Based on the analysis results, it requests relevant cultural data from the server.

[0257] Step 5:

[0258] The server uses generative AI to generate appropriate cultural expressions and solutions based on the context received from the terminal. For example, it can create information that includes advice on appropriate respectful language and nonverbal cues in Japanese companies.

[0259] Step 6:

[0260] The device provides users with generated cultural representations and solutions. These are presented in the form of specific conversation examples and scenarios, and are structured in visual or text format to ensure easy understanding for the user.

[0261] Step 7:

[0262] Users respond to real-world situations based on the information provided and send feedback to the system regarding its usefulness and effectiveness.

[0263] Step 8:

[0264] The server analyzes user feedback and uses it to improve the generated AI model. This feedback helps improve the accuracy of cultural understanding and is reflected in future use.

[0265] Step 9:

[0266] The server generates customized learning content and cultural event information based on the user's past activities and interests, and provides it to the user via their device. This information serves as a supplementary tool to deepen the user's cultural understanding.

[0267] (Example 1)

[0268] 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."

[0269] Misunderstandings and friction often arise in communication between individuals from different cultural backgrounds. These problems stem from differences in cultural backgrounds and values, hindering smooth communication. Traditional methods require considerable effort and time to acquire individual cultural knowledge, making real-time responses difficult. This invention solves these problems and provides a means to facilitate smooth intercultural communication.

[0270] 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.

[0271] In this invention, the server includes means for using an intelligent processing device to collect information about culture and train an analytical model, means for interpreting situational information input by the user to identify appropriate cultural background information, and means for constructing a cultural expression according to the user's conditions using the generative model. This reduces misunderstandings and friction in communication between individuals with different cultural backgrounds and enables smooth information transmission.

[0272] "Cultural information" refers to data that helps in understanding and interpreting customs, business etiquette, and nonverbal communication based on different cultural backgrounds.

[0273] An "analysis model" refers to a program or algorithm that utilizes artificial intelligence technology to analyze and interpret input information.

[0274] A "user" refers to an individual who uses the system to receive advice on cultural context and expression.

[0275] "Status information" refers to specific information related to the current communication situation or problems that users input into the system.

[0276] "Cultural background information" refers to information that provides relevant cultural elements and context in a particular situation.

[0277] A "generative model" refers to AI technology used to construct appropriate cultural representations based on user input.

[0278] "Cultural expression" refers to linguistic expressions and behavioral guidelines that are appropriate for facilitating communication within a specific cultural context.

[0279] This invention is a system for supporting communication between individuals with different cultural backgrounds. This system primarily consists of three elements: a server, a terminal, and a user.

[0280] Server:

[0281] The server collects diverse cultural information from cultural databases and public materials on the Internet. This information includes details about cultural customs and non-verbal communication for facilitating understanding between different cultures. Also, based on this cultural information, the server trains a generative AI model. This model analyzes the collected data and improves the ability to understand different cultural contexts. For example, TensorFlow or PyTorch may be considered as the machine learning framework.

[0282] Terminal:

[0283] The user accesses the system through the terminal. The terminal uses a mobile device or a computer to receive input from the user. This input includes the specific communication challenges and situations the user is facing. The terminal analyzes this input and generates a prompt sentence. This prompt sentence is sent to the server and used to generate an optimal cultural expression.

[0284] User:

[0285] The user can utilize the system to receive specific advice regarding communication challenges. For example, consider the case where the user inputs to the system "I want to propose a new project to my boss, but how should I say it?" Based on this information, the server generates a suitable cultural context and expression and presents it to the user. The information generated in this process serves as a guideline in an actual communication scenario. Also, the user can provide feedback on the provided information. This feedback is utilized by the server to adjust the generative AI model and improve the accuracy of the system.

[0286] Examples of prompt sentences include "Please advise on how to appropriately propose to my boss." By analyzing this prompt sentence and generating expressions that include appropriate cultural contexts, users can communicate smoothly.

[0287] The flow of the specific process in Example 1 will be described using FIG. 11.

[0288] Step 1:

[0289] The server collects cultural information.

[0290] The server collects information from cultural databases on the Internet and reliable materials. At this time, the program automatically searches using cultural keywords to be collected and extracts the necessary data. The input is the data collection conditions and targets, and a dataset of cultural information is formed as the output.

[0291] Step 2:

[0292] The server trains the generation AI model.

[0293] The server trains the generation AI model using the dataset of the collected cultural information. The dataset is data converted into text data or vector format, and a model for deepening cross-cultural understanding is trained using this. The input is the collected data, and the output is the trained generation AI model.

[0294] Step 3:

[0295] The user inputs a communication task through the terminal.

[0296] The user inputs specific situations and challenges they are facing into the terminal. This input information includes details about the person they are communicating with and the situation. The input is the user's text data, and the output is a prompt sentence for parsing that information.

[0297] Step 4:

[0298] The terminal generates a prompt message and sends it to the server.

[0299] The terminal analyzes the text information received from the user and generates an appropriate prompt. This prompt is used by the server to create the most appropriate cultural expression for the user. The input is the user's input information, and the output is the generated prompt.

[0300] Step 5:

[0301] The server generates cultural expressions using a generative AI model.

[0302] The server uses a generative AI model to generate the most appropriate cultural expression based on the received prompt text. This process constructs appropriate expressions that take cultural differences into account, helping users communicate smoothly. The input is the prompt text, and the output is the cultural expression.

[0303] Step 6:

[0304] The device presents the generated cultural representation to the user.

[0305] The terminal displays cultural expressions sent from the server to the user. The user can use this as a reference to improve their own communication. The input is the generated cultural expression, and the output is the displayed content that the user sees.

[0306] Step 7:

[0307] The user inputs feedback to the system.

[0308] The user inputs feedback on the presented cultural expressions into the terminal. This feedback is used to improve the accuracy of the system. The input is the user's feedback, and the output is the evaluation data analyzed by the server.

[0309] (Application Example 1)

[0310] Next, Application Example 1 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".

[0311] Solve the problems of misunderstandings and frictions in communication between individuals with different cultural backgrounds, especially the difficulty of presenting appropriate cultural contexts and product descriptions to users in the virtual space.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example shall be realized by the following means.

[0313] In this invention, the server includes means for using a generative artificial intelligence that collects and learns cultural information, means for analyzing the information input by the user to identify an appropriate cultural context, and means for generating and presenting a product description according to the customer's cultural background in the virtual space. Thereby, it becomes possible to have a culturally appropriate communication and product description without misunderstandings for users with different cultural backgrounds.

[0314] "Different cultural backgrounds" refers to differences in the unique values, beliefs, habits, languages, etc. possessed by individual regions, countries, societies, or individuals.

[0315] "Communication" refers to the process of exchanging information, thoughts, and feelings, which is carried out through verbal and non-verbal means.

[0316] "Cultural information" refers to knowledge, data, and examples related to a particular culture, including history, customs, values, and social norms of conduct.

[0317] "Generative artificial intelligence" refers to artificial intelligence technology that can learn patterns from data and generate human-like responses.

[0318] "Cultural context" refers to the background and assumptions behind information and behaviors within a particular culture, and is an important element for deepening understanding in communication.

[0319] "Product description" refers to an explanation provided to customers that includes information about a product, such as its characteristics, usage, and benefits.

[0320] A "virtual space" refers to a non-physical environment that is artificially created using computer technology.

[0321] "Feedback" refers to the opinions and evaluations that users provide regarding the system's output, and is information used to improve the system.

[0322] The system program for realizing this invention consists of the following elements. First, the server collects cultural information and uses it to train a generative AI model. The model learns patterns based on a vast amount of cultural background data and generates appropriate cultural contexts and expressions in communication between different cultures. Specifically, the GPT-4 is used as the generative AI model.

[0323] Users access the system via devices such as smartphones or head-mounted displays. These devices analyze user input and use a generative AI model to generate responses and product descriptions that are relevant to the cultural context. Standard smart devices can be used as hardware.

[0324] Based on input from the terminal, the server is responsible for generating appropriate cultural representations. In this process, it calculates data to provide product descriptions in a virtual space that are tailored to the user's cultural background. For example, when guiding a tourist from the United States about a Japanese matcha set, it generates information about the history of matcha and its popularity in the United States.

[0325] As a concrete example, the following prompt sentence is input to the AI ​​generation model: "Provide information about the popularity of matcha in American culture and generate cultural expressions that encourage American tourists to purchase a matcha set." This enables product descriptions that are suitable for customers with different cultural backgrounds.

[0326] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0327] Step 1:

[0328] Users access the system via a terminal. Users input basic information and questions into the terminal when they want to communicate with individuals from different cultural backgrounds or understand product information. This input may be in text or voice format.

[0329] Step 2:

[0330] Upon receiving input from the user, the terminal converts the input data into text format and begins analysis. The analysis uses natural language processing techniques to identify the cultural context required for the input information. The output is the analyzed context data.

[0331] Step 3:

[0332] The server receives the analyzed contextual data and utilizes a generative AI model to generate appropriate expressions based on the cultural background. This generative AI model (specifically using GPT-4) generates culturally appropriate expressions based on pre-trained cultural information. The input to the generative AI model is structured as prompt sentences.

[0333] Step 4:

[0334] The generated cultural expressions and product descriptions are sent from the server to the user's terminal. The terminal presents these expressions to the user, providing cultural understanding in the virtual space and appropriate context about the product. The output is displayed in a format that is easy for the user to understand.

[0335] Step 5:

[0336] Users send feedback to the system regarding the information presented. This feedback is recorded to indicate how helpful it was or where improvements are needed. The server uses this feedback to retrain the generating AI model, thereby improving its accuracy over time.

[0337] 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.

[0338] This invention is a system designed to support communication between individuals with different cultural backgrounds. This system combines generative artificial intelligence and an emotion engine to provide appropriate cultural context and expression in response to the user's emotions. Specific embodiments are described below.

[0339] The server collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. The collected data includes information about cultural customs and communication styles, and is used to improve the accuracy of the model.

[0340] Users access the system through their device and input specific communication challenges or situations. During this process, the device uses a built-in emotion engine to recognize the user's emotions in real time. The emotion engine analyzes voice tone, facial expressions, and emotional indicators extracted from text to determine the user's emotional state.

[0341] The server generates appropriate cultural context based on user input and data from the emotion engine, and the generative AI produces the most suitable cultural expressions for the user. For example, if the user is feeling surprised or nervous, suggestions and approaches that take those emotions into consideration will be taken into account.

[0342] The device presents the generated cultural expressions to the user and provides concrete conversation examples and scenarios to help the user understand how to apply them in real-world situations. For example, if a user is feeling anxious during a workplace meeting, the device will offer emotionally-based advice, such as conversational techniques to help them relax and appropriate gestures.

[0343] User feedback is collected by the server, used to retrain the generative AI model, and utilized for future improvements. This allows the system, which combines an emotion engine with generative artificial intelligence, to improve the quality of its cultural understanding with each use.

[0344] Furthermore, the device provides individually customized learning content based on the user's past usage history and sentiment analysis results. This content serves as supplementary material to deepen the user's knowledge and skills in adapting to diverse cultural backgrounds.

[0345] Thus, the present invention combines cultural information and emotion recognition technology to provide an effective solution for facilitating communication between individuals with different cultural backgrounds.

[0346] The following describes the processing flow.

[0347] Step 1:

[0348] The server regularly collects diverse cultural information from open-source databases and online resources. This cultural information includes greetings, business customs, and nonverbal communication patterns in different countries. This data is then organized into a dataset necessary for training generative AI.

[0349] Step 2:

[0350] The server uses the collected data to train a generative AI model. During training, it learns about intercultural communication methods and typical response patterns, and adjusts the model to understand appropriate cultural expressions in each situation.

[0351] Step 3:

[0352] Users access the system using a terminal and input the specific problems or situations they are encountering. At this time, specific examples can be provided, such as "How to greet someone for the first time at a new workplace."

[0353] Step 4:

[0354] The device recognizes the user's emotions using a built-in emotion engine. The emotion engine automatically analyzes the emotional state from facial expressions, voice tone, and the user's text to identify the type and intensity of the emotion.

[0355] Step 5:

[0356] The device sends user input and the results of the emotion engine's analysis to the server. Based on this data, the server identifies the optimal cultural context and uses generative AI to construct a cultural representation best suited to the user.

[0357] Step 6:

[0358] The server sends the generated cultural expressions and suggestions to the terminal, which then presents them to the user. The presentation includes scenarios that consider appropriate expressions, actions, and emotions, and is provided to the user in visual or text format.

[0359] Step 7:

[0360] Users use the information presented to facilitate actual communication. They then provide feedback to the system regarding the advice and system functionality provided.

[0361] Step 8:

[0362] The server analyzes the feedback it receives and uses it to improve the accuracy of the generated AI model. Based on the feedback, it readjusts the model to provide more appropriate support to future users.

[0363] Step 9:

[0364] The server creates personalized learning content based on the user's usage history and sentiment data, and delivers it to the user via their device. This learning content is used as supplementary material to deepen the user's cultural understanding.

[0365] (Example 2)

[0366] 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 glasses 214 will be referred to as the "terminal".

[0367] It is necessary to reduce misunderstandings and frictions that frequently occur in communication between individuals from different cultural backgrounds and to promote smooth mutual understanding. Communication barriers arising from cultural differences are a significant issue in today's increasingly globalized society. Furthermore, it is necessary to consider the emotional state of each individual to realize more effective communication support.

[0368] 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.

[0369] In this invention, the server includes means for using an algorithm to collect and learn cultural information, means for analyzing user input information and emotions to identify an appropriate cultural context, and means for using the algorithm to generate cultural expressions corresponding to the user's emotional state. This enables appropriate communication that takes emotions into consideration, even between individuals from different cultures.

[0370] An "algorithm" is a methodology for collecting and learning cultural information in order to support communication between individuals with different cultural backgrounds.

[0371] "Emotional state" refers to the user's internal state, identified by analyzing emotional indicators extracted from the user's voice, facial expressions, and text.

[0372] "Cultural context" refers to information that describes customs and communication styles within a particular culture, serving as background information necessary for smooth communication.

[0373] "Cultural expression" refers to appropriate forms of dialogue and behavior that are culturally conscious and generated in response to the user's emotional state and circumstances.

[0374] "Feedback" refers to the opinions and reactions that users provide to a system, and is data used to improve the system and optimize its algorithms.

[0375] This invention is a system for supporting communication between individuals with different cultural backgrounds. The system combines a generative AI model and an emotion engine to provide appropriate cultural context and expression that responds to the user's emotions. This embodiment is described in detail below.

[0376] The server collects diverse cultural information via the internet and other data sources. This includes customs and communication styles necessary to promote intercultural understanding, which are used to train a generative AI model. The generative AI model is built using advanced machine learning algorithms and has the ability to generate user-appropriate context based on the cultural information.

[0377] Users access the system through their terminal and input their communication challenges and situations. This input includes specific communication scenarios, such as workplace meetings or conversations with friends. For example, a prompt message like, "I want to convey a congratulatory message to a close friend in a more culturally appropriate way," conveys the user's intentions to the system.

[0378] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expressions, and emotional indicators from text, identifying the user's emotional state in real time. Based on this emotional data, the server generates a cultural context appropriate to the user's situation and provides appropriate cultural expressions through a generative AI model.

[0379] Ultimately, the device displays the generated cultural expressions to the user, showing concrete conversation examples and scenarios. This makes it easier for users to apply them to real-life conversations. Furthermore, user feedback is stored on the server, allowing the generating AI model to be continuously retrained and its cultural understanding to evolve with each use.

[0380] This system can support culturally sensitive communication, especially in international business environments and situations requiring cross-cultural communication.

[0381] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0382] Step 1:

[0383] The server collects cultural information using the internet and other data sources. This collected data includes customs and communication styles related to different cultures. Various cultural information and related documents are provided as input. The server stores this information in a database and uses it as training data for a generative AI model. The output is the cultural dataset necessary for training.

[0384] Step 2:

[0385] Users access the system via a terminal and input their communication challenges or situations. These prompts are entered as linguistic expressions, such as "Please tell me the best way to phrase things when negotiating at work." The input is a text-based prompt, and the output is the user's request, ready for analysis.

[0386] Step 3:

[0387] The device receives input prompts from the user and activates an emotion engine to analyze them. The input consists of the user's voice, facial expressions, and text. The emotion engine analyzes this data to determine the user's emotional state in real time. The output is numerical data representing the user's emotional state.

[0388] Step 4:

[0389] The server combines user prompts with analyzed sentiment data to identify the appropriate cultural context. The input consists of user requests and sentiment data. The server uses advanced data processing techniques and algorithms to infer the most appropriate cultural background. The output is a dataset containing the cultural context.

[0390] Step 5:

[0391] The generative AI model generates cultural expressions that best match the user's emotional state, based on the cultural context created by the server. The input consists of the cultural context and the user's emotional state. The model uses machine learning algorithms to generate expressions appropriate to the selected culture. The output is text data containing specific cultural expressions.

[0392] Step 6:

[0393] The device presents the generated cultural representation to the user. It displays or plays conversational examples and scenarios to illustrate this result. The input is text data provided by the generative AI model. The output is a visualization of the cultural representation and reference materials displayed to the user.

[0394] Step 7:

[0395] Users provide feedback on proposed cultural representations. This feedback is sent back to the server as input. The server collects this feedback and uses it to optimize the generative AI model. The output is data that will be used to improve future generation accuracy.

[0396] (Application Example 2)

[0397] 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."

[0398] In communication between individuals with different cultural backgrounds, cultural differences can sometimes hinder smooth communication. Furthermore, in online purchasing activities, there is a growing demand for personalized experiences tailored to customers' cultural backgrounds and emotions. Addressing these challenges and improving the customer experience is essential.

[0399] 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.

[0400] In this invention, the server includes functions for using artificial intelligence to collect and learn cultural information, analyzing the user's emotions in real time and providing appropriate suggestions to the user based on the results, and generating personalized information based on emotions and cultural background to improve the user's purchasing experience. This makes it possible to facilitate cross-cultural communication and provide a culturally appropriate experience even in online purchasing activities.

[0401] "Artificial intelligence" refers to software that has the ability to make judgments and learn in a way that humans do.

[0402] "Emotional analysis" is the process of recognizing and identifying a user's emotional state from their voice, facial expressions, and text.

[0403] "Cultural context" refers to information that encompasses customs, values, and communication styles based on a particular culture.

[0404] "User feedback" refers to opinions and reactions from users regarding their use of the system provided.

[0405] "Personalized information" refers to content that is customized based on the individual user's cultural background and emotions.

[0406] "Purchase experience" is a concept that refers to the overall experience a user has during the process of selecting and purchasing a product.

[0407] "Nonverbal communication" is a form of communication that does not involve the use of words, and includes, for example, gestures and facial expressions.

[0408] The system for carrying out the present invention is built around artificial intelligence technology for emotion analysis and cultural adaptation. The main components of this system are a server, a terminal, and a user.

[0409] The server first collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. This AI model includes information about cultural customs and communication styles. For this purpose, it utilizes generative AI technologies such as "GPT" and "BERT". The server also collects user feedback and retrains the AI ​​model as needed to achieve a more accurate cultural understanding and sentiment recognition.

[0410] The device analyzes user emotions in real time through voice and text. This analysis utilizes libraries such as "DeepFace" and "NLP," built in Python. The analyzed emotion data is sent to a server and used to generate appropriate cultural context. The device also presents personalized information based on the user's past usage history, facilitating communication in multicultural environments.

[0411] Through this system, users can obtain appropriate cultural representations in various scenarios. For example, if a user is feeling stressed while browsing products in a virtual store, they will be offered relaxation suggestions and customized product information tailored to their emotions.

[0412] As a concrete example, consider a scenario where a Japanese user in their 30s is looking for Japanese tableware. The device recognizes that this user is impressed by the beauty of the products, and the server generates appropriate cultural background information and suggestions.

[0413] Examples of prompt statements include the following:

[0414] "A Japanese user in her 30s is looking for Japanese tableware. She is impressed by the beauty of the products. Please generate a product description suitable for her."

[0415] This system allows users to receive information that aligns with their cultural background and emotions, leading to a more fulfilling shopping experience.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The device accepts user input, including voice, text, and images. This input data is passed to an emotion analysis engine. The device uses Python's "DeepFace" and "NLP" libraries to analyze the user's emotions from their voice tone and facial expressions, and generates an output representing their emotional state.

[0419] Step 2:

[0420] The device sends emotion analysis results and data on the user's cultural background to the server. The server receives this data and passes it as input to a generative AI model. This generative AI model uses the pre-processed input data to calculate a cultural context appropriate for the user and generates the result.

[0421] Step 3:

[0422] The server retrieves suggestions and cultural context as output from the generative AI model. This includes, for example, cultural descriptions related to a specific product or suggestions for relaxation. The server organizes this information and sends it back to the terminal.

[0423] Step 4:

[0424] The device displays suggestions and cultural information received from the server to the user. Customized information based on analyzed emotions is presented on the user's display, providing a more personalized purchasing experience.

[0425] Step 5:

[0426] Users provide feedback on the information presented. The device collects this feedback and sends it to the server. The server uses the feedback to retrain the generative AI model, helping to improve the model's accuracy. This further enhances the quality of future communication.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] [Third Embodiment]

[0431] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0432] 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.

[0433] 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).

[0434] 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.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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".

[0443] This invention is a system that supports communication between individuals with different cultural backgrounds. This system collects vast amounts of cultural information and, based on that information, uses artificial intelligence to provide users with appropriate cultural context and expression. Specifically, it is implemented in the following forms.

[0444] The server periodically collects various cultural information from a cultural database and uses this to train a generative artificial intelligence. The main purpose of the training is to deepen understanding of cultural backgrounds and customs, and to grasp cultural contexts more accurately.

[0445] Users access the system using mobile devices or computers and input specific communication challenges or situations they face through the interface. The device analyzes this input and sets up a cultural context appropriate to the user.

[0446] The server uses artificial intelligence technology to generate the most appropriate cultural representations based on user input. This generated information is provided as concrete scenarios and conversation examples to facilitate understanding of cultural differences.

[0447] For example, consider a scenario where a foreign worker wants to express their opinion to their supervisor in a Japanese workplace. This system provides specific advice on how to show respect and make indirect suggestions within Japanese business culture. It then guides the user with concrete examples of how to express themselves in actual communication situations.

[0448] Furthermore, user feedback is used to readjust the AI ​​model generated by the server. This allows the system to provide more accurate cultural understanding support over time.

[0449] Furthermore, this system provides individually customized learning content based on the user's past usage and needs. This content includes detailed information on nonverbal communication and specific cultural events, helping users navigate diverse cultural situations.

[0450] In this way, the present invention is an effective means of preventing cultural misunderstandings and friction, and of facilitating smooth communication between individuals with different cultural backgrounds.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The server collects cultural information from publicly available databases on the internet and from partner sources. This includes articles, papers, books, and multimedia data such as audio and video. This data is then organized and prepared as a training dataset for generative AI.

[0454] Step 2:

[0455] The server uses the collected cultural information to train a generative AI model. During training, it learns to understand intercultural communication patterns and specific cultural customs, and to provide appropriate responses and behaviors in specific situations.

[0456] Step 3:

[0457] Users access the system using their devices and input specific details about the communication problems and situations they are facing. For example, they might clearly describe a challenge such as "how to make improvement suggestions to my boss."

[0458] Step 4:

[0459] The terminal analyzes user input and identifies the context of the problem that needs to be solved. Based on the analysis results, it requests relevant cultural data from the server.

[0460] Step 5:

[0461] The server uses generative AI to generate appropriate cultural expressions and solutions based on the context received from the terminal. For example, it can create information that includes advice on appropriate respectful language and nonverbal cues in Japanese companies.

[0462] Step 6:

[0463] The device provides users with generated cultural representations and solutions. These are presented in the form of specific conversation examples and scenarios, and are structured in visual or text format to ensure easy understanding for the user.

[0464] Step 7:

[0465] Users respond to real-world situations based on the information provided and send feedback to the system regarding its usefulness and effectiveness.

[0466] Step 8:

[0467] The server analyzes user feedback and uses it to improve the generated AI model. This feedback helps improve the accuracy of cultural understanding and is reflected in future use.

[0468] Step 9:

[0469] The server generates customized learning content and cultural event information based on the user's past activities and interests, and provides it to the user via their device. This information serves as a supplementary tool to deepen the user's cultural understanding.

[0470] (Example 1)

[0471] 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."

[0472] Misunderstandings and friction often arise in communication between individuals from different cultural backgrounds. These problems stem from differences in cultural backgrounds and values, hindering smooth communication. Traditional methods require considerable effort and time to acquire individual cultural knowledge, making real-time responses difficult. This invention solves these problems and provides a means to facilitate smooth intercultural communication.

[0473] 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.

[0474] In this invention, the server includes means for using an intelligent processing device to collect information about culture and train an analytical model, means for interpreting situational information input by the user to identify appropriate cultural background information, and means for constructing a cultural expression according to the user's conditions using the generative model. This reduces misunderstandings and friction in communication between individuals with different cultural backgrounds and enables smooth information transmission.

[0475] "Cultural information" refers to data that helps in understanding and interpreting customs, business etiquette, and nonverbal communication based on different cultural backgrounds.

[0476] An "analysis model" refers to a program or algorithm that utilizes artificial intelligence technology to analyze and interpret input information.

[0477] A "user" refers to an individual who uses the system to receive advice on cultural context and expression.

[0478] "Status information" refers to specific information related to the current communication situation or problems that users input into the system.

[0479] "Cultural background information" refers to information that provides relevant cultural elements and context in a particular situation.

[0480] A "generative model" refers to AI technology used to construct appropriate cultural representations based on user input.

[0481] "Cultural expression" refers to linguistic expressions and behavioral guidelines that are appropriate for facilitating communication within a specific cultural context.

[0482] This invention is a system for supporting communication between individuals with different cultural backgrounds. This system primarily consists of three elements: a server, a terminal, and a user.

[0483] server:

[0484] The server collects diverse cultural information from cultural databases and publicly available materials on the internet. This information includes detailed data on cultural customs and nonverbal communication to aid in intercultural understanding. The server also uses this cultural information to train a generative AI model. This model analyzes the collected data and improves its ability to understand different cultural contexts. For example, TensorFlow or PyTorch could be used as machine learning frameworks.

[0485] Terminal:

[0486] The user accesses the system through a terminal. The terminal receives input from the user using a mobile device or computer. This input includes specific communication challenges and situations the user is facing. The terminal analyzes this input and generates a prompt. This prompt is sent to the server and used to generate the most appropriate cultural expression.

[0487] User:

[0488] Users can use the system to receive specific advice on communication challenges. For example, consider a user who inputs into the system, "I want to propose a new project to my boss, but how should I say it?" Based on this information, the server generates appropriate cultural context and expressions and presents them to the user. The information generated in this process serves as a guide in actual communication situations. Users can also provide feedback on the information provided. This feedback is used by the server to adjust the generated AI model and improve the accuracy of the system.

[0489] Examples of prompts include, "Please advise me on how to make a suitable proposal to my boss." By analyzing these prompts and generating expressions that include appropriate cultural context, users can communicate smoothly.

[0490] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0491] Step 1:

[0492] The server collects cultural information.

[0493] The server collects information from cultural databases and reliable sources on the internet. The program automatically searches using cultural keywords to extract the necessary data. The input consists of data collection conditions and targets, and the output is a dataset of cultural information.

[0494] Step 2:

[0495] The server trains the generative AI model.

[0496] The server trains a generative AI model using a dataset of collected cultural information. This dataset includes text data and data converted into vector formats, which are used to train a model for deepening intercultural understanding. The input is the collected data, and the output is the trained generative AI model.

[0497] Step 3:

[0498] Users input communication tasks through their devices.

[0499] The user inputs specific situations and challenges they are facing into the terminal. This input information includes details about the person they are communicating with and the situation. The input is the user's text data, and the output is a prompt sentence for parsing that information.

[0500] Step 4:

[0501] The terminal generates a prompt message and sends it to the server.

[0502] The terminal analyzes the text information received from the user and generates an appropriate prompt. This prompt is used by the server to create the most appropriate cultural expression for the user. The input is the user's input information, and the output is the generated prompt.

[0503] Step 5:

[0504] The server generates cultural expressions using a generative AI model.

[0505] The server uses a generative AI model to generate the most appropriate cultural expression based on the received prompt text. This process constructs appropriate expressions that take cultural differences into account, helping users communicate smoothly. The input is the prompt text, and the output is the cultural expression.

[0506] Step 6:

[0507] The device presents the generated cultural representation to the user.

[0508] The terminal displays cultural expressions sent from the server to the user. The user can use this as a reference to improve their own communication. The input is the generated cultural expression, and the output is the displayed content that the user sees.

[0509] Step 7:

[0510] Users provide feedback to the system.

[0511] The user inputs feedback on the presented cultural representations into the terminal. This feedback is used to improve the system's accuracy. The input is user feedback, and the output is evaluation data analyzed by the server.

[0512] (Application Example 1)

[0513] 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."

[0514] This solution addresses the challenges of misunderstandings and friction in communication between individuals with different cultural backgrounds, particularly the difficulty of presenting users with appropriate cultural context and product descriptions in virtual spaces.

[0515] 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.

[0516] In this invention, the server includes means for using generative artificial intelligence to collect and learn cultural information, means for analyzing user input to identify an appropriate cultural context, and means for generating and presenting product descriptions in a virtual space that are appropriate to the customer's cultural background. This enables culturally appropriate communication and product descriptions that are free from misunderstandings for users with different cultural backgrounds.

[0517] "Different cultural backgrounds" refers to the differences in values, beliefs, customs, languages, and other characteristics unique to individual regions, countries, societies, or individuals.

[0518] "Communication" refers to the process of exchanging information, thoughts, and feelings, and is carried out through verbal and nonverbal means.

[0519] "Cultural information" refers to knowledge, data, and examples related to a particular culture, including history, customs, values, and social norms of conduct.

[0520] "Generative artificial intelligence" refers to artificial intelligence technology that can learn patterns from data and generate human-like responses.

[0521] "Cultural context" refers to the background and assumptions behind information and behaviors within a particular culture, and is an important element for deepening understanding in communication.

[0522] "Product description" refers to an explanation provided to customers that includes information about a product, such as its characteristics, usage, and benefits.

[0523] A "virtual space" refers to a non-physical environment that is artificially created using computer technology.

[0524] "Feedback" refers to the opinions and evaluations that users provide regarding the system's output, and is information used to improve the system.

[0525] The system program for realizing this invention consists of the following elements. First, the server collects cultural information and uses it to train a generative AI model. The model learns patterns based on a vast amount of cultural background data and generates appropriate cultural contexts and expressions in communication between different cultures. Specifically, the GPT-4 is used as the generative AI model.

[0526] Users access the system via devices such as smartphones or head-mounted displays. These devices analyze user input and use a generative AI model to generate responses and product descriptions that are relevant to the cultural context. Standard smart devices can be used as hardware.

[0527] Based on input from the terminal, the server is responsible for generating appropriate cultural representations. In this process, it calculates data to provide product descriptions in a virtual space that are tailored to the user's cultural background. For example, when guiding a tourist from the United States about a Japanese matcha set, it generates information about the history of matcha and its popularity in the United States.

[0528] As a concrete example, the following prompt sentence is input to the AI ​​generation model: "Provide information about the popularity of matcha in American culture and generate cultural expressions that encourage American tourists to purchase a matcha set." This enables product descriptions that are suitable for customers with different cultural backgrounds.

[0529] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0530] Step 1:

[0531] Users access the system via a terminal. Users input basic information and questions into the terminal when they want to communicate with individuals from different cultural backgrounds or understand product information. This input may be in text or voice format.

[0532] Step 2:

[0533] Upon receiving input from the user, the terminal converts the input data into text format and begins analysis. The analysis uses natural language processing techniques to identify the cultural context required for the input information. The output is the analyzed context data.

[0534] Step 3:

[0535] The server receives the analyzed contextual data and utilizes a generative AI model to generate appropriate expressions based on the cultural background. This generative AI model (specifically using GPT-4) generates culturally appropriate expressions based on pre-trained cultural information. The input to the generative AI model is structured as prompt sentences.

[0536] Step 4:

[0537] The generated cultural expressions and product descriptions are sent from the server to the user's terminal. The terminal presents these expressions to the user, providing cultural understanding in the virtual space and appropriate context about the product. The output is displayed in a format that is easy for the user to understand.

[0538] Step 5:

[0539] Users send feedback to the system regarding the information presented. This feedback is recorded to indicate how helpful it was or where improvements are needed. The server uses this feedback to retrain the generating AI model, thereby improving its accuracy over time.

[0540] 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.

[0541] This invention is a system designed to support communication between individuals with different cultural backgrounds. This system combines generative artificial intelligence and an emotion engine to provide appropriate cultural context and expression in response to the user's emotions. Specific embodiments are described below.

[0542] The server collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. The collected data includes information about cultural customs and communication styles, and is used to improve the accuracy of the model.

[0543] Users access the system through their device and input specific communication challenges or situations. During this process, the device uses a built-in emotion engine to recognize the user's emotions in real time. The emotion engine analyzes voice tone, facial expressions, and emotional indicators extracted from text to determine the user's emotional state.

[0544] The server generates appropriate cultural context based on user input and data from the emotion engine, and the generative AI produces the most suitable cultural expressions for the user. For example, if the user is feeling surprised or nervous, suggestions and approaches that take those emotions into consideration will be taken into account.

[0545] The device presents the generated cultural expressions to the user and provides concrete conversation examples and scenarios to help the user understand how to apply them in real-world situations. For example, if a user is feeling anxious during a workplace meeting, the device will offer emotionally-based advice, such as conversational techniques to help them relax and appropriate gestures.

[0546] User feedback is collected by the server, used to retrain the generative AI model, and utilized for future improvements. This allows the system, which combines an emotion engine with generative artificial intelligence, to improve the quality of its cultural understanding with each use.

[0547] Furthermore, the device provides individually customized learning content based on the user's past usage history and sentiment analysis results. This content serves as supplementary material to deepen the user's knowledge and skills in adapting to diverse cultural backgrounds.

[0548] Thus, the present invention combines cultural information and emotion recognition technology to provide an effective solution for facilitating communication between individuals with different cultural backgrounds.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The server regularly collects diverse cultural information from open-source databases and online resources. This cultural information includes greetings, business customs, and nonverbal communication patterns in different countries. This data is then organized into a dataset necessary for training generative AI.

[0552] Step 2:

[0553] The server uses the collected data to train a generative AI model. During training, it learns about intercultural communication methods and typical response patterns, and adjusts the model to understand appropriate cultural expressions in each situation.

[0554] Step 3:

[0555] Users access the system using a terminal and input the specific problems or situations they are encountering. At this time, specific examples can be provided, such as "How to greet someone for the first time at a new workplace."

[0556] Step 4:

[0557] The device recognizes the user's emotions using a built-in emotion engine. The emotion engine automatically analyzes the emotional state from facial expressions, voice tone, and the user's text to identify the type and intensity of the emotion.

[0558] Step 5:

[0559] The device sends user input and the results of the emotion engine's analysis to the server. Based on this data, the server identifies the optimal cultural context and uses generative AI to construct a cultural representation best suited to the user.

[0560] Step 6:

[0561] The server sends the generated cultural expressions and suggestions to the terminal, which then presents them to the user. The presentation includes scenarios that consider appropriate expressions, actions, and emotions, and is provided to the user in visual or text format.

[0562] Step 7:

[0563] Users use the information presented to facilitate actual communication. They then provide feedback to the system regarding the advice and system functionality provided.

[0564] Step 8:

[0565] The server analyzes the feedback it receives and uses it to improve the accuracy of the generated AI model. Based on the feedback, it readjusts the model to provide more appropriate support to future users.

[0566] Step 9:

[0567] The server creates personalized learning content based on the user's usage history and sentiment data, and delivers it to the user via their device. This learning content is used as supplementary material to deepen the user's cultural understanding.

[0568] (Example 2)

[0569] 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."

[0570] It is necessary to reduce misunderstandings and frictions that frequently occur in communication between individuals from different cultural backgrounds and to promote smooth mutual understanding. Communication barriers arising from cultural differences are a significant issue in today's increasingly globalized society. Furthermore, it is necessary to consider the emotional state of each individual to realize more effective communication support.

[0571] 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.

[0572] In this invention, the server includes means for using an algorithm to collect and learn cultural information, means for analyzing user input information and emotions to identify an appropriate cultural context, and means for using the algorithm to generate cultural expressions corresponding to the user's emotional state. This enables appropriate communication that takes emotions into consideration, even between individuals from different cultures.

[0573] An "algorithm" is a methodology for collecting and learning cultural information in order to support communication between individuals with different cultural backgrounds.

[0574] "Emotional state" refers to the user's internal state, identified by analyzing emotional indicators extracted from the user's voice, facial expressions, and text.

[0575] "Cultural context" refers to information that describes customs and communication styles within a particular culture, serving as background information necessary for smooth communication.

[0576] "Cultural expression" refers to appropriate forms of dialogue and behavior that are culturally conscious and generated in response to the user's emotional state and circumstances.

[0577] "Feedback" refers to the opinions and reactions that users provide to a system, and is data used to improve the system and optimize its algorithms.

[0578] This invention is a system for supporting communication between individuals with different cultural backgrounds. The system combines a generative AI model and an emotion engine to provide appropriate cultural context and expression that responds to the user's emotions. This embodiment is described in detail below.

[0579] The server collects diverse cultural information via the internet and other data sources. This includes customs and communication styles necessary to promote intercultural understanding, which are used to train a generative AI model. The generative AI model is built using advanced machine learning algorithms and has the ability to generate user-appropriate context based on the cultural information.

[0580] Users access the system through their terminal and input their communication challenges and situations. This input includes specific communication scenarios, such as workplace meetings or conversations with friends. For example, a prompt message like, "I want to convey a congratulatory message to a close friend in a more culturally appropriate way," conveys the user's intentions to the system.

[0581] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expressions, and emotional indicators from text, identifying the user's emotional state in real time. Based on this emotional data, the server generates a cultural context appropriate to the user's situation and provides appropriate cultural expressions through a generative AI model.

[0582] Ultimately, the device displays the generated cultural expressions to the user, showing concrete conversation examples and scenarios. This makes it easier for users to apply them to real-life conversations. Furthermore, user feedback is stored on the server, allowing the generating AI model to be continuously retrained and its cultural understanding to evolve with each use.

[0583] This system can support culturally sensitive communication, especially in international business environments and situations requiring cross-cultural communication.

[0584] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0585] Step 1:

[0586] The server collects cultural information using the internet and other data sources. This collected data includes customs and communication styles related to different cultures. Various cultural information and related documents are provided as input. The server stores this information in a database and uses it as training data for a generative AI model. The output is the cultural dataset necessary for training.

[0587] Step 2:

[0588] Users access the system via a terminal and input their communication challenges or situations. These prompts are entered as linguistic expressions, such as "Please tell me the best way to phrase things when negotiating at work." The input is a text-based prompt, and the output is the user's request, ready for analysis.

[0589] Step 3:

[0590] The device receives input prompts from the user and activates an emotion engine to analyze them. The input consists of the user's voice, facial expressions, and text. The emotion engine analyzes this data to determine the user's emotional state in real time. The output is numerical data representing the user's emotional state.

[0591] Step 4:

[0592] The server combines user prompts with analyzed sentiment data to identify the appropriate cultural context. The input consists of user requests and sentiment data. The server uses advanced data processing techniques and algorithms to infer the most appropriate cultural background. The output is a dataset containing the cultural context.

[0593] Step 5:

[0594] The generative AI model generates cultural expressions that best match the user's emotional state, based on the cultural context created by the server. The input consists of the cultural context and the user's emotional state. The model uses machine learning algorithms to generate expressions appropriate to the selected culture. The output is text data containing specific cultural expressions.

[0595] Step 6:

[0596] The device presents the generated cultural representation to the user. It displays or plays conversational examples and scenarios to illustrate this result. The input is text data provided by the generative AI model. The output is a visualization of the cultural representation and reference materials displayed to the user.

[0597] Step 7:

[0598] Users provide feedback on proposed cultural representations. This feedback is sent back to the server as input. The server collects this feedback and uses it to optimize the generative AI model. The output is data that will be used to improve future generation accuracy.

[0599] (Application Example 2)

[0600] 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."

[0601] In communication between individuals with different cultural backgrounds, cultural differences can sometimes hinder smooth communication. Furthermore, in online purchasing activities, there is a growing demand for personalized experiences tailored to customers' cultural backgrounds and emotions. Addressing these challenges and improving the customer experience is essential.

[0602] 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.

[0603] In this invention, the server includes functions for using artificial intelligence to collect and learn cultural information, analyzing the user's emotions in real time and providing appropriate suggestions to the user based on the results, and generating personalized information based on emotions and cultural background to improve the user's purchasing experience. This makes it possible to facilitate cross-cultural communication and provide a culturally appropriate experience even in online purchasing activities.

[0604] "Artificial intelligence" refers to software that has the ability to make judgments and learn in a way that humans do.

[0605] "Emotional analysis" is the process of recognizing and identifying a user's emotional state from their voice, facial expressions, and text.

[0606] "Cultural context" refers to information that encompasses customs, values, and communication styles based on a particular culture.

[0607] "User feedback" refers to opinions and reactions from users regarding their use of the system provided.

[0608] "Personalized information" refers to content that is customized based on the individual user's cultural background and emotions.

[0609] "Purchase experience" is a concept that refers to the overall experience a user has during the process of selecting and purchasing a product.

[0610] "Nonverbal communication" is a form of communication that does not involve the use of words, and includes, for example, gestures and facial expressions.

[0611] The system for carrying out the present invention is built around artificial intelligence technology for emotion analysis and cultural adaptation. The main components of this system are a server, a terminal, and a user.

[0612] The server first collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. This AI model includes information about cultural customs and communication styles. For this purpose, it utilizes generative AI technologies such as "GPT" and "BERT". The server also collects user feedback and retrains the AI ​​model as needed to achieve a more accurate cultural understanding and sentiment recognition.

[0613] The device analyzes user emotions in real time through voice and text. This analysis utilizes libraries such as "DeepFace" and "NLP," built in Python. The analyzed emotion data is sent to a server and used to generate appropriate cultural context. The device also presents personalized information based on the user's past usage history, facilitating communication in multicultural environments.

[0614] Through this system, users can obtain appropriate cultural representations in various scenarios. For example, if a user is feeling stressed while browsing products in a virtual store, they will be offered relaxation suggestions and customized product information tailored to their emotions.

[0615] As a concrete example, consider a scenario where a Japanese user in their 30s is looking for Japanese tableware. The device recognizes that this user is impressed by the beauty of the products, and the server generates appropriate cultural background information and suggestions.

[0616] Examples of prompt statements include the following:

[0617] "A Japanese user in her 30s is looking for Japanese tableware. She is impressed by the beauty of the products. Please generate a product description suitable for her."

[0618] This system allows users to receive information that aligns with their cultural background and emotions, leading to a more fulfilling shopping experience.

[0619] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0620] Step 1:

[0621] The device accepts user input, including voice, text, and images. This input data is passed to an emotion analysis engine. The device uses Python's "DeepFace" and "NLP" libraries to analyze the user's emotions from their voice tone and facial expressions, and generates an output representing their emotional state.

[0622] Step 2:

[0623] The device sends emotion analysis results and data on the user's cultural background to the server. The server receives this data and passes it as input to a generative AI model. This generative AI model uses the pre-processed input data to calculate a cultural context appropriate for the user and generates the result.

[0624] Step 3:

[0625] The server retrieves suggestions and cultural context as output from the generative AI model. This includes, for example, cultural descriptions related to a specific product or suggestions for relaxation. The server organizes this information and sends it back to the terminal.

[0626] Step 4:

[0627] The device displays suggestions and cultural information received from the server to the user. Customized information based on analyzed emotions is presented on the user's display, providing a more personalized purchasing experience.

[0628] Step 5:

[0629] Users provide feedback on the information presented. The device collects this feedback and sends it to the server. The server uses the feedback to retrain the generative AI model, helping to improve the model's accuracy. This further enhances the quality of future communication.

[0630] 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.

[0631] 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.

[0632] 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.

[0633] [Fourth Embodiment]

[0634] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0635] 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.

[0636] 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).

[0637] 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.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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".

[0647] This invention is a system that supports communication between individuals with different cultural backgrounds. This system collects vast amounts of cultural information and, based on that information, uses artificial intelligence to provide users with appropriate cultural context and expression. Specifically, it is implemented in the following forms.

[0648] The server periodically collects various cultural information from a cultural database and uses this to train a generative artificial intelligence. The main purpose of the training is to deepen understanding of cultural backgrounds and customs, and to grasp cultural contexts more accurately.

[0649] Users access the system using mobile devices or computers and input specific communication challenges or situations they face through the interface. The device analyzes this input and sets up a cultural context appropriate to the user.

[0650] The server uses artificial intelligence technology to generate the most appropriate cultural representations based on user input. This generated information is provided as concrete scenarios and conversation examples to facilitate understanding of cultural differences.

[0651] For example, consider a scenario where a foreign worker wants to express their opinion to their supervisor in a Japanese workplace. This system provides specific advice on how to show respect and make indirect suggestions within Japanese business culture. It then guides the user with concrete examples of how to express themselves in actual communication situations.

[0652] Furthermore, user feedback is used to readjust the AI ​​model generated by the server. This allows the system to provide more accurate cultural understanding support over time.

[0653] Furthermore, this system provides individually customized learning content based on the user's past usage and needs. This content includes detailed information on nonverbal communication and specific cultural events, helping users navigate diverse cultural situations.

[0654] In this way, the present invention is an effective means of preventing cultural misunderstandings and friction, and of facilitating smooth communication between individuals with different cultural backgrounds.

[0655] The following describes the processing flow.

[0656] Step 1:

[0657] The server collects cultural information from publicly available databases on the internet and from partner sources. This includes articles, papers, books, and multimedia data such as audio and video. This data is then organized and prepared as a training dataset for generative AI.

[0658] Step 2:

[0659] The server uses the collected cultural information to train a generative AI model. During training, it learns to understand intercultural communication patterns and specific cultural customs, and to provide appropriate responses and behaviors in specific situations.

[0660] Step 3:

[0661] Users access the system using their devices and input specific details about the communication problems and situations they are facing. For example, they might clearly describe a challenge such as "how to make improvement suggestions to my boss."

[0662] Step 4:

[0663] The terminal analyzes user input and identifies the context of the problem that needs to be solved. Based on the analysis results, it requests relevant cultural data from the server.

[0664] Step 5:

[0665] The server uses generative AI to generate appropriate cultural expressions and solutions based on the context received from the terminal. For example, it can create information that includes advice on appropriate respectful language and nonverbal cues in Japanese companies.

[0666] Step 6:

[0667] The device provides users with generated cultural representations and solutions. These are presented in the form of specific conversation examples and scenarios, and are structured in visual or text format to ensure easy understanding for the user.

[0668] Step 7:

[0669] Users respond to real-world situations based on the information provided and send feedback to the system regarding its usefulness and effectiveness.

[0670] Step 8:

[0671] The server analyzes user feedback and uses it to improve the generated AI model. This feedback helps improve the accuracy of cultural understanding and is reflected in future use.

[0672] Step 9:

[0673] The server generates customized learning content and cultural event information based on the user's past activities and interests, and provides it to the user via their device. This information serves as a supplementary tool to deepen the user's cultural understanding.

[0674] (Example 1)

[0675] 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".

[0676] Misunderstandings and friction often arise in communication between individuals from different cultural backgrounds. These problems stem from differences in cultural backgrounds and values, hindering smooth communication. Traditional methods require considerable effort and time to acquire individual cultural knowledge, making real-time responses difficult. This invention solves these problems and provides a means to facilitate smooth intercultural communication.

[0677] 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.

[0678] In this invention, the server includes means for using an intelligent processing device to collect information about culture and train an analytical model, means for interpreting situational information input by the user to identify appropriate cultural background information, and means for constructing a cultural expression according to the user's conditions using the generative model. This reduces misunderstandings and friction in communication between individuals with different cultural backgrounds and enables smooth information transmission.

[0679] "Cultural information" refers to data that helps in understanding and interpreting customs, business etiquette, and nonverbal communication based on different cultural backgrounds.

[0680] An "analysis model" refers to a program or algorithm that utilizes artificial intelligence technology to analyze and interpret input information.

[0681] A "user" refers to an individual who uses the system to receive advice on cultural context and expression.

[0682] "Status information" refers to specific information related to the current communication situation or problems that users input into the system.

[0683] "Cultural background information" refers to information that provides relevant cultural elements and context in a particular situation.

[0684] A "generative model" refers to AI technology used to construct appropriate cultural representations based on user input.

[0685] "Cultural expression" refers to linguistic expressions and behavioral guidelines that are appropriate for facilitating communication within a specific cultural context.

[0686] This invention is a system for supporting communication between individuals with different cultural backgrounds. This system primarily consists of three elements: a server, a terminal, and a user.

[0687] server:

[0688] The server collects diverse cultural information from cultural databases and publicly available materials on the internet. This information includes detailed data on cultural customs and nonverbal communication to aid in intercultural understanding. The server also uses this cultural information to train a generative AI model. This model analyzes the collected data and improves its ability to understand different cultural contexts. For example, TensorFlow or PyTorch could be used as machine learning frameworks.

[0689] Terminal:

[0690] The user accesses the system through a terminal. The terminal receives input from the user using a mobile device or computer. This input includes specific communication challenges and situations the user is facing. The terminal analyzes this input and generates a prompt. This prompt is sent to the server and used to generate the most appropriate cultural expression.

[0691] User:

[0692] Users can use the system to receive specific advice on communication challenges. For example, consider a user who inputs into the system, "I want to propose a new project to my boss, but how should I say it?" Based on this information, the server generates appropriate cultural context and expressions and presents them to the user. The information generated in this process serves as a guide in actual communication situations. Users can also provide feedback on the information provided. This feedback is used by the server to adjust the generated AI model and improve the accuracy of the system.

[0693] Examples of prompts include, "Please advise me on how to make a suitable proposal to my boss." By analyzing these prompts and generating expressions that include appropriate cultural context, users can communicate smoothly.

[0694] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0695] Step 1:

[0696] The server collects cultural information.

[0697] The server collects information from cultural databases and reliable sources on the internet. The program automatically searches using cultural keywords to extract the necessary data. The input consists of data collection conditions and targets, and the output is a dataset of cultural information.

[0698] Step 2:

[0699] The server trains the generative AI model.

[0700] The server trains a generative AI model using a dataset of collected cultural information. This dataset includes text data and data converted into vector formats, which are used to train a model for deepening intercultural understanding. The input is the collected data, and the output is the trained generative AI model.

[0701] Step 3:

[0702] Users input communication tasks through their devices.

[0703] The user inputs specific situations and challenges they are facing into the terminal. This input information includes details about the person they are communicating with and the situation. The input is the user's text data, and the output is a prompt sentence for parsing that information.

[0704] Step 4:

[0705] The terminal generates a prompt message and sends it to the server.

[0706] The terminal analyzes the text information received from the user and generates an appropriate prompt. This prompt is used by the server to create the most appropriate cultural expression for the user. The input is the user's input information, and the output is the generated prompt.

[0707] Step 5:

[0708] The server generates cultural expressions using a generative AI model.

[0709] The server uses a generative AI model to generate the most appropriate cultural expression based on the received prompt text. This process constructs appropriate expressions that take cultural differences into account, helping users communicate smoothly. The input is the prompt text, and the output is the cultural expression.

[0710] Step 6:

[0711] The device presents the generated cultural representation to the user.

[0712] The terminal displays cultural expressions sent from the server to the user. The user can use this as a reference to improve their own communication. The input is the generated cultural expression, and the output is the displayed content that the user sees.

[0713] Step 7:

[0714] Users provide feedback to the system.

[0715] The user inputs feedback on the presented cultural representations into the terminal. This feedback is used to improve the system's accuracy. The input is user feedback, and the output is evaluation data analyzed by the server.

[0716] (Application Example 1)

[0717] 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".

[0718] This solution addresses the challenges of misunderstandings and friction in communication between individuals with different cultural backgrounds, particularly the difficulty of presenting users with appropriate cultural context and product descriptions in virtual spaces.

[0719] 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.

[0720] In this invention, the server includes means for using generative artificial intelligence to collect and learn cultural information, means for analyzing user input to identify an appropriate cultural context, and means for generating and presenting product descriptions in a virtual space that are appropriate to the customer's cultural background. This enables culturally appropriate communication and product descriptions that are free from misunderstandings for users with different cultural backgrounds.

[0721] "Different cultural backgrounds" refers to the differences in values, beliefs, customs, languages, and other characteristics unique to individual regions, countries, societies, or individuals.

[0722] "Communication" refers to the process of exchanging information, thoughts, and feelings, and is carried out through verbal and nonverbal means.

[0723] "Cultural information" refers to knowledge, data, and examples related to a particular culture, including history, customs, values, and social norms of conduct.

[0724] "Generative artificial intelligence" refers to artificial intelligence technology that can learn patterns from data and generate human-like responses.

[0725] "Cultural context" refers to the background and assumptions behind information and behaviors within a particular culture, and is an important element for deepening understanding in communication.

[0726] "Product description" refers to an explanation provided to customers that includes information about a product, such as its characteristics, usage, and benefits.

[0727] A "virtual space" refers to a non-physical environment that is artificially created using computer technology.

[0728] "Feedback" refers to the opinions and evaluations that users provide regarding the system's output, and is information used to improve the system.

[0729] The system program for realizing this invention consists of the following elements. First, the server collects cultural information and uses it to train a generative AI model. The model learns patterns based on a vast amount of cultural background data and generates appropriate cultural contexts and expressions in communication between different cultures. Specifically, the GPT-4 is used as the generative AI model.

[0730] Users access the system via devices such as smartphones or head-mounted displays. These devices analyze user input and use a generative AI model to generate responses and product descriptions that are relevant to the cultural context. Standard smart devices can be used as hardware.

[0731] Based on input from the terminal, the server is responsible for generating appropriate cultural representations. In this process, it calculates data to provide product descriptions in a virtual space that are tailored to the user's cultural background. For example, when guiding a tourist from the United States about a Japanese matcha set, it generates information about the history of matcha and its popularity in the United States.

[0732] As a concrete example, the following prompt sentence is input to the AI ​​generation model: "Provide information about the popularity of matcha in American culture and generate cultural expressions that encourage American tourists to purchase a matcha set." This enables product descriptions that are suitable for customers with different cultural backgrounds.

[0733] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0734] Step 1:

[0735] Users access the system via a terminal. Users input basic information and questions into the terminal when they want to communicate with individuals from different cultural backgrounds or understand product information. This input may be in text or voice format.

[0736] Step 2:

[0737] Upon receiving input from the user, the terminal converts the input data into text format and begins analysis. The analysis uses natural language processing techniques to identify the cultural context required for the input information. The output is the analyzed context data.

[0738] Step 3:

[0739] The server receives the analyzed contextual data and utilizes a generative AI model to generate appropriate expressions based on the cultural background. This generative AI model (specifically using GPT-4) generates culturally appropriate expressions based on pre-trained cultural information. The input to the generative AI model is structured as prompt sentences.

[0740] Step 4:

[0741] The generated cultural expressions and product descriptions are sent from the server to the user's terminal. The terminal presents these expressions to the user, providing cultural understanding in the virtual space and appropriate context about the product. The output is displayed in a format that is easy for the user to understand.

[0742] Step 5:

[0743] Users send feedback to the system regarding the information presented. This feedback is recorded to indicate how helpful it was or where improvements are needed. The server uses this feedback to retrain the generating AI model, thereby improving its accuracy over time.

[0744] 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.

[0745] This invention is a system designed to support communication between individuals with different cultural backgrounds. This system combines generative artificial intelligence and an emotion engine to provide appropriate cultural context and expression in response to the user's emotions. Specific embodiments are described below.

[0746] The server collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. The collected data includes information about cultural customs and communication styles, and is used to improve the accuracy of the model.

[0747] Users access the system through their device and input specific communication challenges or situations. During this process, the device uses a built-in emotion engine to recognize the user's emotions in real time. The emotion engine analyzes voice tone, facial expressions, and emotional indicators extracted from text to determine the user's emotional state.

[0748] The server generates appropriate cultural context based on user input and data from the emotion engine, and the generative AI produces the most suitable cultural expressions for the user. For example, if the user is feeling surprised or nervous, suggestions and approaches that take those emotions into consideration will be taken into account.

[0749] The device presents the generated cultural expressions to the user and provides concrete conversation examples and scenarios to help the user understand how to apply them in real-world situations. For example, if a user is feeling anxious during a workplace meeting, the device will offer emotionally-based advice, such as conversational techniques to help them relax and appropriate gestures.

[0750] User feedback is collected by the server, used to retrain the generative AI model, and utilized for future improvements. This allows the system, which combines an emotion engine with generative artificial intelligence, to improve the quality of its cultural understanding with each use.

[0751] Furthermore, the device provides individually customized learning content based on the user's past usage history and sentiment analysis results. This content serves as supplementary material to deepen the user's knowledge and skills in adapting to diverse cultural backgrounds.

[0752] Thus, the present invention combines cultural information and emotion recognition technology to provide an effective solution for facilitating communication between individuals with different cultural backgrounds.

[0753] The following describes the processing flow.

[0754] Step 1:

[0755] The server regularly collects diverse cultural information from open-source databases and online resources. This cultural information includes greetings, business customs, and nonverbal communication patterns in different countries. This data is then organized into a dataset necessary for training generative AI.

[0756] Step 2:

[0757] The server uses the collected data to train a generative AI model. During training, it learns about intercultural communication methods and typical response patterns, and adjusts the model to understand appropriate cultural expressions in each situation.

[0758] Step 3:

[0759] Users access the system using a terminal and input the specific problems or situations they are encountering. At this time, specific examples can be provided, such as "How to greet someone for the first time at a new workplace."

[0760] Step 4:

[0761] The device recognizes the user's emotions using a built-in emotion engine. The emotion engine automatically analyzes the emotional state from facial expressions, voice tone, and the user's text to identify the type and intensity of the emotion.

[0762] Step 5:

[0763] The device sends user input and the results of the emotion engine's analysis to the server. Based on this data, the server identifies the optimal cultural context and uses generative AI to construct a cultural representation best suited to the user.

[0764] Step 6:

[0765] The server sends the generated cultural expressions and suggestions to the terminal, which then presents them to the user. The presentation includes scenarios that consider appropriate expressions, actions, and emotions, and is provided to the user in visual or text format.

[0766] Step 7:

[0767] Users use the information presented to facilitate actual communication. They then provide feedback to the system regarding the advice and system functionality provided.

[0768] Step 8:

[0769] The server analyzes the feedback it receives and uses it to improve the accuracy of the generated AI model. Based on the feedback, it readjusts the model to provide more appropriate support to future users.

[0770] Step 9:

[0771] The server creates personalized learning content based on the user's usage history and sentiment data, and delivers it to the user via their device. This learning content is used as supplementary material to deepen the user's cultural understanding.

[0772] (Example 2)

[0773] 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".

[0774] It is necessary to reduce misunderstandings and frictions that frequently occur in communication between individuals from different cultural backgrounds and to promote smooth mutual understanding. Communication barriers arising from cultural differences are a significant issue in today's increasingly globalized society. Furthermore, it is necessary to consider the emotional state of each individual to realize more effective communication support.

[0775] 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.

[0776] In this invention, the server includes means for using an algorithm to collect and learn cultural information, means for analyzing user input information and emotions to identify an appropriate cultural context, and means for using the algorithm to generate cultural expressions corresponding to the user's emotional state. This enables appropriate communication that takes emotions into consideration, even between individuals from different cultures.

[0777] An "algorithm" is a methodology for collecting and learning cultural information in order to support communication between individuals with different cultural backgrounds.

[0778] "Emotional state" refers to the user's internal state, identified by analyzing emotional indicators extracted from the user's voice, facial expressions, and text.

[0779] "Cultural context" refers to information that describes customs and communication styles within a particular culture, serving as background information necessary for smooth communication.

[0780] "Cultural expression" refers to appropriate forms of dialogue and behavior that are culturally conscious and generated in response to the user's emotional state and circumstances.

[0781] "Feedback" refers to the opinions and reactions that users provide to a system, and is data used to improve the system and optimize its algorithms.

[0782] This invention is a system for supporting communication between individuals with different cultural backgrounds. The system combines a generative AI model and an emotion engine to provide appropriate cultural context and expression that responds to the user's emotions. This embodiment is described in detail below.

[0783] The server collects diverse cultural information via the internet and other data sources. This includes customs and communication styles necessary to promote intercultural understanding, which are used to train a generative AI model. The generative AI model is built using advanced machine learning algorithms and has the ability to generate user-appropriate context based on the cultural information.

[0784] Users access the system through their terminal and input their communication challenges and situations. This input includes specific communication scenarios, such as workplace meetings or conversations with friends. For example, a prompt message like, "I want to convey a congratulatory message to a close friend in a more culturally appropriate way," conveys the user's intentions to the system.

[0785] The device utilizes a built-in emotion engine to analyze the user's voice tone, facial expressions, and emotional indicators from text, identifying the user's emotional state in real time. Based on this emotional data, the server generates a cultural context appropriate to the user's situation and provides appropriate cultural expressions through a generative AI model.

[0786] Ultimately, the device displays the generated cultural expressions to the user, showing concrete conversation examples and scenarios. This makes it easier for users to apply them to real-life conversations. Furthermore, user feedback is stored on the server, allowing the generating AI model to be continuously retrained and its cultural understanding to evolve with each use.

[0787] This system can support culturally sensitive communication, especially in international business environments and situations requiring cross-cultural communication.

[0788] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0789] Step 1:

[0790] The server collects cultural information using the internet and other data sources. This collected data includes customs and communication styles related to different cultures. Various cultural information and related documents are provided as input. The server stores this information in a database and uses it as training data for a generative AI model. The output is the cultural dataset necessary for training.

[0791] Step 2:

[0792] Users access the system via a terminal and input their communication challenges or situations. These prompts are entered as linguistic expressions, such as "Please tell me the best way to phrase things when negotiating at work." The input is a text-based prompt, and the output is the user's request, ready for analysis.

[0793] Step 3:

[0794] The device receives input prompts from the user and activates an emotion engine to analyze them. The input consists of the user's voice, facial expressions, and text. The emotion engine analyzes this data to determine the user's emotional state in real time. The output is numerical data representing the user's emotional state.

[0795] Step 4:

[0796] The server combines user prompts with analyzed sentiment data to identify the appropriate cultural context. The input consists of user requests and sentiment data. The server uses advanced data processing techniques and algorithms to infer the most appropriate cultural background. The output is a dataset containing the cultural context.

[0797] Step 5:

[0798] The generative AI model generates cultural expressions that best match the user's emotional state, based on the cultural context created by the server. The input consists of the cultural context and the user's emotional state. The model uses machine learning algorithms to generate expressions appropriate to the selected culture. The output is text data containing specific cultural expressions.

[0799] Step 6:

[0800] The device presents the generated cultural representation to the user. It displays or plays conversational examples and scenarios to illustrate this result. The input is text data provided by the generative AI model. The output is a visualization of the cultural representation and reference materials displayed to the user.

[0801] Step 7:

[0802] Users provide feedback on proposed cultural representations. This feedback is sent back to the server as input. The server collects this feedback and uses it to optimize the generative AI model. The output is data that will be used to improve future generation accuracy.

[0803] (Application Example 2)

[0804] 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".

[0805] In communication between individuals with different cultural backgrounds, cultural differences can sometimes hinder smooth communication. Furthermore, in online purchasing activities, there is a growing demand for personalized experiences tailored to customers' cultural backgrounds and emotions. Addressing these challenges and improving the customer experience is essential.

[0806] 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.

[0807] In this invention, the server includes functions for using artificial intelligence to collect and learn cultural information, analyzing the user's emotions in real time and providing appropriate suggestions to the user based on the results, and generating personalized information based on emotions and cultural background to improve the user's purchasing experience. This makes it possible to facilitate cross-cultural communication and provide a culturally appropriate experience even in online purchasing activities.

[0808] "Artificial intelligence" refers to software that has the ability to make judgments and learn in a way that humans do.

[0809] "Emotional analysis" is the process of recognizing and identifying a user's emotional state from their voice, facial expressions, and text.

[0810] "Cultural context" refers to information that encompasses customs, values, and communication styles based on a particular culture.

[0811] "User feedback" refers to opinions and reactions from users regarding their use of the system provided.

[0812] "Personalized information" refers to content that is customized based on the individual user's cultural background and emotions.

[0813] "Purchase experience" is a concept that refers to the overall experience a user has during the process of selecting and purchasing a product.

[0814] "Nonverbal communication" is a form of communication that does not involve the use of words, and includes, for example, gestures and facial expressions.

[0815] The system for carrying out the present invention is built around artificial intelligence technology for emotion analysis and cultural adaptation. The main components of this system are a server, a terminal, and a user.

[0816] The server first collects a wide range of cultural information from the internet and other data sources, and uses this to train a generative AI model. This AI model includes information about cultural customs and communication styles. For this purpose, it utilizes generative AI technologies such as "GPT" and "BERT". The server also collects user feedback and retrains the AI ​​model as needed to achieve a more accurate cultural understanding and sentiment recognition.

[0817] The device analyzes user emotions in real time through voice and text. This analysis utilizes libraries such as "DeepFace" and "NLP," built in Python. The analyzed emotion data is sent to a server and used to generate appropriate cultural context. The device also presents personalized information based on the user's past usage history, facilitating communication in multicultural environments.

[0818] Through this system, users can obtain appropriate cultural representations in various scenarios. For example, if a user is feeling stressed while browsing products in a virtual store, they will be offered relaxation suggestions and customized product information tailored to their emotions.

[0819] As a concrete example, consider a scenario where a Japanese user in their 30s is looking for Japanese tableware. The device recognizes that this user is impressed by the beauty of the products, and the server generates appropriate cultural background information and suggestions.

[0820] Examples of prompt statements include the following:

[0821] "A Japanese user in her 30s is looking for Japanese tableware. She is impressed by the beauty of the products. Please generate a product description suitable for her."

[0822] This system allows users to receive information that aligns with their cultural background and emotions, leading to a more fulfilling shopping experience.

[0823] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0824] Step 1:

[0825] The device accepts user input, including voice, text, and images. This input data is passed to an emotion analysis engine. The device uses Python's "DeepFace" and "NLP" libraries to analyze the user's emotions from their voice tone and facial expressions, and generates an output representing their emotional state.

[0826] Step 2:

[0827] The device sends emotion analysis results and data on the user's cultural background to the server. The server receives this data and passes it as input to a generative AI model. This generative AI model uses the pre-processed input data to calculate a cultural context appropriate for the user and generates the result.

[0828] Step 3:

[0829] The server retrieves suggestions and cultural context as output from the generative AI model. This includes, for example, cultural descriptions related to a specific product or suggestions for relaxation. The server organizes this information and sends it back to the terminal.

[0830] Step 4:

[0831] The device displays suggestions and cultural information received from the server to the user. Customized information based on analyzed emotions is presented on the user's display, providing a more personalized purchasing experience.

[0832] Step 5:

[0833] Users provide feedback on the information presented. The device collects this feedback and sends it to the server. The server uses the feedback to retrain the generative AI model, helping to improve the model's accuracy. This further enhances the quality of future communication.

[0834] 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.

[0835] 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.

[0836] 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 robot 414.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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."

[0843] 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.

[0844] 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.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] The following is further disclosed regarding the embodiments described above.

[0856] (Claim 1)

[0857] To support communication between multiple individuals with different cultural backgrounds, a means of using generative artificial intelligence that collects and learns cultural information,

[0858] A means of analyzing user input to identify the appropriate cultural context,

[0859] A means for generating cultural expressions according to the user's situation using the aforementioned artificial intelligence for generation,

[0860] A means of presenting the generated cultural expression to the user,

[0861] A means for collecting user feedback and retraining the model of the generative artificial intelligence,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, further comprising means for providing personalized learning content based on the user's usage history.

[0865] (Claim 3)

[0866] The system according to claim 1, further comprising means for generating a detailed information guide regarding nonverbal communication and cultural events provided to the user.

[0867] "Example 1"

[0868] (Claim 1)

[0869] A means of using an intelligent processing device to collect information on culture and train an analytical model,

[0870] A means of interpreting situational information entered by the user to identify appropriate cultural background information,

[0871] Using the aforementioned generative model, a means for constructing cultural expressions according to the user's conditions,

[0872] A means of presenting constructed cultural expressions to users,

[0873] A means for collecting evaluation information from users and retraining the generative model,

[0874] A means of conducting a training process to improve the learning of a generative model based on collected cultural information,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, further comprising means for providing personalized learning content based on the user's past interactions.

[0878] (Claim 3)

[0879] The system according to claim 1, further comprising means for generating a detailed explanatory guide regarding nonverbal communication and cultural background provided to the user.

[0880] "Application Example 1"

[0881] (Claim 1)

[0882] To support communication between multiple individuals with different cultural backgrounds, a means of using generative artificial intelligence that collects and learns cultural information,

[0883] A means of analyzing user input to identify the appropriate cultural context,

[0884] A means for generating cultural expressions according to the user's situation using the aforementioned artificial intelligence for generation,

[0885] A means of presenting the generated cultural expression to the user,

[0886] A means for collecting user feedback and retraining the model of the generative artificial intelligence,

[0887] A means of generating and presenting product descriptions tailored to the customer's cultural background in a virtual space,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for providing personalized learning content based on the user's usage history.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for generating a detailed information guide regarding nonverbal communication and cultural events provided to the user.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] To support communication between multiple individuals with different cultural backgrounds, a means of using algorithms that collect and learn cultural information,

[0896] A means of analyzing user input and emotions to identify the appropriate cultural context,

[0897] A means for generating cultural expressions corresponding to the user's emotional state using the aforementioned algorithm,

[0898] A means of presenting generated cultural expressions to users and providing appropriate conversation examples,

[0899] A means for collecting user feedback and optimizing the algorithm model,

[0900] A mechanism that includes this.

[0901] (Claim 2)

[0902] The mechanism according to claim 1, further comprising means for providing personalized learning information based on the user's usage history and sentiment data.

[0903] (Claim 3)

[0904] The mechanism according to claim 1, further comprising means for generating detailed guides on nonverbal communication and multicultural events provided to the user.

[0905] "Application example 2 of combining emotional engines"

[0906] (Claim 1)

[0907] To support communication between multiple individuals with different cultural backgrounds, the system uses artificial intelligence to collect and learn cultural information,

[0908] A function that analyzes information entered by the user to identify the appropriate cultural context,

[0909] The aforementioned artificial intelligence is used to generate cultural expressions that are appropriate to the user's situation,

[0910] A function that presents generated cultural expressions to the user,

[0911] A function to collect user feedback and retrain the AI ​​learning model,

[0912] A feature that analyzes user emotions in real time and provides appropriate suggestions to the user based on the results,

[0913] Features that generate personalized information based on emotions and cultural background to improve the user's purchasing experience,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, further comprising a function to provide personalized learning information based on the user's usage history.

[0917] (Claim 3)

[0918] The system according to claim 1, further comprising a function to generate detailed informational guidance regarding nonverbal communication and cultural activities provided to the user. [Explanation of Symbols]

[0919] 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 communication between multiple individuals with different cultural backgrounds, a means of using generative artificial intelligence that collects and learns cultural information, A means of analyzing user input to identify the appropriate cultural context, A means for generating cultural expressions according to the user's situation using the aforementioned artificial intelligence for generation, A means of presenting the generated cultural expression to the user, A means for collecting user feedback and retraining the model of the generative artificial intelligence, A system that includes this.

2. The system according to claim 1, further comprising means for providing personalized learning content based on the user's usage history.

3. The system according to claim 1, further comprising means for generating a detailed information guide regarding nonverbal communication and cultural events provided to the user.

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