system

Abstract

We provide the system. [Solution] Information processing means that automatically generates different dialogue scenarios according to the user's interests and occupation, A dialogue control means that processes dialogues in real time according to a generated dialogue scenario, An evaluation method that detects errors in the user's pronunciation and grammar and provides feedback for improvement, A life support tool that proposes English expressions appropriate to real-world situations, A system that includes this.

Description

Technical Field

[0002] , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, while the importance of English as a global communication ability has been increasing, the opportunity to actually use it in daily life is limited, which has become an issue. In addition, it is difficult to provide specific scenarios according to individual interests and occupations in conventional language learning methods, and furthermore, there is a lack of a function to correct pronunciation and grammar mistakes of learners in real time. Therefore, there is a need to provide an effective and personalized English learning environment.

Means for Solving the Problems

[0005] This invention provides a system that automatically generates different dialogue scenarios according to the user's interests and occupation. It includes an evaluation means that processes the user's dialogue in real time based on the generated scenarios, detects errors in the user's pronunciation and grammar, and provides immediate feedback for improvement. Furthermore, by incorporating a life support means that suggests appropriate English expressions according to everyday situations, it enables users to gain practical English usage experience. This allows users to learn English effectively regardless of location.

[0006] "Information processing means" refers to a device or program that acquires input data from a user and has the function of automatically generating different dialogue scenarios according to the user's interests and occupation.

[0007] "Dialogue control means" refers to a device or program that has the function of processing dialogues executed in real time according to a generated dialogue scenario and controlling communication with the user.

[0008] "Evaluation means" refers to a device or program that has the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0009] A "life support tool" is a device or program that suggests English expressions appropriate to real-world situations and has the function of supporting the user's use of English in their daily life.

[0010] "Voice conversion means" refers to a device or program that has the function of converting voice input from a user into text format. [Brief explanation of the drawing]

[0011] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0015] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0017] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system that provides customized English conversation scenarios tailored to the user's interests and occupation, and supports conversations based on those scenarios. This system is centered around generative AI and provides users with a real-time English learning experience through smart glasses or mobile devices.

[0033] When a user uses the system, they first launch smart glasses or a dedicated application and input basic information such as their interests, occupation, and English level. This data is then sent to an AI that generates conversational scenarios tailored to the user. For example, healthcare professionals are provided with conversational scenarios based on hospital consultation situations.

[0034] The server sends data to the terminal to control the interaction with the user based on the generated dialogue scenario. This dialogue control means that prompts following the flow of the conversation are displayed on the smart glasses, allowing the user to proceed with the conversation with the AI ​​accordingly. For example, if the user asks "How can I assist you today?" in the scenario, the AI ​​will provide a response such as "I'm looking for advice on how to manage stress at work."

[0035] The terminal receives the user's speech as voice input and uses a speech-to-text conversion system to send it to the server. Meanwhile, the evaluation system analyzes this voice data, instantly detecting errors in the user's pronunciation and grammar, and generating feedback in real time. As a result, the user can receive specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0036] Furthermore, when users need English in various everyday situations, the life support system suggests appropriate phrases for each situation. This feature allows users to respond quickly when ordering food or shopping while traveling. For example, by using smart glasses at a local restaurant, users can quickly refer to phrases such as "May I have the menu, please?"

[0037] Thus, the present invention provides users with an individualized English learning environment and realizes a system that supports efficient learning regardless of time or place.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user launches the smart glasses or a dedicated application and enters their name, occupation, areas of interest, and English proficiency level on the interface.

[0041] Step 2:

[0042] The device receives data entered by the user and sends it to the server as profile data. This profile data includes the user's interests, occupation, and English proficiency level.

[0043] Step 3:

[0044] The server analyzes the received profile data and uses a generation AI to automatically generate customized conversation scenarios tailored to the user. For example, a scenario related to sales negotiations will be generated for a sales professional.

[0045] Step 4:

[0046] The server sends the generated dialogue scenario to the device, making it visible on the user's smart glasses. This prepares the user to engage in the dialogue while visually reviewing the scenario.

[0047] Step 5:

[0048] The user initiates a conversation with the AI ​​by following prompts on the smart glasses. During this process, the user's speech is sent to the device via voice input.

[0049] Step 6:

[0050] The device converts the collected voice data into text data using speech recognition technology and sends that data to the server in real time.

[0051] Step 7:

[0052] The server analyzes the voice data and generates an appropriate response. Utilizing AI's natural language processing capabilities, it creates contextually appropriate responses tailored to the user's utterances and sends them to the device.

[0053] Step 8:

[0054] The device displays the response from the server on the smart glasses' display and, if necessary, provides audio output to communicate the response to the user.

[0055] Step 9:

[0056] The server evaluates the user's pronunciation and grammar and generates real-time feedback. For example, it can correct the pronunciation of specific words or suggest improvements to grammatical structure.

[0057] Step 10:

[0058] The device visually displays the generated feedback on the smart glasses and presents the user with specific ways to improve.

[0059] Step 11:

[0060] Users can request assistance with using English in their daily lives as needed. This request is forwarded to the server by the device.

[0061] Step 12:

[0062] The server generates appropriate English phrases and guidance based on everyday situations and sends them to the terminal. This allows users to communicate smoothly in a way that is appropriate for each situation.

[0063] (Example 1)

[0064] 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."

[0065] In modern society, there is a growing need for efficient English language learning tailored to individual interests and occupations. However, traditional learning methods struggle to automatically generate user-friendly dialogue scenarios, and furthermore, they have difficulty detecting pronunciation and grammatical errors in real time and providing immediate feedback. Additionally, there has been a lack of timely means to provide English expressions relevant to real-world situations.

[0066] 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.

[0067] In this invention, the server includes an information processing structure that automatically generates different dialogue content according to the user's interests and occupation, a conversation control structure that immediately processes conversations executed according to the generated dialogue content, and an evaluation structure that detects errors in the user's pronunciation and grammar and provides suggestions for improvement. This makes it possible to provide the user with the most suitable English dialogue scenario in real time and support efficient English learning.

[0068] An "information processing structure" is an element of a system that collects data based on the user's interests and occupation, and automatically generates different dialogue content based on that data.

[0069] A "conversation control structure" is a system element that facilitates smooth, real-time conversations with users based on the generated dialogue content.

[0070] The "evaluation structure" is a system element that analyzes user voice data, detects errors in pronunciation and grammar, and immediately provides specific feedback for improvement.

[0071] A "daily support structure" is a system element that proposes language expressions to users that are appropriate to real-world situations, thereby supporting language use in daily life.

[0072] A "voice conversion structure" is an element of a system that receives voice data from a user and converts it into text data.

[0073] This invention is a system that provides English conversation scenarios based on individual interests and occupations to support users' English learning. To implement this, an information processing system centered on a generative AI model is required.

[0074] Users of this system first launch an application on their smart glasses or mobile device. Here, users input information such as their interests, occupation, and English level. Upon receiving the user's input, the device sends that data to the server.

[0075] The server uses a generative AI model to analyze the received user information. It then generates a dialogue scenario suitable for the user using prompts. For example, a prompt such as "Generate an English conversation used in a medical scenario" might be used.

[0076] Based on the generated dialogue scenario, the server sends data to the device to control the conversation with the user. The device then uses this data to display the flow of the conversation on the smart glasses, allowing the user to refer to it and continue the conversation with the AI.

[0077] The user's voice input is received by the device and converted into text data using a speech-to-text structure. The converted text data is then sent back to the server. On the server side, an evaluation structure analyzes the voice data, identifies pronunciation and grammatical errors, and generates feedback in real time. For example, it might provide specific feedback such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0078] Furthermore, as language support for everyday life, the server prompts users with expressions appropriate to situations they encounter in their daily lives. In this example, it instantly presents the phrase "May I have the menu, please?" which a traveler might use in a restaurant. In this way, users can improve their English skills at their own pace through a personalized learning experience.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The user launches an application on smart glasses or a mobile device and enters basic information such as interests, occupation, and English level. This information becomes the initial input data for the system. The entered information is then transmitted by the terminal to the information processing structure.

[0082] Step 2:

[0083] The terminal transfers user input information to the server. The server receives this information and inputs it, along with prompts, into the generating AI model. These prompts might include something like, "Generate an English conversation used in a medical scenario." The server generates a customized conversation scenario based on the user's interests and occupation. The output is the data of the generated conversation scenario.

[0084] Step 3:

[0085] The server creates dialogue control data based on the generated dialogue scenario and sends it to the terminal. The terminal receives this data and displays appropriate conversation topics and prompts on the smart glasses' display. The user refers to these prompts and begins interacting with the AI. The output is the flow of the dialogue that the user should follow.

[0086] Step 4:

[0087] The user interacts with the AI ​​via voice by following the displayed prompts. The device receives the user's voice as voice input and converts it into text data using a speech-to-text conversion structure. This text data is sent to the server and used for processing in the next step.

[0088] Step 5:

[0089] The server analyzes the received text data using an evaluation structure to detect errors in the user's pronunciation and grammar. It then generates feedback based on the detected errors and sends it to the terminal in real time. The user receives specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0090] Step 6:

[0091] The server operates a daily support structure to suggest appropriate English expressions based on the user's living environment and daily situations. This provides concrete examples that meet the needs of travelers and in daily life. As a result, users can quickly refer to practical expressions such as "May I have the menu, please?"

[0092] (Application Example 1)

[0093] 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."

[0094] In today's increasingly globalized society, businesses are required to communicate effectively with customers from diverse cultures and backgrounds. However, many frontline staff face the challenge of being unable to adequately serve customers due to language barriers. Therefore, there is a need for systems that smoothly support foreign language communication.

[0095] 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.

[0096] In this invention, the server includes an information processing element that automatically generates different dialogue scenarios according to the user's interests and occupation, a dialogue control element that processes the dialogue executed according to the generated dialogue scenario in real time, and an evaluation element that detects errors in the user's pronunciation and grammar and provides feedback for improvement. This enables smooth intercultural communication by allowing on-site staff to provide appropriate foreign language phrases in real time.

[0097] An "information processing element" is an element that has the function of automatically generating different dialogue scenarios tailored to the user's interests and occupation.

[0098] A "dialogue control element" is an element that has the function of processing dialogue in real time, according to the generated dialogue scenario.

[0099] "Evaluation elements" are elements that have the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0100] "Life support elements" are elements that have the function of suggesting the most appropriate language expression to the user according to the situation in the real world.

[0101] A "support element" is an element that has the function of providing language phrases that enable users to effectively communicate with foreigners in environments such as the workplace.

[0102] The system of this invention is primarily implemented using AR-enabled smart glasses and a server. The server first receives information about the user's interests and occupation entered through the smart glasses. This information is used to automatically generate the optimal dialogue scenario for the user using a generative AI model. The generated scenario is then transmitted to the smart glasses to control the dialogue in real time.

[0103] The device (smart glasses) displays specific prompts to the user based on dialogue scenarios sent from the server. In addition, the device is equipped with software that performs speech conversion and evaluation, converting the user's voice input into text data and sending it to the server. This evaluation primarily detects errors in the user's pronunciation and grammar, and provides immediate feedback. Specifically, this includes minor corrections to pronunciation and identification of grammatical errors.

[0104] Users can use this system, for example, when assisting foreign customers as staff in a physical store. If a customer asks, "Does this product come in other colors?", the smart glasses will display a response phrase such as, "Yes, it comes in several colors. Would you like to see them?" This prompt is generated by a generative AI model that uses prompt sentences like the following to suggest the most appropriate language expression for the situation.

[0105] Examples of prompts for a generative AI model:

[0106] "If the user is a store employee, please explain in English how to respond when a customer asks about the color of a product."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The user puts on smart glasses and launches a dedicated application. The user enters their interests, occupation, and English level. This information is sent to the server by the device as input data.

[0110] Step 2:

[0111] The server sends prompt messages to the generating AI model based on the user information it receives. These prompt messages cause the AI ​​to generate a dialogue scenario suitable for the user. The generating AI analyzes the input data and returns the optimal dialogue scenario as generated data.

[0112] Step 3:

[0113] The server receives the generated dialogue scenario and creates specific dialogue prompts based on that scenario. This becomes the output data and is sent to the smart glasses.

[0114] Step 4:

[0115] The device (smart glasses) displays received conversational prompts in the user's field of view. The user follows these prompts during customer service conversations.

[0116] Step 5:

[0117] When a user inputs voice data during a conversation with a customer, that voice data is converted into text data. This conversion process takes place on the terminal and the text data is sent to the server.

[0118] Step 6:

[0119] The server analyzes the received text data using evaluation elements. It detects errors in the user's pronunciation and grammar, and generates feedback based on the evaluation results.

[0120] Step 7:

[0121] The generated feedback is immediately sent to the device and displayed to the user. The user can then use this feedback to improve their English expression.

[0122] 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.

[0123] This invention aims to further enhance the user experience by combining an emotion engine with a system that provides customized English dialogue scenarios tailored to the user's interests and occupation. This system supports real-time English learning using generative AI, an emotion engine, and smart glasses or mobile devices.

[0124] When a user uses the system, they launch the smart glasses application and enter basic information into the interface. This information includes the user's interests, occupation, and current English proficiency, and is sent to the server. The server uses generative AI to automatically generate conversational scenarios based on the user's profile data. For example, for a user whose interest is sports, a scenario related to sporting events will be generated.

[0125] The generated dialogue scenario is transmitted via the device to the smart glasses and displayed in the user's field of view. Here, the user receives adaptive feedback on their emotional state through an emotion engine. This emotion engine can analyze the user's voice tone and facial expressions to recognize the stress and difficulties the user experiences during the dialogue.

[0126] For example, if a user has difficulty pronouncing a particular phrase, the emotion engine can sense the user's frustration and provide gentle, encouraging feedback through dialogue control mechanisms, such as, "Try to relax a little and try again. We'll practice together." In this way, the emotion engine's data serves as a new metric for improving the user's learning experience.

[0127] The evaluation system analyzes the user's pronunciation and grammar in real time and suggests specific areas for improvement. In addition, a life support system assists the user in using English in their daily life. This system enables less stressful communication by suggesting appropriate English phrases based on the user's emotional state.

[0128] This comprehensive system structure allows users to smoothly progress in their English learning while receiving individually adapted feedback. The adoption of an emotion engine provides a flexible learning environment that takes into account the user's psychological state, enabling a more effective and human-centered approach to English learning.

[0129] The following describes the processing flow.

[0130] Step 1:

[0131] The user activates the smart glasses, accesses a dedicated app, and enters basic information such as their interests, occupation, and English level.

[0132] Step 2:

[0133] The device sends the information received from the user to the server as profile data. The profile reflects the user's individual attributes.

[0134] Step 3:

[0135] The server analyzes profile data and generates personalized conversation scenarios using AI. For example, for a salesperson, it creates a scenario for a customer negotiation.

[0136] Step 4:

[0137] The generated dialogue scenario is sent from the server to the terminal and displayed on the smart glasses. This allows the user to visually recognize the scenario and prepare for the interaction.

[0138] Step 5:

[0139] The user follows the prompts displayed on the smart glasses to begin the conversation. The audio of the conversation is transmitted to the device.

[0140] Step 6:

[0141] The terminal converts user speech into text data using speech recognition technology and transfers it to the server. The converted data is then used for analysis.

[0142] Step 7:

[0143] The server uses dialogue control mechanisms to generate real-time responses based on the user's utterances. The AI ​​understands the context and creates appropriate responses.

[0144] Step 8:

[0145] Furthermore, the server's emotion engine analyzes the user's voice tone and facial expressions to assess their emotional state. Based on this assessment, it constructs emotionally sensitive feedback, such as "Let's try to relax a little while we talk."

[0146] Step 9:

[0147] The device displays the evaluation results on smart glasses and provides the user with adapted feedback and emotion-based advice.

[0148] Step 10:

[0149] The server uses evaluation tools to monitor the user's pronunciation and grammar, and provides real-time feedback on areas for improvement. Specific instructions are then displayed.

[0150] Step 11:

[0151] When a user requests English assistance in everyday situations, such as looking for a phrase while shopping, the request is sent to the server via the device.

[0152] Step 12:

[0153] The server generates and provides users with English phrases and appropriate action suggestions based on their emotional state, ensuring that support tailored to each situation is provided.

[0154] (Example 2)

[0155] 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".

[0156] The challenge lies in creating a system that efficiently provides customized dialogue scenarios tailored to users' interests and occupations, while also considering their emotional state to support language learning. Furthermore, it is essential to provide an effective learning experience through adaptive feedback based on each user's individual language proficiency.

[0157] 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.

[0158] In this invention, the server includes information processing means for automatically generating different dialogue scenarios according to the user's interests and occupation, dialogue control means for processing dialogues executed according to the generated dialogue scenarios in real time, and emotion analysis means for analyzing the user's voice tone and facial expressions to evaluate their emotional state and provide adaptive feedback. This enables the provision of real-time dialogue scenarios based on the user's interests and occupation, and emotionally adapted feedback.

[0159] "Information processing means" refers to means that provide functions for automatically generating dialogue scenarios tailored to the user's interests and occupation.

[0160] "Dialogue control means" refers to means for processing user interactions in real time based on generated dialogue scenarios.

[0161] An "evaluation tool" is a tool that has the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0162] "Life support tools" are tools that have the function of suggesting appropriate language expressions to users in accordance with real-world situations.

[0163] "Emotional analysis means" refers to a method of analyzing a user's voice tone and facial expressions to evaluate their emotional state and provide appropriate feedback.

[0164] A "dialogue scenario generation method" using "generative AI" is a method that uses a generative AI model to generate customized dialogue scenarios based on the user's profile data.

[0165] A "voice conversion method" is a means for converting voice input from a user into text format.

[0166] This invention provides a system that offers customized English conversation scenarios based on the user's interests and occupation, and enhances the learning experience using an emotion engine. This system is implemented using a generative AI model, smart glasses or a mobile device, a server, and emotion analysis technology.

[0167] Users launch an application on smart glasses or a mobile device and input their interests, occupation, and English proficiency using a dedicated interface. This input information is transmitted to a server via the internet.

[0168] The server utilizes a generative AI model as an information processing tool based on the information it receives. This generative AI model is a natural language processing engine that runs on the cloud, and an example of a large-scale model is "GPT". The server sends prompt sentences tailored to the user's interests to the generative AI, which automatically generates dialogue scenarios that match the user's characteristics. For example, if the user is interested in cooking, the server will generate dialogue scenarios related to cooking recipes and cooking techniques.

[0169] The generated dialogue scenario is transferred to the smart glasses via the terminal and displayed in the user's field of view. Here, the terminal plays a role in controlling the scenario-based dialogue with the user in real time.

[0170] As an emotion analysis tool, the server analyzes the user's voice tone and facial expressions to evaluate the user's emotional state in real time. This analysis utilizes speech recognition software and image recognition software, specifically open-source speech recognition libraries and facial expression evaluation algorithms.

[0171] As the user engages in dialogue, if the emotion analysis system detects the user's stress or difficulty, the server generates appropriate feedback and sends it to the user via the device. For example, if the user is struggling with a particular English phrase, the server might provide feedback such as, "Calm down and try again. Let's repeat it together."

[0172] Furthermore, the server uses evaluation tools to analyze the user's pronunciation and grammar and suggests specific areas for improvement. The life support tools suggest useful English phrases for the user's daily life, helping to improve the user's communication skills.

[0173] As a concrete example, here is an example of a prompt to the generating AI: "If the user's interest is travel and their English proficiency is intermediate, please suggest the most helpful dialogue scenarios for the travel destinations the user will be visiting." In response to this prompt, the AI ​​will generate dialogue scenarios related to everyday conversations and tourist attractions in the travel destinations.

[0174] In summary, the present invention provides users with an individually optimized English learning experience and realizes a flexible and human-centered learning environment by utilizing an emotion engine.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The user launches an app on smart glasses or a mobile device and enters personal information. Specifically, the user interacts with an interface to enter basic data such as interests, occupation, and English proficiency. This input data forms the basis for designing a customized learning experience for the user.

[0178] Step 2:

[0179] The terminal retrieves information entered by the user and sends it to the server. The input data includes information about interests, occupation, and English proficiency, and this data serves as initial data for data processing on the server side.

[0180] Step 3:

[0181] The server creates prompts for the AI ​​model based on the received user information and instructs it to generate dialogue scenarios. Specifically, the server analyzes the received data and generates prompts such as, "If the user's interest is music and their English ability is beginner level, please generate a simple conversation scenario for a music festival." The server uses this as input to the AI ​​model and outputs dialogue scenarios adapted to each user.

[0182] Step 4:

[0183] The server sends a customized dialogue scenario derived from the generated AI model to the terminal. The terminal's role is to transfer this output scenario to the smart glasses and display it directly to the user. The user then conducts a dialogue exercise based on this displayed information.

[0184] Step 5:

[0185] The user initiates a conversation, and the emotion analysis system analyzes the user's voice tone and facial expressions in real time. The input is the user's voice and video data, which the emotion analysis system outputs as an emotional state. This analysis includes voice recognition and facial expression analysis, allowing the application to track changes in emotion.

[0186] Step 6:

[0187] The server receives data from the emotion analysis system and generates feedback based on the user's emotional state. For example, if the server detects that the user is stressed, it will output supportive feedback such as, "Try to relax a little and try again slowly." This allows the user to adjust their next steps based on the feedback.

[0188] Step 7:

[0189] The server uses evaluation tools to analyze the user's pronunciation and grammar. The input is the user's spoken content, which the server analyzes and provides output that specifically identifies errors and areas for improvement. Based on the evaluation, the user receives specific guidance for improving their proficiency.

[0190] Step 8:

[0191] The life support system suggests everyday language expressions based on the user's emotional state and daily feedback. This allows users to communicate more smoothly in real-life situations and receive support tailored to their daily lives.

[0192] (Application Example 2)

[0193] 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."

[0194] In today's multicultural society, smooth communication between individuals with different languages ​​and cultures remains challenging. Language barriers are particularly significant obstacles to customer service and business transactions in tourist destinations and international business settings. Furthermore, traditional language learning and interpretation services struggle to understand and respond appropriately to the other person's emotions, creating limitations in building interpersonal relationships.

[0195] 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.

[0196] In this invention, the server includes means for converting observable human speech into text using information identification means, means for automatically generating dialogue scenarios using a reproducible artificial intelligence model, and means for analyzing human emotional states using expression analysis means. This enables users to overcome language and emotional barriers in intercultural communication and communicate smoothly in real time.

[0197] "Information identification means" refers to a technological device for detecting sounds and images in the environment, acquiring those signals, and analyzing them.

[0198] A "generative artificial intelligence model" is an algorithm or system that has the ability to autonomously create new data or scenarios based on input information.

[0199] A "dialogue scenario" is a pre-designed and generated transcript of the expected flow and content of a conversation in a specific scene or situation.

[0200] "Management means" refer to methods and devices for coordinating various processes and data flows within a system to ensure smooth operation.

[0201] "Representation analysis means" refers to technologies that judge and classify human intentions and emotional states based on acquired data.

[0202] "Means of providing feedback" refer to methods and devices for returning appropriate information and advice to users based on analysis results.

[0203] "Everyday life scenarios" refer to situations and scenarios that are commonly experienced in daily life.

[0204] "Means for proposing linguistic expressions" refer to functions or systems for generating and presenting linguistic phrases that are appropriate for specific situations or emotional states.

[0205] To implement this invention, it is necessary to build a system that supports smooth intercultural communication by coordinating the user's smart glasses, mobile device, and server. This system consists of the following main modules.

[0206] First, the system acquires the voices of the user and their conversation partner in real time using identification methods from smart glasses or mobile devices. The acquired voice data is converted into text data using speech-to-text conversion methods such as Google Cloud Speech-to-Text API. The converted text data is sent to a server, and an appropriate conversation scenario is automatically generated using a reproducible artificial intelligence model, such as OpenAI's GPT.

[0207] The server uses expression analysis tools to understand the user's emotional state, which is analyzed from their voice, and provides feedback that corresponds to that emotion. Software such as IBM Watson® Tone Analyzer can be used in this process to achieve optimal communication based on the user's emotional state.

[0208] This allows the device to appropriately display generated dialogue scenarios and feedback within the user's field of view, supporting the user in smooth communication with their conversation partner. A concrete application example would be in a physical store in a tourist area, where staff are assisting international customers. In this scenario, it would function as an auxiliary tool to enable staff to quickly provide the information requested by the customer.

[0209] An example of a prompt message input to the generating AI would be: "As a store employee, you must explain a product to a tourist. The tourist is interested. How would you respond in a friendly manner?" Based on this, the AI ​​generates appropriate scenarios and suggestions and provides them to the user, thereby improving cross-cultural communication.

[0210] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0211] Step 1:

[0212] The user activates smart glasses or a mobile device and captures the other person's voice with a microphone during conversation. The input is an audio signal, which is acquired by an information identification mechanism. The output is audio data.

[0213] Step 2:

[0214] The device converts the acquired audio data into text data using a speech-to-text API or similar method. The input is audio data, which is then processed to generate text data as output.

[0215] Step 3:

[0216] The server uses a reproducible artificial intelligence model to analyze text data and automatically generate dialogue scenarios. Input consists of text data and user profile information, which are used to generate prompts. Output is a dialogue scenario tailored to a specific situation.

[0217] Step 4:

[0218] The terminal analyzes the user's emotional state using IBM Watson Tone Analyzer and other tools, along with the dialogue scenario sent from the server. Input is speech tone data used in conjunction with text data generated from speech, and output is the result of the emotion analysis.

[0219] Step 5:

[0220] The server provides appropriate feedback to the user based on the sentiment analysis results, assisting in the conversation in real time. The input is the sentiment analysis results, and the output is a feedback message that resonates with the user's feelings.

[0221] Step 6:

[0222] The user communicates smoothly with the other party based on the displayed dialogue scenario and feedback. The device updates the information displayed during the dialogue in the user's field of view according to the situation. Input is visual information and feedback, and output is the improved dialogue situation.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] [Second Embodiment]

[0227] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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).

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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".

[0239] This invention is a system that provides customized English conversation scenarios tailored to the user's interests and occupation, and supports conversations based on those scenarios. This system is centered around generative AI and provides users with a real-time English learning experience through smart glasses or mobile devices.

[0240] When a user uses the system, they first launch smart glasses or a dedicated application and input basic information such as their interests, occupation, and English level. This data is then sent to an AI that generates conversational scenarios tailored to the user. For example, healthcare professionals are provided with conversational scenarios based on hospital consultation situations.

[0241] The server sends data to the terminal to control the interaction with the user based on the generated dialogue scenario. This dialogue control means that prompts following the flow of the conversation are displayed on the smart glasses, allowing the user to proceed with the conversation with the AI ​​accordingly. For example, if the user asks "How can I assist you today?" in the scenario, the AI ​​will provide a response such as "I'm looking for advice on how to manage stress at work."

[0242] The terminal receives the user's speech as voice input and uses a speech-to-text conversion system to send it to the server. Meanwhile, the evaluation system analyzes this voice data, instantly detecting errors in the user's pronunciation and grammar, and generating feedback in real time. As a result, the user can receive specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0243] Furthermore, when users need English in various everyday situations, the life support system suggests appropriate phrases for each situation. This feature allows users to respond quickly when ordering food or shopping while traveling. For example, by using smart glasses at a local restaurant, users can quickly refer to phrases such as "May I have the menu, please?"

[0244] Thus, the present invention provides users with an individualized English learning environment and realizes a system that supports efficient learning regardless of time or place.

[0245] The following describes the processing flow.

[0246] Step 1:

[0247] The user launches the smart glasses or a dedicated application and enters their name, occupation, areas of interest, and English proficiency level on the interface.

[0248] Step 2:

[0249] The device receives data entered by the user and sends it to the server as profile data. This profile data includes the user's interests, occupation, and English proficiency level.

[0250] Step 3:

[0251] The server analyzes the received profile data and uses a generation AI to automatically generate customized conversation scenarios tailored to the user. For example, a scenario related to sales negotiations will be generated for a sales professional.

[0252] Step 4:

[0253] The server sends the generated dialogue scenario to the device, making it visible on the user's smart glasses. This prepares the user to engage in the dialogue while visually reviewing the scenario.

[0254] Step 5:

[0255] The user initiates a conversation with the AI ​​by following prompts on the smart glasses. During this process, the user's speech is sent to the device via voice input.

[0256] Step 6:

[0257] The device converts the collected voice data into text data using speech recognition technology and sends that data to the server in real time.

[0258] Step 7:

[0259] The server analyzes the voice data and generates an appropriate response. Utilizing AI's natural language processing capabilities, it creates contextually appropriate responses tailored to the user's utterances and sends them to the device.

[0260] Step 8:

[0261] The device displays the response from the server on the smart glasses' display and, if necessary, provides audio output to communicate the response to the user.

[0262] Step 9:

[0263] The server evaluates the user's pronunciation and grammar and generates real-time feedback. For example, it can correct the pronunciation of specific words or suggest improvements to grammatical structure.

[0264] Step 10:

[0265] The device visually displays the generated feedback on the smart glasses and presents the user with specific ways to improve.

[0266] Step 11:

[0267] Users can request assistance with using English in their daily lives as needed. This request is forwarded to the server by the device.

[0268] Step 12:

[0269] The server generates appropriate English phrases and guidance based on everyday situations and sends them to the terminal. This allows users to communicate smoothly in a way that is appropriate for each situation.

[0270] (Example 1)

[0271] 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."

[0272] In modern society, there is a growing need for efficient English language learning tailored to individual interests and occupations. However, traditional learning methods struggle to automatically generate user-friendly dialogue scenarios, and furthermore, they have difficulty detecting pronunciation and grammatical errors in real time and providing immediate feedback. Additionally, there has been a lack of timely means to provide English expressions relevant to real-world situations.

[0273] 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.

[0274] In this invention, the server includes an information processing structure that automatically generates different dialogue content according to the user's interests and occupation, a conversation control structure that immediately processes conversations executed according to the generated dialogue content, and an evaluation structure that detects errors in the user's pronunciation and grammar and provides suggestions for improvement. This makes it possible to provide the user with the most suitable English dialogue scenario in real time and support efficient English learning.

[0275] An "information processing structure" is an element of a system that collects data based on the user's interests and occupation, and automatically generates different dialogue content based on that data.

[0276] A "conversation control structure" is a system element that facilitates smooth, real-time conversations with users based on the generated dialogue content.

[0277] The "evaluation structure" is a system element that analyzes user voice data, detects errors in pronunciation and grammar, and immediately provides specific feedback for improvement.

[0278] The "Daily Support Structure" is an element of a system that proposes language expressions according to real-world situations to users and supports language use in daily life.

[0279] The "Voice Conversion Structure" is an element of a system that receives voice data from users and converts it into text data.

[0280] This invention is a system that provides English conversation scenarios based on individual interests and occupations and supports users' English learning. To implement this, an information processing system centered on a generative AI model is required.

[0281] The user, who is the user of this system, first launches an application on a smart glass or a mobile device. Here, the user inputs information such as their interests, occupation, English level, etc. Upon receiving the user's input, the terminal sends the data to the server.

[0282] The server uses the generative AI model to analyze the received user information. Then, it generates a conversation scenario suitable for the user using a prompt sentence. For example, a prompt sentence such as "Please generate an English conversation used in a medical scenario" is used.

[0283] Based on the generated conversation scenario, the server sends data for controlling the conversation with the user to the terminal. Based on this data, the terminal displays the flow of the conversation on the smart glass, and the user can proceed with the conversation with the AI while referring to it.

[0284] The user's voice input is received by the terminal and converted into text data using the voice conversion structure. The converted text data is sent to the server again. On the server side, the evaluation structure analyzes the voice data, identifies pronunciation and grammar errors, and generates real-time feedback. For example, it provides specific feedback such as "There is an error in pronunciation. Emphasize the s in'stress'."

[0285] Furthermore, as language support in daily life, the server prompts expressions suitable for the situations that users face in their daily lives. In this embodiment, an example is instantaneously presenting the phrase "May I have the menu, please?", which a traveler uses in a restaurant. In this way, users can improve their English skills at their own pace through individualized learning experiences.

[0286] The flow of the specific process in Example 1 will be described using FIG. 11.

[0287] Step 1:

[0288] The user launches an application on a smart glass or a mobile device and inputs basic information such as interests, occupation, and English level. This information input becomes the initial input data of the system. The input information is transmitted by the terminal to the information processing structure.

[0289] Step 2:

[0290] The terminal transfers the user's input information to the server. The server receives this and inputs it together with a prompt sentence to the generated AI model. This prompt sentence includes, for example, something like "Please generate an English conversation used in a medical scenario". The server generates a customized dialogue scenario based on the user's interests and occupation. The output is the data of the generated dialogue scenario.

[0291] Step 3:

[0292] Based on the generated dialogue scenario, the server creates dialogue control data and transmits this to the terminal. The terminal receives this data and displays appropriate conversation topics and prompts on the display of the smart glass. The user starts a conversation with the AI referring to this prompt. The output is the flow of the conversation that the user should proceed with.

[0293] Step 4:

[0294] The user interacts with the AI ​​via voice by following the displayed prompts. The device receives the user's voice as voice input and converts it into text data using a speech-to-text conversion structure. This text data is sent to the server and used for processing in the next step.

[0295] Step 5:

[0296] The server analyzes the received text data using an evaluation structure to detect errors in the user's pronunciation and grammar. It then generates feedback based on the detected errors and sends it to the terminal in real time. The user receives specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0297] Step 6:

[0298] The server operates a daily support structure to suggest appropriate English expressions based on the user's living environment and daily situations. This provides concrete examples that meet the needs of travelers and in daily life. As a result, users can quickly refer to practical expressions such as "May I have the menu, please?"

[0299] (Application Example 1)

[0300] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0301] In today's increasingly globalized society, businesses are required to communicate effectively with customers from diverse cultures and backgrounds. However, many frontline staff face the challenge of being unable to adequately serve customers due to language barriers. Therefore, there is a need for systems that smoothly support foreign language communication.

[0302] The specific processing by the specific processing unit 290 of the data processing apparatus 12 in Application Example 1 is realized by the following means.

[0303] In this invention, the server includes an information processing element that automatically generates different dialogue scenarios according to the interests and occupations of users, a dialogue control element that processes the dialogue executed according to the generated dialogue scenario in real time, and an evaluation element that detects errors in the pronunciation and grammar of users and provides feedback for improvement. Thereby, by the on-site staff providing appropriate foreign language phrases in real time, it becomes possible to smoothly conduct communication between different cultures.

[0304] The "information processing element" is an element having a function for automatically generating different dialogue scenarios according to the interests and occupations of users.

[0305] The "dialogue control element" is an element having a function for processing the dialogue executed according to the generated dialogue scenario in real time.

[0306] The "evaluation element" is an element having a function for detecting errors in the pronunciation and grammar of users and providing feedback for improvement thereon.

[0307] The "life support element" is an element having a function for proposing the optimal language expression to the user according to the situation of the real world.

[0308] The "assistance element" is an element having a function for providing language phrases for the user to effectively communicate with foreigners in an environment such as the workplace.

[0309] The system of this invention is mainly realized by using AR-compatible smart glasses and a server. The server first receives information about the interests and occupations input by the user through the smart glasses. This information is used to automatically generate an optimal dialogue scenario for the user by utilizing the generation AI model. The generated scenario is transmitted to the smart glasses to control the dialogue in real time.

[0310] The device (smart glasses) displays specific prompts to the user based on dialogue scenarios sent from the server. In addition, the device is equipped with software that performs speech conversion and evaluation, converting the user's voice input into text data and sending it to the server. This evaluation primarily detects errors in the user's pronunciation and grammar, and provides immediate feedback. Specifically, this includes minor corrections to pronunciation and identification of grammatical errors.

[0311] Users can use this system, for example, when assisting foreign customers as staff in a physical store. If a customer asks, "Does this product come in other colors?", the smart glasses will display a response phrase such as, "Yes, it comes in several colors. Would you like to see them?" This prompt is generated by a generative AI model that uses prompt sentences like the following to suggest the most appropriate language expression for the situation.

[0312] Examples of prompts for a generative AI model:

[0313] "If the user is a store employee, please explain in English how to respond when a customer asks about the color of a product."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user puts on smart glasses and launches a dedicated application. The user enters their interests, occupation, and English level. This information is sent to the server by the device as input data.

[0317] Step 2:

[0318] The server sends prompt messages to the generating AI model based on the user information it receives. These prompt messages cause the AI ​​to generate a dialogue scenario suitable for the user. The generating AI analyzes the input data and returns the optimal dialogue scenario as generated data.

[0319] Step 3:

[0320] The server receives the generated dialogue scenario and creates specific dialogue prompts based on that scenario. This becomes the output data and is sent to the smart glasses.

[0321] Step 4:

[0322] The device (smart glasses) displays received conversational prompts in the user's field of view. The user follows these prompts during customer service conversations.

[0323] Step 5:

[0324] When a user inputs voice data during a conversation with a customer, that voice data is converted into text data. This conversion process takes place on the terminal and the text data is sent to the server.

[0325] Step 6:

[0326] The server analyzes the received text data using evaluation elements. It detects errors in the user's pronunciation and grammar, and generates feedback based on the evaluation results.

[0327] Step 7:

[0328] The generated feedback is immediately sent to the device and displayed to the user. The user can then use this feedback to improve their English expression.

[0329] 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.

[0330] This invention aims to further enhance the user experience by combining an emotion engine with a system that provides customized English dialogue scenarios tailored to the user's interests and occupation. This system supports real-time English learning using generative AI, an emotion engine, and smart glasses or mobile devices.

[0331] When a user uses the system, they launch the smart glasses application and enter basic information into the interface. This information includes the user's interests, occupation, and current English proficiency, and is sent to the server. The server uses generative AI to automatically generate conversational scenarios based on the user's profile data. For example, for a user whose interest is sports, a scenario related to sporting events will be generated.

[0332] The generated dialogue scenario is transmitted via the device to the smart glasses and displayed in the user's field of view. Here, the user receives adaptive feedback on their emotional state through an emotion engine. This emotion engine can analyze the user's voice tone and facial expressions to recognize the stress and difficulties the user experiences during the dialogue.

[0333] For example, if a user has difficulty pronouncing a particular phrase, the emotion engine can sense the user's frustration and provide gentle, encouraging feedback through dialogue control mechanisms, such as, "Try to relax a little and try again. We'll practice together." In this way, the emotion engine's data serves as a new metric for improving the user's learning experience.

[0334] The evaluation system analyzes the user's pronunciation and grammar in real time and suggests specific areas for improvement. In addition, a life support system assists the user in using English in their daily life. This system enables less stressful communication by suggesting appropriate English phrases based on the user's emotional state.

[0335] This comprehensive system structure allows users to smoothly progress in their English learning while receiving individually adapted feedback. The adoption of an emotion engine provides a flexible learning environment that takes into account the user's psychological state, enabling a more effective and human-centered approach to English learning.

[0336] The following describes the processing flow.

[0337] Step 1:

[0338] The user activates the smart glasses, accesses a dedicated app, and enters basic information such as their interests, occupation, and English level.

[0339] Step 2:

[0340] The device sends the information received from the user to the server as profile data. The profile reflects the user's individual attributes.

[0341] Step 3:

[0342] The server analyzes profile data and generates personalized conversation scenarios using AI. For example, for a salesperson, it creates a scenario for a customer negotiation.

[0343] Step 4:

[0344] The generated dialogue scenario is sent from the server to the terminal and displayed on the smart glasses. This allows the user to visually recognize the scenario and prepare for the interaction.

[0345] Step 5:

[0346] The user follows the prompts displayed on the smart glasses to begin the conversation. The audio of the conversation is transmitted to the device.

[0347] Step 6:

[0348] The terminal converts user speech into text data using speech recognition technology and transfers it to the server. The converted data is then used for analysis.

[0349] Step 7:

[0350] The server uses dialogue control mechanisms to generate real-time responses based on the user's utterances. The AI ​​understands the context and creates appropriate responses.

[0351] Step 8:

[0352] Furthermore, the server's emotion engine analyzes the user's voice tone and facial expressions to assess their emotional state. Based on this assessment, it constructs emotionally sensitive feedback, such as "Let's try to relax a little while we talk."

[0353] Step 9:

[0354] The device displays the evaluation results on smart glasses and provides the user with adapted feedback and emotion-based advice.

[0355] Step 10:

[0356] The server uses evaluation tools to monitor the user's pronunciation and grammar, and provides real-time feedback on areas for improvement. Specific instructions are then displayed.

[0357] Step 11:

[0358] When a user requests English assistance in everyday situations, such as looking for a phrase while shopping, the request is sent to the server via the device.

[0359] Step 12:

[0360] The server generates and provides users with English phrases and appropriate action suggestions based on their emotional state, ensuring that support tailored to each situation is provided.

[0361] (Example 2)

[0362] 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".

[0363] The challenge lies in creating a system that efficiently provides customized dialogue scenarios tailored to users' interests and occupations, while also considering their emotional state to support language learning. Furthermore, it is essential to provide an effective learning experience through adaptive feedback based on each user's individual language proficiency.

[0364] 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.

[0365] In this invention, the server includes information processing means for automatically generating different dialogue scenarios according to the user's interests and occupation, dialogue control means for processing dialogues executed according to the generated dialogue scenarios in real time, and emotion analysis means for analyzing the user's voice tone and facial expressions to evaluate their emotional state and provide adaptive feedback. This enables the provision of real-time dialogue scenarios based on the user's interests and occupation, and emotionally adapted feedback.

[0366] "Information processing means" refers to means that provide functions for automatically generating dialogue scenarios tailored to the user's interests and occupation.

[0367] "Dialogue control means" refers to means for processing user interactions in real time based on generated dialogue scenarios.

[0368] An "evaluation tool" is a tool that has the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0369] "Life support tools" are tools that have the function of suggesting appropriate language expressions to users in accordance with real-world situations.

[0370] "Emotional analysis means" refers to a method of analyzing a user's voice tone and facial expressions to evaluate their emotional state and provide appropriate feedback.

[0371] A "dialogue scenario generation method" using "generative AI" is a method that uses a generative AI model to generate customized dialogue scenarios based on the user's profile data.

[0372] A "voice conversion method" is a means for converting voice input from a user into text format.

[0373] This invention provides a system that offers customized English conversation scenarios based on the user's interests and occupation, and enhances the learning experience using an emotion engine. This system is implemented using a generative AI model, smart glasses or a mobile device, a server, and emotion analysis technology.

[0374] Users launch an application on smart glasses or a mobile device and input their interests, occupation, and English proficiency using a dedicated interface. This input information is transmitted to a server via the internet.

[0375] The server utilizes a generative AI model as an information processing tool based on the information it receives. This generative AI model is a natural language processing engine that runs on the cloud, and an example of a large-scale model is "GPT". The server sends prompt sentences tailored to the user's interests to the generative AI, which automatically generates dialogue scenarios that match the user's characteristics. For example, if the user is interested in cooking, the server will generate dialogue scenarios related to cooking recipes and cooking techniques.

[0376] The generated dialogue scenario is transferred to the smart glasses via the terminal and displayed in the user's field of view. Here, the terminal plays a role in controlling the scenario-based dialogue with the user in real time.

[0377] As an emotion analysis tool, the server analyzes the user's voice tone and facial expressions to evaluate the user's emotional state in real time. This analysis utilizes speech recognition software and image recognition software, specifically open-source speech recognition libraries and facial expression evaluation algorithms.

[0378] As the user engages in dialogue, if the emotion analysis system detects the user's stress or difficulty, the server generates appropriate feedback and sends it to the user via the device. For example, if the user is struggling with a particular English phrase, the server might provide feedback such as, "Calm down and try again. Let's repeat it together."

[0379] Furthermore, the server uses evaluation tools to analyze the user's pronunciation and grammar and suggests specific areas for improvement. The life support tools suggest useful English phrases for the user's daily life, helping to improve the user's communication skills.

[0380] As a concrete example, here is an example of a prompt to the generating AI: "If the user's interest is travel and their English proficiency is intermediate, please suggest the most helpful dialogue scenarios for the travel destinations the user will be visiting." In response to this prompt, the AI ​​will generate dialogue scenarios related to everyday conversations and tourist attractions in the travel destinations.

[0381] In summary, the present invention provides users with an individually optimized English learning experience and realizes a flexible and human-centered learning environment by utilizing an emotion engine.

[0382] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0383] Step 1:

[0384] The user launches an app on smart glasses or a mobile device and enters personal information. Specifically, the user interacts with an interface to enter basic data such as interests, occupation, and English proficiency. This input data forms the basis for designing a customized learning experience for the user.

[0385] Step 2:

[0386] The terminal retrieves information entered by the user and sends it to the server. The input data includes information about interests, occupation, and English proficiency, and this data serves as initial data for data processing on the server side.

[0387] Step 3:

[0388] The server creates prompts for the AI ​​model based on the received user information and instructs it to generate dialogue scenarios. Specifically, the server analyzes the received data and generates prompts such as, "If the user's interest is music and their English ability is beginner level, please generate a simple conversation scenario for a music festival." The server uses this as input to the AI ​​model and outputs dialogue scenarios adapted to each user.

[0389] Step 4:

[0390] The server sends a customized dialogue scenario derived from the generated AI model to the terminal. The terminal's role is to transfer this output scenario to the smart glasses and display it directly to the user. The user then conducts a dialogue exercise based on this displayed information.

[0391] Step 5:

[0392] The user initiates a conversation, and the emotion analysis system analyzes the user's voice tone and facial expressions in real time. The input is the user's voice and video data, which the emotion analysis system outputs as an emotional state. This analysis includes voice recognition and facial expression analysis, allowing the application to track changes in emotion.

[0393] Step 6:

[0394] The server receives data from the emotion analysis system and generates feedback based on the user's emotional state. For example, if the server detects that the user is stressed, it will output supportive feedback such as, "Try to relax a little and try again slowly." This allows the user to adjust their next steps based on the feedback.

[0395] Step 7:

[0396] The server uses evaluation tools to analyze the user's pronunciation and grammar. The input is the user's spoken content, which the server analyzes and provides output that specifically identifies errors and areas for improvement. Based on the evaluation, the user receives specific guidance for improving their proficiency.

[0397] Step 8:

[0398] The life support system suggests everyday language expressions based on the user's emotional state and daily feedback. This allows users to communicate more smoothly in real-life situations and receive support tailored to their daily lives.

[0399] (Application Example 2)

[0400] 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."

[0401] In today's multicultural society, smooth communication between individuals with different languages ​​and cultures remains challenging. Language barriers are particularly significant obstacles to customer service and business transactions in tourist destinations and international business settings. Furthermore, traditional language learning and interpretation services struggle to understand and respond appropriately to the other person's emotions, creating limitations in building interpersonal relationships.

[0402] 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.

[0403] In this invention, the server includes means for converting observable human speech into text using information identification means, means for automatically generating dialogue scenarios using a reproducible artificial intelligence model, and means for analyzing human emotional states using expression analysis means. This enables users to overcome language and emotional barriers in intercultural communication and communicate smoothly in real time.

[0404] "Information identification means" refers to a technological device for detecting sounds and images in the environment, acquiring those signals, and analyzing them.

[0405] A "generative artificial intelligence model" is an algorithm or system that has the ability to autonomously create new data or scenarios based on input information.

[0406] A "dialogue scenario" is a pre-designed and generated transcript of the expected flow and content of a conversation in a specific scene or situation.

[0407] "Management means" refer to methods and devices for coordinating various processes and data flows within a system to ensure smooth operation.

[0408] "Representation analysis means" refers to technologies that judge and classify human intentions and emotional states based on acquired data.

[0409] "Means of providing feedback" refer to methods and devices for returning appropriate information and advice to users based on analysis results.

[0410] "Everyday life scenarios" refer to situations and scenarios that are commonly experienced in daily life.

[0411] "Means for proposing linguistic expressions" refer to functions or systems for generating and presenting linguistic phrases that are appropriate for specific situations or emotional states.

[0412] To implement this invention, it is necessary to build a system that supports smooth intercultural communication by coordinating the user's smart glasses, mobile device, and server. This system consists of the following main modules.

[0413] First, the system acquires the voices of the user and their conversation partner in real time using identification methods from smart glasses or mobile devices. The acquired voice data is converted into text data using speech-to-text conversion tools such as the Google Cloud Speech-to-Text API. The converted text data is sent to a server, where an appropriate conversation scenario is automatically generated using a reproducible artificial intelligence model, such as OpenAI's GPT.

[0414] The server uses expression analysis tools to understand the user's emotional state, which is analyzed from their voice, and provides feedback that corresponds to that emotion. Software such as IBM Watson Tone Analyzer can be used in this process to achieve optimal communication based on the user's emotional state.

[0415] This allows the device to appropriately display generated dialogue scenarios and feedback within the user's field of view, supporting the user in smooth communication with their conversation partner. A concrete application example would be in a physical store in a tourist area, where staff are assisting international customers. In this scenario, it would function as an auxiliary tool to enable staff to quickly provide the information requested by the customer.

[0416] An example of a prompt message input to the generating AI would be: "As a store employee, you must explain a product to a tourist. The tourist is interested. How would you respond in a friendly manner?" Based on this, the AI ​​generates appropriate scenarios and suggestions and provides them to the user, thereby improving cross-cultural communication.

[0417] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0418] Step 1:

[0419] The user activates smart glasses or a mobile device and captures the other person's voice with a microphone during conversation. The input is an audio signal, which is acquired by an information identification mechanism. The output is audio data.

[0420] Step 2:

[0421] The device converts the acquired audio data into text data using a speech-to-text API or similar method. The input is audio data, which is then processed to generate text data as output.

[0422] Step 3:

[0423] The server uses a reproducible artificial intelligence model to analyze text data and automatically generate dialogue scenarios. Input consists of text data and user profile information, which are used to generate prompts. Output is a dialogue scenario tailored to a specific situation.

[0424] Step 4:

[0425] The terminal analyzes the user's emotional state using IBM Watson Tone Analyzer and other tools, along with the dialogue scenario sent from the server. Input is speech tone data used in conjunction with text data generated from speech, and output is the result of the emotion analysis.

[0426] Step 5:

[0427] The server provides appropriate feedback to the user based on the sentiment analysis results, assisting in the conversation in real time. The input is the sentiment analysis results, and the output is a feedback message that resonates with the user's feelings.

[0428] Step 6:

[0429] The user communicates smoothly with the other party based on the displayed dialogue scenario and feedback. The device updates the information displayed during the dialogue in the user's field of view according to the situation. Input is visual information and feedback, and output is the improved dialogue situation.

[0430] 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.

[0431] 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.

[0432] 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.

[0433] [Third Embodiment]

[0434] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0435] 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.

[0436] 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).

[0437] 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.

[0438] 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.

[0439] 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).

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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".

[0446] This invention is a system that provides customized English conversation scenarios tailored to the user's interests and occupation, and supports conversations based on those scenarios. This system is centered around generative AI and provides users with a real-time English learning experience through smart glasses or mobile devices.

[0447] When a user uses the system, they first launch smart glasses or a dedicated application and input basic information such as their interests, occupation, and English level. This data is then sent to an AI that generates conversational scenarios tailored to the user. For example, healthcare professionals are provided with conversational scenarios based on hospital consultation situations.

[0448] The server sends data to the terminal to control the interaction with the user based on the generated dialogue scenario. This dialogue control means that prompts following the flow of the conversation are displayed on the smart glasses, allowing the user to proceed with the conversation with the AI ​​accordingly. For example, if the user asks "How can I assist you today?" in the scenario, the AI ​​will provide a response such as "I'm looking for advice on how to manage stress at work."

[0449] The terminal receives the user's speech as voice input and uses a speech-to-text conversion system to send it to the server. Meanwhile, the evaluation system analyzes this voice data, instantly detecting errors in the user's pronunciation and grammar, and generating feedback in real time. As a result, the user can receive specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0450] Furthermore, when users need English in various everyday situations, the life support system suggests appropriate phrases for each situation. This feature allows users to respond quickly when ordering food or shopping while traveling. For example, by using smart glasses at a local restaurant, users can quickly refer to phrases such as "May I have the menu, please?"

[0451] Thus, the present invention provides users with an individualized English learning environment and realizes a system that supports efficient learning regardless of time or place.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The user launches the smart glasses or a dedicated application and enters their name, occupation, areas of interest, and English proficiency level on the interface.

[0455] Step 2:

[0456] The device receives data entered by the user and sends it to the server as profile data. This profile data includes the user's interests, occupation, and English proficiency level.

[0457] Step 3:

[0458] The server analyzes the received profile data and uses a generation AI to automatically generate customized conversation scenarios tailored to the user. For example, a scenario related to sales negotiations will be generated for a sales professional.

[0459] Step 4:

[0460] The server sends the generated dialogue scenario to the device, making it visible on the user's smart glasses. This prepares the user to engage in the dialogue while visually reviewing the scenario.

[0461] Step 5:

[0462] The user initiates a conversation with the AI ​​by following prompts on the smart glasses. During this process, the user's speech is sent to the device via voice input.

[0463] Step 6:

[0464] The device converts the collected voice data into text data using speech recognition technology and sends that data to the server in real time.

[0465] Step 7:

[0466] The server analyzes the voice data and generates an appropriate response. Utilizing AI's natural language processing capabilities, it creates contextually appropriate responses tailored to the user's utterances and sends them to the device.

[0467] Step 8:

[0468] The device displays the response from the server on the smart glasses' display and, if necessary, provides audio output to communicate the response to the user.

[0469] Step 9:

[0470] The server evaluates the user's pronunciation and grammar and generates real-time feedback. For example, it can correct the pronunciation of specific words or suggest improvements to grammatical structure.

[0471] Step 10:

[0472] The device visually displays the generated feedback on the smart glasses and presents the user with specific ways to improve.

[0473] Step 11:

[0474] Users can request assistance with using English in their daily lives as needed. This request is forwarded to the server by the device.

[0475] Step 12:

[0476] The server generates appropriate English phrases and guidance based on everyday situations and sends them to the terminal. This allows users to communicate smoothly in a way that is appropriate for each situation.

[0477] (Example 1)

[0478] 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."

[0479] In modern society, there is a growing need for efficient English language learning tailored to individual interests and occupations. However, traditional learning methods struggle to automatically generate user-friendly dialogue scenarios, and furthermore, they have difficulty detecting pronunciation and grammatical errors in real time and providing immediate feedback. Additionally, there has been a lack of timely means to provide English expressions relevant to real-world situations.

[0480] 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.

[0481] In this invention, the server includes an information processing structure that automatically generates different dialogue content according to the user's interests and occupation, a conversation control structure that immediately processes conversations executed according to the generated dialogue content, and an evaluation structure that detects errors in the user's pronunciation and grammar and provides suggestions for improvement. This makes it possible to provide the user with the most suitable English dialogue scenario in real time and support efficient English learning.

[0482] An "information processing structure" is an element of a system that collects data based on the user's interests and occupation, and automatically generates different dialogue content based on that data.

[0483] A "conversation control structure" is a system element that facilitates smooth, real-time conversations with users based on the generated dialogue content.

[0484] The "evaluation structure" is a system element that analyzes user voice data, detects errors in pronunciation and grammar, and immediately provides specific feedback for improvement.

[0485] A "daily support structure" is a system element that proposes language expressions to users that are appropriate to real-world situations, thereby supporting language use in daily life.

[0486] A "voice conversion structure" is an element of a system that receives voice data from a user and converts it into text data.

[0487] This invention is a system that provides English conversation scenarios based on individual interests and occupations to support users' English learning. To implement this, an information processing system centered on a generative AI model is required.

[0488] Users of this system first launch an application on their smart glasses or mobile device. Here, users input information such as their interests, occupation, and English level. Upon receiving the user's input, the device sends that data to the server.

[0489] The server uses a generative AI model to analyze the received user information. It then generates a dialogue scenario suitable for the user using prompts. For example, a prompt such as "Generate an English conversation used in a medical scenario" might be used.

[0490] Based on the generated dialogue scenario, the server sends data to the device to control the conversation with the user. The device then uses this data to display the flow of the conversation on the smart glasses, allowing the user to refer to it and continue the conversation with the AI.

[0491] The user's voice input is received by the device and converted into text data using a speech-to-text structure. The converted text data is then sent back to the server. On the server side, an evaluation structure analyzes the voice data, identifies pronunciation and grammatical errors, and generates feedback in real time. For example, it might provide specific feedback such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0492] Furthermore, as language support for everyday life, the server prompts users with expressions appropriate to situations they encounter in their daily lives. In this example, it instantly presents the phrase "May I have the menu, please?" which a traveler might use in a restaurant. In this way, users can improve their English skills at their own pace through a personalized learning experience.

[0493] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0494] Step 1:

[0495] The user launches an application on smart glasses or a mobile device and enters basic information such as interests, occupation, and English level. This information becomes the initial input data for the system. The entered information is then transmitted by the terminal to the information processing structure.

[0496] Step 2:

[0497] The terminal transfers user input information to the server. The server receives this information and inputs it, along with prompts, into the generating AI model. These prompts might include something like, "Generate an English conversation used in a medical scenario." The server generates a customized conversation scenario based on the user's interests and occupation. The output is the data of the generated conversation scenario.

[0498] Step 3:

[0499] The server creates dialogue control data based on the generated dialogue scenario and sends it to the terminal. The terminal receives this data and displays appropriate conversation topics and prompts on the smart glasses' display. The user refers to these prompts and begins interacting with the AI. The output is the flow of the dialogue that the user should follow.

[0500] Step 4:

[0501] The user interacts with the AI ​​via voice by following the displayed prompts. The device receives the user's voice as voice input and converts it into text data using a speech-to-text conversion structure. This text data is sent to the server and used for processing in the next step.

[0502] Step 5:

[0503] The server analyzes the received text data using an evaluation structure to detect errors in the user's pronunciation and grammar. It then generates feedback based on the detected errors and sends it to the terminal in real time. The user receives specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0504] Step 6:

[0505] The server operates a daily support structure to suggest appropriate English expressions based on the user's living environment and daily situations. This provides concrete examples that meet the needs of travelers and in daily life. As a result, users can quickly refer to practical expressions such as "May I have the menu, please?"

[0506] (Application Example 1)

[0507] 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."

[0508] In today's increasingly globalized society, businesses are required to communicate effectively with customers from diverse cultures and backgrounds. However, many frontline staff face the challenge of being unable to adequately serve customers due to language barriers. Therefore, there is a need for systems that smoothly support foreign language communication.

[0509] 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.

[0510] In this invention, the server includes an information processing element that automatically generates different dialogue scenarios according to the user's interests and occupation, a dialogue control element that processes the dialogue executed according to the generated dialogue scenario in real time, and an evaluation element that detects errors in the user's pronunciation and grammar and provides feedback for improvement. This enables smooth intercultural communication by allowing on-site staff to provide appropriate foreign language phrases in real time.

[0511] An "information processing element" is an element that has the function of automatically generating different dialogue scenarios tailored to the user's interests and occupation.

[0512] A "dialogue control element" is an element that has the function of processing dialogue in real time, according to the generated dialogue scenario.

[0513] "Evaluation elements" are elements that have the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0514] "Life support elements" are elements that have the function of suggesting the most appropriate language expression to the user according to the situation in the real world.

[0515] A "support element" is an element that has the function of providing language phrases that enable users to effectively communicate with foreigners in environments such as the workplace.

[0516] The system of this invention is primarily implemented using AR-enabled smart glasses and a server. The server first receives information about the user's interests and occupation entered through the smart glasses. This information is used to automatically generate the optimal dialogue scenario for the user using a generative AI model. The generated scenario is then transmitted to the smart glasses to control the dialogue in real time.

[0517] The device (smart glasses) displays specific prompts to the user based on dialogue scenarios sent from the server. In addition, the device is equipped with software that performs speech conversion and evaluation, converting the user's voice input into text data and sending it to the server. This evaluation primarily detects errors in the user's pronunciation and grammar, and provides immediate feedback. Specifically, this includes minor corrections to pronunciation and identification of grammatical errors.

[0518] Users can use this system, for example, when assisting foreign customers as staff in a physical store. If a customer asks, "Does this product come in other colors?", the smart glasses will display a response phrase such as, "Yes, it comes in several colors. Would you like to see them?" This prompt is generated by a generative AI model that uses prompt sentences like the following to suggest the most appropriate language expression for the situation.

[0519] Examples of prompts for a generative AI model:

[0520] "If the user is a store employee, please explain in English how to respond when a customer asks about the color of a product."

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The user puts on smart glasses and launches a dedicated application. The user enters their interests, occupation, and English level. This information is sent to the server by the device as input data.

[0524] Step 2:

[0525] The server sends prompt messages to the generating AI model based on the user information it receives. These prompt messages cause the AI ​​to generate a dialogue scenario suitable for the user. The generating AI analyzes the input data and returns the optimal dialogue scenario as generated data.

[0526] Step 3:

[0527] The server receives the generated dialogue scenario and creates specific dialogue prompts based on that scenario. This becomes the output data and is sent to the smart glasses.

[0528] Step 4:

[0529] The device (smart glasses) displays received conversational prompts in the user's field of view. The user follows these prompts during customer service conversations.

[0530] Step 5:

[0531] When a user inputs voice data during a conversation with a customer, that voice data is converted into text data. This conversion process takes place on the terminal and the text data is sent to the server.

[0532] Step 6:

[0533] The server analyzes the received text data using evaluation elements. It detects errors in the user's pronunciation and grammar, and generates feedback based on the evaluation results.

[0534] Step 7:

[0535] The generated feedback is immediately sent to the device and displayed to the user. The user can then use this feedback to improve their English expression.

[0536] 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.

[0537] This invention aims to further enhance the user experience by combining an emotion engine with a system that provides customized English dialogue scenarios tailored to the user's interests and occupation. This system supports real-time English learning using generative AI, an emotion engine, and smart glasses or mobile devices.

[0538] When a user uses the system, they launch the smart glasses application and enter basic information into the interface. This information includes the user's interests, occupation, and current English proficiency, and is sent to the server. The server uses generative AI to automatically generate conversational scenarios based on the user's profile data. For example, for a user whose interest is sports, a scenario related to sporting events will be generated.

[0539] The generated dialogue scenario is transmitted via the device to the smart glasses and displayed in the user's field of view. Here, the user receives adaptive feedback on their emotional state through an emotion engine. This emotion engine can analyze the user's voice tone and facial expressions to recognize the stress and difficulties the user experiences during the dialogue.

[0540] For example, if a user has difficulty pronouncing a particular phrase, the emotion engine can sense the user's frustration and provide gentle, encouraging feedback through dialogue control mechanisms, such as, "Try to relax a little and try again. We'll practice together." In this way, the emotion engine's data serves as a new metric for improving the user's learning experience.

[0541] The evaluation system analyzes the user's pronunciation and grammar in real time and suggests specific areas for improvement. In addition, a life support system assists the user in using English in their daily life. This system enables less stressful communication by suggesting appropriate English phrases based on the user's emotional state.

[0542] This comprehensive system structure allows users to smoothly progress in their English learning while receiving individually adapted feedback. The adoption of an emotion engine provides a flexible learning environment that takes into account the user's psychological state, enabling a more effective and human-centered approach to English learning.

[0543] The following describes the processing flow.

[0544] Step 1:

[0545] The user activates the smart glasses, accesses a dedicated app, and enters basic information such as their interests, occupation, and English level.

[0546] Step 2:

[0547] The device sends the information received from the user to the server as profile data. The profile reflects the user's individual attributes.

[0548] Step 3:

[0549] The server analyzes profile data and generates personalized conversation scenarios using AI. For example, for a salesperson, it creates a scenario for a customer negotiation.

[0550] Step 4:

[0551] The generated dialogue scenario is sent from the server to the terminal and displayed on the smart glasses. This allows the user to visually recognize the scenario and prepare for the interaction.

[0552] Step 5:

[0553] The user follows the prompts displayed on the smart glasses to begin the conversation. The audio of the conversation is transmitted to the device.

[0554] Step 6:

[0555] The terminal converts user speech into text data using speech recognition technology and transfers it to the server. The converted data is then used for analysis.

[0556] Step 7:

[0557] The server uses dialogue control mechanisms to generate real-time responses based on the user's utterances. The AI ​​understands the context and creates appropriate responses.

[0558] Step 8:

[0559] Furthermore, the server's emotion engine analyzes the user's voice tone and facial expressions to assess their emotional state. Based on this assessment, it constructs emotionally sensitive feedback, such as "Let's try to relax a little while we talk."

[0560] Step 9:

[0561] The device displays the evaluation results on smart glasses and provides the user with adapted feedback and emotion-based advice.

[0562] Step 10:

[0563] The server uses evaluation tools to monitor the user's pronunciation and grammar, and provides real-time feedback on areas for improvement. Specific instructions are then displayed.

[0564] Step 11:

[0565] When a user requests English assistance in everyday situations, such as looking for a phrase while shopping, the request is sent to the server via the device.

[0566] Step 12:

[0567] The server generates and provides users with English phrases and appropriate action suggestions based on their emotional state, ensuring that support tailored to each situation is provided.

[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] The challenge lies in creating a system that efficiently provides customized dialogue scenarios tailored to users' interests and occupations, while also considering their emotional state to support language learning. Furthermore, it is essential to provide an effective learning experience through adaptive feedback based on each user's individual language proficiency.

[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 information processing means for automatically generating different dialogue scenarios according to the user's interests and occupation, dialogue control means for processing dialogues executed according to the generated dialogue scenarios in real time, and emotion analysis means for analyzing the user's voice tone and facial expressions to evaluate their emotional state and provide adaptive feedback. This enables the provision of real-time dialogue scenarios based on the user's interests and occupation, and emotionally adapted feedback.

[0573] "Information processing means" refers to means that provide functions for automatically generating dialogue scenarios tailored to the user's interests and occupation.

[0574] "Dialogue control means" refers to means for processing user interactions in real time based on generated dialogue scenarios.

[0575] An "evaluation tool" is a tool that has the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0576] "Life support tools" are tools that have the function of suggesting appropriate language expressions to users in accordance with real-world situations.

[0577] "Emotional analysis means" refers to a method of analyzing a user's voice tone and facial expressions to evaluate their emotional state and provide appropriate feedback.

[0578] A "dialogue scenario generation method" using "generative AI" is a method that uses a generative AI model to generate customized dialogue scenarios based on the user's profile data.

[0579] A "voice conversion method" is a means for converting voice input from a user into text format.

[0580] This invention provides a system that offers customized English conversation scenarios based on the user's interests and occupation, and enhances the learning experience using an emotion engine. This system is implemented using a generative AI model, smart glasses or a mobile device, a server, and emotion analysis technology.

[0581] Users launch an application on smart glasses or a mobile device and input their interests, occupation, and English proficiency using a dedicated interface. This input information is transmitted to a server via the internet.

[0582] The server utilizes a generative AI model as an information processing tool based on the information it receives. This generative AI model is a natural language processing engine that runs on the cloud, and an example of a large-scale model is "GPT". The server sends prompt sentences tailored to the user's interests to the generative AI, which automatically generates dialogue scenarios that match the user's characteristics. For example, if the user is interested in cooking, the server will generate dialogue scenarios related to cooking recipes and cooking techniques.

[0583] The generated dialogue scenario is transferred to the smart glasses via the terminal and displayed in the user's field of view. Here, the terminal plays a role in controlling the scenario-based dialogue with the user in real time.

[0584] As an emotion analysis tool, the server analyzes the user's voice tone and facial expressions to evaluate the user's emotional state in real time. This analysis utilizes speech recognition software and image recognition software, specifically open-source speech recognition libraries and facial expression evaluation algorithms.

[0585] As the user engages in dialogue, if the emotion analysis system detects the user's stress or difficulty, the server generates appropriate feedback and sends it to the user via the device. For example, if the user is struggling with a particular English phrase, the server might provide feedback such as, "Calm down and try again. Let's repeat it together."

[0586] Furthermore, the server uses evaluation tools to analyze the user's pronunciation and grammar and suggests specific areas for improvement. The life support tools suggest useful English phrases for the user's daily life, helping to improve the user's communication skills.

[0587] As a concrete example, here is an example of a prompt to the generating AI: "If the user's interest is travel and their English proficiency is intermediate, please suggest the most helpful dialogue scenarios for the travel destinations the user will be visiting." In response to this prompt, the AI ​​will generate dialogue scenarios related to everyday conversations and tourist attractions in the travel destinations.

[0588] In summary, the present invention provides users with an individually optimized English learning experience and realizes a flexible and human-centered learning environment by utilizing an emotion engine.

[0589] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0590] Step 1:

[0591] The user launches an app on smart glasses or a mobile device and enters personal information. Specifically, the user interacts with an interface to enter basic data such as interests, occupation, and English proficiency. This input data forms the basis for designing a customized learning experience for the user.

[0592] Step 2:

[0593] The terminal retrieves information entered by the user and sends it to the server. The input data includes information about interests, occupation, and English proficiency, and this data serves as initial data for data processing on the server side.

[0594] Step 3:

[0595] The server creates prompts for the AI ​​model based on the received user information and instructs it to generate dialogue scenarios. Specifically, the server analyzes the received data and generates prompts such as, "If the user's interest is music and their English ability is beginner level, please generate a simple conversation scenario for a music festival." The server uses this as input to the AI ​​model and outputs dialogue scenarios adapted to each user.

[0596] Step 4:

[0597] The server sends a customized dialogue scenario derived from the generated AI model to the terminal. The terminal's role is to transfer this output scenario to the smart glasses and display it directly to the user. The user then conducts a dialogue exercise based on this displayed information.

[0598] Step 5:

[0599] The user initiates a conversation, and the emotion analysis system analyzes the user's voice tone and facial expressions in real time. The input is the user's voice and video data, which the emotion analysis system outputs as an emotional state. This analysis includes voice recognition and facial expression analysis, allowing the application to track changes in emotion.

[0600] Step 6:

[0601] The server receives data from the emotion analysis system and generates feedback based on the user's emotional state. For example, if the server detects that the user is stressed, it will output supportive feedback such as, "Try to relax a little and try again slowly." This allows the user to adjust their next steps based on the feedback.

[0602] Step 7:

[0603] The server uses evaluation tools to analyze the user's pronunciation and grammar. The input is the user's spoken content, which the server analyzes and provides output that specifically identifies errors and areas for improvement. Based on the evaluation, the user receives specific guidance for improving their proficiency.

[0604] Step 8:

[0605] The life support system suggests everyday language expressions based on the user's emotional state and daily feedback. This allows users to communicate more smoothly in real-life situations and receive support tailored to their daily lives.

[0606] (Application Example 2)

[0607] 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."

[0608] In today's multicultural society, smooth communication between individuals with different languages ​​and cultures remains challenging. Language barriers are particularly significant obstacles to customer service and business transactions in tourist destinations and international business settings. Furthermore, traditional language learning and interpretation services struggle to understand and respond appropriately to the other person's emotions, creating limitations in building interpersonal relationships.

[0609] 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.

[0610] In this invention, the server includes means for converting observable human speech into text using information identification means, means for automatically generating dialogue scenarios using a reproducible artificial intelligence model, and means for analyzing human emotional states using expression analysis means. This enables users to overcome language and emotional barriers in intercultural communication and communicate smoothly in real time.

[0611] "Information identification means" refers to a technological device for detecting sounds and images in the environment, acquiring those signals, and analyzing them.

[0612] A "generative artificial intelligence model" is an algorithm or system that has the ability to autonomously create new data or scenarios based on input information.

[0613] A "dialogue scenario" is a pre-designed and generated transcript of the expected flow and content of a conversation in a specific scene or situation.

[0614] "Management means" refer to methods and devices for coordinating various processes and data flows within a system to ensure smooth operation.

[0615] "Representation analysis means" refers to technologies that judge and classify human intentions and emotional states based on acquired data.

[0616] "Means of providing feedback" refer to methods and devices for returning appropriate information and advice to users based on analysis results.

[0617] "Everyday life scenarios" refer to situations and scenarios that are commonly experienced in daily life.

[0618] "Means for proposing linguistic expressions" refer to functions or systems for generating and presenting linguistic phrases that are appropriate for specific situations or emotional states.

[0619] To implement this invention, it is necessary to build a system that supports smooth intercultural communication by coordinating the user's smart glasses, mobile device, and server. This system consists of the following main modules.

[0620] First, the system acquires the voices of the user and their conversation partner in real time using identification methods from smart glasses or mobile devices. The acquired voice data is converted into text data using speech-to-text conversion tools such as the Google Cloud Speech-to-Text API. The converted text data is sent to a server, where an appropriate conversation scenario is automatically generated using a reproducible artificial intelligence model, such as OpenAI's GPT.

[0621] The server uses expression analysis tools to understand the user's emotional state, which is analyzed from their voice, and provides feedback that corresponds to that emotion. Software such as IBM Watson Tone Analyzer can be used in this process to achieve optimal communication based on the user's emotional state.

[0622] This allows the device to appropriately display generated dialogue scenarios and feedback within the user's field of view, supporting the user in smooth communication with their conversation partner. A concrete application example would be in a physical store in a tourist area, where staff are assisting international customers. In this scenario, it would function as an auxiliary tool to enable staff to quickly provide the information requested by the customer.

[0623] An example of a prompt message input to the generating AI would be: "As a store employee, you must explain a product to a tourist. The tourist is interested. How would you respond in a friendly manner?" Based on this, the AI ​​generates appropriate scenarios and suggestions and provides them to the user, thereby improving cross-cultural communication.

[0624] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0625] Step 1:

[0626] The user activates smart glasses or a mobile device and captures the other person's voice with a microphone during conversation. The input is an audio signal, which is acquired by an information identification mechanism. The output is audio data.

[0627] Step 2:

[0628] The device converts the acquired audio data into text data using a speech-to-text API or similar method. The input is audio data, which is then processed to generate text data as output.

[0629] Step 3:

[0630] The server uses a reproducible artificial intelligence model to analyze text data and automatically generate dialogue scenarios. Input consists of text data and user profile information, which are used to generate prompts. Output is a dialogue scenario tailored to a specific situation.

[0631] Step 4:

[0632] The terminal analyzes the user's emotional state using IBM Watson Tone Analyzer and other tools, along with the dialogue scenario sent from the server. Input is speech tone data used in conjunction with text data generated from speech, and output is the result of the emotion analysis.

[0633] Step 5:

[0634] The server provides appropriate feedback to the user based on the sentiment analysis results, assisting in the conversation in real time. The input is the sentiment analysis results, and the output is a feedback message that resonates with the user's feelings.

[0635] Step 6:

[0636] The user communicates smoothly with the other party based on the displayed dialogue scenario and feedback. The device updates the information displayed during the dialogue in the user's field of view according to the situation. Input is visual information and feedback, and output is the improved dialogue situation.

[0637] 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.

[0638] 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.

[0639] 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.

[0640] [Fourth Embodiment]

[0641] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0642] 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.

[0643] 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).

[0644] 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.

[0645] 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.

[0646] 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).

[0647] 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.

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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".

[0654] This invention is a system that provides customized English conversation scenarios tailored to the user's interests and occupation, and supports conversations based on those scenarios. This system is centered around generative AI and provides users with a real-time English learning experience through smart glasses or mobile devices.

[0655] When a user uses the system, they first launch smart glasses or a dedicated application and input basic information such as their interests, occupation, and English level. This data is then sent to an AI that generates conversational scenarios tailored to the user. For example, healthcare professionals are provided with conversational scenarios based on hospital consultation situations.

[0656] The server sends data to the terminal to control the interaction with the user based on the generated dialogue scenario. This dialogue control means that prompts following the flow of the conversation are displayed on the smart glasses, allowing the user to proceed with the conversation with the AI ​​accordingly. For example, if the user asks "How can I assist you today?" in the scenario, the AI ​​will provide a response such as "I'm looking for advice on how to manage stress at work."

[0657] The terminal receives the user's speech as voice input and uses a speech-to-text conversion system to send it to the server. Meanwhile, the evaluation system analyzes this voice data, instantly detecting errors in the user's pronunciation and grammar, and generating feedback in real time. As a result, the user can receive specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0658] Furthermore, when users need English in various everyday situations, the life support system suggests appropriate phrases for each situation. This feature allows users to respond quickly when ordering food or shopping while traveling. For example, by using smart glasses at a local restaurant, users can quickly refer to phrases such as "May I have the menu, please?"

[0659] Thus, the present invention provides users with an individualized English learning environment and realizes a system that supports efficient learning regardless of time or place.

[0660] The following describes the processing flow.

[0661] Step 1:

[0662] The user launches the smart glasses or a dedicated application and enters their name, occupation, areas of interest, and English proficiency level on the interface.

[0663] Step 2:

[0664] The device receives data entered by the user and sends it to the server as profile data. This profile data includes the user's interests, occupation, and English proficiency level.

[0665] Step 3:

[0666] The server analyzes the received profile data and uses a generation AI to automatically generate customized conversation scenarios tailored to the user. For example, a scenario related to sales negotiations will be generated for a sales professional.

[0667] Step 4:

[0668] The server sends the generated dialogue scenario to the device, making it visible on the user's smart glasses. This prepares the user to engage in the dialogue while visually reviewing the scenario.

[0669] Step 5:

[0670] The user initiates a conversation with the AI ​​by following prompts on the smart glasses. During this process, the user's speech is sent to the device via voice input.

[0671] Step 6:

[0672] The device converts the collected voice data into text data using speech recognition technology and sends that data to the server in real time.

[0673] Step 7:

[0674] The server analyzes the voice data and generates an appropriate response. Utilizing AI's natural language processing capabilities, it creates contextually appropriate responses tailored to the user's utterances and sends them to the device.

[0675] Step 8:

[0676] The device displays the response from the server on the smart glasses' display and, if necessary, provides audio output to communicate the response to the user.

[0677] Step 9:

[0678] The server evaluates the user's pronunciation and grammar and generates real-time feedback. For example, it can correct the pronunciation of specific words or suggest improvements to grammatical structure.

[0679] Step 10:

[0680] The device visually displays the generated feedback on the smart glasses and presents the user with specific ways to improve.

[0681] Step 11:

[0682] Users can request assistance with using English in their daily lives as needed. This request is forwarded to the server by the device.

[0683] Step 12:

[0684] The server generates appropriate English phrases and guidance based on everyday situations and sends them to the terminal. This allows users to communicate smoothly in a way that is appropriate for each situation.

[0685] (Example 1)

[0686] 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".

[0687] In modern society, there is a growing need for efficient English language learning tailored to individual interests and occupations. However, traditional learning methods struggle to automatically generate user-friendly dialogue scenarios, and furthermore, they have difficulty detecting pronunciation and grammatical errors in real time and providing immediate feedback. Additionally, there has been a lack of timely means to provide English expressions relevant to real-world situations.

[0688] 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.

[0689] In this invention, the server includes an information processing structure that automatically generates different dialogue content according to the user's interests and occupation, a conversation control structure that immediately processes conversations executed according to the generated dialogue content, and an evaluation structure that detects errors in the user's pronunciation and grammar and provides suggestions for improvement. This makes it possible to provide the user with the most suitable English dialogue scenario in real time and support efficient English learning.

[0690] An "information processing structure" is an element of a system that collects data based on the user's interests and occupation, and automatically generates different dialogue content based on that data.

[0691] A "conversation control structure" is a system element that facilitates smooth, real-time conversations with users based on the generated dialogue content.

[0692] The "evaluation structure" is a system element that analyzes user voice data, detects errors in pronunciation and grammar, and immediately provides specific feedback for improvement.

[0693] A "daily support structure" is a system element that proposes language expressions to users that are appropriate to real-world situations, thereby supporting language use in daily life.

[0694] A "voice conversion structure" is an element of a system that receives voice data from a user and converts it into text data.

[0695] This invention is a system that provides English conversation scenarios based on individual interests and occupations to support users' English learning. To implement this, an information processing system centered on a generative AI model is required.

[0696] Users of this system first launch an application on their smart glasses or mobile device. Here, users input information such as their interests, occupation, and English level. Upon receiving the user's input, the device sends that data to the server.

[0697] The server uses a generative AI model to analyze the received user information. It then generates a dialogue scenario suitable for the user using prompts. For example, a prompt such as "Generate an English conversation used in a medical scenario" might be used.

[0698] Based on the generated dialogue scenario, the server sends data to the device to control the conversation with the user. The device then uses this data to display the flow of the conversation on the smart glasses, allowing the user to refer to it and continue the conversation with the AI.

[0699] The user's voice input is received by the device and converted into text data using a speech-to-text structure. The converted text data is then sent back to the server. On the server side, an evaluation structure analyzes the voice data, identifies pronunciation and grammatical errors, and generates feedback in real time. For example, it might provide specific feedback such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0700] Furthermore, as language support for everyday life, the server prompts users with expressions appropriate to situations they encounter in their daily lives. In this example, it instantly presents the phrase "May I have the menu, please?" which a traveler might use in a restaurant. In this way, users can improve their English skills at their own pace through a personalized learning experience.

[0701] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0702] Step 1:

[0703] The user launches an application on smart glasses or a mobile device and enters basic information such as interests, occupation, and English level. This information becomes the initial input data for the system. The entered information is then transmitted by the terminal to the information processing structure.

[0704] Step 2:

[0705] The terminal transfers user input information to the server. The server receives this information and inputs it, along with prompts, into the generating AI model. These prompts might include something like, "Generate an English conversation used in a medical scenario." The server generates a customized conversation scenario based on the user's interests and occupation. The output is the data of the generated conversation scenario.

[0706] Step 3:

[0707] The server creates dialogue control data based on the generated dialogue scenario and sends it to the terminal. The terminal receives this data and displays appropriate conversation topics and prompts on the smart glasses' display. The user refers to these prompts and begins interacting with the AI. The output is the flow of the dialogue that the user should follow.

[0708] Step 4:

[0709] The user interacts with the AI ​​via voice by following the displayed prompts. The device receives the user's voice as voice input and converts it into text data using a speech-to-text conversion structure. This text data is sent to the server and used for processing in the next step.

[0710] Step 5:

[0711] The server analyzes the received text data using an evaluation structure to detect errors in the user's pronunciation and grammar. It then generates feedback based on the detected errors and sends it to the terminal in real time. The user receives specific improvement suggestions, such as, "There is a pronunciation error. Emphasize the 's' in 'stress'."

[0712] Step 6:

[0713] The server operates a daily support structure to suggest appropriate English expressions based on the user's living environment and daily situations. This provides concrete examples that meet the needs of travelers and in daily life. As a result, users can quickly refer to practical expressions such as "May I have the menu, please?"

[0714] (Application Example 1)

[0715] 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".

[0716] In today's increasingly globalized society, businesses are required to communicate effectively with customers from diverse cultures and backgrounds. However, many frontline staff face the challenge of being unable to adequately serve customers due to language barriers. Therefore, there is a need for systems that smoothly support foreign language communication.

[0717] 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.

[0718] In this invention, the server includes an information processing element that automatically generates different dialogue scenarios according to the user's interests and occupation, a dialogue control element that processes the dialogue executed according to the generated dialogue scenario in real time, and an evaluation element that detects errors in the user's pronunciation and grammar and provides feedback for improvement. This enables smooth intercultural communication by allowing on-site staff to provide appropriate foreign language phrases in real time.

[0719] An "information processing element" is an element that has the function of automatically generating different dialogue scenarios tailored to the user's interests and occupation.

[0720] A "dialogue control element" is an element that has the function of processing dialogue in real time, according to the generated dialogue scenario.

[0721] "Evaluation elements" are elements that have the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0722] "Life support elements" are elements that have the function of suggesting the most appropriate language expression to the user according to the situation in the real world.

[0723] A "support element" is an element that has the function of providing language phrases that enable users to effectively communicate with foreigners in environments such as the workplace.

[0724] The system of this invention is primarily implemented using AR-enabled smart glasses and a server. The server first receives information about the user's interests and occupation entered through the smart glasses. This information is used to automatically generate the optimal dialogue scenario for the user using a generative AI model. The generated scenario is then transmitted to the smart glasses to control the dialogue in real time.

[0725] The device (smart glasses) displays specific prompts to the user based on dialogue scenarios sent from the server. In addition, the device is equipped with software that performs speech conversion and evaluation, converting the user's voice input into text data and sending it to the server. This evaluation primarily detects errors in the user's pronunciation and grammar, and provides immediate feedback. Specifically, this includes minor corrections to pronunciation and identification of grammatical errors.

[0726] Users can use this system, for example, when assisting foreign customers as staff in a physical store. If a customer asks, "Does this product come in other colors?", the smart glasses will display a response phrase such as, "Yes, it comes in several colors. Would you like to see them?" This prompt is generated by a generative AI model that uses prompt sentences like the following to suggest the most appropriate language expression for the situation.

[0727] Examples of prompts for a generative AI model:

[0728] "If the user is a store employee, please explain in English how to respond when a customer asks about the color of a product."

[0729] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0730] Step 1:

[0731] The user puts on smart glasses and launches a dedicated application. The user enters their interests, occupation, and English level. This information is sent to the server by the device as input data.

[0732] Step 2:

[0733] The server sends prompt messages to the generating AI model based on the user information it receives. These prompt messages cause the AI ​​to generate a dialogue scenario suitable for the user. The generating AI analyzes the input data and returns the optimal dialogue scenario as generated data.

[0734] Step 3:

[0735] The server receives the generated dialogue scenario and creates specific dialogue prompts based on that scenario. This becomes the output data and is sent to the smart glasses.

[0736] Step 4:

[0737] The device (smart glasses) displays received conversational prompts in the user's field of view. The user follows these prompts during customer service conversations.

[0738] Step 5:

[0739] When a user inputs voice data during a conversation with a customer, that voice data is converted into text data. This conversion process takes place on the terminal and the text data is sent to the server.

[0740] Step 6:

[0741] The server analyzes the received text data using evaluation elements. It detects errors in the user's pronunciation and grammar, and generates feedback based on the evaluation results.

[0742] Step 7:

[0743] The generated feedback is immediately sent to the device and displayed to the user. The user can then use this feedback to improve their English expression.

[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 aims to further enhance the user experience by combining an emotion engine with a system that provides customized English dialogue scenarios tailored to the user's interests and occupation. This system supports real-time English learning using generative AI, an emotion engine, and smart glasses or mobile devices.

[0746] When a user uses the system, they launch the smart glasses application and enter basic information into the interface. This information includes the user's interests, occupation, and current English proficiency, and is sent to the server. The server uses generative AI to automatically generate conversational scenarios based on the user's profile data. For example, for a user whose interest is sports, a scenario related to sporting events will be generated.

[0747] The generated dialogue scenario is transmitted via the device to the smart glasses and displayed in the user's field of view. Here, the user receives adaptive feedback on their emotional state through an emotion engine. This emotion engine can analyze the user's voice tone and facial expressions to recognize the stress and difficulties the user experiences during the dialogue.

[0748] For example, if a user has difficulty pronouncing a particular phrase, the emotion engine can sense the user's frustration and provide gentle, encouraging feedback through dialogue control mechanisms, such as, "Try to relax a little and try again. We'll practice together." In this way, the emotion engine's data serves as a new metric for improving the user's learning experience.

[0749] The evaluation system analyzes the user's pronunciation and grammar in real time and suggests specific areas for improvement. In addition, a life support system assists the user in using English in their daily life. This system enables less stressful communication by suggesting appropriate English phrases based on the user's emotional state.

[0750] This comprehensive system structure allows users to smoothly progress in their English learning while receiving individually adapted feedback. The adoption of an emotion engine provides a flexible learning environment that takes into account the user's psychological state, enabling a more effective and human-centered approach to English learning.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The user activates the smart glasses, accesses a dedicated app, and enters basic information such as their interests, occupation, and English level.

[0754] Step 2:

[0755] The device sends the information received from the user to the server as profile data. The profile reflects the user's individual attributes.

[0756] Step 3:

[0757] The server analyzes profile data and generates personalized conversation scenarios using AI. For example, for a salesperson, it creates a scenario for a customer negotiation.

[0758] Step 4:

[0759] The generated dialogue scenario is sent from the server to the terminal and displayed on the smart glasses. This allows the user to visually recognize the scenario and prepare for the interaction.

[0760] Step 5:

[0761] The user follows the prompts displayed on the smart glasses to begin the conversation. The audio of the conversation is transmitted to the device.

[0762] Step 6:

[0763] The terminal converts user speech into text data using speech recognition technology and transfers it to the server. The converted data is then used for analysis.

[0764] Step 7:

[0765] The server uses dialogue control mechanisms to generate real-time responses based on the user's utterances. The AI ​​understands the context and creates appropriate responses.

[0766] Step 8:

[0767] Furthermore, the server's emotion engine analyzes the user's voice tone and facial expressions to assess their emotional state. Based on this assessment, it constructs emotionally sensitive feedback, such as "Let's try to relax a little while we talk."

[0768] Step 9:

[0769] The device displays the evaluation results on smart glasses and provides the user with adapted feedback and emotion-based advice.

[0770] Step 10:

[0771] The server uses evaluation tools to monitor the user's pronunciation and grammar, and provides real-time feedback on areas for improvement. Specific instructions are then displayed.

[0772] Step 11:

[0773] When a user requests English assistance in everyday situations, such as looking for a phrase while shopping, the request is sent to the server via the device.

[0774] Step 12:

[0775] The server generates and provides users with English phrases and appropriate action suggestions based on their emotional state, ensuring that support tailored to each situation is provided.

[0776] (Example 2)

[0777] 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".

[0778] The challenge lies in creating a system that efficiently provides customized dialogue scenarios tailored to users' interests and occupations, while also considering their emotional state to support language learning. Furthermore, it is essential to provide an effective learning experience through adaptive feedback based on each user's individual language proficiency.

[0779] 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.

[0780] In this invention, the server includes information processing means for automatically generating different dialogue scenarios according to the user's interests and occupation, dialogue control means for processing dialogues executed according to the generated dialogue scenarios in real time, and emotion analysis means for analyzing the user's voice tone and facial expressions to evaluate their emotional state and provide adaptive feedback. This enables the provision of real-time dialogue scenarios based on the user's interests and occupation, and emotionally adapted feedback.

[0781] "Information processing means" refers to means that provide functions for automatically generating dialogue scenarios tailored to the user's interests and occupation.

[0782] "Dialogue control means" refers to means for processing user interactions in real time based on generated dialogue scenarios.

[0783] An "evaluation tool" is a tool that has the function of detecting errors in the user's pronunciation and grammar and providing feedback for improvement.

[0784] "Life support tools" are tools that have the function of suggesting appropriate language expressions to users in accordance with real-world situations.

[0785] "Emotional analysis means" refers to a method of analyzing a user's voice tone and facial expressions to evaluate their emotional state and provide appropriate feedback.

[0786] A "dialogue scenario generation method" using "generative AI" is a method that uses a generative AI model to generate customized dialogue scenarios based on the user's profile data.

[0787] A "voice conversion method" is a means for converting voice input from a user into text format.

[0788] This invention provides a system that offers customized English conversation scenarios based on the user's interests and occupation, and enhances the learning experience using an emotion engine. This system is implemented using a generative AI model, smart glasses or a mobile device, a server, and emotion analysis technology.

[0789] Users launch an application on smart glasses or a mobile device and input their interests, occupation, and English proficiency using a dedicated interface. This input information is transmitted to a server via the internet.

[0790] The server utilizes a generative AI model as an information processing tool based on the information it receives. This generative AI model is a natural language processing engine that runs on the cloud, and an example of a large-scale model is "GPT". The server sends prompt sentences tailored to the user's interests to the generative AI, which automatically generates dialogue scenarios that match the user's characteristics. For example, if the user is interested in cooking, the server will generate dialogue scenarios related to cooking recipes and cooking techniques.

[0791] The generated dialogue scenario is transferred to the smart glasses via the terminal and displayed in the user's field of view. Here, the terminal plays a role in controlling the scenario-based dialogue with the user in real time.

[0792] As an emotion analysis tool, the server analyzes the user's voice tone and facial expressions to evaluate the user's emotional state in real time. This analysis utilizes speech recognition software and image recognition software, specifically open-source speech recognition libraries and facial expression evaluation algorithms.

[0793] As the user engages in dialogue, if the emotion analysis system detects the user's stress or difficulty, the server generates appropriate feedback and sends it to the user via the device. For example, if the user is struggling with a particular English phrase, the server might provide feedback such as, "Calm down and try again. Let's repeat it together."

[0794] Furthermore, the server uses evaluation tools to analyze the user's pronunciation and grammar and suggests specific areas for improvement. The life support tools suggest useful English phrases for the user's daily life, helping to improve the user's communication skills.

[0795] As a concrete example, here is an example of a prompt to the generating AI: "If the user's interest is travel and their English proficiency is intermediate, please suggest the most helpful dialogue scenarios for the travel destinations the user will be visiting." In response to this prompt, the AI ​​will generate dialogue scenarios related to everyday conversations and tourist attractions in the travel destinations.

[0796] In summary, the present invention provides users with an individually optimized English learning experience and realizes a flexible and human-centered learning environment by utilizing an emotion engine.

[0797] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0798] Step 1:

[0799] The user launches an app on smart glasses or a mobile device and enters personal information. Specifically, the user interacts with an interface to enter basic data such as interests, occupation, and English proficiency. This input data forms the basis for designing a customized learning experience for the user.

[0800] Step 2:

[0801] The terminal retrieves information entered by the user and sends it to the server. The input data includes information about interests, occupation, and English proficiency, and this data serves as initial data for data processing on the server side.

[0802] Step 3:

[0803] The server creates prompts for the AI ​​model based on the received user information and instructs it to generate dialogue scenarios. Specifically, the server analyzes the received data and generates prompts such as, "If the user's interest is music and their English ability is beginner level, please generate a simple conversation scenario for a music festival." The server uses this as input to the AI ​​model and outputs dialogue scenarios adapted to each user.

[0804] Step 4:

[0805] The server sends a customized dialogue scenario derived from the generated AI model to the terminal. The terminal's role is to transfer this output scenario to the smart glasses and display it directly to the user. The user then conducts a dialogue exercise based on this displayed information.

[0806] Step 5:

[0807] The user initiates a conversation, and the emotion analysis system analyzes the user's voice tone and facial expressions in real time. The input is the user's voice and video data, which the emotion analysis system outputs as an emotional state. This analysis includes voice recognition and facial expression analysis, allowing the application to track changes in emotion.

[0808] Step 6:

[0809] The server receives data from the emotion analysis system and generates feedback based on the user's emotional state. For example, if the server detects that the user is stressed, it will output supportive feedback such as, "Try to relax a little and try again slowly." This allows the user to adjust their next steps based on the feedback.

[0810] Step 7:

[0811] The server uses evaluation tools to analyze the user's pronunciation and grammar. The input is the user's spoken content, which the server analyzes and provides output that specifically identifies errors and areas for improvement. Based on the evaluation, the user receives specific guidance for improving their proficiency.

[0812] Step 8:

[0813] The life support system suggests everyday language expressions based on the user's emotional state and daily feedback. This allows users to communicate more smoothly in real-life situations and receive support tailored to their daily lives.

[0814] (Application Example 2)

[0815] 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".

[0816] In today's multicultural society, smooth communication between individuals with different languages ​​and cultures remains challenging. Language barriers are particularly significant obstacles to customer service and business transactions in tourist destinations and international business settings. Furthermore, traditional language learning and interpretation services struggle to understand and respond appropriately to the other person's emotions, creating limitations in building interpersonal relationships.

[0817] 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.

[0818] In this invention, the server includes means for converting observable human speech into text using information identification means, means for automatically generating dialogue scenarios using a reproducible artificial intelligence model, and means for analyzing human emotional states using expression analysis means. This enables users to overcome language and emotional barriers in intercultural communication and communicate smoothly in real time.

[0819] "Information identification means" refers to a technological device for detecting sounds and images in the environment, acquiring those signals, and analyzing them.

[0820] A "generative artificial intelligence model" is an algorithm or system that has the ability to autonomously create new data or scenarios based on input information.

[0821] A "dialogue scenario" is a pre-designed and generated transcript of the expected flow and content of a conversation in a specific scene or situation.

[0822] "Management means" refer to methods and devices for coordinating various processes and data flows within a system to ensure smooth operation.

[0823] "Representation analysis means" refers to technologies that judge and classify human intentions and emotional states based on acquired data.

[0824] "Means of providing feedback" refer to methods and devices for returning appropriate information and advice to users based on analysis results.

[0825] "Everyday life scenarios" refer to situations and scenarios that are commonly experienced in daily life.

[0826] "Means for proposing linguistic expressions" refer to functions or systems for generating and presenting linguistic phrases that are appropriate for specific situations or emotional states.

[0827] To implement this invention, it is necessary to build a system that supports smooth intercultural communication by coordinating the user's smart glasses, mobile device, and server. This system consists of the following main modules.

[0828] First, the system acquires the voices of the user and their conversation partner in real time using identification methods from smart glasses or mobile devices. The acquired voice data is converted into text data using speech-to-text conversion tools such as the Google Cloud Speech-to-Text API. The converted text data is sent to a server, where an appropriate conversation scenario is automatically generated using a reproducible artificial intelligence model, such as OpenAI's GPT.

[0829] The server uses expression analysis tools to understand the user's emotional state, which is analyzed from their voice, and provides feedback that corresponds to that emotion. Software such as IBM Watson Tone Analyzer can be used in this process to achieve optimal communication based on the user's emotional state.

[0830] This allows the device to appropriately display generated dialogue scenarios and feedback within the user's field of view, supporting the user in smooth communication with their conversation partner. A concrete application example would be in a physical store in a tourist area, where staff are assisting international customers. In this scenario, it would function as an auxiliary tool to enable staff to quickly provide the information requested by the customer.

[0831] An example of a prompt message input to the generating AI would be: "As a store employee, you must explain a product to a tourist. The tourist is interested. How would you respond in a friendly manner?" Based on this, the AI ​​generates appropriate scenarios and suggestions and provides them to the user, thereby improving cross-cultural communication.

[0832] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0833] Step 1:

[0834] The user activates smart glasses or a mobile device and captures the other person's voice with a microphone during conversation. The input is an audio signal, which is acquired by an information identification mechanism. The output is audio data.

[0835] Step 2:

[0836] The device converts the acquired audio data into text data using a speech-to-text API or similar method. The input is audio data, which is then processed to generate text data as output.

[0837] Step 3:

[0838] The server uses a reproducible artificial intelligence model to analyze text data and automatically generate dialogue scenarios. Input consists of text data and user profile information, which are used to generate prompts. Output is a dialogue scenario tailored to a specific situation.

[0839] Step 4:

[0840] The terminal analyzes the user's emotional state using IBM Watson Tone Analyzer and other tools, along with the dialogue scenario sent from the server. Input is speech tone data used in conjunction with text data generated from speech, and output is the result of the emotion analysis.

[0841] Step 5:

[0842] The server provides appropriate feedback to the user based on the sentiment analysis results, assisting in the conversation in real time. The input is the sentiment analysis results, and the output is a feedback message that resonates with the user's feelings.

[0843] Step 6:

[0844] The user communicates smoothly with the other party based on the displayed dialogue scenario and feedback. The device updates the information displayed during the dialogue in the user's field of view according to the situation. Input is visual information and feedback, and output is the improved dialogue situation.

[0845] 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.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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."

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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 to be incorporated by reference.

[0866] The following is further disclosed regarding the embodiments described above.

[0867] (Claim 1)

[0868] An information processing method that automatically generates different dialogue scenarios tailored to the user's interests and occupation,

[0869] A dialogue control means that processes dialogues in real time according to a generated dialogue scenario,

[0870] An evaluation method that detects errors in the user's pronunciation and grammar and provides feedback for improvement,

[0871] A life support tool that proposes English expressions appropriate to real-world situations,

[0872] A system that includes this.

[0873] (Claim 2)

[0874] The system according to claim 1, further comprising means for customizing the dialogue scenarios presented based on the user's English proficiency.

[0875] (Claim 3)

[0876] The system according to claim 1, further comprising a speech conversion means for converting voice input from a user into text format.

[0877] "Example 1"

[0878] (Claim 1)

[0879] An information processing structure that automatically generates different dialogue content according to the user's interests and occupation,

[0880] A conversation control structure that immediately processes conversations executed according to the generated dialogue content,

[0881] An evaluation structure that detects errors in user pronunciation and grammar and provides suggestions for improvement,

[0882] A daily support structure that proposes language expressions that correspond to real-world situations,

[0883] A speech conversion structure that converts user voice data into text data,

[0884] A system that includes this.

[0885] (Claim 2)

[0886] The system according to claim 1, further comprising a structure that personalizes the dialogue content presented based on the user's language ability.

[0887] (Claim 3)

[0888] The system according to claim 1, further comprising a structure that reflects the results in real-life situations.

[0889] "Application Example 1"

[0890] (Claim 1)

[0891] Information processing elements that automatically generate different dialogue scenarios tailored to the user's interests and occupation,

[0892] A dialogue control element that processes dialogue in real time according to a generated dialogue scenario,

[0893] Evaluation elements that detect user pronunciation and grammatical errors and provide feedback for improvement,

[0894] Life support elements that propose language expressions appropriate to real-world situations,

[0895] Support elements that provide appropriate language phrases for users to effectively communicate with foreigners in the workplace,

[0896] A system that includes this.

[0897] (Claim 2)

[0898] The system according to claim 1, further comprising components for customizing dialogue scenarios presented based on the user's language ability.

[0899] (Claim 3)

[0900] The system according to claim 1, further comprising a speech conversion element that converts voice input from a user into text format.

[0901] "Example 2 of combining an emotion engine"

[0902] (Claim 1)

[0903] An information processing method that automatically generates different dialogue scenarios tailored to the user's interests and occupation,

[0904] A dialogue control means that processes dialogues in real time according to a generated dialogue scenario,

[0905] An evaluation method that detects errors in the user's pronunciation and grammar and provides feedback for improvement,

[0906] A life support tool that proposes language expressions appropriate to real-world situations,

[0907] An emotion analysis means that analyzes the user's voice tone and facial expressions to evaluate their emotional state and provides adaptive feedback,

[0908] A dialogue scenario generation method that uses generated AI to provide real-time feedback,

[0909] A system that includes this.

[0910] (Claim 2)

[0911] The system according to claim 1, further comprising means for customizing the dialogue scenarios presented based on the user's language ability.

[0912] (Claim 3)

[0913] The system according to claim 1, further comprising a speech conversion means for converting voice input from a user into text format.

[0914] "Application example 2 when combining with an emotional engine"

[0915] (Claim 1)

[0916] A means for converting observable human speech into text using an information identification means,

[0917] A means for automatically generating dialogue scenarios using a reproducible artificial intelligence model,

[0918] A management means for processing dialogues in real time, which are executed according to the generated dialogue scenarios,

[0919] A means of analyzing human emotional states using expression analysis methods,

[0920] A means of providing appropriate feedback based on the analyzed emotional state,

[0921] A means of proposing language expressions appropriate to various situations in daily life,

[0922] A system that includes this.

[0923] (Claim 2)

[0924] The system according to claim 1, further comprising means for adjusting the dialogue scenarios presented based on the user's language ability.

[0925] (Claim 3)

[0926] The system according to claim 1, further comprising means for analyzing observed video and audio signals. [Explanation of symbols]

[0927] 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. An information processing method that automatically generates different dialogue scenarios tailored to the user's interests and occupation, A dialogue control means that processes dialogues in real time according to a generated dialogue scenario, An evaluation method that detects errors in the user's pronunciation and grammar and provides feedback for improvement, A life support tool that proposes English expressions appropriate to real-world situations, A system that includes this.

2. The system according to claim 1, further comprising means for customizing the dialogue scenarios presented based on the user's English proficiency.

3. The system according to claim 1, further comprising a speech conversion means for converting voice input from a user into text format.

Images