system

Abstract

The system according to this embodiment aims to efficiently improve the user's language skills and cultural knowledge. [Solution] The system according to the embodiment comprises a reception unit, an evaluation unit, a generation unit, a simulation unit, and a translation unit. The reception unit receives user input. The evaluation unit evaluates the user's current level based on the information received by the reception unit. The generation unit generates a learning plan based on the information evaluated by the evaluation unit. The simulation unit provides a cross-cultural simulation based on the learning plan generated by the generation unit. The translation unit performs real-time translation based on the learning plan generated by the generation unit.

Description

Technical Field

[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 the conventional technology, there is a problem that the support for efficiently improving the user's language skills and cultural knowledge is insufficient.

[0005] The system according to the embodiment aims to efficiently improve the user's language skills and cultural knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an evaluation unit, a generation unit, a simulation unit, and a translation unit. The reception unit receives user input. The evaluation unit evaluates the user's current level based on the information received by the reception unit. The generation unit generates a learning plan based on the information evaluated by the evaluation unit. The simulation unit provides a cross-cultural simulation based on the learning plan generated by the generation unit. The translation unit performs real-time translation based on the learning plan generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently improve the user's language skills and cultural knowledge. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the numbered 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 applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] 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.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The GlobalConnect AI system, according to an embodiment of the present invention, is an innovative AI agent that supports intercultural understanding and language learning. This GlobalConnect AI system improves the user's language skills and cultural knowledge by accepting user input, evaluating the user's current level, generating a learning plan, and providing intercultural simulations and real-time translation. For example, if a user inputs "I want to learn English," the GlobalConnect AI system evaluates the user's current English level and proposes an optimal learning plan. The learning plan includes conversation practice utilizing real-time translation and practical scenarios through intercultural simulations. For example, by simulating a business meeting in English, the user can improve their communication skills in real business situations. The GlobalConnect AI system is also available to businesses, serving as a tool for employees of multinational companies to improve their intercultural understanding and language skills. Through the GlobalConnect AI system, companies can reduce employee training costs and strengthen their international competitiveness. Thus, the GlobalConnect AI system is an innovative AI agent that provides personalized learning experiences tailored to individual and corporate needs, supporting intercultural understanding and language learning. This allows the GlobalConnect AI system to effectively support users' intercultural understanding and language learning.

[0029] The GlobalConnect AI system according to this embodiment comprises a reception unit, an evaluation unit, a generation unit, a simulation unit, and a translation unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard interface for receiving text input. The reception unit may also provide a microphone interface for receiving voice input. Furthermore, the reception unit may also provide a camera interface for receiving image input. For example, the reception unit provides a keyboard for the user to input text and receives the text entered by the user. In the case of voice input, the reception unit receives the voice spoken by the user through the microphone and converts it into text using speech recognition technology. In the case of image input, the reception unit receives an image taken by the user through the camera and analyzes it using image recognition technology. The evaluation unit evaluates the user's current level based on the information received by the reception unit. The evaluation unit uses, for example, algorithms to evaluate the user's language ability and cultural understanding. For example, the evaluation unit analyzes the text entered by the user and evaluates their language ability. The evaluation unit can also analyze the user's voice input and evaluate pronunciation and fluency. Furthermore, the evaluation unit can analyze the user's image input and evaluate their cultural understanding. For example, the evaluation unit analyzes the text entered by the user using natural language processing technology and evaluates the level of grammar and vocabulary. In the case of voice input, the evaluation unit uses speech recognition technology to evaluate the accuracy and fluency of pronunciation. In the case of image input, the evaluation unit uses image recognition technology to analyze cultural symbols and gestures and evaluate the user's cultural understanding. The generation unit generates a learning plan based on the information evaluated by the evaluation unit. For example, the generation unit proposes optimal learning goals and learning content based on the user's evaluation results. For example, the generation unit generates a learning plan that includes grammar reinforcement and vocabulary expansion depending on the user's language ability. The generation unit can also generate a learning plan that includes cross-cultural simulations and practical scenarios depending on the user's cultural understanding. For example, the generation unit generates a learning plan aimed at grammar reinforcement based on the user's evaluation results.The system can also generate learning plans aimed at expanding vocabulary. In the case of cross-cultural simulation, the generation unit generates scenarios to help users improve their communication skills in real business situations. The simulation unit provides cross-cultural simulations based on the learning plans generated by the generation unit. The simulation unit provides, for example, an interface for users to experience cross-cultural simulations. For example, the simulation unit provides a virtual environment for users to simulate business meetings. The simulation unit can also provide feedback to help users acquire practical skills through cross-cultural simulations. For example, the simulation unit provides feedback to help users simulate business meetings in a virtual environment and improve their communication skills in real business situations. The translation unit performs real-time translations based on the learning plans generated by the generation unit. The translation unit provides real-time translations when users perform cross-cultural simulations. For example, the translation unit translates into Japanese in real time when users simulate conversations in English. The translation unit can also provide real-time translations when users learn through cross-cultural simulations. For example, the translation unit translates into Japanese in real time when users simulate business meetings in English, supporting the user's understanding. As a result, the GlobalConnect AI system according to this embodiment can effectively support users' cross-cultural understanding and language learning.

[0030] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit provides a keyboard interface for receiving text input. Specifically, it receives text data entered by the user using the keyboard in real time and transmits it to the system. The reception unit can also provide a microphone interface for receiving voice input. In the case of voice input, it receives the voice spoken by the user through the microphone and converts it to text using speech recognition technology. Speech recognition technology includes a process of extracting features from the voice and converting the voice to text using a language model. Furthermore, the reception unit can also provide a camera interface for receiving image input. In the case of image input, it receives images taken by the user through the camera and analyzes them using image recognition technology. Image recognition technology includes a process of extracting features from the image and performing object recognition and scene analysis. For example, the reception unit provides a keyboard for the user to enter text and receives the text entered by the user. In the case of voice input, the reception unit receives the voice spoken by the user through the microphone and converts it to text using speech recognition technology. In the case of image input, the reception unit receives images captured by the user via a camera and analyzes them using image recognition technology. This allows the reception unit to handle diverse user input formats and flexibly accept information. Furthermore, the reception unit can centrally manage user input data and process it efficiently in cooperation with other departments. For example, the reception unit can send the received data to a cloud server, making it accessible to the evaluation and generation units. The reception unit can also adjust the frequency and accuracy of data reception, enabling flexible responses to specific situations and conditions. As a result, the reception unit can receive data efficiently and effectively, improving the overall system performance.

[0031] The evaluation unit assesses the user's current level based on the information received by the reception unit. The evaluation unit uses algorithms to evaluate, for example, the user's language ability and cultural understanding. Specifically, it analyzes the text entered by the user using natural language processing technology to evaluate grammar and vocabulary levels. Natural language processing technology includes morphological analysis, syntactic analysis, and semantic analysis, which are combined to evaluate the user's language ability in detail. The evaluation unit can also analyze the user's voice input to evaluate pronunciation and fluency. Evaluating voice input involves converting speech to text using speech recognition technology and then analyzing that text. Furthermore, the evaluation unit can analyze the user's image input to evaluate cultural understanding. Evaluating image input involves extracting image features using image recognition technology and analyzing cultural symbols and gestures. For example, the evaluation unit analyzes the text entered by the user using natural language processing technology to evaluate grammar and vocabulary levels. In the case of voice input, the evaluation unit uses speech recognition technology to evaluate the accuracy and fluency of pronunciation. In the case of image input, the evaluation unit uses image recognition technology to analyze cultural symbols and gestures and assess the user's level of cultural understanding. This allows the evaluation unit to analyze the user's diverse input data in detail and accurately assess the user's current level. Furthermore, the evaluation unit can utilize historical data and statistical information to perform long-term evaluations and trend analyses. For example, it can track the progress of a specific user based on past evaluation data and develop a future learning plan. In addition, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the evaluation unit to handle not only real-time evaluations but also long-term evaluations and anomaly detection, improving the overall reliability and security of the system.

[0032] The generation unit generates a learning plan based on the information evaluated by the evaluation unit. For example, the generation unit proposes optimal learning goals and content based on the user's evaluation results. Specifically, it generates a learning plan that includes grammar reinforcement and vocabulary expansion, depending on the user's language ability. The generation unit analyzes the user's evaluation results using natural language processing technology to identify the user's weaknesses and areas that need strengthening. For example, a learning plan aimed at grammar reinforcement would propose content that focuses on grammar items the user finds difficult. A learning plan aimed at vocabulary expansion would propose content that focuses on vocabulary frequently used by the user in daily life and business situations. Furthermore, the generation unit can also generate learning plans that include cross-cultural simulations and practical scenarios, depending on the user's level of cultural understanding. For example, in the case of cross-cultural simulations, the generation unit generates scenarios to help the user improve their communication skills in actual business situations. The scenarios include cross-cultural business manners and communication points, and are designed to allow the user to acquire practical skills. In addition, the generation unit can monitor the user's progress in real time and modify the learning plan as needed. For example, if a user achieves a specific learning goal, the generation unit sets a new learning goal and updates the learning plan. Furthermore, if a user is experiencing difficulties in their learning, the generation unit adjusts the learning content to support them in learning effectively. This allows the generation unit to generate an optimal learning plan based on the user's evaluation results, maximizing the user's learning effectiveness.

[0033] The simulation unit provides cross-cultural simulations based on learning plans generated by the generation unit. For example, the simulation unit provides an interface for users to experience cross-cultural simulations. Specifically, it provides a virtual environment for users to simulate business meetings. This virtual environment includes 3D models and avatars that recreate business scenes, allowing users to simulate as if it were a real business meeting. The simulation unit can also provide feedback to help users acquire practical skills through cross-cultural simulations. For example, the simulation unit provides feedback to help users improve their communication skills in real business situations after simulating business meetings in the virtual environment. This feedback includes the user's statements, attitude, and gestures, specifically pointing out areas for improvement. Furthermore, the simulation unit can monitor the user's progress in real time and modify the simulation content as needed. For example, if a user completes a specific simulation scenario, the simulation unit provides a new scenario to support continuous learning. Also, if a user finds the simulation difficult, the simulation unit adjusts the difficulty level of the scenario to support effective learning. In this way, the simulation unit enables users to acquire practical skills and deepen their understanding of different cultures through cross-cultural simulations.

[0034] The translation unit performs real-time translation based on the learning plan generated by the generation unit. For example, the translation unit provides real-time translation when a user is performing cross-cultural simulations. Specifically, when a user simulates a conversation in English, it translates it into Japanese in real time. The translation unit uses speech recognition technology to convert the user's speech into text and inputs that text into the translation engine. The translation engine uses natural language processing technology to translate the text and provides it to the user. For example, when a user simulates a business meeting in English, the translation unit translates the user's speech into Japanese in real time to support the user in understanding it. The translation unit can also provide real-time translation when a user is learning through cross-cultural simulations. For example, when a user is performing a cross-cultural simulation, the translation unit translates the user's speech in real time to support the user in effectively learning cross-cultural communication. Furthermore, the translation unit can monitor the user's progress in real time and modify the translation content as needed. For example, when a user clears a particular simulation scenario, the translation unit provides translations corresponding to new scenarios to support the user in continuing to learn. Furthermore, if a user encounters difficulties with translation, the translation team will adjust the translation to support the user in learning effectively. This allows the translation team to enable users to learn effectively through cross-cultural simulations and deepen their understanding of different cultures.

[0035] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0036] The reception desk can provide additional information based on the user's areas of interest, depending on the input. For example, if the user is interested in language learning, the reception desk can suggest relevant learning resources. For example, if the user is interested in intercultural understanding, the reception desk can provide relevant simulation scenarios. Also, if the user is interested in business English, the reception desk can suggest a learning plan tailored to business situations. In this way, the user's learning experience can be improved by providing additional information based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's areas of interest data into a generating AI and have the generating AI perform the task of providing additional information.

[0037] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can provide input options related to the culture and language of that region. For example, if the user is traveling, the reception desk can provide options for inputting information about their travel destination. Furthermore, if the user moves to a different region, the reception desk can automatically switch to input options appropriate for that region. This improves user convenience by providing region-specific input options, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing region-specific input options.

[0038] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, if a user posts about language learning on social media, the reception desk can suggest relevant input content. For example, if a user posts about intercultural understanding, the reception desk can suggest relevant simulation scenarios. Furthermore, if a user posts about business English, the reception desk can suggest input content tailored to business situations. In this way, by analyzing a user's social media activity, relevant input content can be suggested, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0039] The evaluation unit can improve the accuracy of its evaluations by referring to the user's past learning history. For example, the evaluation unit can assess the user's current level based on what the user has learned in the past. For example, the evaluation unit can assess the user's strengths and weaknesses in a specific area from the user's past learning history. The evaluation unit can also analyze the user's past learning history to improve the accuracy of its evaluations. In this way, the accuracy of the evaluations can be improved by referring to the user's past learning history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0040] The evaluation unit can customize the evaluation based on the user's current living situation and learning environment. For example, if the user is busy, the evaluation unit can provide a method for conducting a quick evaluation. For example, if the user is relaxed, the evaluation unit can provide a method for conducting a detailed evaluation. The evaluation unit can also provide the optimal evaluation method according to the user's learning environment. In this way, by customizing the evaluation based on the user's current living situation and learning environment, the evaluation can be provided to the user at its best. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's living situation data into a generating AI and have the generating AI perform the evaluation customization.

[0041] The evaluation unit can apply region-specific evaluation criteria, taking into account the user's geographical location. For example, if the user is in a specific region, the evaluation unit can apply evaluation criteria related to the culture and language of that region. For example, if the user is traveling, the evaluation unit can adjust the evaluation criteria based on information about the travel destination. Furthermore, if the user moves to a different region, the evaluation unit can automatically switch to evaluation criteria appropriate for that region. This allows for the application of region-specific evaluation criteria by considering the user's geographical location, thereby improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI perform the application of region-specific evaluation criteria.

[0042] The evaluation unit can analyze a user's social media activity and provide relevant evaluation information. For example, if a user posts about language learning on social media, the evaluation unit can provide relevant evaluation information. For example, if a user posts about intercultural understanding, the evaluation unit can provide relevant evaluation information. Furthermore, if a user posts about business English, the evaluation unit can provide evaluation information specific to business settings. In this way, by analyzing a user's social media activity, relevant evaluation information can be provided, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant evaluation information.

[0043] The generation unit can improve the accuracy of the learning plan by referring to the user's past learning history. For example, the generation unit can adjust the current learning plan based on what the user has learned in the past. For example, the generation unit can create a learning plan by considering the user's strengths and weaknesses in a specific field from the user's past learning history. The generation unit can also analyze the user's past learning history and provide an optimal learning plan. In this way, the accuracy of the learning plan can be improved by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

[0044] The generation unit can customize learning plans based on the user's current lifestyle and learning environment. For example, if the user is busy, the generation unit can provide a plan for learning in a short amount of time. For example, if the user is relaxed, the generation unit can provide a detailed learning plan. The generation unit can also provide an optimal learning plan according to the user's learning environment. In this way, by customizing the learning plan based on the user's current lifestyle and learning environment, it is possible to provide the optimal learning plan for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle data into a generation AI and have the generation AI perform the customization of the learning plan.

[0045] The generation unit can provide region-specific learning plans by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can provide a learning plan related to the culture and language of that region. For example, if the user is traveling, the generation unit can adjust the learning plan based on information about the travel destination. Furthermore, if the user moves to a different region, the generation unit can automatically switch to a learning plan suitable for that region. In this way, by taking into account the user's geographical location information, region-specific learning plans can be provided, improving user convenience. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of providing region-specific learning plans.

[0046] The generation unit can analyze a user's social media activity and propose relevant learning plans. For example, if a user posts about language learning on social media, the generation unit can propose relevant learning plans. For example, if a user posts about intercultural understanding, the generation unit can propose relevant simulation scenarios. Furthermore, if a user posts about business English, the generation unit can propose learning plans tailored to business situations. In this way, by analyzing a user's social media activity, relevant learning plans can be proposed, improving the user's learning experience. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI propose relevant learning plans.

[0047] The simulation unit can improve the accuracy of simulations by referring to the user's past simulation history. For example, the simulation unit can adjust the current simulation based on the results of simulations previously performed by the user. For example, the simulation unit can create a simulation considering the strengths and weaknesses in a particular scenario based on the user's past simulation history. The simulation unit can also analyze the user's past simulation history and provide the optimal simulation scenario. This allows for improved simulation accuracy by referring to the user's past simulation history. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past simulation history data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0048] The simulation unit can customize the simulation based on the user's current living situation and learning environment. For example, if the user is busy, the simulation unit can provide a scenario to complete the simulation in a short amount of time. For example, if the user is relaxed, the simulation unit can provide a detailed simulation scenario. The simulation unit can also provide an optimal simulation scenario according to the user's learning environment. In this way, by customizing the simulation based on the user's current living situation and learning environment, the system can provide the user with the best possible simulation experience. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's living situation data into a generating AI and have the generating AI perform the simulation customization.

[0049] The simulation unit can provide region-specific simulation scenarios by taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit can provide simulation scenarios related to the culture and language of that region. For example, if the user is traveling, the simulation unit can adjust the simulation scenario based on information about the travel destination. Furthermore, if the user moves to a different region, the simulation unit can automatically switch to a simulation scenario appropriate for that region. In this way, by taking into account the user's geographical location information, region-specific simulation scenarios can be provided, improving user convenience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing region-specific simulation scenarios.

[0050] The simulation unit can analyze a user's social media activity and propose relevant simulation scenarios. For example, if a user posts about language learning on social media, the simulation unit can propose relevant simulation scenarios. For example, if a user posts about intercultural understanding, the simulation unit can propose relevant simulation scenarios. Furthermore, if a user posts about business English, the simulation unit can propose simulation scenarios specifically tailored to business situations. In this way, by analyzing a user's social media activity, relevant simulation scenarios can be proposed, thereby improving the user's learning experience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of relevant simulation scenarios.

[0051] The translation unit can improve the accuracy of translations by referring to the user's past translation history. For example, the translation unit can adjust the current translation based on the content the user has translated in the past. For example, the translation unit can create a translation by considering the user's strengths and weaknesses in a particular field from the user's past translation history. The translation unit can also analyze the user's past translation history and provide the optimal translation. In this way, the accuracy of translations can be improved by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's past translation history data into a generating AI and have the generating AI perform the translation accuracy improvement.

[0052] The translation unit can customize translations based on the user's current living situation and learning environment. For example, if the user is busy, the translation unit can provide a method to deliver a translation in a short time. For example, if the user is relaxed, the translation unit can provide a detailed translation. The translation unit can also provide the optimal translation method according to the user's learning environment. In this way, by customizing translations based on the user's current living situation and learning environment, the translation unit can provide the best possible translation for the user. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's living situation data into a generating AI and have the generating AI perform the translation customization.

[0053] The translation unit can provide region-specific translations by taking into account the user's geographical location. For example, if the user is in a specific region, the translation unit can provide translations related to the culture and language of that region. For example, if the user is traveling, the translation unit can adjust the translation based on information about the travel destination. Furthermore, if the user moves to a different region, the translation unit can automatically switch to translations appropriate for that region. This improves user convenience by providing region-specific translations that take into account the user's geographical location. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing region-specific translations.

[0054] The translation unit can analyze a user's social media activity and suggest relevant translations. For example, if a user posts about language learning on social media, the translation unit can suggest relevant translations. For example, if a user posts about intercultural understanding, the translation unit can suggest relevant translations. Furthermore, if a user posts about business English, the translation unit can suggest translations tailored to business situations. This allows the translation unit to analyze a user's social media activity, suggest relevant translations, and improve user convenience. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input user social media activity data into a generating AI and have the generating AI suggest relevant translations.

[0055] The translation unit can adjust the expression of the translation to take into account the user's health condition. For example, if the user is tired, the translation unit can provide a concise and easy-to-understand translation. For example, if the user is healthy, the translation unit can provide a detailed translation. Furthermore, if the user is unwell, the translation unit can provide a translation using gentle language. In this way, by adjusting the expression of the translation according to the user's health condition, the translation can be provided to the user in the most optimal way. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's health condition data into a generating AI and have the generating AI perform the adjustment of the expression of the translation.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The GlobalConnect AI system can further analyze a user's learning style and suggest the most suitable learning method. For example, users who prefer visual learning can be provided with a learning plan that makes extensive use of visual aids and infographics. Users who prefer auditory learning can be offered a learning plan that utilizes audio materials and podcasts. Furthermore, users who prefer practical learning can be provided with a learning plan that includes interactive simulations and role-playing. By suggesting the most suitable learning method according to the user's learning style, the system can maximize learning effectiveness.

[0058] The GlobalConnect AI system can analyze a user's past learning data and visualize their learning progress. For example, it can display what the user has learned in the past using graphs and charts, allowing them to see their progress at a glance. It can also list the goals the user has achieved and the skills they have acquired, allowing them to feel a sense of accomplishment from their learning. Furthermore, it can identify areas where the user struggles and suggest a plan for focused learning. By visualizing the user's learning progress, it can help maintain motivation and support effective learning.

[0059] The GlobalConnect AI system can provide information on region-specific cultures and customs, taking into account the user's geographical location. For example, if a user is in a specific region, it can provide information on the local culture and customs to deepen their understanding of different cultures. If a user is traveling, it can provide information on the culture and customs of their destination to facilitate communication. Furthermore, if a user moves to a different region, the system can automatically switch to information on cultures and customs appropriate for that region. This allows the system to support cross-cultural understanding by providing information on region-specific cultures and customs, taking the user's geographical location into consideration.

[0060] The GlobalConnect AI system can analyze a user's social media activity and suggest relevant learning resources. For example, if a user posts about language learning on social media, it can suggest relevant online courses and materials. If a user posts about cross-cultural understanding, it can suggest relevant simulation scenarios and articles. Furthermore, if a user posts about business English, it can suggest a learning plan tailored to business situations. In this way, by analyzing a user's social media activity, it can suggest relevant learning resources and improve the learning experience.

[0061] The GlobalConnect AI system can adjust the learning pace based on the user's health condition. For example, if the user is tired, it can slow down the learning pace and encourage them to take a break. Conversely, if the user is healthy, it can speed up the learning pace and provide more information. Furthermore, if the user is unwell, it can temporarily halt the learning pace and wait until they recover. This allows for an effective learning environment by adjusting the learning pace according to the user's health condition.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk provides a keyboard interface for receiving text input, a microphone interface for receiving voice input, and a camera interface for receiving image input. Step 2: The evaluation unit assesses the user's current level based on the information received by the reception unit. The evaluation unit uses algorithms to assess the user's language ability and cultural understanding. For example, in the case of text input, natural language processing technology is used to assess the level of grammar and vocabulary. In the case of voice input, speech recognition technology is used to assess the accuracy and fluency of pronunciation. In the case of image input, image recognition technology is used to analyze cultural symbols and gestures and assess the level of cultural understanding. Step 3: The generation unit generates a learning plan based on the information evaluated by the evaluation unit. The generation unit proposes optimal learning goals and content based on the user's evaluation results. For example, it generates a learning plan that includes grammar reinforcement and vocabulary expansion depending on language ability. It also generates a learning plan that includes cross-cultural simulations and practical scenarios depending on cultural understanding. Step 4: The simulation unit provides cross-cultural simulations based on the learning plan generated by the generation unit. The simulation unit provides an interface for the user to experience the cross-cultural simulation and provides feedback to help the user acquire practical skills. For example, it improves the user's communication skills through a simulation of a business meeting in a virtual environment. Step 5: The translation unit performs real-time translation based on the learning plan generated by the generation unit. The translation unit provides real-time translation when the user performs cross-cultural simulations. For example, when simulating a conversation in English, it translates it into Japanese to support the user's understanding.

[0064] (Example of form 2) The GlobalConnect AI system, according to an embodiment of the present invention, is an innovative AI agent that supports intercultural understanding and language learning. This GlobalConnect AI system improves the user's language skills and cultural knowledge by accepting user input, evaluating the user's current level, generating a learning plan, and providing intercultural simulations and real-time translation. For example, if a user inputs "I want to learn English," the GlobalConnect AI system evaluates the user's current English level and proposes an optimal learning plan. The learning plan includes conversation practice utilizing real-time translation and practical scenarios through intercultural simulations. For example, by simulating a business meeting in English, the user can improve their communication skills in real business situations. The GlobalConnect AI system is also available to businesses, serving as a tool for employees of multinational companies to improve their intercultural understanding and language skills. Through the GlobalConnect AI system, companies can reduce employee training costs and strengthen their international competitiveness. Thus, the GlobalConnect AI system is an innovative AI agent that provides personalized learning experiences tailored to individual and corporate needs, supporting intercultural understanding and language learning. This allows the GlobalConnect AI system to effectively support users' intercultural understanding and language learning.

[0065] The GlobalConnect AI system according to this embodiment comprises a reception unit, an evaluation unit, a generation unit, a simulation unit, and a translation unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit provides, for example, a keyboard interface for receiving text input. The reception unit may also provide a microphone interface for receiving voice input. Furthermore, the reception unit may also provide a camera interface for receiving image input. For example, the reception unit provides a keyboard for the user to input text and receives the text entered by the user. In the case of voice input, the reception unit receives the voice spoken by the user through the microphone and converts it into text using speech recognition technology. In the case of image input, the reception unit receives an image taken by the user through the camera and analyzes it using image recognition technology. The evaluation unit evaluates the user's current level based on the information received by the reception unit. The evaluation unit uses, for example, algorithms to evaluate the user's language ability and cultural understanding. For example, the evaluation unit analyzes the text entered by the user and evaluates their language ability. The evaluation unit can also analyze the user's voice input and evaluate pronunciation and fluency. Furthermore, the evaluation unit can analyze the user's image input and evaluate their cultural understanding. For example, the evaluation unit analyzes the text entered by the user using natural language processing technology and evaluates the level of grammar and vocabulary. In the case of voice input, the evaluation unit uses speech recognition technology to evaluate the accuracy and fluency of pronunciation. In the case of image input, the evaluation unit uses image recognition technology to analyze cultural symbols and gestures and evaluate the user's cultural understanding. The generation unit generates a learning plan based on the information evaluated by the evaluation unit. For example, the generation unit proposes optimal learning goals and learning content based on the user's evaluation results. For example, the generation unit generates a learning plan that includes grammar reinforcement and vocabulary expansion depending on the user's language ability. The generation unit can also generate a learning plan that includes cross-cultural simulations and practical scenarios depending on the user's cultural understanding. For example, the generation unit generates a learning plan aimed at grammar reinforcement based on the user's evaluation results.The system can also generate learning plans aimed at expanding vocabulary. In the case of cross-cultural simulation, the generation unit generates scenarios to help users improve their communication skills in real business situations. The simulation unit provides cross-cultural simulations based on the learning plans generated by the generation unit. The simulation unit provides, for example, an interface for users to experience cross-cultural simulations. For example, the simulation unit provides a virtual environment for users to simulate business meetings. The simulation unit can also provide feedback to help users acquire practical skills through cross-cultural simulations. For example, the simulation unit provides feedback to help users simulate business meetings in a virtual environment and improve their communication skills in real business situations. The translation unit performs real-time translations based on the learning plans generated by the generation unit. The translation unit provides real-time translations when users perform cross-cultural simulations. For example, the translation unit translates into Japanese in real time when users simulate conversations in English. The translation unit can also provide real-time translations when users learn through cross-cultural simulations. For example, the translation unit translates into Japanese in real time when users simulate business meetings in English, supporting the user's understanding. As a result, the GlobalConnect AI system according to this embodiment can effectively support users' cross-cultural understanding and language learning.

[0066] The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception unit provides a keyboard interface for receiving text input. Specifically, it receives text data entered by the user using the keyboard in real time and transmits it to the system. The reception unit can also provide a microphone interface for receiving voice input. In the case of voice input, it receives the voice spoken by the user through the microphone and converts it to text using speech recognition technology. Speech recognition technology includes a process of extracting features from the voice and converting the voice to text using a language model. Furthermore, the reception unit can also provide a camera interface for receiving image input. In the case of image input, it receives images taken by the user through the camera and analyzes them using image recognition technology. Image recognition technology includes a process of extracting features from the image and performing object recognition and scene analysis. For example, the reception unit provides a keyboard for the user to enter text and receives the text entered by the user. In the case of voice input, the reception unit receives the voice spoken by the user through the microphone and converts it to text using speech recognition technology. In the case of image input, the reception unit receives images captured by the user via a camera and analyzes them using image recognition technology. This allows the reception unit to handle diverse user input formats and flexibly accept information. Furthermore, the reception unit can centrally manage user input data and process it efficiently in cooperation with other departments. For example, the reception unit can send the received data to a cloud server, making it accessible to the evaluation and generation units. The reception unit can also adjust the frequency and accuracy of data reception, enabling flexible responses to specific situations and conditions. As a result, the reception unit can receive data efficiently and effectively, improving the overall system performance.

[0067] The evaluation unit assesses the user's current level based on the information received by the reception unit. The evaluation unit uses algorithms to evaluate, for example, the user's language ability and cultural understanding. Specifically, it analyzes the text entered by the user using natural language processing technology to evaluate grammar and vocabulary levels. Natural language processing technology includes morphological analysis, syntactic analysis, and semantic analysis, which are combined to evaluate the user's language ability in detail. The evaluation unit can also analyze the user's voice input to evaluate pronunciation and fluency. Evaluating voice input involves converting speech to text using speech recognition technology and then analyzing that text. Furthermore, the evaluation unit can analyze the user's image input to evaluate cultural understanding. Evaluating image input involves extracting image features using image recognition technology and analyzing cultural symbols and gestures. For example, the evaluation unit analyzes the text entered by the user using natural language processing technology to evaluate grammar and vocabulary levels. In the case of voice input, the evaluation unit uses speech recognition technology to evaluate the accuracy and fluency of pronunciation. In the case of image input, the evaluation unit uses image recognition technology to analyze cultural symbols and gestures and assess the user's level of cultural understanding. This allows the evaluation unit to analyze the user's diverse input data in detail and accurately assess the user's current level. Furthermore, the evaluation unit can utilize historical data and statistical information to perform long-term evaluations and trend analyses. For example, it can track the progress of a specific user based on past evaluation data and develop a future learning plan. In addition, the evaluation unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue early warnings. This allows the evaluation unit to handle not only real-time evaluations but also long-term evaluations and anomaly detection, improving the overall reliability and security of the system.

[0068] The generation unit generates a learning plan based on the information evaluated by the evaluation unit. For example, the generation unit proposes optimal learning goals and content based on the user's evaluation results. Specifically, it generates a learning plan that includes grammar reinforcement and vocabulary expansion, depending on the user's language ability. The generation unit analyzes the user's evaluation results using natural language processing technology to identify the user's weaknesses and areas that need strengthening. For example, a learning plan aimed at grammar reinforcement would propose content that focuses on grammar items the user finds difficult. A learning plan aimed at vocabulary expansion would propose content that focuses on vocabulary frequently used by the user in daily life and business situations. Furthermore, the generation unit can also generate learning plans that include cross-cultural simulations and practical scenarios, depending on the user's level of cultural understanding. For example, in the case of cross-cultural simulations, the generation unit generates scenarios to help the user improve their communication skills in actual business situations. The scenarios include cross-cultural business manners and communication points, and are designed to allow the user to acquire practical skills. In addition, the generation unit can monitor the user's progress in real time and modify the learning plan as needed. For example, if a user achieves a specific learning goal, the generation unit sets a new learning goal and updates the learning plan. Furthermore, if a user is experiencing difficulties in their learning, the generation unit adjusts the learning content to support them in learning effectively. This allows the generation unit to generate an optimal learning plan based on the user's evaluation results, maximizing the user's learning effectiveness.

[0069] The simulation unit provides cross-cultural simulations based on learning plans generated by the generation unit. For example, the simulation unit provides an interface for users to experience cross-cultural simulations. Specifically, it provides a virtual environment for users to simulate business meetings. This virtual environment includes 3D models and avatars that recreate business scenes, allowing users to simulate as if it were a real business meeting. The simulation unit can also provide feedback to help users acquire practical skills through cross-cultural simulations. For example, the simulation unit provides feedback to help users improve their communication skills in real business situations after simulating business meetings in the virtual environment. This feedback includes the user's statements, attitude, and gestures, specifically pointing out areas for improvement. Furthermore, the simulation unit can monitor the user's progress in real time and modify the simulation content as needed. For example, if a user completes a specific simulation scenario, the simulation unit provides a new scenario to support continuous learning. Also, if a user finds the simulation difficult, the simulation unit adjusts the difficulty level of the scenario to support effective learning. In this way, the simulation unit enables users to acquire practical skills and deepen their understanding of different cultures through cross-cultural simulations.

[0070] The translation unit performs real-time translation based on the learning plan generated by the generation unit. For example, the translation unit provides real-time translation when a user is performing cross-cultural simulations. Specifically, when a user simulates a conversation in English, it translates it into Japanese in real time. The translation unit uses speech recognition technology to convert the user's speech into text and inputs that text into the translation engine. The translation engine uses natural language processing technology to translate the text and provides it to the user. For example, when a user simulates a business meeting in English, the translation unit translates the user's speech into Japanese in real time to support the user in understanding it. The translation unit can also provide real-time translation when a user is learning through cross-cultural simulations. For example, when a user is performing a cross-cultural simulation, the translation unit translates the user's speech in real time to support the user in effectively learning cross-cultural communication. Furthermore, the translation unit can monitor the user's progress in real time and modify the translation content as needed. For example, when a user clears a particular simulation scenario, the translation unit provides translations corresponding to new scenarios to support the user in continuing to learn. Furthermore, if a user encounters difficulties with translation, the translation team will adjust the translation to support the user in learning effectively. This allows the translation team to enable users to learn effectively through cross-cultural simulations and deepen their understanding of different cultures.

[0071] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is stressed, the reception desk can start with simple questions and gradually request more detailed information. For example, if the user is relaxed, the reception desk can provide an interface that requests detailed input. Also, if the user is in a hurry, the reception desk can prioritize requesting the most important information. In this way, by adjusting the priority of input content according to the user's emotions, the system can provide the user with the best possible input experience. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0072] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest information that the user will use at a specific time of day based on the user's past input history. In this way, by analyzing the user's past input history, the optimal input method can be suggested and input efficiency can be improved. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0073] The reception desk can provide additional information based on the user's areas of interest, depending on the input. For example, if the user is interested in language learning, the reception desk can suggest relevant learning resources. For example, if the user is interested in intercultural understanding, the reception desk can provide relevant simulation scenarios. Also, if the user is interested in business English, the reception desk can suggest a learning plan tailored to business situations. In this way, the user's learning experience can be improved by providing additional information based on the user's areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's areas of interest data into a generating AI and have the generating AI perform the task of providing additional information.

[0074] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. For example, if the user is having fun, the reception unit can provide an interface with bright colors to make the input process more enjoyable. Also, if the user is tired, the reception unit can provide a simple and highly visible interface to facilitate the input process. In this way, by adjusting the design of the input interface according to the user's emotions, a comfortable input environment can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0075] The reception desk can provide region-specific input options, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk can provide input options related to the culture and language of that region. For example, if the user is traveling, the reception desk can provide options for inputting information about their travel destination. Furthermore, if the user moves to a different region, the reception desk can automatically switch to input options appropriate for that region. This improves user convenience by providing region-specific input options, taking into account the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing region-specific input options.

[0076] The reception desk can analyze a user's social media activity and suggest relevant input content. For example, if a user posts about language learning on social media, the reception desk can suggest relevant input content. For example, if a user posts about intercultural understanding, the reception desk can suggest relevant simulation scenarios. Furthermore, if a user posts about business English, the reception desk can suggest input content tailored to business situations. In this way, by analyzing a user's social media activity, relevant input content can be suggested, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input content.

[0077] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is relaxed, the evaluation unit can apply detailed evaluation criteria. For example, if the user is tense, the evaluation unit can apply simplified evaluation criteria. Also, if the user is in a hurry, the evaluation unit can apply criteria for a quick evaluation. In this way, by adjusting the evaluation criteria according to the user's emotions, the evaluation unit can provide the optimal evaluation for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0078] The evaluation unit can improve the accuracy of its evaluations by referring to the user's past learning history. For example, the evaluation unit can assess the user's current level based on what the user has learned in the past. For example, the evaluation unit can assess the user's strengths and weaknesses in a specific area from the user's past learning history. The evaluation unit can also analyze the user's past learning history to improve the accuracy of its evaluations. In this way, the accuracy of the evaluations can be improved by referring to the user's past learning history. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the user's past learning history data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluations.

[0079] The evaluation unit can customize the evaluation based on the user's current living situation and learning environment. For example, if the user is busy, the evaluation unit can provide a method for conducting a quick evaluation. For example, if the user is relaxed, the evaluation unit can provide a method for conducting a detailed evaluation. The evaluation unit can also provide the optimal evaluation method according to the user's learning environment. In this way, by customizing the evaluation based on the user's current living situation and learning environment, the evaluation can be provided to the user at its best. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's living situation data into a generating AI and have the generating AI perform the evaluation customization.

[0080] The evaluation unit can estimate the user's emotions and adjust the feedback method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide feedback in gentle words. For example, if the user is relaxed, the evaluation unit can provide detailed feedback. Also, if the user is in a hurry, the evaluation unit can provide concise feedback. In this way, by adjusting the feedback method of the evaluation results according to the user's emotions, the optimal feedback can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The evaluation unit can apply region-specific evaluation criteria, taking into account the user's geographical location. For example, if the user is in a specific region, the evaluation unit can apply evaluation criteria related to the culture and language of that region. For example, if the user is traveling, the evaluation unit can adjust the evaluation criteria based on information about the travel destination. Furthermore, if the user moves to a different region, the evaluation unit can automatically switch to evaluation criteria appropriate for that region. This allows for the application of region-specific evaluation criteria by considering the user's geographical location, thereby improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the user's geographical location data into a generating AI and have the generating AI perform the application of region-specific evaluation criteria.

[0082] The evaluation unit can analyze a user's social media activity and provide relevant evaluation information. For example, if a user posts about language learning on social media, the evaluation unit can provide relevant evaluation information. For example, if a user posts about intercultural understanding, the evaluation unit can provide relevant evaluation information. Furthermore, if a user posts about business English, the evaluation unit can provide evaluation information specific to business settings. In this way, by analyzing a user's social media activity, relevant evaluation information can be provided, improving the accuracy of the evaluation. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input user social media activity data into a generating AI and have the generating AI perform the task of providing relevant evaluation information.

[0083] The generation unit can estimate the user's emotions and adjust the content of the learning plan based on the estimated emotions. For example, if the user is relaxed, the generation unit can provide a detailed learning plan. For example, if the user is stressed, the generation unit can provide a simplified learning plan. Also, if the user is in a hurry, the generation unit can provide a plan to quickly advance the learning process. In this way, by adjusting the content of the learning plan according to the user's emotions, the optimal learning plan can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0084] The generation unit can improve the accuracy of the learning plan by referring to the user's past learning history. For example, the generation unit can adjust the current learning plan based on what the user has learned in the past. For example, the generation unit can create a learning plan by considering the user's strengths and weaknesses in a specific field from the user's past learning history. The generation unit can also analyze the user's past learning history and provide an optimal learning plan. In this way, the accuracy of the learning plan can be improved by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI perform the task of improving the accuracy of the learning plan.

[0085] The generation unit can customize learning plans based on the user's current lifestyle and learning environment. For example, if the user is busy, the generation unit can provide a plan for learning in a short amount of time. For example, if the user is relaxed, the generation unit can provide a detailed learning plan. The generation unit can also provide an optimal learning plan according to the user's learning environment. In this way, by customizing the learning plan based on the user's current lifestyle and learning environment, it is possible to provide the optimal learning plan for the user. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's lifestyle data into a generation AI and have the generation AI perform the customization of the learning plan.

[0086] The generation unit can estimate the user's emotions and prioritize learning plans based on those emotions. For example, if the user is relaxed, the generation unit may prioritize a detailed learning plan. If the user is stressed, the generation unit may prioritize a simplified learning plan. If the user is in a hurry, the generation unit may prioritize a plan for rapid learning. This allows the system to provide the user with the optimal learning plan by prioritizing learning plans according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0087] The generation unit can provide region-specific learning plans by taking into account the user's geographical location information. For example, if the user is in a specific region, the generation unit can provide a learning plan related to the culture and language of that region. For example, if the user is traveling, the generation unit can adjust the learning plan based on information about the travel destination. Furthermore, if the user moves to a different region, the generation unit can automatically switch to a learning plan suitable for that region. In this way, by taking into account the user's geographical location information, region-specific learning plans can be provided, improving user convenience. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location information data into a generation AI and have the generation AI perform the task of providing region-specific learning plans.

[0088] The generation unit can analyze a user's social media activity and propose relevant learning plans. For example, if a user posts about language learning on social media, the generation unit can propose relevant learning plans. For example, if a user posts about intercultural understanding, the generation unit can propose relevant simulation scenarios. Furthermore, if a user posts about business English, the generation unit can propose learning plans tailored to business situations. In this way, by analyzing a user's social media activity, relevant learning plans can be proposed, improving the user's learning experience. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI propose relevant learning plans.

[0089] The simulation unit can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is relaxed, the simulation unit can provide a detailed simulation scenario. For example, if the user is tense, the simulation unit can provide a simplified simulation scenario. Also, if the user is in a hurry, the simulation unit can provide a scenario to quickly advance the simulation. In this way, by adjusting the simulation scenario according to the user's emotions, the optimal simulation experience can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0090] The simulation unit can improve the accuracy of simulations by referring to the user's past simulation history. For example, the simulation unit can adjust the current simulation based on the results of simulations previously performed by the user. For example, the simulation unit can create a simulation considering the strengths and weaknesses in a particular scenario based on the user's past simulation history. The simulation unit can also analyze the user's past simulation history and provide the optimal simulation scenario. This allows for improved simulation accuracy by referring to the user's past simulation history. Some or all of the above processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's past simulation history data into a generating AI and have the generating AI perform the simulation accuracy improvement.

[0091] The simulation unit can customize the simulation based on the user's current living situation and learning environment. For example, if the user is busy, the simulation unit can provide a scenario to complete the simulation in a short amount of time. For example, if the user is relaxed, the simulation unit can provide a detailed simulation scenario. The simulation unit can also provide an optimal simulation scenario according to the user's learning environment. In this way, by customizing the simulation based on the user's current living situation and learning environment, the system can provide the user with the best possible simulation experience. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's living situation data into a generating AI and have the generating AI perform the simulation customization.

[0092] The simulation unit can estimate the user's emotions and adjust the simulation's feedback method based on the estimated user emotions. For example, if the user is nervous, the simulation unit can provide feedback in gentle words. For example, if the user is relaxed, the simulation unit can provide detailed feedback. Also, if the user is in a hurry, the simulation unit can provide concise feedback. In this way, by adjusting the simulation's feedback method according to the user's emotions, the system can provide the user with the most optimal feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0093] The simulation unit can provide region-specific simulation scenarios by taking into account the user's geographical location information. For example, if the user is in a specific region, the simulation unit can provide simulation scenarios related to the culture and language of that region. For example, if the user is traveling, the simulation unit can adjust the simulation scenario based on information about the travel destination. Furthermore, if the user moves to a different region, the simulation unit can automatically switch to a simulation scenario appropriate for that region. In this way, by taking into account the user's geographical location information, region-specific simulation scenarios can be provided, improving user convenience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of providing region-specific simulation scenarios.

[0094] The simulation unit can analyze a user's social media activity and propose relevant simulation scenarios. For example, if a user posts about language learning on social media, the simulation unit can propose relevant simulation scenarios. For example, if a user posts about intercultural understanding, the simulation unit can propose relevant simulation scenarios. Furthermore, if a user posts about business English, the simulation unit can propose simulation scenarios specifically tailored to business situations. In this way, by analyzing a user's social media activity, relevant simulation scenarios can be proposed, thereby improving the user's learning experience. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of relevant simulation scenarios.

[0095] The translation unit can estimate the user's emotions and adjust the translation's expression based on the estimated emotions. For example, if the user is nervous, the translation unit can provide a gentle translation. For example, if the user is relaxed, the translation unit can provide a detailed translation. Also, if the user is in a hurry, the translation unit can provide a concise translation. In this way, by adjusting the translation's expression according to the user's emotions, the optimal translation can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The translation unit can improve the accuracy of translations by referring to the user's past translation history. For example, the translation unit can adjust the current translation based on the content the user has translated in the past. For example, the translation unit can create a translation by considering the user's strengths and weaknesses in a particular field from the user's past translation history. The translation unit can also analyze the user's past translation history and provide the optimal translation. In this way, the accuracy of translations can be improved by referring to the user's past translation history. Some or all of the above processes in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's past translation history data into a generating AI and have the generating AI perform the translation accuracy improvement.

[0097] The translation unit can customize translations based on the user's current living situation and learning environment. For example, if the user is busy, the translation unit can provide a method to deliver a translation in a short time. For example, if the user is relaxed, the translation unit can provide a detailed translation. The translation unit can also provide the optimal translation method according to the user's learning environment. In this way, by customizing translations based on the user's current living situation and learning environment, the translation unit can provide the best possible translation for the user. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's living situation data into a generating AI and have the generating AI perform the translation customization.

[0098] The translation unit can estimate the user's emotions and determine translation priorities based on the estimated emotions. For example, if the user is relaxed, the translation unit may prioritize detailed translations. For example, if the user is stressed, the translation unit may prioritize simplified translations. Also, if the user is in a hurry, the translation unit may prioritize methods that allow for rapid translation. This allows the system to provide the user with the best possible translation by prioritizing translations according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not using AI. For example, the translation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0099] The translation unit can provide region-specific translations by taking into account the user's geographical location. For example, if the user is in a specific region, the translation unit can provide translations related to the culture and language of that region. For example, if the user is traveling, the translation unit can adjust the translation based on information about the travel destination. Furthermore, if the user moves to a different region, the translation unit can automatically switch to translations appropriate for that region. This improves user convenience by providing region-specific translations that take into account the user's geographical location. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's geographical location data into a generating AI and have the generating AI perform the task of providing region-specific translations.

[0100] The translation unit can analyze a user's social media activity and suggest relevant translations. For example, if a user posts about language learning on social media, the translation unit can suggest relevant translations. For example, if a user posts about intercultural understanding, the translation unit can suggest relevant translations. Furthermore, if a user posts about business English, the translation unit can suggest translations tailored to business situations. This allows the translation unit to analyze a user's social media activity, suggest relevant translations, and improve user convenience. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input user social media activity data into a generating AI and have the generating AI suggest relevant translations.

[0101] The translation unit can adjust the expression of the translation to take into account the user's health condition. For example, if the user is tired, the translation unit can provide a concise and easy-to-understand translation. For example, if the user is healthy, the translation unit can provide a detailed translation. Furthermore, if the user is unwell, the translation unit can provide a translation using gentle language. In this way, by adjusting the expression of the translation according to the user's health condition, the translation can be provided to the user in the most optimal way. Some or all of the above processing in the translation unit may be performed using AI, for example, or not using AI. For example, the translation unit can input the user's health condition data into a generating AI and have the generating AI perform the adjustment of the expression of the translation.

[0102] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0103] The GlobalConnect AI system can further analyze a user's learning style and suggest the most suitable learning method. For example, users who prefer visual learning can be provided with a learning plan that makes extensive use of visual aids and infographics. Users who prefer auditory learning can be offered a learning plan that utilizes audio materials and podcasts. Furthermore, users who prefer practical learning can be provided with a learning plan that includes interactive simulations and role-playing. By suggesting the most suitable learning method according to the user's learning style, the system can maximize learning effectiveness.

[0104] The GlobalConnect AI system can estimate a user's emotions and adjust the learning pace based on those emotions. For example, if a user is stressed, it can slow down the learning pace and provide relaxing content. Conversely, if a user is focused, it can speed up the learning pace and provide more information. Furthermore, if a user is tired, it can temporarily pause the learning process and encourage a break. By adjusting the learning pace according to the user's emotions, it can provide an effective learning environment.

[0105] The GlobalConnect AI system can analyze a user's past learning data and visualize their learning progress. For example, it can display what the user has learned in the past using graphs and charts, allowing them to see their progress at a glance. It can also list the goals the user has achieved and the skills they have acquired, allowing them to feel a sense of accomplishment from their learning. Furthermore, it can identify areas where the user struggles and suggest a plan for focused learning. By visualizing the user's learning progress, it can help maintain motivation and support effective learning.

[0106] The GlobalConnect AI system can estimate a user's emotions and adjust the difficulty level of learning content based on those emotions. For example, if a user is relaxed, it can provide more challenging content to offer a more engaging learning experience. Conversely, if a user is stressed, it can provide easier content to reduce the learning burden. Furthermore, if a user is enjoying themselves, it can provide content with game elements to make learning more fun. By adjusting the difficulty level of learning content according to the user's emotions, it can provide a more effective learning experience.

[0107] The GlobalConnect AI system can provide information on region-specific cultures and customs, taking into account the user's geographical location. For example, if a user is in a specific region, it can provide information on the local culture and customs to deepen their understanding of different cultures. If a user is traveling, it can provide information on the culture and customs of their destination to facilitate communication. Furthermore, if a user moves to a different region, the system can automatically switch to information on cultures and customs appropriate for that region. This allows the system to support cross-cultural understanding by providing information on region-specific cultures and customs, taking the user's geographical location into consideration.

[0108] The GlobalConnect AI system can estimate a user's emotions and adjust the learning feedback method based on those emotions. For example, if a user is nervous, it can provide feedback in gentle language to reassure them. If a user is relaxed, it can provide detailed feedback and specifically point out areas for improvement in their learning. Furthermore, if a user is in a hurry, it can provide concise feedback to help them quickly move on to the next step. In this way, by adjusting the learning feedback method according to the user's emotions, it can provide effective feedback.

[0109] The GlobalConnect AI system can analyze a user's social media activity and suggest relevant learning resources. For example, if a user posts about language learning on social media, it can suggest relevant online courses and materials. If a user posts about cross-cultural understanding, it can suggest relevant simulation scenarios and articles. Furthermore, if a user posts about business English, it can suggest a learning plan tailored to business situations. In this way, by analyzing a user's social media activity, it can suggest relevant learning resources and improve the learning experience.

[0110] The GlobalConnect AI system can estimate a user's emotions and provide support to boost their learning motivation based on those emotions. For example, if a user is discouraged, it can provide encouraging messages to help them regain their motivation. If a user is focused, it can set goals that give them a sense of accomplishment and visualize their learning progress. Furthermore, if a user is enjoying themselves, it can provide content with game elements to make learning more fun. In this way, by providing support to boost learning motivation according to the user's emotions, it can support effective learning.

[0111] The GlobalConnect AI system can adjust the learning pace based on the user's health condition. For example, if the user is tired, it can slow down the learning pace and encourage them to take a break. Conversely, if the user is healthy, it can speed up the learning pace and provide more information. Furthermore, if the user is unwell, it can temporarily halt the learning pace and wait until they recover. This allows for an effective learning environment by adjusting the learning pace according to the user's health condition.

[0112] The GlobalConnect AI system can estimate a user's emotions and set learning goals based on those emotions. For example, if the user is relaxed, it can set long-term goals and help them learn systematically. If the user is stressed, it can set short-term goals to help them feel a sense of accomplishment. Furthermore, if the user is enjoying themselves, it can set challenging goals to increase their motivation to learn. In this way, by setting learning goals according to the user's emotions, it can support effective learning.

[0113] The following briefly describes the processing flow for example form 2.

[0114] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk provides a keyboard interface for receiving text input, a microphone interface for receiving voice input, and a camera interface for receiving image input. Step 2: The evaluation unit assesses the user's current level based on the information received by the reception unit. The evaluation unit uses algorithms to assess the user's language ability and cultural understanding. For example, in the case of text input, natural language processing technology is used to assess the level of grammar and vocabulary. In the case of voice input, speech recognition technology is used to assess the accuracy and fluency of pronunciation. In the case of image input, image recognition technology is used to analyze cultural symbols and gestures and assess the level of cultural understanding. Step 3: The generation unit generates a learning plan based on the information evaluated by the evaluation unit. The generation unit proposes optimal learning goals and content based on the user's evaluation results. For example, it generates a learning plan that includes grammar reinforcement and vocabulary expansion depending on language ability. It also generates a learning plan that includes cross-cultural simulations and practical scenarios depending on cultural understanding. Step 4: The simulation unit provides cross-cultural simulations based on the learning plan generated by the generation unit. The simulation unit provides an interface for the user to experience the cross-cultural simulation and provides feedback to help the user acquire practical skills. For example, it improves the user's communication skills through a simulation of a business meeting in a virtual environment. Step 5: The translation unit performs real-time translation based on the learning plan generated by the generation unit. The translation unit provides real-time translation when the user performs cross-cultural simulations. For example, when simulating a conversation in English, it translates it into Japanese to support the user's understanding.

[0115] 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.

[0116] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0117] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements described above, including the reception unit, evaluation unit, generation unit, simulation unit, and translation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit receives user input using the keyboard interface and microphone interface of the smart device 14. The evaluation unit evaluates the user's current level using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12. The simulation unit provides cross-cultural simulation using the control unit 46A of the smart device 14. The translation unit performs real-time translation using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0120] 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.

[0121] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0122] 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.

[0123] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0124] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0125] 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.

[0126] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0127] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0128] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0129] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0131] 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.

[0132] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0133] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements described above, including the reception unit, evaluation unit, generation unit, simulation unit, and translation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the smart glasses 214. The evaluation unit evaluates the user's current level using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12. The simulation unit provides cross-cultural simulation using the control unit 46A of the smart glasses 214. The translation unit performs real-time translation using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0136] 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.

[0137] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0138] 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.

[0139] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0140] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0141] 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.

[0142] 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.

[0143] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0144] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0145] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0147] 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.

[0148] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0149] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] Each of the multiple elements described above, including the reception unit, evaluation unit, generation unit, simulation unit, and translation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the headset terminal 314. The evaluation unit evaluates the user's current level using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12. The simulation unit provides cross-cultural simulation using the control unit 46A of the headset terminal 314. The translation unit performs real-time translation using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0152] 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.

[0153] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0154] 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.

[0155] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0156] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0157] 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.

[0158] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0159] 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.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] 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.

[0165] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the reception unit, evaluation unit, generation unit, simulation unit, and translation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the reception unit receives user input using the microphone 238 and camera 42 of the robot 414. The evaluation unit evaluates the user's current level using the specific processing unit 290 of the data processing unit 12. The generation unit generates a learning plan using the specific processing unit 290 of the data processing unit 12. The simulation unit provides cross-cultural simulation using the control unit 46A of the robot 414. The translation unit performs real-time translation using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] 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.

[0169] Figure 9 shows the 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.

[0170] 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.

[0171] 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.

[0172] 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, and motorcycles, 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 based, for example, 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.

[0173] 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."

[0174] 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.

[0175] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0184] 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 other things 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.

[0185] 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.

[0186] (Note 1) A reception area that receives user input, An evaluation unit that evaluates the user's current level based on the information received by the reception unit, A generation unit that generates a learning plan based on the information evaluated by the evaluation unit, A simulation unit provides cross-cultural simulations based on the learning plan generated by the generation unit, The system comprises a translation unit that performs real-time translation based on the learning plan generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is Based on the input, additional information will be provided that is tailored to the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Provide region-specific input options, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The evaluation unit, By referring to the user's past learning history, the accuracy of evaluations can be improved. The system described in Appendix 1, characterized by the features described herein. (Note 10) The evaluation unit, Customize the assessment based on the user's current living situation and learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The evaluation unit, It estimates the user's emotions and adjusts the feedback method for evaluation results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The evaluation unit, Apply region-specific evaluation criteria, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The evaluation unit, Analyze users' social media activity and provide relevant evaluation information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is By referencing the user's past learning history, we can improve the accuracy of learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Customize learning plans based on the user's current living situation and learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and prioritizes the learning plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is We provide region-specific learning plans that take into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Analyze users' social media activity and suggest relevant learning plans. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, Referencing the user's past simulation history improves the accuracy of the simulation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, Customize the simulation based on the user's current living situation and learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation's feedback method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned simulation unit, It provides region-specific simulation scenarios, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, We analyze users' social media activity and propose relevant simulation scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned translation department, It estimates the user's emotions and adjusts the translation's expression based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned translation department, Improve translation accuracy by referring to the user's past translation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, Customize translations based on the user's current living situation and learning environment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned translation department, It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned translation department, Providing region-specific translations that take the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned translation department, Analyze users' social media activity and suggest relevant translations. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned translation department, The translation style is adjusted to take the user's health condition into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0187] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that receives user input, An evaluation unit that evaluates the user's current level based on the information received by the reception unit, A generation unit that generates a learning plan based on the information evaluated by the evaluation unit, A simulation unit provides cross-cultural simulations based on the learning plan generated by the generation unit, The system comprises a translation unit that performs real-time translation based on the learning plan generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the priority of input content based on the estimated user emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is Based on the input, additional information will be provided that is tailored to the user's areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is Provide region-specific input options, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is Analyzes users' social media activity and suggests relevant inputs. The system according to feature 1.

8. The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system according to feature 1.

9. The evaluation unit, By referring to the user's past learning history, the accuracy of evaluations can be improved. The system according to feature 1.

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