system

Abstract

The system according to this embodiment aims to support learning exchanges that promote intercultural understanding and broaden international perspectives. [Solution] The system according to the embodiment comprises a reception unit, a selection unit, a pairing unit, a support unit, and a translation unit. The reception unit receives input from the user regarding their interests and learning objectives. The selection unit analyzes the information received by the reception unit and selects the most suitable learning partner. The pairing unit pairs the user with the learning partner selected by the selection unit. The support unit assists the learning interaction with the learning partner paired by the pairing unit. The translation unit provides real-time translation in the learning interaction supported by the support 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 prior art, learning exchanges for cross-cultural understanding and expanding international perspectives have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to support learning exchanges for cross-cultural understanding and expanding international perspectives.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a selection unit, a pairing unit, a support unit, and a translation unit. The reception unit receives input from the user regarding their interests and learning objectives. The selection unit analyzes the information received by the reception unit and selects the most suitable learning partner. The pairing unit pairs the user with the learning partner selected by the selection unit. The support unit assists with learning interaction with the learning partner paired by the pairing unit. The translation unit provides real-time translation during the learning interaction supported by the support unit. [Effects of the Invention]

[0007] The system according to this embodiment can support learning exchanges aimed at fostering intercultural understanding and broadening international perspectives. [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 manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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] [[ID=十七]] 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 receiving 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 receiving 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 system according to an embodiment of the present invention is a system that provides a platform for students to learn and interact with peers around the world. In this GlobalConnect system, the user inputs their interests and learning objectives, and a generating AI analyzes this information to select the most suitable learning partner from students overseas. The selected partner is paired with the user, and learning interaction begins. At this time, real-time translation is provided using natural language processing, eliminating language barriers. In addition, collaborative project support is provided, such as group problem-solving and research activities. Furthermore, as an intercultural understanding program, international understanding is deepened through cultural introductions and discussions. Finally, to provide a safe communication environment, personal information is protected and inappropriate content is filtered. For example, the user inputs their interests and learning objectives. At this time, the user inputs their interests and learning objectives in detail. For example, they input information such as "I am interested in science" or "I want to learn English." This information is input to the generating AI. Next, the generating AI analyzes the input information and selects the most suitable learning partner. The generating AI selects the most suitable partner from students overseas based on the user's interests and learning objectives. For example, a user who is interested in science will be selected as a student overseas who is also interested in science. Users are paired with selected partners, and learning exchanges begin. Real-time translation is provided using natural language processing. For example, if a Japanese-speaking user is paired with an English-speaking partner, real-time translation between Japanese and English is performed, eliminating language barriers. Furthermore, collaborative project support is provided, assisting with group problem-solving and research activities. For instance, when working on a scientific project collaboratively, the generative AI supports the project's progress. Intercultural understanding programs include cultural introductions and discussions. For example, programs introducing Japanese culture and forums for discussing different cultures are provided. Finally, to ensure a safe communication environment, personal information is protected and inappropriate content is filtered. For example, if chat content is inappropriate, the generative AI automatically filters it, maintaining a safe environment.This system allows students to be exposed to diverse values ​​and simultaneously develop autonomy and international awareness. It also serves as a valuable tool for educational institutions seeking to promote global education. Through this, the GlobalConnect system enables students to engage in learning exchanges with peers around the world, fostering intercultural understanding and communication skills.

[0029] The GlobalConnect system according to this embodiment comprises a reception unit, a selection unit, a pairing unit, a support unit, and a translation unit. The reception unit receives input from the user regarding their interests and learning objectives. These include, but are not limited to, specific academic fields, hobbies, and skill improvement. The reception unit provides, for example, an interface for the user to input their interests and learning objectives. For example, the reception unit allows the user to input their interests and learning objectives in text format. The reception unit also allows the user to input their interests and learning objectives using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice input into text data. The selection unit uses a generative AI to analyze the information entered by the reception unit and select the most suitable learning partner. For example, the selection unit selects the most suitable partner from overseas students based on the user's interests and learning objectives. For example, the selection unit uses a generative AI to analyze the user's interests and learning objectives and selects overseas students with common interests. The selection unit can also use a generative AI to select the most suitable learning partner considering the user's skill level and learning style. The pairing unit pairs users with learning partners selected by the selection unit. The pairing unit uses algorithms to match users with selected learning partners. For example, the pairing unit uses a matching algorithm to pair users with learning partners. The pairing unit can also adjust the timing of pairing. For example, the pairing unit considers the user's schedule and the learning partner's schedule to perform pairing at the optimal time. The support unit supports learning interaction with learning partners paired by the pairing unit. For example, the support unit supports group problem-solving and research activities. For example, the support unit supports group discussions and collaborative projects. The support unit can also provide programs to deepen intercultural understanding through cultural introductions and discussions. For example, the support unit provides a venue for cultural presentations and intercultural discussions. The support unit can also protect personal information and filter inappropriate content. For example, the support unit monitors chat content and filters out inappropriate content.The translation unit provides real-time translation in learning exchanges supported by the support unit. The translation unit uses, for example, natural language processing technology to eliminate language barriers between users and learning partners. For example, the translation unit performs real-time translation between Japanese and English. The translation unit can also support multiple language pairs. For example, the translation unit supports not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. As a result, the GlobalConnect system according to this embodiment can select the optimal learning partner based on the user's interests and learning objectives, and support learning exchanges to cultivate intercultural understanding and communication skills.

[0030] The reception desk allows users to input their interests and learning objectives. These interests and objectives may include, but are not limited to, specific academic fields, hobbies, or skill development. The reception desk provides an interface for users to input their interests and learning objectives. For example, the reception desk allows users to input their interests and learning objectives in text format. Alternatively, the reception desk allows users to input their interests and learning objectives using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice input into text data. Specifically, the reception desk provides an interface that users can access through a web browser or mobile application. Users can input their interests and learning objectives in a text box or by pressing a microphone button for voice input. In the case of voice input, speech recognition technology analyzes the user's speech and converts it into text data. This speech recognition technology includes noise cancellation to remove ambient noise and achieve high-precision speech recognition. Furthermore, the reception desk saves the information entered by the user for later reference. For example, when a user logs in again, it displays previously entered interests and learning objectives, allowing them to modify or add to them as needed. This allows the reception department to accurately understand the user's interests and learning objectives, and to smoothly provide that information to the next step, the selection department.

[0031] The selection unit uses generative AI to analyze the information entered by the reception unit and select the most suitable learning partner. For example, the selection unit selects the most suitable partner from overseas students based on the user's interests and learning objectives. For example, the selection unit uses generative AI to analyze the user's interests and learning objectives and select overseas students with similar interests. The selection unit can also use generative AI to select the most suitable learning partner considering the user's skill level and learning style. Specifically, the selection unit uses generative AI to analyze the user's input information using natural language processing technology to understand the user's interests and learning objectives. The generative AI classifies the user's input information using topic models and clustering algorithms to identify overseas students with similar interests. Furthermore, the generative AI selects the most suitable learning partner considering the user's skill level and learning style. For example, the generative AI analyzes the user's past learning history and evaluation data to evaluate the user's skill level. The generative AI also collects information about the user's learning style and selects the most suitable learning partner based on learning styles such as visual learners, auditory learners, and experiential learners. This allows the selection unit to choose the learning partner best suited to the user's interests and learning objectives, thereby improving the user's learning experience.

[0032] The pairing unit pairs the user with a learning partner selected by the selection unit. The pairing unit uses an algorithm to match the selected learning partner with the user. For example, the pairing unit uses a matching algorithm to pair the user with a learning partner. The pairing unit can also adjust the timing of pairing. For example, the pairing unit considers the user's schedule and the learning partner's schedule to perform pairing at the optimal time. Specifically, the pairing unit collects schedule information from the user and the learning partner and calculates the optimal pairing timing. The pairing unit considers the time zones of the user and the learning partner and identifies time slots when both are available. Furthermore, the pairing unit adjusts the means of communication between the user and the learning partner. For example, the pairing unit provides communication methods such as video calls, voice calls, and chats to enable the user and the learning partner to interact in the most optimal way. To improve the success rate of pairing, the pairing unit can also analyze past pairing data and improve the optimal pairing algorithm. This allows the pairing unit to achieve smooth interaction between the user and the learning partner and maximize learning effectiveness.

[0033] The Support Department assists learning interactions with learning partners paired by the Pairing Department. For example, the Support Department supports group problem-solving and research activities. For instance, it supports group discussions and collaborative projects. The Support Department can also provide programs to deepen intercultural understanding through cultural introductions and discussions. For example, it provides venues for cultural presentations and intercultural discussions. The Support Department can also implement measures to protect personal information and filter inappropriate content. For example, it monitors chat content and filters out inappropriate material. Specifically, the Support Department provides tasks and projects for users and learning partners to work on together and monitors their progress. The Support Department provides appropriate advice and support for problems and questions users may encounter. Furthermore, the Support Department provides programs to deepen intercultural understanding, offering opportunities for users and learning partners to understand each other's cultures and values. For example, it provides cultural introduction videos and intercultural discussion topics to help users and learning partners deepen their interactions. In addition, the Support Department implements security measures to protect users' personal information and introduces systems to filter inappropriate content and behavior. This allows the support department to facilitate safe and effective learning exchanges between users and learning partners, promoting improved intercultural understanding and communication skills.

[0034] The Translation Unit provides real-time translation in learning exchanges supported by the Support Unit. The Translation Unit uses, for example, natural language processing technology to bridge the language barrier between users and learning partners. For instance, it performs real-time translation between Japanese and English. The Translation Unit can also support multiple language pairs. For example, it supports not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. Specifically, the Translation Unit automatically detects the languages ​​used by the user and learning partner and performs real-time translation. Using natural language processing technology, the Translation Unit analyzes user utterances and text messages to provide appropriate translations. For example, it translates what a user says in Japanese into English and conveys it to the learning partner. It also translates what a learning partner says in English into Japanese and conveys it to the user. To improve translation accuracy, the Translation Unit uses advanced translation algorithms that consider context and technical terms. Furthermore, the Translation Unit collects user feedback to continuously improve translation quality. This allows the translation department to facilitate smooth communication between users and learning partners, supporting learning exchanges that transcend language barriers.

[0035] The support department can assist with group problem-solving and research activities. For example, it can support group discussions. For instance, it can provide a platform for group members to conduct online discussions. The support department can also support collaborative projects. For example, it can provide tools and resources for group members to work on a project together. The support department may also have functions to manage project progress. For example, it can track project progress and notify group members of the progress. This can improve collaborative learning abilities by supporting group problem-solving and research activities.

[0036] The support department can provide programs that deepen intercultural understanding through cultural introductions and discussions. For example, the support department can provide cultural presentations. For example, the support department can provide a platform for users to give presentations introducing their own country's culture. The support department can also provide intercultural discussions. For example, the support department can provide a forum for discussing different cultures. The support department may also have functions to set discussion themes and participant roles. For example, the support department can set discussion themes and assign roles to participants. This allows for a broader international perspective by deepening intercultural understanding through cultural introductions and discussions.

[0037] The support department can implement measures to protect personal information and filter inappropriate content. For example, the support department can encrypt data. For example, the support department can encrypt and protect users' personal information. The support department can also implement access control. For example, the support department can restrict the permissions to access users' personal information. The support department can also set up a privacy policy. For example, the support department can set up a privacy policy regarding the handling of users' personal information and notify users. The support department can also perform keyword filtering. For example, the support department can filter chat content if it contains inappropriate keywords. The support department can also perform content moderation. For example, the support department can monitor chat content and automatically delete inappropriate content. The support department can also provide a reporting system. For example, the support department can provide a function for users to report inappropriate content. This allows for a secure communication environment by protecting personal information and filtering inappropriate content.

[0038] The selection system can choose the most suitable learning partner based on the user's interests and learning objectives. For example, the system can select a learning partner with common interests. For instance, if the user is interested in science, the system will select a learning partner who is also interested in science. The system can also select a learning partner considering skill level. For example, the system will select a learning partner that matches the user's skill level. The system can also select a learning partner considering learning style. For example, the system will select a learning partner that matches the user's learning style. This allows for effective learning interaction by selecting the most suitable learning partner based on the user's interests and learning objectives.

[0039] The translation unit can provide real-time translation using natural language processing. For example, it can perform morphological analysis. For instance, it can break down the input text into morphemes and analyze the meaning of each morpheme. The translation unit can also perform grammatical analysis. For example, it can analyze the grammatical structure of the input text and understand the meaning of the sentence. The translation unit can also perform semantic analysis. For example, it can analyze the meaning of the input text and generate an appropriate translation. The translation unit can also support multiple language pairs. For example, it can support not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. This allows for the elimination of language barriers and smoother communication by providing real-time translation using natural language processing.

[0040] The input system can analyze a user's past input history and suggest the most suitable input format. For example, it can automatically display suggestions based on interests and learning objectives that the user has frequently entered in the past. For instance, it can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The input system can also predict and suggest interests and learning objectives that the user will use at specific times of day based on their past input history. By analyzing the user's past input history, it can suggest the most suitable input format and improve input efficiency.

[0041] The input system can filter user input based on their current learning status and areas of interest when users enter their interests and learning objectives. For example, the input system may prioritize displaying interests and learning objectives related to the subject the user is currently studying. For example, the input system may filter relevant input based on the user's areas of interest. The input system can also suggest appropriate input based on the user's learning progress. By filtering input based on the user's current learning status and areas of interest, the system can provide users with highly relevant information.

[0042] The reception system can prioritize retrieving highly relevant input content when users input their interests and learning objectives, taking into account their geographical location. For example, if a user is in a specific region, the reception system will prioritize displaying learning content related to that region. For instance, the reception system can suggest relevant learning partners based on the user's geographical location. The reception system can also prioritize displaying region-specific learning resources, taking the user's location into consideration. This allows the system to provide region-specific information by prioritizing the retrieval of highly relevant input content, taking into account the user's geographical location.

[0043] The reception desk can analyze the user's social media activity when they input their interests and learning objectives, and retrieve relevant input content. For example, the reception desk can suggest relevant learning content based on the user's interests and concerns on social media. For example, the reception desk can obtain information from the user's social media activity that is useful for selecting a learning partner. The reception desk can also analyze the user's social media activity history and suggest the most suitable learning content. In this way, by analyzing the user's social media activity, it is possible to provide input content that is based on the user's interests and concerns.

[0044] The selection unit can choose the most suitable learning partner by considering the user's past learning history. For example, the selection unit can select a relevant learning partner based on the content the user has previously studied. For example, the selection unit can suggest the most suitable learning partner based on the user's past learning history. The selection unit can also analyze the user's learning history and select a learning partner with similar interests. By selecting the most suitable partner considering the user's past learning history, effective learning interaction can be achieved.

[0045] The selection unit can customize the selection criteria for learning partners based on the user's current learning status and areas of interest. For example, the selection unit can select relevant learning partners based on the subjects the user is currently studying. For example, the selection unit can select the most suitable learning partner based on the user's areas of interest. The selection unit can also select an appropriate learning partner according to the user's learning progress. In this way, by customizing the selection criteria based on the user's current learning status and areas of interest, the system can select the most suitable learning partner for the user.

[0046] The selection unit can select the most suitable learning partner by considering the user's geographical location. For example, if the user is in a specific region, the selection unit will select a learning partner relevant to that region. For instance, the selection unit will suggest relevant learning partners based on the user's geographical location. The selection unit can also select region-specific learning partners by considering the user's location. This allows the system to provide region-specific information by selecting the most suitable partner based on the user's geographical location.

[0047] The selection unit can analyze the user's social media activity when selecting a learning partner and select a relevant partner. For example, the selection unit can suggest a relevant learning partner based on the user's interests and preferences on social media. For example, the selection unit can obtain information useful for selecting a learning partner from the user's social media activity. The selection unit can also analyze the user's social media activity history and select the most suitable learning partner. This allows for the selection of a learning partner based on the user's interests and preferences by analyzing the user's social media activity.

[0048] The pairing unit can select the optimal pairing method during pairing by considering the user's past pairing history. For example, the pairing unit can select the optimal pairing method based on the user's past pairing history with partners. For example, the pairing unit can suggest the optimal pairing method based on the user's past pairing history. The pairing unit can also analyze the user's pairing history and select a pairing method with a partner who shares similar interests. By selecting the optimal pairing method while considering the user's past pairing history, effective pairing can be achieved.

[0049] The pairing unit can customize pairing content based on the user's current learning status and areas of interest during pairing. For example, the pairing unit can customize relevant pairing content based on the subject the user is currently studying. For example, the pairing unit can customize optimal pairing content based on the user's areas of interest. The pairing unit can also customize appropriate pairing content according to the user's learning progress. In this way, by customizing pairing content based on the user's current learning status and areas of interest, the optimal pairing for the user can be achieved.

[0050] The pairing unit can select the optimal pairing method during pairing, taking into account the user's geographical location information. For example, if the user is in a specific region, the pairing unit will select a pairing method relevant to that region. For instance, the pairing unit will propose a relevant pairing method based on the user's geographical location information. The pairing unit can also select a region-specific pairing method, taking into account the user's location information. This allows the unit to provide region-specific information by selecting the optimal pairing method while considering the user's geographical location information.

[0051] The pairing unit can analyze the user's social media activity during pairing and obtain relevant pairing content. For example, the pairing unit can suggest relevant pairing content based on the user's interests and preferences on social media. For instance, the pairing unit can obtain information useful for pairing from the user's social media activity. The pairing unit can also analyze the user's social media activity history and suggest optimal pairing content. This allows the system to provide pairing content based on the user's interests and preferences by analyzing their social media activity.

[0052] The support department can select the optimal support method when assisting with learning exchanges, taking into account the user's past learning history. For example, the support department can provide relevant support methods based on what the user has learned in the past. For example, the support department can suggest the optimal support method based on the user's past learning history. The support department can also analyze the user's learning history and provide support methods with learning partners who share similar interests. By selecting the optimal support method considering the user's past learning history, effective learning exchanges can be achieved.

[0053] The support department can customize the support provided during learning exchanges based on the user's current learning status and areas of interest. For example, the support department can provide relevant support based on the subjects the user is currently studying. For example, the support department can provide optimal support based on the user's areas of interest. The support department can also provide appropriate support according to the user's learning progress. In this way, by customizing the support based on the user's current learning status and areas of interest, the support department can provide the best possible support for the user.

[0054] The support department can select the optimal support method when assisting with learning exchanges, taking into account the user's geographical location. For example, if the user is in a specific region, the support department will provide support methods relevant to that region. For instance, the support department will propose relevant support methods based on the user's geographical location. The support department can also provide region-specific support methods, taking the user's location into consideration. This allows for the provision of region-specific information by selecting the optimal support method while considering the user's geographical location.

[0055] The support department can analyze users' social media activity and obtain relevant support content when providing support for learning exchanges. For example, the support department can provide relevant support content based on users' interests and preferences on social media. For example, the support department can obtain information useful for support from users' social media activity. The support department can also analyze users' social media activity history and provide optimal support content. In this way, by analyzing users' social media activity, it is possible to provide support content based on users' interests and preferences.

[0056] The translation unit can select the optimal translation method during translation by considering the user's past translation history. For example, the translation unit can provide relevant translation methods based on the content the user has previously translated. For instance, the translation unit can suggest the optimal translation method based on the user's past translation history. The translation unit can also analyze the user's translation history and provide translation methods for content of similar interest. This allows for effective translation by selecting the optimal translation method while considering the user's past translation history.

[0057] The translation unit can customize the translation content based on the user's current learning status and areas of interest during the translation process. For example, the translation unit can provide relevant translation content based on the subject the user is currently studying. For example, the translation unit can provide the most suitable translation content based on the user's areas of interest. The translation unit can also provide appropriate translation content according to the user's learning progress. In this way, by customizing the translation content based on the user's current learning status and areas of interest, it is possible to provide the best possible translation for the user.

[0058] The translation unit can select the optimal translation method while considering the user's geographical location. For example, if the user is in a specific region, the translation unit will provide a translation method relevant to that region. For instance, the translation unit will suggest a relevant translation method based on the user's geographical location. The translation unit can also provide region-specific translation methods while considering the user's location. This allows for the provision of region-specific information by selecting the optimal translation method while considering the user's geographical location.

[0059] The translation department can analyze the user's social media activity during translation and obtain relevant translation content. For example, the translation department can provide relevant translation content based on the user's interests and preferences on social media. For instance, the translation department can obtain information useful for translation from the user's social media activity. The translation department can also analyze the user's social media activity history and provide optimal translation content. This allows the translation department to provide content based on the user's interests and preferences by analyzing the user's social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can analyze a user's past input history and suggest the most suitable input format. For example, it can automatically display suggestions for interests and learning objectives that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest interests and learning objectives that the user will use at specific times of day based on their past input history. In this way, by analyzing a user's past input history, the system can suggest the most suitable input format for the user and improve input efficiency.

[0062] The selection unit can choose the most suitable learning partner by considering the user's past learning history. For example, it can select a relevant learning partner based on the content the user has previously studied. It can also suggest the most suitable learning partner based on the user's past learning history. Furthermore, it can analyze the user's learning history and select a learning partner with similar interests. By selecting the most suitable partner considering the user's past learning history, effective learning exchange can be achieved.

[0063] The pairing unit can select the optimal pairing method during pairing, taking into account the user's past pairing history. For example, it can select the optimal pairing method based on the user's past pairing history with partners. It can also suggest the optimal pairing method based on the user's past pairing history. Furthermore, it can analyze the user's pairing history and select a pairing method with partners who share similar interests. By selecting the optimal pairing method while considering the user's past pairing history, effective pairing can be achieved.

[0064] The support department can select the most appropriate support method when assisting with learning exchanges, taking into account the user's past learning history. For example, it can provide relevant support methods based on what the user has learned in the past. It can also suggest the most appropriate support method based on the user's past learning history. Furthermore, it can analyze the user's learning history and provide support methods that match the user with learning partners who share similar interests. By selecting the most appropriate support method considering the user's past learning history, effective learning exchanges can be achieved.

[0065] The translation department can select the most suitable translation method by considering the user's past translation history. For example, it can provide relevant translation methods based on content the user has previously translated. It can also suggest the most suitable translation method based on the user's past translation history. Furthermore, it can analyze the user's translation history and provide translation methods for content of similar interest. By selecting the most suitable translation method considering the user's past translation history, effective translation can be achieved.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk inputs the user's interests and learning objectives. These include specific academic fields, hobbies, skill development, etc. The reception desk provides an interface for the user to input their interests and learning objectives, accepting input in text format or voice input. In the case of voice input, speech recognition technology is used to convert it into text data. Step 2: The selection department analyzes the information entered by the reception department and selects the most suitable learning partner. The selection department uses a generative AI to consider the user's interests, learning objectives, skill level, and learning style, and selects the most suitable partner from overseas students with similar interests. Step 3: The pairing unit pairs the user with the learning partner selected by the selection unit. The pairing unit uses a matching algorithm to pair the user with the learning partner, and performs the pairing at the optimal time considering the user's schedule and the learning partner's schedule. Step 4: The support department assists with learning exchanges with learning partners paired by the pairing department. The support department provides opportunities for group discussions, collaborative projects, cultural introductions, and intercultural discussions, and implements measures to protect personal information and filter inappropriate content. Step 5: The translation unit provides real-time translation in learning exchanges supported by the support unit. The translation unit uses natural language processing technology to eliminate language barriers between users and learning partners and supports multiple language pairs.

[0068] (Example of form 2) The GlobalConnect system according to an embodiment of the present invention is a system that provides a platform for students to learn and interact with peers around the world. In this GlobalConnect system, the user inputs their interests and learning objectives, and a generating AI analyzes this information to select the most suitable learning partner from students overseas. The selected partner is paired with the user, and learning interaction begins. At this time, real-time translation is provided using natural language processing, eliminating language barriers. In addition, collaborative project support is provided, such as group problem-solving and research activities. Furthermore, as an intercultural understanding program, international understanding is deepened through cultural introductions and discussions. Finally, to provide a safe communication environment, personal information is protected and inappropriate content is filtered. For example, the user inputs their interests and learning objectives. At this time, the user inputs their interests and learning objectives in detail. For example, they input information such as "I am interested in science" or "I want to learn English." This information is input to the generating AI. Next, the generating AI analyzes the input information and selects the most suitable learning partner. The generating AI selects the most suitable partner from students overseas based on the user's interests and learning objectives. For example, a user who is interested in science will be selected as a student overseas who is also interested in science. Users are paired with selected partners, and learning exchanges begin. Real-time translation is provided using natural language processing. For example, if a Japanese-speaking user is paired with an English-speaking partner, real-time translation between Japanese and English is performed, eliminating language barriers. Furthermore, collaborative project support is provided, assisting with group problem-solving and research activities. For instance, when working on a scientific project collaboratively, the generative AI supports the project's progress. Intercultural understanding programs include cultural introductions and discussions. For example, programs introducing Japanese culture and forums for discussing different cultures are provided. Finally, to ensure a safe communication environment, personal information is protected and inappropriate content is filtered. For example, if chat content is inappropriate, the generative AI automatically filters it, maintaining a safe environment.This system allows students to be exposed to diverse values ​​and simultaneously develop autonomy and international awareness. It also serves as a valuable tool for educational institutions seeking to promote global education. Through this, the GlobalConnect system enables students to engage in learning exchanges with peers around the world, fostering intercultural understanding and communication skills.

[0069] The GlobalConnect system according to this embodiment comprises a reception unit, a selection unit, a pairing unit, a support unit, and a translation unit. The reception unit receives input from the user regarding their interests and learning objectives. These include, but are not limited to, specific academic fields, hobbies, and skill improvement. The reception unit provides, for example, an interface for the user to input their interests and learning objectives. For example, the reception unit allows the user to input their interests and learning objectives in text format. The reception unit also allows the user to input their interests and learning objectives using voice input. For example, the reception unit uses speech recognition technology to convert the user's voice input into text data. The selection unit uses a generative AI to analyze the information entered by the reception unit and select the most suitable learning partner. For example, the selection unit selects the most suitable partner from overseas students based on the user's interests and learning objectives. For example, the selection unit uses a generative AI to analyze the user's interests and learning objectives and selects overseas students with common interests. The selection unit can also use a generative AI to select the most suitable learning partner considering the user's skill level and learning style. The pairing unit pairs users with learning partners selected by the selection unit. The pairing unit uses algorithms to match users with selected learning partners. For example, the pairing unit uses a matching algorithm to pair users with learning partners. The pairing unit can also adjust the timing of pairing. For example, the pairing unit considers the user's schedule and the learning partner's schedule to perform pairing at the optimal time. The support unit supports learning interaction with learning partners paired by the pairing unit. For example, the support unit supports group problem-solving and research activities. For example, the support unit supports group discussions and collaborative projects. The support unit can also provide programs to deepen intercultural understanding through cultural introductions and discussions. For example, the support unit provides a venue for cultural presentations and intercultural discussions. The support unit can also protect personal information and filter inappropriate content. For example, the support unit monitors chat content and filters out inappropriate content.The translation unit provides real-time translation in learning exchanges supported by the support unit. The translation unit uses, for example, natural language processing technology to eliminate language barriers between users and learning partners. For example, the translation unit performs real-time translation between Japanese and English. The translation unit can also support multiple language pairs. For example, the translation unit supports not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. As a result, the GlobalConnect system according to this embodiment can select the optimal learning partner based on the user's interests and learning objectives, and support learning exchanges to cultivate intercultural understanding and communication skills.

[0070] The reception desk allows users to input their interests and learning objectives. These interests and objectives may include, but are not limited to, specific academic fields, hobbies, or skill development. The reception desk provides an interface for users to input their interests and learning objectives. For example, the reception desk allows users to input their interests and learning objectives in text format. Alternatively, the reception desk allows users to input their interests and learning objectives using voice input. For example, the reception desk uses speech recognition technology to convert the user's voice input into text data. Specifically, the reception desk provides an interface that users can access through a web browser or mobile application. Users can input their interests and learning objectives in a text box or by pressing a microphone button for voice input. In the case of voice input, speech recognition technology analyzes the user's speech and converts it into text data. This speech recognition technology includes noise cancellation to remove ambient noise and achieve high-precision speech recognition. Furthermore, the reception desk saves the information entered by the user for later reference. For example, when a user logs in again, it displays previously entered interests and learning objectives, allowing them to modify or add to them as needed. This allows the reception department to accurately understand the user's interests and learning objectives, and to smoothly provide that information to the next step, the selection department.

[0071] The selection unit uses generative AI to analyze the information entered by the reception unit and select the most suitable learning partner. For example, the selection unit selects the most suitable partner from overseas students based on the user's interests and learning objectives. For example, the selection unit uses generative AI to analyze the user's interests and learning objectives and select overseas students with similar interests. The selection unit can also use generative AI to select the most suitable learning partner considering the user's skill level and learning style. Specifically, the selection unit uses generative AI to analyze the user's input information using natural language processing technology to understand the user's interests and learning objectives. The generative AI classifies the user's input information using topic models and clustering algorithms to identify overseas students with similar interests. Furthermore, the generative AI selects the most suitable learning partner considering the user's skill level and learning style. For example, the generative AI analyzes the user's past learning history and evaluation data to evaluate the user's skill level. The generative AI also collects information about the user's learning style and selects the most suitable learning partner based on learning styles such as visual learners, auditory learners, and experiential learners. This allows the selection unit to choose the learning partner best suited to the user's interests and learning objectives, thereby improving the user's learning experience.

[0072] The pairing unit pairs the user with a learning partner selected by the selection unit. The pairing unit uses an algorithm to match the selected learning partner with the user. For example, the pairing unit uses a matching algorithm to pair the user with a learning partner. The pairing unit can also adjust the timing of pairing. For example, the pairing unit considers the user's schedule and the learning partner's schedule to perform pairing at the optimal time. Specifically, the pairing unit collects schedule information from the user and the learning partner and calculates the optimal pairing timing. The pairing unit considers the time zones of the user and the learning partner and identifies time slots when both are available. Furthermore, the pairing unit adjusts the means of communication between the user and the learning partner. For example, the pairing unit provides communication methods such as video calls, voice calls, and chats to enable the user and the learning partner to interact in the most optimal way. To improve the success rate of pairing, the pairing unit can also analyze past pairing data and improve the optimal pairing algorithm. This allows the pairing unit to achieve smooth interaction between the user and the learning partner and maximize learning effectiveness.

[0073] The Support Department assists learning interactions with learning partners paired by the Pairing Department. For example, the Support Department supports group problem-solving and research activities. For instance, it supports group discussions and collaborative projects. The Support Department can also provide programs to deepen intercultural understanding through cultural introductions and discussions. For example, it provides venues for cultural presentations and intercultural discussions. The Support Department can also implement measures to protect personal information and filter inappropriate content. For example, it monitors chat content and filters out inappropriate material. Specifically, the Support Department provides tasks and projects for users and learning partners to work on together and monitors their progress. The Support Department provides appropriate advice and support for problems and questions users may encounter. Furthermore, the Support Department provides programs to deepen intercultural understanding, offering opportunities for users and learning partners to understand each other's cultures and values. For example, it provides cultural introduction videos and intercultural discussion topics to help users and learning partners deepen their interactions. In addition, the Support Department implements security measures to protect users' personal information and introduces systems to filter inappropriate content and behavior. This allows the support department to facilitate safe and effective learning exchanges between users and learning partners, promoting improved intercultural understanding and communication skills.

[0074] The Translation Unit provides real-time translation in learning exchanges supported by the Support Unit. The Translation Unit uses, for example, natural language processing technology to bridge the language barrier between users and learning partners. For instance, it performs real-time translation between Japanese and English. The Translation Unit can also support multiple language pairs. For example, it supports not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. Specifically, the Translation Unit automatically detects the languages ​​used by the user and learning partner and performs real-time translation. Using natural language processing technology, the Translation Unit analyzes user utterances and text messages to provide appropriate translations. For example, it translates what a user says in Japanese into English and conveys it to the learning partner. It also translates what a learning partner says in English into Japanese and conveys it to the user. To improve translation accuracy, the Translation Unit uses advanced translation algorithms that consider context and technical terms. Furthermore, the Translation Unit collects user feedback to continuously improve translation quality. This allows the translation department to facilitate smooth communication between users and learning partners, supporting learning exchanges that transcend language barriers.

[0075] The support department can assist with group problem-solving and research activities. For example, it can support group discussions. For instance, it can provide a platform for group members to conduct online discussions. The support department can also support collaborative projects. For example, it can provide tools and resources for group members to work on a project together. The support department may also have functions to manage project progress. For example, it can track project progress and notify group members of the progress. This can improve collaborative learning abilities by supporting group problem-solving and research activities.

[0076] The support department can provide programs that deepen intercultural understanding through cultural introductions and discussions. For example, the support department can provide cultural presentations. For example, the support department can provide a platform for users to give presentations introducing their own country's culture. The support department can also provide intercultural discussions. For example, the support department can provide a forum for discussing different cultures. The support department may also have functions to set discussion themes and participant roles. For example, the support department can set discussion themes and assign roles to participants. This allows for a broader international perspective by deepening intercultural understanding through cultural introductions and discussions.

[0077] The support department can implement measures to protect personal information and filter inappropriate content. For example, the support department can encrypt data. For example, the support department can encrypt and protect users' personal information. The support department can also implement access control. For example, the support department can restrict the permissions to access users' personal information. The support department can also set up a privacy policy. For example, the support department can set up a privacy policy regarding the handling of users' personal information and notify users. The support department can also perform keyword filtering. For example, the support department can filter chat content if it contains inappropriate keywords. The support department can also perform content moderation. For example, the support department can monitor chat content and automatically delete inappropriate content. The support department can also provide a reporting system. For example, the support department can provide a function for users to report inappropriate content. This allows for a secure communication environment by protecting personal information and filtering inappropriate content.

[0078] The selection system can choose the most suitable learning partner based on the user's interests and learning objectives. For example, the system can select a learning partner with common interests. For instance, if the user is interested in science, the system will select a learning partner who is also interested in science. The system can also select a learning partner considering skill level. For example, the system will select a learning partner that matches the user's skill level. The system can also select a learning partner considering learning style. For example, the system will select a learning partner that matches the user's learning style. This allows for effective learning interaction by selecting the most suitable learning partner based on the user's interests and learning objectives.

[0079] The translation unit can provide real-time translation using natural language processing. For example, it can perform morphological analysis. For instance, it can break down the input text into morphemes and analyze the meaning of each morpheme. The translation unit can also perform grammatical analysis. For example, it can analyze the grammatical structure of the input text and understand the meaning of the sentence. The translation unit can also perform semantic analysis. For example, it can analyze the meaning of the input text and generate an appropriate translation. The translation unit can also support multiple language pairs. For example, it can support not only Japanese and English, but also Japanese and French, English and Spanish, and other language pairs. This allows for the elimination of language barriers and smoother communication by providing real-time translation using natural language processing.

[0080] The reception desk can estimate the user's emotions and adjust the input method for interests and learning objectives based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface and minimizes the input steps. For example, if the user is relaxed, the reception desk provides detailed input options and suggests a customizable input method. If the user is in a hurry, the reception desk prioritizes voice input to allow for quick input of interests and learning objectives. This ensures that users can comfortably input their interests and learning objectives by adjusting the input method according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The input system can analyze a user's past input history and suggest the most suitable input format. For example, it can automatically display suggestions based on interests and learning objectives that the user has frequently entered in the past. For instance, it can prioritize suggesting input methods that the user has used in the past (voice, text, etc.). The input system can also predict and suggest interests and learning objectives that the user will use at specific times of day based on their past input history. By analyzing the user's past input history, it can suggest the most suitable input format and improve input efficiency.

[0082] The input system can filter user input based on their current learning status and areas of interest when users enter their interests and learning objectives. For example, the input system may prioritize displaying interests and learning objectives related to the subject the user is currently studying. For example, the input system may filter relevant input based on the user's areas of interest. The input system can also suggest appropriate input based on the user's learning progress. By filtering input based on the user's current learning status and areas of interest, the system can provide users with highly relevant information.

[0083] The reception desk can estimate the user's emotions and prioritize input content based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize displaying important input content. For example, if the user is relaxed, the reception desk will prioritize displaying detailed input content. If the user is in a hurry, the reception desk will prioritize displaying content that can be entered quickly. In this way, by prioritizing input content according to the user's emotions, the system enables the user to prioritize entering important information. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception system can prioritize retrieving highly relevant input content when users input their interests and learning objectives, taking into account their geographical location. For example, if a user is in a specific region, the reception system will prioritize displaying learning content related to that region. For instance, the reception system can suggest relevant learning partners based on the user's geographical location. The reception system can also prioritize displaying region-specific learning resources, taking the user's location into consideration. This allows the system to provide region-specific information by prioritizing the retrieval of highly relevant input content, taking into account the user's geographical location.

[0085] The reception desk can analyze the user's social media activity when they input their interests and learning objectives, and retrieve relevant input content. For example, the reception desk can suggest relevant learning content based on the user's interests and concerns on social media. For example, the reception desk can obtain information from the user's social media activity that is useful for selecting a learning partner. The reception desk can also analyze the user's social media activity history and suggest the most suitable learning content. In this way, by analyzing the user's social media activity, it is possible to provide input content that is based on the user's interests and concerns.

[0086] The selection unit can estimate the user's emotions and adjust the selection criteria for learning partners based on the estimated emotions. For example, if the user is relaxed, the selection unit will select a learning partner that is also relaxed. For example, if the user is stressed, the selection unit will select a learning partner that can provide support. If the user is excited, the selection unit can also select a learning partner that is also excited. In this way, by adjusting the selection criteria for learning partners according to the user's emotions, the optimal learning partner for the user can be selected. 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.

[0087] The selection unit can choose the most suitable learning partner by considering the user's past learning history. For example, the selection unit can select a relevant learning partner based on the content the user has previously studied. For example, the selection unit can suggest the most suitable learning partner based on the user's past learning history. The selection unit can also analyze the user's learning history and select a learning partner with similar interests. By selecting the most suitable partner considering the user's past learning history, effective learning interaction can be achieved.

[0088] The selection unit can customize the selection criteria for learning partners based on the user's current learning status and areas of interest. For example, the selection unit can select relevant learning partners based on the subjects the user is currently studying. For example, the selection unit can select the most suitable learning partner based on the user's areas of interest. The selection unit can also select an appropriate learning partner according to the user's learning progress. In this way, by customizing the selection criteria based on the user's current learning status and areas of interest, the system can select the most suitable learning partner for the user.

[0089] The selection unit can estimate the user's emotions and adjust the order in which it displays the learning partner selection results based on the estimated user emotions. For example, if the user is relaxed, the selection unit will prioritize displaying selection results that contain detailed information. For example, if the user is stressed, the selection unit will prioritize displaying selection results that contain concise information. The selection unit can also quickly display selection results if the user is in a hurry. This allows the user to quickly find the optimal selection result by adjusting the order in which the learning partner selection results are displayed according to the user's emotions. 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.

[0090] The selection unit can select the most suitable learning partner by considering the user's geographical location. For example, if the user is in a specific region, the selection unit will select a learning partner relevant to that region. For instance, the selection unit will suggest relevant learning partners based on the user's geographical location. The selection unit can also select region-specific learning partners by considering the user's location. This allows the system to provide region-specific information by selecting the most suitable partner based on the user's geographical location.

[0091] The selection unit can analyze the user's social media activity when selecting a learning partner and select a relevant partner. For example, the selection unit can suggest a relevant learning partner based on the user's interests and preferences on social media. For example, the selection unit can obtain information useful for selecting a learning partner from the user's social media activity. The selection unit can also analyze the user's social media activity history and select the most suitable learning partner. This allows for the selection of a learning partner based on the user's interests and preferences by analyzing the user's social media activity.

[0092] The pairing unit can estimate the user's emotions and adjust the timing of pairing based on the estimated emotions. For example, if the user is relaxed, the pairing unit flexibly adjusts the timing of pairing. For example, if the user is stressed, the pairing unit performs pairing quickly. If the user is excited, the pairing unit can also perform pairing at an appropriate time. In this way, by adjusting the timing of pairing according to the user's emotions, pairing can be performed at the optimal time 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.

[0093] The pairing unit can select the optimal pairing method during pairing by considering the user's past pairing history. For example, the pairing unit can select the optimal pairing method based on the user's past pairing history with partners. For example, the pairing unit can suggest the optimal pairing method based on the user's past pairing history. The pairing unit can also analyze the user's pairing history and select a pairing method with a partner who shares similar interests. By selecting the optimal pairing method while considering the user's past pairing history, effective pairing can be achieved.

[0094] The pairing unit can customize pairing content based on the user's current learning status and areas of interest during pairing. For example, the pairing unit can customize relevant pairing content based on the subject the user is currently studying. For example, the pairing unit can customize optimal pairing content based on the user's areas of interest. The pairing unit can also customize appropriate pairing content according to the user's learning progress. In this way, by customizing pairing content based on the user's current learning status and areas of interest, the optimal pairing for the user can be achieved.

[0095] The pairing unit can estimate the user's emotions and determine pairing priorities based on the estimated emotions. For example, if the user is relaxed, the pairing unit will prioritize pairings that include detailed information. For example, if the user is stressed, the pairing unit will prioritize pairings that include concise information. The pairing unit can also perform quick pairings if the user is in a hurry. This allows for quick and optimal pairing for the user by determining pairing priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The pairing unit can select the optimal pairing method during pairing, taking into account the user's geographical location information. For example, if the user is in a specific region, the pairing unit will select a pairing method relevant to that region. For instance, the pairing unit will propose a relevant pairing method based on the user's geographical location information. The pairing unit can also select a region-specific pairing method, taking into account the user's location information. This allows the unit to provide region-specific information by selecting the optimal pairing method while considering the user's geographical location information.

[0097] The pairing unit can analyze the user's social media activity during pairing and obtain relevant pairing content. For example, the pairing unit can suggest relevant pairing content based on the user's interests and preferences on social media. For instance, the pairing unit can obtain information useful for pairing from the user's social media activity. The pairing unit can also analyze the user's social media activity history and suggest optimal pairing content. This allows the system to provide pairing content based on the user's interests and preferences by analyzing their social media activity.

[0098] The support unit can estimate the user's emotions and adjust the learning interaction support method based on the estimated user emotions. For example, if the user is relaxed, the support unit will provide detailed support. For example, if the user is stressed, the support unit will provide concise support. The support unit can also provide rapid support if the user is in a hurry. In this way, by adjusting the learning interaction support method according to the user's emotions, the optimal support 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.

[0099] The support department can select the optimal support method when assisting with learning exchanges, taking into account the user's past learning history. For example, the support department can provide relevant support methods based on what the user has learned in the past. For example, the support department can suggest the optimal support method based on the user's past learning history. The support department can also analyze the user's learning history and provide support methods with learning partners who share similar interests. By selecting the optimal support method considering the user's past learning history, effective learning exchanges can be achieved.

[0100] The support department can customize the support provided during learning exchanges based on the user's current learning status and areas of interest. For example, the support department can provide relevant support based on the subjects the user is currently studying. For example, the support department can provide optimal support based on the user's areas of interest. The support department can also provide appropriate support according to the user's learning progress. In this way, by customizing the support based on the user's current learning status and areas of interest, the support department can provide the best possible support for the user.

[0101] The support unit can estimate the user's emotions and adjust the order in which it displays learning interaction support content based on the estimated emotions. For example, if the user is relaxed, the support unit will prioritize displaying support content containing detailed information. For example, if the user is stressed, the support unit will prioritize displaying support content containing concise information. The support unit can also quickly display support content if the user is in a hurry. This allows the user to quickly find the most suitable support content by adjusting the order in which learning interaction support content is displayed according to the user's emotions. 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.

[0102] The support department can select the optimal support method when assisting with learning exchanges, taking into account the user's geographical location. For example, if the user is in a specific region, the support department will provide support methods relevant to that region. For instance, the support department will propose relevant support methods based on the user's geographical location. The support department can also provide region-specific support methods, taking the user's location into consideration. This allows for the provision of region-specific information by selecting the optimal support method while considering the user's geographical location.

[0103] The support department can analyze users' social media activity and obtain relevant support content when providing support for learning exchanges. For example, the support department can provide relevant support content based on users' interests and preferences on social media. For example, the support department can obtain information useful for support from users' social media activity. The support department can also analyze users' social media activity history and provide optimal support content. In this way, by analyzing users' social media activity, it is possible to provide support content based on users' interests and preferences.

[0104] The translation unit can estimate the user's emotions and adjust the translation's expression based on those emotions. For example, if the user is relaxed, the translation unit will provide a detailed translation. For example, if the user is stressed, the translation unit will provide a concise translation. The translation unit can also provide a rapid translation if the user is in a hurry. This allows the translation to be optimized for the user by adjusting the expression of the translation according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The translation unit can select the optimal translation method during translation by considering the user's past translation history. For example, the translation unit can provide relevant translation methods based on the content the user has previously translated. For instance, the translation unit can suggest the optimal translation method based on the user's past translation history. The translation unit can also analyze the user's translation history and provide translation methods for content of similar interest. This allows for effective translation by selecting the optimal translation method while considering the user's past translation history.

[0106] The translation unit can customize the translation content based on the user's current learning status and areas of interest during the translation process. For example, the translation unit can provide relevant translation content based on the subject the user is currently studying. For example, the translation unit can provide the most suitable translation content based on the user's areas of interest. The translation unit can also provide appropriate translation content according to the user's learning progress. In this way, by customizing the translation content based on the user's current learning status and areas of interest, it is possible to provide the best possible translation for the user.

[0107] The translation unit can estimate the user's emotions and prioritize translations based on those emotions. For example, if the user is relaxed, the translation unit will prioritize detailed translations. For example, if the user is stressed, the translation unit will prioritize concise translations. The translation unit can also provide quick translations if the user is in a hurry. This allows the system to quickly provide the user with the most suitable translation by prioritizing translations according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The translation unit can select the optimal translation method while considering the user's geographical location. For example, if the user is in a specific region, the translation unit will provide a translation method relevant to that region. For instance, the translation unit will suggest a relevant translation method based on the user's geographical location. The translation unit can also provide region-specific translation methods while considering the user's location. This allows for the provision of region-specific information by selecting the optimal translation method while considering the user's geographical location.

[0109] The translation department can analyze the user's social media activity during translation and obtain relevant translation content. For example, the translation department can provide relevant translation content based on the user's interests and preferences on social media. For instance, the translation department can obtain information useful for translation from the user's social media activity. The translation department can also analyze the user's social media activity history and provide optimal translation content. This allows the translation department to provide content based on the user's interests and preferences by analyzing the user's social media activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The reception system can estimate the user's emotions and adjust the input method for interests and learning objectives based on those estimates. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick input of interests and learning objectives. In this way, by adjusting the input method according to the user's emotions, it is possible to ensure that users can comfortably input their interests and learning objectives.

[0112] The selection unit can estimate the user's emotions and adjust the selection criteria for learning partners based on those estimated emotions. For example, if the user is relaxed, it can select a learning partner who is also relaxed. If the user is stressed, it can select a learning partner who can provide support. Furthermore, if the user is excited, it can select a learning partner who is also excited. By adjusting the selection criteria for learning partners according to the user's emotions, the system can select the most suitable learning partner for the user.

[0113] The pairing unit can estimate the user's emotions and adjust the timing of pairing based on those emotions. For example, if the user is relaxed, the timing of pairing can be flexibly adjusted. If the user is stressed, pairing can be performed quickly. Furthermore, if the user is excited, pairing can be performed at an appropriate time. In this way, by adjusting the timing of pairing according to the user's emotions, pairing can be performed at the optimal time for the user.

[0114] The support unit can estimate the user's emotions and adjust the learning interaction support method based on the estimated emotions. For example, if the user is relaxed, it can provide detailed support. If the user is stressed, it can provide concise support. Furthermore, if the user is in a hurry, it can provide support quickly. In this way, by adjusting the learning interaction support method according to the user's emotions, it is possible to provide the optimal support for the user.

[0115] The translation unit can estimate the user's emotions and adjust the translation's expression based on that estimation. For example, if the user is relaxed, it can provide a detailed translation. If the user is stressed, it can provide a concise translation. Furthermore, if the user is in a hurry, it can provide a quick translation. In this way, by adjusting the translation's expression according to the user's emotions, it can provide the most suitable translation for the user.

[0116] The reception desk can analyze a user's past input history and suggest the most suitable input format. For example, it can automatically display suggestions for interests and learning objectives that the user has frequently entered in the past. It can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest interests and learning objectives that the user will use at specific times of day based on their past input history. In this way, by analyzing a user's past input history, the system can suggest the most suitable input format for the user and improve input efficiency.

[0117] The selection unit can choose the most suitable learning partner by considering the user's past learning history. For example, it can select a relevant learning partner based on the content the user has previously studied. It can also suggest the most suitable learning partner based on the user's past learning history. Furthermore, it can analyze the user's learning history and select a learning partner with similar interests. By selecting the most suitable partner considering the user's past learning history, effective learning exchange can be achieved.

[0118] The pairing unit can select the optimal pairing method during pairing, taking into account the user's past pairing history. For example, it can select the optimal pairing method based on the user's past pairing history with partners. It can also suggest the optimal pairing method based on the user's past pairing history. Furthermore, it can analyze the user's pairing history and select a pairing method with partners who share similar interests. By selecting the optimal pairing method while considering the user's past pairing history, effective pairing can be achieved.

[0119] The support department can select the most appropriate support method when assisting with learning exchanges, taking into account the user's past learning history. For example, it can provide relevant support methods based on what the user has learned in the past. It can also suggest the most appropriate support method based on the user's past learning history. Furthermore, it can analyze the user's learning history and provide support methods that match the user with learning partners who share similar interests. By selecting the most appropriate support method considering the user's past learning history, effective learning exchanges can be achieved.

[0120] The translation department can select the most suitable translation method by considering the user's past translation history. For example, it can provide relevant translation methods based on content the user has previously translated. It can also suggest the most suitable translation method based on the user's past translation history. Furthermore, it can analyze the user's translation history and provide translation methods for content of similar interest. By selecting the most suitable translation method considering the user's past translation history, effective translation can be achieved.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reception desk inputs the user's interests and learning objectives. These include specific academic fields, hobbies, skill development, etc. The reception desk provides an interface for the user to input their interests and learning objectives, accepting input in text format or voice input. In the case of voice input, speech recognition technology is used to convert it into text data. Step 2: The selection department analyzes the information entered by the reception department and selects the most suitable learning partner. The selection department uses a generative AI to consider the user's interests, learning objectives, skill level, and learning style, and selects the most suitable partner from overseas students with similar interests. Step 3: The pairing unit pairs the user with the learning partner selected by the selection unit. The pairing unit uses a matching algorithm to pair the user with the learning partner, and performs the pairing at the optimal time considering the user's schedule and the learning partner's schedule. Step 4: The support department assists with learning exchanges with learning partners paired by the pairing department. The support department provides opportunities for group discussions, collaborative projects, cultural introductions, and intercultural discussions, and implements measures to protect personal information and filter inappropriate content. Step 5: The translation unit provides real-time translation in learning exchanges supported by the support unit. The translation unit uses natural language processing technology to eliminate language barriers between users and learning partners and supports multiple language pairs.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, selection unit, pairing unit, support 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 is implemented by the reception device 38 of the smart device 14 and provides an interface for the user to input their interests and learning objectives. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generation AI to select the optimal learning partner. The pairing unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the selected learning partner with the user. The support unit is implemented by the control unit 46A of the smart device 14 and supports learning interaction. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time translation using natural language processing technology. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, selection unit, pairing unit, support unit, and translation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and provides an interface for the user to input their interests and learning objectives by voice. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to select the optimal learning partner. The pairing unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the selected learning partner with the user. The support unit is implemented by the control unit 46A of the smart glasses 214 and supports learning interaction. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time translation using natural language processing technology. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reception unit, selection unit, pairing unit, support 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 is implemented by the microphone 238 of the headset terminal 314 and provides an interface for the user to input their interests and learning objectives by voice. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generation AI to select the optimal learning partner. The pairing unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the selected learning partner with the user. The support unit is implemented by the control unit 46A of the headset terminal 314 and supports learning interaction. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time translation using natural language processing technology. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reception unit, selection unit, pairing unit, support unit, and translation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and provides an interface for the user to input their interests and learning objectives by voice. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input information using a generating AI to select the optimal learning partner. The pairing unit is implemented by the specific processing unit 290 of the data processing unit 12 and matches the selected learning partner with the user. The support unit is implemented by the control unit 46A of the robot 414 and supports learning interaction. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides real-time translation using natural language processing technology. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reception area where users input their interests and learning objectives, A selection unit analyzes the information entered by the reception unit and selects the most suitable learning partner, A pairing unit that pairs with a learning partner selected by the aforementioned selection unit, A support unit that assists learning interaction with a learning partner paired by the aforementioned pairing unit, The system comprises a translation unit that provides real-time translation in learning exchanges supported by the aforementioned support unit. A system characterized by the following features. (Note 2) The aforementioned support unit, Supporting group problem-solving and research activities. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned support unit, We offer programs that deepen intercultural understanding through cultural introductions and discussions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned support unit, We implement measures to protect personal information and filter inappropriate content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned selection unit is Select the optimal learning partner based on the user's interests and learning objectives. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned translation department, Providing real-time translation using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for interests and learning objectives based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input format. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users enter their interests and learning goals, the system filters their input based on their current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users input their interests and learning objectives, the system prioritizes retrieving highly relevant input content by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users input their interests and learning objectives, the system analyzes their social media activity and retrieves relevant input data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned selection unit is It estimates the user's emotions and adjusts the selection criteria for learning partners based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned selection unit is When selecting a learning partner, the system will consider the user's past learning history to select the most suitable partner. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned selection unit is When selecting a learning partner, the selection criteria are customized based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned selection unit is It estimates the user's emotions and adjusts the order in which the learning partner selection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned selection unit is When selecting a learning partner, the system will consider the user's geographical location to select the most suitable partner. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned selection unit is When selecting learning partners, we analyze users' social media activity and select relevant partners. The system described in Appendix 1, characterized by the features described herein. (Note 19) The pairing unit is, It estimates the user's emotions and adjusts the pairing timing based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The pairing unit is, During pairing, the system selects the optimal pairing method by considering the user's past pairing history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The pairing unit is, During pairing, the pairing settings are customized based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 22) The pairing unit is, It estimates the user's emotions and determines pairing priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The pairing unit is, During pairing, the system selects the optimal pairing method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The pairing unit is, During pairing, the system analyzes the user's social media activity and retrieves relevant pairing information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned support unit, It estimates the user's emotions and adjusts the learning interaction support methods based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned support unit, When supporting learning exchanges, the optimal support method is selected by considering the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned support unit, When supporting learning exchanges, the support content is customized based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned support unit, The system estimates the user's emotions and adjusts the order in which learning interaction support content is displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned support unit, When supporting learning exchanges, the optimal support method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned support unit, When supporting learning exchanges, we analyze users' social media activity and obtain relevant support content. The system described in Appendix 1, characterized by the features described herein. (Note 31) 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 32) The aforementioned translation department, During translation, the system selects the optimal translation method by considering the user's past translation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned translation department, During translation, the translation content is customized based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) 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 35) The aforementioned translation department, During translation, the system selects the optimal translation method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned translation department, During translation, the system analyzes the user's social media activity to retrieve relevant translation content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 where users input their interests and learning objectives, A selection unit analyzes the information entered by the reception unit and selects the most suitable learning partner, A pairing unit that pairs with a learning partner selected by the aforementioned selection unit, A support unit that assists learning interaction with a learning partner paired by the aforementioned pairing unit, The system comprises a translation unit that provides real-time translation in learning exchanges supported by the aforementioned support unit. A system characterized by the following features.

2. The aforementioned support unit, Supporting group problem-solving and research activities. The system according to feature 1.

3. The aforementioned support unit, We offer programs that deepen intercultural understanding through cultural introductions and discussions. The system according to feature 1.

4. The aforementioned support unit, We implement measures to protect personal information and filter inappropriate content. The system according to feature 1.

5. The aforementioned selection unit is Select the optimal learning partner based on the user's interests and learning objectives. The system according to feature 1.

6. The aforementioned translation department, Providing real-time translation using natural language processing. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the input method for interests and learning objectives based on the estimated user emotions. The system according to feature 1.

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

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