system

Abstract

The system according to this embodiment aims to provide urban students with opportunities to study at local educational institutions and to make effective use of local educational resources. [Solution] The system according to this embodiment comprises an analysis unit, a proposal unit, a lecture unit, an interpretation unit, and a field trip unit. The analysis unit analyzes students' interests and learning styles. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit. The lecture unit provides real-time lectures based on the curriculum proposed by the proposal unit. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit. The field trip unit provides virtual field trips based on the curriculum proposed by the proposal unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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, there was a problem that students in urban areas had limited opportunities to study at local educational institutions, and the effective utilization of local educational resources was not fully carried out.

[0005] The system according to the embodiment aims to provide students in urban areas with opportunities to study at local educational institutions and effectively utilize local educational resources.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a proposal unit, a lecture unit, an interpretation unit, and a field trip unit. The analysis unit analyzes students' interests, preferences, and learning styles. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit. The lecture unit provides real-time lectures based on the curriculum proposed by the proposal unit. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit. The field trip unit provides virtual field trips based on the curriculum proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment provides urban students with opportunities to study at local educational institutions and enables effective utilization of local educational resources. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0019] The smart device 14 comprises a computer 36, a 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 Remote Campus Connect System, according to an embodiment of the present invention, is a system that utilizes generative AI to provide a virtual study abroad program that allows urban students to study at universities and vocational schools in rural areas. This system aims to revitalize rural educational institutions and promote future migration of young people to rural areas. The Remote Campus Connect System analyzes students' interests and learning styles and proposes the most suitable rural educational institutions and curricula. For example, if a student is interested in science, the system suggests a suitable rural university or vocational school. Next, learning in a virtual space begins. The system enables students to participate in real-time lectures in a high-definition 3D environment generated by the generative AI. For example, a student can participate in a lecture at a rural university from their home and interact with other students in the region. This virtual space replicates the actual campus scenery and classroom environment, allowing students to experience it as if they were actually there. Furthermore, the system is equipped with an AI-powered simultaneous interpretation function, providing a learning experience that transcends language barriers. For example, when taking a foreign language lecture, the AI ​​provides real-time interpretation, allowing students to understand the lecture in their native language. The system also includes a virtual field trip function that allows students to experience rural culture and lifestyle. Students can visit local tourist destinations and cultural facilities in a virtual space, directly experiencing the charm of regional areas. For example, students can experience local festivals and traditional crafts through virtual tours. In this way, the Remote Campus Connect system functions not merely as an online learning tool, but as an educational platform connecting urban and rural areas. It aims to simultaneously achieve the effective utilization of local educational resources and stimulate young people's interest in rural areas, thereby revitalizing local educational institutions and promoting future migration of young people to rural areas. Through this, the Remote Campus Connect system can propose the most suitable educational institutions and curricula based on students' interests and learning styles, and by providing real-time lectures and virtual field trips, it can revitalize local educational institutions and promote migration of young people to rural areas.

[0029] The remote campus connect system according to this embodiment comprises an analysis unit, a proposal unit, a lecture unit, an interpretation unit, and a field trip unit. The analysis unit analyzes students' interests and learning styles. The analysis unit analyzes students' interests and learning styles based on information entered by students, for example. For example, the analysis unit can analyze students' interests and learning styles using survey results, learning history, behavioral data, etc. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit, for example. For example, the proposal unit can make proposals considering the evaluation criteria of educational institutions, the content and objectives of the curriculum, etc. The lecture unit provides real-time lectures based on the curriculum proposed by the proposal unit. The lecture unit can provide real-time lectures, for example, based on the curriculum proposed by the proposal unit, including live streaming and interactive elements. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit in real time. For example, the interpretation department can perform simultaneous interpretation, taking into account the interpretation system used and the languages ​​supported. The field trip department provides virtual field trips based on the curriculum proposed by the proposal department. The field trip department can provide virtual field trips based on the curriculum proposed by the proposal department, taking into account the technologies used and the content of the experiences. As a result, the remote campus connect system according to this embodiment can propose the most suitable educational institution and curriculum based on students' interests and learning styles, and by providing real-time lectures and virtual field trips, it can revitalize local educational institutions and promote the migration of young people to rural areas.

[0030] The analytics department analyzes students' interests and learning styles. Specifically, it conducts a detailed analysis of students' interests and learning styles based on information they provide. For example, the analytics department can analyze students' interests and learning styles from multiple perspectives using survey results, learning history, and behavioral data. Survey results are important data for understanding what subjects and topics students are interested in, and learning history serves as an indicator of what learning methods have been effective in the past. Behavioral data provides information about the learning environment, such as what times of day students tend to study and what devices they use. By integrating this data and using AI for pattern recognition and clustering, it is possible to identify the optimal learning style and interests for each individual student. Furthermore, the analytics department can monitor students' learning progress and performance in real time and provide feedback as needed. This allows students to enjoy an optimal learning environment based on their own learning style and interests.

[0031] The Proposal Department proposes the most suitable local educational institutions and curricula based on the information analyzed by the Analysis Department. Specifically, it selects the most suitable local educational institutions and curricula based on data on students' interests and learning styles obtained by the Analysis Department. The Proposal Department makes proposals by comprehensively considering the evaluation criteria of educational institutions, the content and objectives of the curriculum, and the characteristics of the educational institutions. For example, it selects educational institutions that have strengths in specific fields or educational institutions that offer specialized curricula that match students' interests. The Proposal Department also proposes curricula that match students' learning styles. For example, it proposes curricula that emphasize fieldwork and experiments for students who prefer practical learning, and curricula that focus on lectures and discussions for students who prefer theoretical learning. Furthermore, the Proposal Department can also propose curricula that match students' future career paths and goals. This allows students to choose the educational institution and curriculum that best suits their interests and learning style, enabling them to achieve effective learning.

[0032] The lecture department will provide real-time lectures based on the curriculum proposed by the proposal department. Specifically, it will provide real-time lectures, including live streaming and interactive elements, based on the curriculum selected by the proposal department. The lecture department will utilize the latest online education technologies to enable students to participate remotely. For example, lectures will be delivered in real time via live streaming, allowing students to participate from home or any location. By incorporating interactive elements, students can ask questions and participate in discussions during lectures. Furthermore, the lecture department can also provide recorded lectures on demand, allowing students to learn at their own pace. In this way, the lecture department will provide an environment in which students can participate in lectures in real time and enjoy an interactive learning experience.

[0033] The Interpretation Department provides simultaneous interpretation of lectures delivered by the Lecture Department. Specifically, it provides real-time simultaneous interpretation of lectures delivered by the Lecture Department, enabling students to participate in lectures without experiencing language barriers. The Interpretation Department performs simultaneous interpretation considering the interpretation system used and the languages ​​supported. For example, it can introduce an AI-powered automatic interpretation system that can translate lecture content into multiple languages ​​in real time. This allows students who speak different languages ​​to participate in the same lecture and understand the same content. In addition, the Interpretation Department employs professional interpreters to accurately interpret specialized terminology and technical content in specific fields. Furthermore, the Interpretation Department can interpret student questions and discussions in real time, providing them without compromising the interactive elements of the lectures. In this way, the Interpretation Department can remove language barriers and realize a global learning environment.

[0034] The Field Trip Department provides virtual field trips based on the curriculum proposed by the Proposal Department. Specifically, it provides virtual field trips based on the curriculum selected by the Proposal Department, taking into consideration the technologies used and the content of the experiences. The Field Trip Department utilizes the latest VR (Virtual Reality) and AR (Augmented Reality) technologies to provide an environment where students can have realistic experiences without actually going to the site. For example, using VR goggles, students can experience a field trip in a virtual space and learn about the local scenery, culture, and history. Furthermore, by using AR technology to overlay virtual information onto the actual scenery, a more interactive experience can be provided. In addition, the Field Trip Department can invite experts and guides to provide real-time commentary during the virtual field trip. This allows students to experience virtual field trips based on their interests and gain a learning experience that is close to an actual site visit.

[0035] The analysis department can analyze students' interests and learning styles based on the information they input. For example, the analysis department can analyze students' interests and learning styles based on the information they input. For example, the analysis department can analyze students' interests and learning styles using survey results, learning history, behavioral data, etc. This allows for more appropriate suggestions to be made by analyzing students' interests and learning styles based on the information they input. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information entered by students into a generating AI and have the generating AI perform the analysis of interests and learning styles.

[0036] The proposal department can propose the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal department can propose the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal department can make proposals considering the evaluation criteria of educational institutions, the content and objectives of curricula, etc. In this way, by proposing the most suitable educational institutions and curricula based on the analysis results, a learning environment suitable for students can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the information analyzed by the analysis department into a generating AI and have the generating AI execute a proposal for the most suitable educational institutions and curricula.

[0037] The lecture department can provide real-time lectures based on the curriculum proposed by the proposal department. For example, the lecture department can provide real-time lectures that include live streaming and interactive elements based on the curriculum proposed by the proposal department. This allows students to experience what it is like to be in an actual lecture by providing real-time lectures based on the proposed curriculum. Some or all of the above processes in the lecture department may be performed using AI, for example, or not using AI. For example, the lecture department can input the curriculum proposed by the proposal department into a generative AI and have the generative AI perform the delivery of real-time lectures.

[0038] The interpretation department can simultaneously interpret lectures provided by the lecture department. For example, the interpretation department can simultaneously interpret lectures provided by the lecture department in real time. For example, the interpretation department can perform simultaneous interpretation while considering the interpretation system used and the supported languages. This allows the simultaneous interpretation function to provide a learning experience that transcends language barriers. Some or all of the above processing in the interpretation department may be performed using AI, for example, or without AI. For example, the interpretation department can input lectures provided by the lecture department into a generating AI and have the generating AI perform simultaneous interpretation.

[0039] The Field Trip Department can provide virtual field trips based on the curriculum proposed by the Proposal Department. For example, the Field Trip Department can provide virtual field trips based on the curriculum proposed by the Proposal Department, taking into account the technologies used and the content of the experiences. This allows the virtual field trip function to provide opportunities to experience local culture and lifestyle. Some or all of the above processing in the Field Trip Department may be performed using AI, for example, or without AI. For example, the Field Trip Department can input the curriculum proposed by the Proposal Department into a generating AI and have the generating AI perform the provision of virtual field trips.

[0040] The analysis department can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis department can use AI to select the optimal analysis algorithm based on subjects and areas in which the student has previously received high marks. For example, the analysis department can use AI to select an analysis algorithm to suggest a complementary learning style based on subjects and areas in which the student has previously struggled. The analysis department can also use AI to select the optimal analysis algorithm based on the student's history of participating in online courses and workshops. By selecting the optimal analysis algorithm based on past learning history, more accurate analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input a student's past learning history into a generating AI and have the generating AI select the optimal analysis algorithm.

[0041] The analysis department can improve the accuracy of its analysis based on students' current learning status and living environment. For example, the AI ​​can improve the accuracy of the analysis based on the progress of assignments and projects that students are currently working on. For example, the AI ​​can improve the accuracy of the analysis by considering the student's living environment (e.g., home internet connection status and availability of study space). The AI ​​can also improve the accuracy of the analysis based on the student's current learning resources (e.g., textbooks and reference books being used). This allows for more appropriate suggestions by improving the accuracy of the analysis based on the current learning status and living environment. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data on students' current learning status and living environment into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0042] The analysis unit can analyze region-specific learning styles and interests by considering students' geographical location information. For example, the analysis unit can use AI to analyze region-specific learning styles by considering the culture and customs of the area where students live. For example, the analysis unit can use AI to analyze region-specific interests by considering the characteristics of educational institutions in the area where students live. Furthermore, the analysis unit can use AI to analyze region-specific learning styles and interests by considering the industry and economic conditions of the area where students live. In this way, region-specific learning styles and interests can be analyzed by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input students' geographical location information into a generating AI and have the generating AI perform an analysis of region-specific learning styles and interests.

[0043] The analysis department can analyze students' social media activity and extract relevant interests and learning styles. For example, the analysis department can use AI to extract relevant interests based on accounts and groups that students follow on social media. For example, the analysis department can use AI to extract relevant learning styles based on content and comments that students share on social media. The analysis department can also use AI to extract relevant interests and learning styles based on online communities that students participate in on social media. In this way, relevant interests and learning styles can be extracted by analyzing social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input students' social media activity data into a generating AI and have the generating AI perform the extraction of relevant interests and learning styles.

[0044] The proposal department can adjust the level of detail of proposals based on the importance of educational institutions and curricula. For example, the proposal department can use AI to adjust the level of detail of proposals to provide detailed information for highly important educational institutions and curricula. For example, the proposal department can use AI to adjust the level of detail of proposals to provide concise information for less important educational institutions and curricula. The proposal department can also use AI to adjust the level of detail of proposals to prioritize providing highly important information according to students' interests. In this way, by adjusting the level of detail of proposals based on importance, information that is important to students can be prioritized. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input data on the importance of educational institutions and curricula into a generating AI and have the generating AI perform the adjustment of the level of detail of proposals.

[0045] The suggestion unit can apply different suggestion algorithms depending on the student's learning history and interests when making suggestions. For example, the AI ​​can apply different suggestion algorithms based on subjects or areas in which the student has previously received high marks. For example, the AI ​​can apply complementary suggestion algorithms based on subjects or areas in which the student has previously struggled. The suggestion unit can also apply the most suitable suggestion algorithm based on the student's interests. This allows for more appropriate suggestions by applying different suggestion algorithms according to learning history and interests. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the student's learning history and interest data into a generating AI and have the generating AI apply different suggestion algorithms.

[0046] The proposal department can determine the priority of proposals based on the availability dates of educational institutions and curricula. For example, the proposal department can use AI to prioritize proposals for educational institutions and curricula with upcoming availability dates. For example, the proposal department can use AI to prioritize proposals for educational institutions and curricula with later availability dates, postponing their proposals. The proposal department can also use AI to determine the priority of proposals to ensure they are made at the optimal time for students, according to their schedules. This allows proposals to be made at the most opportune time for students by prioritizing them based on availability dates. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the availability dates of educational institutions and curricula into a generating AI and have the generating AI determine the priority of proposals.

[0047] The suggestion function can adjust the order of suggestions based on the relevance of educational institutions and curricula during the suggestion process. For example, the AI ​​can adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's interests. For example, the AI ​​can adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's learning history. The AI ​​can also adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's future career goals. By adjusting the order of suggestions based on relevance, the AI ​​can prioritize providing students with the most relevant information. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can input relevance data of educational institutions and curricula into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0048] The lecture system can provide optimal lecture content by referring to students' past learning history during lectures. For example, the lecture system can use AI to provide optimal lecture content based on subjects and areas in which students have previously received high marks. For example, the lecture system can use AI to provide supplementary lecture content based on subjects and areas in which students have previously struggled. The lecture system can also use AI to provide optimal lecture content based on students' past learning history. This allows for more effective learning for students by providing optimal lecture content based on their past learning history. Some or all of the above processes in the lecture system may be performed using AI, or not. For example, the lecture system can input students' past learning history data into a generating AI and have the generating AI perform the task of providing optimal lecture content.

[0049] The lecture system can adjust the pace of the lecture based on the students' current learning progress. For example, the lecture system can use AI to adjust the lecture pace based on the progress of assignments or projects that students are currently working on. For example, the lecture system can use AI to adjust the lecture pace based on the students' current level of understanding. The lecture system can also use AI to adjust the lecture pace based on the students' current learning resources (for example, textbooks or reference books they are using). This allows students to learn at an optimal pace by adjusting the lecture pace based on their current learning progress. Some or all of the above processes in the lecture system may be performed using AI, or not. For example, the lecture system can input data on students' current learning progress into a generating AI and have the generating AI adjust the lecture pace.

[0050] The lecture system can provide optimal lecture content by considering the geographical location of students during lectures. For example, the lecture system can use AI to provide optimal lecture content by considering the culture and customs of the area where students live. For example, the lecture system can use AI to provide optimal lecture content by considering the characteristics of educational institutions in the area where students live. Furthermore, the lecture system can use AI to provide optimal lecture content by considering the industry and economic conditions of the area where students live. In this way, by considering geographical location information, region-specific lecture content can be provided. Some or all of the above processing in the lecture system may be performed using AI, for example, or without AI. For example, the lecture system can input the geographical location information of students into a generating AI and have the generating AI perform the task of providing optimal lecture content.

[0051] The lecture department can analyze students' social media activity during lectures and provide relevant lecture content. For example, the lecture department can use AI to provide relevant lecture content based on accounts and groups that students follow on social media. For example, the lecture department can use AI to provide relevant lecture content based on content and comments that students share on social media. The lecture department can also use AI to provide relevant lecture content based on online communities that students participate in on social media. In this way, relevant lecture content can be provided by analyzing social media activity. Some or all of the above processing in the lecture department may be performed using AI, for example, or without AI. For example, the lecture department can input students' social media activity data into a generating AI and have the generating AI provide relevant lecture content.

[0052] The interpreting department can adjust the level of detail in the interpretation based on the importance of the lecture content. For example, the AI ​​can adjust the level of detail in the interpretation to provide a detailed interpretation for highly important lecture content. For example, the AI ​​can adjust the level of detail in the interpretation to provide a concise interpretation for less important lecture content. The interpreting department can also adjust the level of detail in the interpretation to prioritize highly important information according to the students' interests. In this way, by adjusting the level of detail in the interpretation based on the importance of the lecture content, information that is important to the students can be prioritized in the interpretation. Some or all of the above processing in the interpreting department may be performed using AI, for example, or without AI. For example, the interpreting department can input lecture content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the interpretation.

[0053] The interpretation department can apply different interpretation algorithms depending on the student's language proficiency during interpretation. For example, if a student has high language proficiency, the AI ​​can apply a different interpretation algorithm to provide a detailed interpretation that includes specialized terminology. For example, if a student has low language proficiency, the AI ​​can apply a different interpretation algorithm to provide a concise and easy-to-understand interpretation. The interpretation department can also apply different interpretation algorithms to the AI ​​to apply the most suitable interpretation algorithm depending on the student's language proficiency. This allows for more appropriate interpretations to be provided by applying different interpretation algorithms according to the student's language proficiency. Some or all of the above processing in the interpretation department may be performed using AI, for example, or without AI. For example, the interpretation department can input student language proficiency data into a generating AI and have the generating AI apply different interpretation algorithms.

[0054] The interpretation department can determine the priority of interpretations based on the timing of lecture content delivery. For example, the AI ​​can determine the priority of interpretations so that lecture content with an upcoming delivery date is interpreted first. For example, the AI ​​can determine the priority of interpretations so that lecture content with a distant delivery date is interpreted later. The AI ​​can also determine the priority of interpretations so that interpretations are performed at the optimal time according to the student's schedule. In this way, by determining the priority of interpretations based on the delivery date, interpretations can be performed at the optimal time for the student. Some or all of the above processes in the interpretation department may be performed using AI, or they may not. For example, the interpretation department can input lecture content delivery date data into a generating AI and have the generating AI perform the determination of interpretation priorities.

[0055] The interpretation department can adjust the order of interpretation based on the relevance of the lecture content during interpretation. For example, the AI ​​can adjust the order of interpretation to prioritize lecture content that is most relevant to the student's interests. For example, the AI ​​can adjust the order of interpretation to prioritize lecture content that is most relevant based on the student's learning history. The AI ​​can also adjust the order of interpretation to prioritize lecture content that is most relevant based on the student's future career goals. In this way, by adjusting the order of interpretation based on relevance, the information most relevant to the student can be prioritized. Some or all of the above processing in the interpretation department may be performed using AI, for example, or not using AI. For example, the interpretation department can input relevance data of the lecture content into a generating AI and have the generating AI perform the adjustment of the order of interpretation.

[0056] The Field Trip Department can provide optimal field trip content by referencing students' past interests. For example, the Field Trip Department can use AI to provide optimal field trip content based on tourist destinations and cultural facilities that students have shown interest in in the past. For example, the Field Trip Department can use AI to provide optimal field trip content based on the history of events and workshops that students have participated in in the past. The Field Trip Department can also use AI to provide optimal field trip content based on students' past interests. This allows for more effective learning for students by providing optimal field trip content based on their past interests. Some or all of the above processing in the Field Trip Department may be performed using AI, or not using AI. For example, the Field Trip Department can input students' past interest data into a generating AI and have the generating AI perform the task of providing optimal field trip content.

[0057] The field trip unit can customize the content of field trips based on students' current learning status. For example, the field trip unit can use AI to customize the field trip content based on the progress of assignments or projects that students are currently working on. For example, the field trip unit can use AI to customize the field trip content based on students' current level of understanding. The field trip unit can also use AI to customize the field trip content based on students' current learning resources (for example, textbooks and reference books they are using). This allows for optimal learning for students by customizing the field trip content based on their current learning status. Some or all of the above processes in the field trip unit may be performed using AI, or not. For example, the field trip unit can input students' current learning status data into a generating AI and have the generating AI perform the customization of the field trip content.

[0058] The field trip unit can provide optimal field trip content by considering the geographical location information of students. For example, the field trip unit can use AI to provide optimal field trip content by considering the culture and customs of the area where the student lives. For example, the field trip unit can use AI to provide optimal field trip content by considering the characteristics of educational institutions in the area where the student lives. Furthermore, the field trip unit can use AI to provide optimal field trip content by considering the industry and economic conditions of the area where the student lives. In this way, by considering geographical location information, it is possible to provide field trip content specific to the region. Some or all of the above processing in the field trip unit may be performed using AI, for example, or without AI. For example, the field trip unit can input the student's geographical location information into a generating AI and have the generating AI perform the task of providing optimal field trip content.

[0059] The Field Trip Department can analyze students' social media activity during field trips and provide relevant content. For example, the Field Trip Department can use AI to provide relevant field trip content based on accounts and groups that students follow on social media. For example, the Field Trip Department can use AI to provide relevant field trip content based on content and comments that students share on social media. The Field Trip Department can also use AI to provide relevant field trip content based on online communities that students participate in on social media. In this way, relevant field trip content can be provided by analyzing social media activity. Some or all of the above processing in the Field Trip Department may be performed using AI, for example, or without AI. For example, the Field Trip Department can input students' social media activity data into a generating AI and have the generating AI provide relevant field trip content.

[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 remote campus connect system can also include a progress management unit that visualizes students' learning progress. This unit can, for example, display students' learning progress using graphs and charts, allowing for a visual understanding of their learning achievements. For instance, it can display in real time how far along a student is in relation to their goals. Furthermore, if a student is falling behind, the unit can suggest supplementary learning resources. By visualizing learning progress, students can more easily understand their own learning situation and increase their motivation to learn.

[0062] The remote campus connect system can also include an assessment unit to evaluate students' learning performance. This unit can, for example, analyze students' assignments and test results to assess their learning performance. It can also identify students' strengths and weaknesses and provide individualized feedback. Furthermore, the assessment unit can suggest the next learning steps based on the student's progress. This allows students to objectively understand their own learning situation and identify areas for improvement by evaluating their learning performance.

[0063] The remote campus connect system can also include an environment adjustment unit to further optimize the student learning environment. This unit monitors the student's learning environment (lighting, volume, temperature, etc.) and provides an optimal environment. For example, it adjusts the brightness of the lighting to create an environment conducive to student concentration. It can also play background music to help students relax. By optimizing the learning environment in this way, students can learn more effectively.

[0064] The remote campus connect system can also include a community building section to foster student learning communities. This section could, for example, provide online forums or chat rooms where students can interact with each other. It could also enable students to join groups with shared interests. Furthermore, the community building section could create an environment where students help and support each other's learning. This allows students to engage in their studies without feeling isolated by forming learning communities.

[0065] The remote campus connect system can also include a customization section that provides personalized learning plans tailored to students' learning styles. This customization section can create optimal learning plans based on students' learning styles (visual, auditory, tactile, etc.). For example, it can provide a plan with a lot of visual content for students with a visual learning style, and a plan with a lot of audio content for students with an auditory learning style. This allows students to learn more effectively by providing customized learning plans that match their learning style.

[0066] The remote campus connect system can also include an outcomes sharing section for sharing students' learning outcomes. This section could provide a platform for students to share projects and reports they have created with other students and faculty. For example, it could offer online presentation features for students to present their work. Furthermore, the outcomes sharing section could allow students to evaluate and provide feedback on other students' work. This allows students to learn from and grow together by sharing their learning outcomes.

[0067] The remote campus connect system can also include a resource management unit to manage students' learning resources. This unit centrally manages the learning resources students need (textbooks, reference books, online courses, etc.). For example, it allows students to easily search for and access resources. It can also track students' progress as they use resources and suggest additional resources as needed. This management of learning resources enables students to learn more efficiently.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The analysis department analyzes students' interests, learning styles, and other relevant information. For example, they analyze data such as survey results, learning history, and behavioral data based on information entered by students. Step 2: The proposal department proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal will take into account the evaluation criteria for educational institutions, the content and objectives of the curriculum, and other factors. Step 3: The lecture department will provide real-time lectures based on the curriculum proposed by the proposal department. For example, they will provide real-time lectures that include live streaming and interactive elements. Step 4: The interpreting department provides simultaneous interpretation of the lectures delivered by the lecture department. For example, they perform real-time simultaneous interpretation, taking into account the interpretation system used and the languages ​​supported. Step 5: The Field Trip Department will provide a virtual field trip based on the curriculum proposed by the Proposal Department. For example, the virtual field trip will be provided taking into consideration the technologies used and the content of the experiences.

[0070] (Example of form 2) The Remote Campus Connect System, according to an embodiment of the present invention, is a system that utilizes generative AI to provide a virtual study abroad program that allows urban students to study at universities and vocational schools in rural areas. This system aims to revitalize rural educational institutions and promote future migration of young people to rural areas. The Remote Campus Connect System analyzes students' interests and learning styles and proposes the most suitable rural educational institutions and curricula. For example, if a student is interested in science, the system suggests a suitable rural university or vocational school. Next, learning in a virtual space begins. The system enables students to participate in real-time lectures in a high-definition 3D environment generated by the generative AI. For example, a student can participate in a lecture at a rural university from their home and interact with other students in the region. This virtual space replicates the actual campus scenery and classroom environment, allowing students to experience it as if they were actually there. Furthermore, the system is equipped with an AI-powered simultaneous interpretation function, providing a learning experience that transcends language barriers. For example, when taking a foreign language lecture, the AI ​​provides real-time interpretation, allowing students to understand the lecture in their native language. The system also includes a virtual field trip function that allows students to experience rural culture and lifestyle. Students can visit local tourist destinations and cultural facilities in a virtual space, directly experiencing the charm of regional areas. For example, students can experience local festivals and traditional crafts through virtual tours. In this way, the Remote Campus Connect system functions not merely as an online learning tool, but as an educational platform connecting urban and rural areas. It aims to simultaneously achieve the effective utilization of local educational resources and stimulate young people's interest in rural areas, thereby revitalizing local educational institutions and promoting future migration of young people to rural areas. Through this, the Remote Campus Connect system can propose the most suitable educational institutions and curricula based on students' interests and learning styles, and by providing real-time lectures and virtual field trips, it can revitalize local educational institutions and promote migration of young people to rural areas.

[0071] The remote campus connect system according to this embodiment comprises an analysis unit, a proposal unit, a lecture unit, an interpretation unit, and a field trip unit. The analysis unit analyzes students' interests and learning styles. The analysis unit analyzes students' interests and learning styles based on information entered by students, for example. For example, the analysis unit can analyze students' interests and learning styles using survey results, learning history, behavioral data, etc. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit. The proposal unit proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis unit, for example. For example, the proposal unit can make proposals considering the evaluation criteria of educational institutions, the content and objectives of the curriculum, etc. The lecture unit provides real-time lectures based on the curriculum proposed by the proposal unit. The lecture unit can provide real-time lectures, for example, based on the curriculum proposed by the proposal unit, including live streaming and interactive elements. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit. The interpretation unit provides simultaneous interpretation of the lectures provided by the lecture unit in real time. For example, the interpretation department can perform simultaneous interpretation, taking into account the interpretation system used and the languages ​​supported. The field trip department provides virtual field trips based on the curriculum proposed by the proposal department. The field trip department can provide virtual field trips based on the curriculum proposed by the proposal department, taking into account the technologies used and the content of the experiences. As a result, the remote campus connect system according to this embodiment can propose the most suitable educational institution and curriculum based on students' interests and learning styles, and by providing real-time lectures and virtual field trips, it can revitalize local educational institutions and promote the migration of young people to rural areas.

[0072] The analytics department analyzes students' interests and learning styles. Specifically, it conducts a detailed analysis of students' interests and learning styles based on information they provide. For example, the analytics department can analyze students' interests and learning styles from multiple perspectives using survey results, learning history, and behavioral data. Survey results are important data for understanding what subjects and topics students are interested in, and learning history serves as an indicator of what learning methods have been effective in the past. Behavioral data provides information about the learning environment, such as what times of day students tend to study and what devices they use. By integrating this data and using AI for pattern recognition and clustering, it is possible to identify the optimal learning style and interests for each individual student. Furthermore, the analytics department can monitor students' learning progress and performance in real time and provide feedback as needed. This allows students to enjoy an optimal learning environment based on their own learning style and interests.

[0073] The Proposal Department proposes the most suitable local educational institutions and curricula based on the information analyzed by the Analysis Department. Specifically, it selects the most suitable local educational institutions and curricula based on data on students' interests and learning styles obtained by the Analysis Department. The Proposal Department makes proposals by comprehensively considering the evaluation criteria of educational institutions, the content and objectives of the curriculum, and the characteristics of the educational institutions. For example, it selects educational institutions that have strengths in specific fields or educational institutions that offer specialized curricula that match students' interests. The Proposal Department also proposes curricula that match students' learning styles. For example, it proposes curricula that emphasize fieldwork and experiments for students who prefer practical learning, and curricula that focus on lectures and discussions for students who prefer theoretical learning. Furthermore, the Proposal Department can also propose curricula that match students' future career paths and goals. This allows students to choose the educational institution and curriculum that best suits their interests and learning style, enabling them to achieve effective learning.

[0074] The lecture department will provide real-time lectures based on the curriculum proposed by the proposal department. Specifically, it will provide real-time lectures, including live streaming and interactive elements, based on the curriculum selected by the proposal department. The lecture department will utilize the latest online education technologies to enable students to participate remotely. For example, lectures will be delivered in real time via live streaming, allowing students to participate from home or any location. By incorporating interactive elements, students can ask questions and participate in discussions during lectures. Furthermore, the lecture department can also provide recorded lectures on demand, allowing students to learn at their own pace. In this way, the lecture department will provide an environment in which students can participate in lectures in real time and enjoy an interactive learning experience.

[0075] The Interpretation Department provides simultaneous interpretation of lectures delivered by the Lecture Department. Specifically, it provides real-time simultaneous interpretation of lectures delivered by the Lecture Department, enabling students to participate in lectures without experiencing language barriers. The Interpretation Department performs simultaneous interpretation considering the interpretation system used and the languages ​​supported. For example, it can introduce an AI-powered automatic interpretation system that can translate lecture content into multiple languages ​​in real time. This allows students who speak different languages ​​to participate in the same lecture and understand the same content. In addition, the Interpretation Department employs professional interpreters to accurately interpret specialized terminology and technical content in specific fields. Furthermore, the Interpretation Department can interpret student questions and discussions in real time, providing them without compromising the interactive elements of the lectures. In this way, the Interpretation Department can remove language barriers and realize a global learning environment.

[0076] The Field Trip Department provides virtual field trips based on the curriculum proposed by the Proposal Department. Specifically, it provides virtual field trips based on the curriculum selected by the Proposal Department, taking into consideration the technologies used and the content of the experiences. The Field Trip Department utilizes the latest VR (Virtual Reality) and AR (Augmented Reality) technologies to provide an environment where students can have realistic experiences without actually going to the site. For example, using VR goggles, students can experience a field trip in a virtual space and learn about the local scenery, culture, and history. Furthermore, by using AR technology to overlay virtual information onto the actual scenery, a more interactive experience can be provided. In addition, the Field Trip Department can invite experts and guides to provide real-time commentary during the virtual field trip. This allows students to experience virtual field trips based on their interests and gain a learning experience that is close to an actual site visit.

[0077] The analysis department can analyze students' interests and learning styles based on the information they input. For example, the analysis department can analyze students' interests and learning styles based on the information they input. For example, the analysis department can analyze students' interests and learning styles using survey results, learning history, behavioral data, etc. This allows for more appropriate suggestions to be made by analyzing students' interests and learning styles based on the information they input. Some or all of the above processing in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input information entered by students into a generating AI and have the generating AI perform the analysis of interests and learning styles.

[0078] The proposal department can propose the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal department can propose the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal department can make proposals considering the evaluation criteria of educational institutions, the content and objectives of curricula, etc. In this way, by proposing the most suitable educational institutions and curricula based on the analysis results, a learning environment suitable for students can be provided. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the information analyzed by the analysis department into a generating AI and have the generating AI execute a proposal for the most suitable educational institutions and curricula.

[0079] The lecture department can provide real-time lectures based on the curriculum proposed by the proposal department. For example, the lecture department can provide real-time lectures that include live streaming and interactive elements based on the curriculum proposed by the proposal department. This allows students to experience what it is like to be in an actual lecture by providing real-time lectures based on the proposed curriculum. Some or all of the above processes in the lecture department may be performed using AI, for example, or not using AI. For example, the lecture department can input the curriculum proposed by the proposal department into a generative AI and have the generative AI perform the delivery of real-time lectures.

[0080] The interpretation department can simultaneously interpret lectures provided by the lecture department. For example, the interpretation department can simultaneously interpret lectures provided by the lecture department in real time. For example, the interpretation department can perform simultaneous interpretation while considering the interpretation system used and the supported languages. This allows the simultaneous interpretation function to provide a learning experience that transcends language barriers. Some or all of the above processing in the interpretation department may be performed using AI, for example, or without AI. For example, the interpretation department can input lectures provided by the lecture department into a generating AI and have the generating AI perform simultaneous interpretation.

[0081] The Field Trip Department can provide virtual field trips based on the curriculum proposed by the Proposal Department. For example, the Field Trip Department can provide virtual field trips based on the curriculum proposed by the Proposal Department, taking into account the technologies used and the content of the experiences. This allows the virtual field trip function to provide opportunities to experience local culture and lifestyle. Some or all of the above processing in the Field Trip Department may be performed using AI, for example, or without AI. For example, the Field Trip Department can input the curriculum proposed by the Proposal Department into a generating AI and have the generating AI perform the provision of virtual field trips.

[0082] The analysis unit can estimate students' emotions and adjust the analysis method for interests and learning styles based on the estimated emotions. For example, if a student is stressed, the AI ​​can adjust the analysis method to suggest a relaxing learning style. For example, if a student is excited, the AI ​​can adjust the analysis method to suggest a learning style that enhances concentration. The analysis unit can also adjust the analysis method to suggest a less burdensome learning style if a student is tired. In this way, by adjusting the analysis method based on students' emotions, a more appropriate learning style can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI adjust the analysis method for interests and learning styles.

[0083] The analysis department can analyze a student's past learning history and select the optimal analysis algorithm. For example, the analysis department can use AI to select the optimal analysis algorithm based on subjects and areas in which the student has previously received high marks. For example, the analysis department can use AI to select an analysis algorithm to suggest a complementary learning style based on subjects and areas in which the student has previously struggled. The analysis department can also use AI to select the optimal analysis algorithm based on the student's history of participating in online courses and workshops. By selecting the optimal analysis algorithm based on past learning history, more accurate analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input a student's past learning history into a generating AI and have the generating AI select the optimal analysis algorithm.

[0084] The analysis department can improve the accuracy of its analysis based on students' current learning status and living environment. For example, the AI ​​can improve the accuracy of the analysis based on the progress of assignments and projects that students are currently working on. For example, the AI ​​can improve the accuracy of the analysis by considering the student's living environment (e.g., home internet connection status and availability of study space). The AI ​​can also improve the accuracy of the analysis based on the student's current learning resources (e.g., textbooks and reference books being used). This allows for more appropriate suggestions by improving the accuracy of the analysis based on the current learning status and living environment. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input data on students' current learning status and living environment into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0085] The analysis unit can estimate students' emotions and prioritize analysis results based on the estimated emotions. For example, if a student is stressed, the AI ​​in the analysis unit prioritizes suggesting relaxing learning styles. For example, if a student is excited, the AI ​​in the analysis unit prioritizes suggesting learning styles that enhance concentration. The analysis unit can also prioritize suggesting less burdensome learning styles if a student is tired. By prioritizing analysis results based on students' emotions, a more appropriate learning style can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI determine the priority of analysis results.

[0086] The analysis unit can analyze region-specific learning styles and interests by considering students' geographical location information. For example, the analysis unit can use AI to analyze region-specific learning styles by considering the culture and customs of the area where students live. For example, the analysis unit can use AI to analyze region-specific interests by considering the characteristics of educational institutions in the area where students live. Furthermore, the analysis unit can use AI to analyze region-specific learning styles and interests by considering the industry and economic conditions of the area where students live. In this way, region-specific learning styles and interests can be analyzed by considering geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input students' geographical location information into a generating AI and have the generating AI perform an analysis of region-specific learning styles and interests.

[0087] The analysis department can analyze students' social media activity and extract relevant interests and learning styles. For example, the analysis department can use AI to extract relevant interests based on accounts and groups that students follow on social media. For example, the analysis department can use AI to extract relevant learning styles based on content and comments that students share on social media. The analysis department can also use AI to extract relevant interests and learning styles based on online communities that students participate in on social media. In this way, relevant interests and learning styles can be extracted by analyzing social media activity. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input students' social media activity data into a generating AI and have the generating AI perform the extraction of relevant interests and learning styles.

[0088] The proposal unit can estimate the student's emotions and adjust the way the proposal is presented based on those emotions. For example, if the student is relaxed, the AI ​​can adjust the presentation to make it more approachable. If the student is nervous, the AI ​​can adjust the presentation to make it calmer. If the student is excited, the AI ​​can adjust the presentation to make it more energetic. By adjusting the presentation based on the student's emotions, more appropriate proposals can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input student emotion data into a generative AI and have the generative AI adjust the presentation of the proposal.

[0089] The proposal department can adjust the level of detail of proposals based on the importance of educational institutions and curricula. For example, the proposal department can use AI to adjust the level of detail of proposals to provide detailed information for highly important educational institutions and curricula. For example, the proposal department can use AI to adjust the level of detail of proposals to provide concise information for less important educational institutions and curricula. The proposal department can also use AI to adjust the level of detail of proposals to prioritize providing highly important information according to students' interests. In this way, by adjusting the level of detail of proposals based on importance, information that is important to students can be prioritized. Some or all of the above processing in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input data on the importance of educational institutions and curricula into a generating AI and have the generating AI perform the adjustment of the level of detail of proposals.

[0090] The suggestion unit can apply different suggestion algorithms depending on the student's learning history and interests when making suggestions. For example, the AI ​​can apply different suggestion algorithms based on subjects or areas in which the student has previously received high marks. For example, the AI ​​can apply complementary suggestion algorithms based on subjects or areas in which the student has previously struggled. The suggestion unit can also apply the most suitable suggestion algorithm based on the student's interests. This allows for more appropriate suggestions by applying different suggestion algorithms according to learning history and interests. Some or all of the above processing in the suggestion unit may be performed using AI, or not. For example, the suggestion unit can input the student's learning history and interest data into a generating AI and have the generating AI apply different suggestion algorithms.

[0091] The suggestion section can estimate a student's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if a student is in a hurry, the AI ​​can adjust the length of the suggestion to make it short and to the point. For example, if a student is relaxed, the AI ​​can adjust the length of the suggestion to make it longer and include more detailed information. The suggestion section can also adjust the length of the suggestion to make it visually stimulating if a student is excited. By adjusting the length of the suggestion based on the student's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input student emotion data into a generative AI and have the generative AI adjust the length of the suggestion.

[0092] The proposal department can determine the priority of proposals based on the availability dates of educational institutions and curricula. For example, the proposal department can use AI to prioritize proposals for educational institutions and curricula with upcoming availability dates. For example, the proposal department can use AI to prioritize proposals for educational institutions and curricula with later availability dates, postponing their proposals. The proposal department can also use AI to determine the priority of proposals to ensure they are made at the optimal time for students, according to their schedules. This allows proposals to be made at the most opportune time for students by prioritizing them based on availability dates. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input data on the availability dates of educational institutions and curricula into a generating AI and have the generating AI determine the priority of proposals.

[0093] The suggestion function can adjust the order of suggestions based on the relevance of educational institutions and curricula during the suggestion process. For example, the AI ​​can adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's interests. For example, the AI ​​can adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's learning history. The AI ​​can also adjust the order of suggestions to prioritize educational institutions and curricula most relevant to the student's future career goals. By adjusting the order of suggestions based on relevance, the AI ​​can prioritize providing students with the most relevant information. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function can input relevance data of educational institutions and curricula into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0094] The lecture system can estimate students' emotions and adjust the lecture pace based on those estimates. For example, if students are relaxed, the AI ​​can adjust the pace to be more relaxed. If students are nervous, the AI ​​can adjust the pace to be more calming. If students are excited, the AI ​​can adjust the pace to be more energetic. By adjusting the lecture pace based on students' emotions, a more appropriate lecture can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lecture system may be performed using AI or not. For example, the lecture system can input student emotion data into a generative AI and have the generative AI adjust the lecture pace.

[0095] The lecture system can provide optimal lecture content by referring to students' past learning history during lectures. For example, the lecture system can use AI to provide optimal lecture content based on subjects and areas in which students have previously received high marks. For example, the lecture system can use AI to provide supplementary lecture content based on subjects and areas in which students have previously struggled. The lecture system can also use AI to provide optimal lecture content based on students' past learning history. This allows for more effective learning for students by providing optimal lecture content based on their past learning history. Some or all of the above processes in the lecture system may be performed using AI, or not. For example, the lecture system can input students' past learning history data into a generating AI and have the generating AI perform the task of providing optimal lecture content.

[0096] The lecture system can adjust the pace of the lecture based on the students' current learning progress. For example, the lecture system can use AI to adjust the lecture pace based on the progress of assignments or projects that students are currently working on. For example, the lecture system can use AI to adjust the lecture pace based on the students' current level of understanding. The lecture system can also use AI to adjust the lecture pace based on the students' current learning resources (for example, textbooks or reference books they are using). This allows students to learn at an optimal pace by adjusting the lecture pace based on their current learning progress. Some or all of the above processes in the lecture system may be performed using AI, or not. For example, the lecture system can input data on students' current learning progress into a generating AI and have the generating AI adjust the lecture pace.

[0097] The lecture department can estimate students' emotions and prioritize lectures based on those estimated emotions. For example, if students are relaxed, the lecture department can use AI to prioritize lectures that promote relaxation. For example, if students are nervous, the lecture department can use AI to prioritize lectures with a calm atmosphere. The lecture department can also use AI to prioritize lectures that are energetic if students are excited. This allows for the provision of more appropriate lectures by prioritizing lectures based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the lecture department may be performed using AI or not. For example, the lecture department can input student emotion data into a generative AI and have the generative AI determine the lecture priorities.

[0098] The lecture system can provide optimal lecture content by considering the geographical location of students during lectures. For example, the lecture system can use AI to provide optimal lecture content by considering the culture and customs of the area where students live. For example, the lecture system can use AI to provide optimal lecture content by considering the characteristics of educational institutions in the area where students live. Furthermore, the lecture system can use AI to provide optimal lecture content by considering the industry and economic conditions of the area where students live. In this way, by considering geographical location information, region-specific lecture content can be provided. Some or all of the above processing in the lecture system may be performed using AI, for example, or without AI. For example, the lecture system can input the geographical location information of students into a generating AI and have the generating AI perform the task of providing optimal lecture content.

[0099] The lecture department can analyze students' social media activity during lectures and provide relevant lecture content. For example, the lecture department can use AI to provide relevant lecture content based on accounts and groups that students follow on social media. For example, the lecture department can use AI to provide relevant lecture content based on content and comments that students share on social media. The lecture department can also use AI to provide relevant lecture content based on online communities that students participate in on social media. In this way, relevant lecture content can be provided by analyzing social media activity. Some or all of the above processing in the lecture department may be performed using AI, for example, or without AI. For example, the lecture department can input students' social media activity data into a generating AI and have the generating AI provide relevant lecture content.

[0100] The interpreting department can estimate the student's emotions and adjust the interpretation style based on the estimated emotions. For example, if the student is relaxed, the AI ​​can adjust the interpretation style to be more approachable. For example, if the student is nervous, the AI ​​can adjust the interpretation style to be more calm. The AI ​​can also adjust the interpretation style to be more energetic if the student is excited. By adjusting the interpretation style based on the student's emotions, a more appropriate interpretation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interpreting department may be performed using AI or not. For example, the interpreting department can input student emotion data into a generative AI and have the generative AI perform the adjustment of the interpretation style.

[0101] The interpreting department can adjust the level of detail in the interpretation based on the importance of the lecture content. For example, the AI ​​can adjust the level of detail in the interpretation to provide a detailed interpretation for highly important lecture content. For example, the AI ​​can adjust the level of detail in the interpretation to provide a concise interpretation for less important lecture content. The interpreting department can also adjust the level of detail in the interpretation to prioritize highly important information according to the students' interests. In this way, by adjusting the level of detail in the interpretation based on the importance of the lecture content, information that is important to the students can be prioritized in the interpretation. Some or all of the above processing in the interpreting department may be performed using AI, for example, or without AI. For example, the interpreting department can input lecture content importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the interpretation.

[0102] The interpretation department can apply different interpretation algorithms depending on the student's language proficiency during interpretation. For example, if a student has high language proficiency, the AI ​​can apply a different interpretation algorithm to provide a detailed interpretation that includes specialized terminology. For example, if a student has low language proficiency, the AI ​​can apply a different interpretation algorithm to provide a concise and easy-to-understand interpretation. The interpretation department can also apply different interpretation algorithms to the AI ​​to apply the most suitable interpretation algorithm depending on the student's language proficiency. This allows for more appropriate interpretations to be provided by applying different interpretation algorithms according to the student's language proficiency. Some or all of the above processing in the interpretation department may be performed using AI, for example, or without AI. For example, the interpretation department can input student language proficiency data into a generating AI and have the generating AI apply different interpretation algorithms.

[0103] The interpreting unit can estimate the student's emotions and adjust the length of the interpretation based on the estimated emotions. For example, if the student is in a hurry, the AI ​​can adjust the length of the interpretation to provide a short, to-the-point translation. For example, if the student is relaxed, the AI ​​can adjust the length of the interpretation to provide a longer translation that includes more detailed information. The AI ​​can also adjust the length of the interpretation to add visually stimulating effects if the student is excited. By adjusting the length of the interpretation based on the student's emotions, a more appropriate interpretation can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interpreting unit may be performed using AI or not. For example, the interpreting unit can input student emotion data into a generative AI and have the generative AI adjust the length of the interpretation.

[0104] The interpretation department can determine the priority of interpretations based on the timing of lecture content delivery. For example, the AI ​​can determine the priority of interpretations so that lecture content with an upcoming delivery date is interpreted first. For example, the AI ​​can determine the priority of interpretations so that lecture content with a distant delivery date is interpreted later. The AI ​​can also determine the priority of interpretations so that interpretations are performed at the optimal time according to the student's schedule. In this way, by determining the priority of interpretations based on the delivery date, interpretations can be performed at the optimal time for the student. Some or all of the above processes in the interpretation department may be performed using AI, or they may not. For example, the interpretation department can input lecture content delivery date data into a generating AI and have the generating AI perform the determination of interpretation priorities.

[0105] The interpretation department can adjust the order of interpretation based on the relevance of the lecture content during interpretation. For example, the AI ​​can adjust the order of interpretation to prioritize lecture content that is most relevant to the student's interests. For example, the AI ​​can adjust the order of interpretation to prioritize lecture content that is most relevant based on the student's learning history. The AI ​​can also adjust the order of interpretation to prioritize lecture content that is most relevant based on the student's future career goals. In this way, by adjusting the order of interpretation based on relevance, the information most relevant to the student can be prioritized. Some or all of the above processing in the interpretation department may be performed using AI, for example, or not using AI. For example, the interpretation department can input relevance data of the lecture content into a generating AI and have the generating AI perform the adjustment of the order of interpretation.

[0106] The field trip unit can estimate students' emotions and adjust the content of the field trip based on the estimated emotions. For example, if a student is relaxed, the AI ​​adjusts the content to provide a relaxing field trip. For example, if a student is nervous, the AI ​​adjusts the content to provide a calm atmosphere. The field trip unit can also adjust the content to provide an energetic field trip if a student is excited. In this way, by adjusting the content of the field trip based on students' emotions, more appropriate field trips can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the field trip unit may be performed using AI or not using AI. For example, the field trip unit can input student emotion data into a generative AI and have the generative AI perform the adjustment of the field trip content.

[0107] The Field Trip Department can provide optimal field trip content by referencing students' past interests. For example, the Field Trip Department can use AI to provide optimal field trip content based on tourist destinations and cultural facilities that students have shown interest in in the past. For example, the Field Trip Department can use AI to provide optimal field trip content based on the history of events and workshops that students have participated in in the past. The Field Trip Department can also use AI to provide optimal field trip content based on students' past interests. This allows for more effective learning for students by providing optimal field trip content based on their past interests. Some or all of the above processing in the Field Trip Department may be performed using AI, or not using AI. For example, the Field Trip Department can input students' past interest data into a generating AI and have the generating AI perform the task of providing optimal field trip content.

[0108] The field trip unit can customize the content of field trips based on students' current learning status. For example, the field trip unit can use AI to customize the field trip content based on the progress of assignments or projects that students are currently working on. For example, the field trip unit can use AI to customize the field trip content based on students' current level of understanding. The field trip unit can also use AI to customize the field trip content based on students' current learning resources (for example, textbooks and reference books they are using). This allows for optimal learning for students by customizing the field trip content based on their current learning status. Some or all of the above processes in the field trip unit may be performed using AI, or not. For example, the field trip unit can input students' current learning status data into a generating AI and have the generating AI perform the customization of the field trip content.

[0109] The field trip unit can estimate students' emotions and prioritize field trips based on those emotions. For example, if students are relaxed, the AI ​​will prioritize providing relaxing field trips. For example, if students are nervous, the AI ​​will prioritize providing field trips with a calm atmosphere. The AI ​​can also prioritize providing energetic field trips if students are excited. By prioritizing field trips based on students' emotions, more appropriate field trips can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the field trip unit may be performed using AI or not using AI. For example, the field trip department can input student emotional data into a generating AI and have the AI ​​determine the priority of field trips.

[0110] The field trip unit can provide optimal field trip content by considering the geographical location information of students. For example, the field trip unit can use AI to provide optimal field trip content by considering the culture and customs of the area where the student lives. For example, the field trip unit can use AI to provide optimal field trip content by considering the characteristics of educational institutions in the area where the student lives. Furthermore, the field trip unit can use AI to provide optimal field trip content by considering the industry and economic conditions of the area where the student lives. In this way, by considering geographical location information, it is possible to provide field trip content specific to the region. Some or all of the above processing in the field trip unit may be performed using AI, for example, or without AI. For example, the field trip unit can input the student's geographical location information into a generating AI and have the generating AI perform the task of providing optimal field trip content.

[0111] The Field Trip Department can analyze students' social media activity during field trips and provide relevant content. For example, the Field Trip Department can use AI to provide relevant field trip content based on accounts and groups that students follow on social media. For example, the Field Trip Department can use AI to provide relevant field trip content based on content and comments that students share on social media. The Field Trip Department can also use AI to provide relevant field trip content based on online communities that students participate in on social media. In this way, relevant field trip content can be provided by analyzing social media activity. Some or all of the above processing in the Field Trip Department may be performed using AI, for example, or without AI. For example, the Field Trip Department can input students' social media activity data into a generating AI and have the generating AI provide relevant field trip content.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The remote campus connect system can also include a health management department to monitor students' health. This department could, for example, monitor students' heart rates and sleep patterns to understand their health status. For instance, if a student is experiencing stress, the health management department could use AI to analyze health data and suggest a more relaxing learning style. It could also send notifications to students encouraging rest if they are feeling tired. This allows for a more effective learning environment by adjusting learning styles based on students' health status.

[0114] The remote campus connect system can also include a progress management unit that visualizes students' learning progress. This unit can, for example, display students' learning progress using graphs and charts, allowing for a visual understanding of their learning achievements. For instance, it can display in real time how far along a student is in relation to their goals. Furthermore, if a student is falling behind, the unit can suggest supplementary learning resources. By visualizing learning progress, students can more easily understand their own learning situation and increase their motivation to learn.

[0115] The remote campus connect system can also include an assessment unit to evaluate students' learning performance. This unit can, for example, analyze students' assignments and test results to assess their learning performance. It can also identify students' strengths and weaknesses and provide individualized feedback. Furthermore, the assessment unit can suggest the next learning steps based on the student's progress. This allows students to objectively understand their own learning situation and identify areas for improvement by evaluating their learning performance.

[0116] The remote campus connect system can also include an environment adjustment unit to further optimize the student learning environment. This unit monitors the student's learning environment (lighting, volume, temperature, etc.) and provides an optimal environment. For example, it adjusts the brightness of the lighting to create an environment conducive to student concentration. It can also play background music to help students relax. By optimizing the learning environment in this way, students can learn more effectively.

[0117] The remote campus connect system can also include a motivation management unit to further enhance student learning motivation. This unit can, for example, set learning goals for students and provide rewards based on their achievement. For instance, it could award virtual badges or points to students who achieve their goals. Furthermore, the motivation management unit can boost students' motivation by providing positive feedback on their learning. This, in turn, improves learning motivation, enabling students to continue their studies consistently.

[0118] The remote campus connect system can also include a community building section to foster student learning communities. This section could, for example, provide online forums or chat rooms where students can interact with each other. It could also enable students to join groups with shared interests. Furthermore, the community building section could create an environment where students help and support each other's learning. This allows students to engage in their studies without feeling isolated by forming learning communities.

[0119] The remote campus connect system can also include a customization section that provides personalized learning plans tailored to students' learning styles. This customization section can create optimal learning plans based on students' learning styles (visual, auditory, tactile, etc.). For example, it can provide a plan with a lot of visual content for students with a visual learning style, and a plan with a lot of audio content for students with an auditory learning style. This allows students to learn more effectively by providing customized learning plans that match their learning style.

[0120] The remote campus connect system can also include an outcomes sharing section for sharing students' learning outcomes. This section could provide a platform for students to share projects and reports they have created with other students and faculty. For example, it could offer online presentation features for students to present their work. Furthermore, the outcomes sharing section could allow students to evaluate and provide feedback on other students' work. This allows students to learn from and grow together by sharing their learning outcomes.

[0121] The remote campus connect system can also include a resource management unit to manage students' learning resources. This unit centrally manages the learning resources students need (textbooks, reference books, online courses, etc.). For example, it allows students to easily search for and access resources. It can also track students' progress as they use resources and suggest additional resources as needed. This management of learning resources enables students to learn more efficiently.

[0122] The remote campus connect system can also include an emotion management unit that estimates students' emotions and adjusts the learning progress based on those estimates. For example, if a student is feeling stressed, the emotion management unit provides relaxing learning content. For example, if a student is excited, the emotion management unit provides learning content to improve their concentration. The emotion management unit can also send notifications to encourage students to rest if they are tired. This allows for a more effective learning environment by adjusting the learning progress based on students' emotions.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The analysis department analyzes students' interests, learning styles, and other relevant information. For example, they analyze data such as survey results, learning history, and behavioral data based on information entered by students. Step 2: The proposal department proposes the most suitable local educational institutions and curricula based on the information analyzed by the analysis department. For example, the proposal will take into account the evaluation criteria for educational institutions, the content and objectives of the curriculum, and other factors. Step 3: The lecture department will provide real-time lectures based on the curriculum proposed by the proposal department. For example, they will provide real-time lectures that include live streaming and interactive elements. Step 4: The interpreting department provides simultaneous interpretation of the lectures delivered by the lecture department. For example, they perform real-time simultaneous interpretation, taking into account the interpretation system used and the languages ​​supported. Step 5: The Field Trip Department will provide a virtual field trip based on the curriculum proposed by the Proposal Department. For example, the virtual field trip will be provided taking into consideration the technologies used and the content of the experiences.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the analysis unit, proposal unit, lecture unit, interpretation unit, and field trip unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by, for example, the control unit 46A of the smart device 14. The interpretation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The field trip unit is implemented by, for example, the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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 (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 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the analysis unit, proposal unit, lecture unit, interpretation unit, and field trip unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by, for example, the control unit 46A of the smart glasses 214. The interpretation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The field trip unit is implemented by, for example, the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.).

[0157] 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.

[0158] 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 (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 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.

[0159] 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.

[0160] Each of the multiple elements described above, including the analysis unit, proposal unit, lecture unit, interpretation unit, and field trip unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by, for example, the control unit 46A of the headset terminal 314. The interpretation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The field trip unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.).

[0174] 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.

[0175] 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 (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 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.

[0176] 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 external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0177] Each of the multiple elements described above, including the analysis unit, proposal unit, lecture unit, interpretation unit, and field trip unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The lecture unit is implemented by, for example, the control unit 46A of the robot 414. The interpretation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12. The field trip unit is implemented by, for example, the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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."

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] (Note 1) The analysis department analyzes students' interests and learning styles, Based on the information analyzed by the aforementioned analysis department, the proposal department proposes the most suitable local educational institutions and curricula. The lecture department provides real-time lectures based on the curriculum proposed by the aforementioned proposal department, The interpretation department provides simultaneous interpretation of the lectures offered by the aforementioned lecture department, The field trip unit provides a virtual field trip based on the curriculum proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We analyze students' interests, preferences, and learning styles based on the information they provide. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, Based on the information analyzed by the aforementioned analysis department, we propose the most suitable local educational institutions and curricula. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned lecture section, The proposed curriculum will be used to provide real-time lectures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned interpretation department, To provide simultaneous interpretation of lectures delivered by the aforementioned lecture department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned field trip section is A virtual field trip will be provided based on the curriculum proposed by the aforementioned proposal department. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is We estimate students' emotions and adjust the analysis methods for their interests and learning styles based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is Analyze students' past learning history and select the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is Improve the accuracy of the analysis based on students' current learning status and living environment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is The system estimates students' emotions and prioritizes the analysis results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is We analyze region-specific learning styles and interests, taking into account students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Analyze students' social media activity to extract relevant interests and learning styles. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, We estimate the students' emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the educational institution and curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the student's learning history and interests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, Estimate the students' emotions and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, When submitting proposals, prioritize them based on the educational institution and the timing of curriculum delivery. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of educational institutions and curricula. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned lecture section, The system estimates students' emotions and adjusts the lecture pace based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned lecture section, During lectures, we provide the most suitable lecture content by referring to students' past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned lecture section, During lectures, the pace of the lecture is adjusted based on the students' current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned lecture section, The system estimates students' emotions and prioritizes lectures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned lecture section, During lectures, we provide optimal lecture content while taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned lecture section, During lectures, analyze students' social media activity and provide relevant lecture content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned interpretation department, The system estimates the students' emotions and adjusts the interpretation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned interpretation department, When interpreting, adjust the level of detail in the interpretation based on the importance of the lecture content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned interpretation department, During interpretation, different interpretation algorithms are applied depending on the student's language proficiency. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned interpretation department, The system estimates the student's emotions and adjusts the length of the interpretation based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned interpretation department, When interpreting, the priority of interpreters is determined based on when the lecture content is provided. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned interpretation department, During interpretation, the order of interpretation is adjusted based on the relevance of the lecture content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned field trip section is We estimate students' emotions and adjust the content of field trips based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned field trip section is During field trips, we provide content that is best suited to the students' past interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned field trip section is During field trips, the content is customized based on the students' current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned field trip section is Estimate students' emotions and prioritize field trips based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned field trip section is During field trips, we provide the most suitable content considering the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned field trip section is During field trips, we analyze students' social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0197] 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. The analysis department analyzes students' interests and learning styles, Based on the information analyzed by the aforementioned analysis department, the proposal department proposes the most suitable local educational institutions and curricula. The lecture department provides real-time lectures based on the curriculum proposed by the aforementioned proposal department, The interpretation department provides simultaneous interpretation of the lectures offered by the aforementioned lecture department, The field trip unit provides a virtual field trip based on the curriculum proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit is We analyze students' interests, preferences, and learning styles based on the information they provide. The system according to feature 1.

3. The aforementioned proposal section is, Based on the information analyzed by the aforementioned analysis department, we propose the most suitable local educational institutions and curricula. The system according to feature 1.

4. The aforementioned lecture section, The proposed curriculum will be used to provide real-time lectures. The system according to feature 1.

5. The aforementioned interpretation department, Simultaneous interpretation of lectures provided by the aforementioned lecture department. The system according to feature 1.

6. The aforementioned field trip section is A virtual field trip will be provided based on the curriculum proposed by the aforementioned proposal department. The system according to feature 1.

7. The aforementioned analysis unit is We estimate students' emotions and adjust the analysis methods for their interests and learning styles based on those estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit is Analyze students' past learning history and select the optimal analysis algorithm. The system according to feature 1.

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