System

Abstract

An object of the system according to the embodiment is to recommend an optimal destination of study based on a student's desire and to efficiently arrange a place to stay and perform an entrance procedure.SOLUTION: A system includes a desire analysis part, a recommendation part, an arrangement part, and a procedure part. The request analysis part analyzes the request and condition of the student. The recommendation unit recommends an optimal destination of study based on the desire and the condition analyzed by the desire analysis unit. The arrangement unit arranges a staying place based on the destination after study recommended by the recommendation unit. The procedure part performs an entrance procedure and a visa application on the basis of the staying place arranged by the arrangement part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, the process of students finding their desired study abroad destination, arranging accommodation, and completing the enrollment procedures was complicated and time-consuming.

[0005] The system according to the embodiment aims to recommend the most suitable study abroad destination based on the student's wishes, and to efficiently arrange accommodation and complete admission procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a preference analysis unit, a recommendation unit, an arrangements unit, and a procedure unit. The preference analysis unit analyzes the student's preferences and conditions. The recommendation unit recommends the most suitable study abroad destination based on the preferences and conditions analyzed by the preference analysis unit. The arrangements unit arranges accommodation based on the study abroad destination recommended by the recommendation unit. The procedure unit carries out admission procedures and visa applications based on the accommodation arranged by the arrangements unit. [Effects of the Invention]

[0007] The system according to the embodiment can recommend the most suitable study abroad destination based on the student's preferences, and can efficiently arrange accommodation and complete admission procedures. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The study abroad support system according to an embodiment of the present invention is a system in which AI handles procedures such as recommendations for the student's desired study abroad destination and preparations for stay and enrollment, etc. As a result, the study abroad support system can automatically recommend the most suitable study abroad destination, arrange accommodation, complete enrollment procedures, and apply for a visa based on the student's wishes and conditions.

[0029] The study abroad support system according to the embodiment includes a preference analysis unit, a recommendation unit, an arrangement unit, and a procedure unit. The preference analysis unit analyzes a student's preferences and conditions. For example, the preference analysis unit analyzes conditions such as the student's major, budget, climate, and culture entered by the student. The preference analysis unit can also analyze the student's past academic performance and extracurricular activity data. The recommendation unit recommends an optimal study abroad destination based on the preferences and conditions analyzed by the preference analysis unit. For example, the recommendation unit uses a generation AI to create a list of study abroad destinations that match the student's preferences. The arrangement unit arranges accommodation based on the study abroad destination recommended by the recommendation unit. For example, the arrangement unit arranges the optimal accommodation based on the student's desired accommodation type (dormitory, apartment, homestay, etc.), budget, and location. The procedure unit performs admission procedures and visa applications based on the accommodation arranged by the arrangement unit. For example, the procedure unit automatically prepares and submits necessary documents, applies for entrance exams, and applies for visas. As a result, the study abroad support system according to the embodiment can automatically recommend study abroad destinations, arrange accommodation, complete admission procedures, and apply for visas based on the student's wishes and conditions.

[0030] The preference analysis unit analyzes students' past academic performance and extracurricular activity data and can recommend the most suitable study abroad destination based on that data. For example, the preference analysis unit imports students' past transcripts and extracurricular activity records into a database, and AI analyzes the data. For example, it can recommend prestigious universities to students with excellent grades, and universities with strong extracurricular activities to students with active extracurricular activities. The preference analysis unit also analyzes students' past academic performance and recommends universities with strong specialties to students who have achieved excellent results in a particular subject. For example, it can recommend prestigious universities for mathematics to students who excel in mathematics. The preference analysis unit also analyzes extracurricular activity data and recommends universities with strong specialties to students who are enthusiastic about a particular sport or club activity. For example, it can recommend universities with strong soccer clubs to students who are active in soccer. This allows the system to recommend the most suitable study abroad destination based on students' past academic performance and extracurricular activity data.

[0031] The preference analysis unit takes into account a student's future career goals and can recommend the study abroad destination that best suits those goals. For example, the preference analysis unit inputs a student's future career goals, and the AI ​​recommends the university that best suits those goals. For example, for a student aiming for international business, it would recommend an international business school. The preference analysis unit also recommends universities that have strong ties with related industries and companies based on the student's career goals. For example, for a student aiming for the IT industry, it would recommend a university that has strong ties with IT companies. The preference analysis unit also takes into account a student's career goals and recommends universities that offer programs and courses that are best suited to those goals. For example, for a student aiming to start a business, it would recommend a university with a comprehensive entrepreneurial support program. This makes it possible to recommend the optimal study abroad destination based on the student's future career goals.

[0032] The arrangement department can take into account a student's lifestyle and hobbies and arrange the most suitable accommodation for them. For example, the arrangement department inputs information about a student's lifestyle and hobbies, and the AI ​​arranges the most suitable accommodation based on that. For example, for a student who likes to go to bed early and rise early, the AI ​​can arrange accommodation in a quiet environment. The arrangement department also takes into account a student's hobbies and interests and arranges the most suitable accommodation for them. For example, for a student who likes sports, the AI ​​can arrange accommodation with excellent sports facilities. The arrangement department also lists the most suitable accommodations based on the student's lifestyle and hobbies. For example, for a student who likes to cook, the AI ​​can arrange accommodation with a well-equipped kitchen. This makes it possible to arrange the most suitable accommodation based on a student's lifestyle and hobbies.

[0033] The arrangement department can analyze safety and security information about the destination and arrange the safest place to stay. For example, the arrangement department collects safety and security information about the destination, and AI analyzes it to arrange the safest place to stay. For example, arranging accommodation in an area with a low crime rate. The arrangement department also analyzes security information about the destination in real time, and AI arranges the safest place to stay. For example, selecting a safe place to stay based on recent security information. The arrangement department also collects data about the safety of the destination, and AI arranges the optimal place to stay based on that data. For example, arranging accommodation near a police station or fire station. This allows the safest place to be arranged based on the safety and security information of the destination.

[0034] The procedure unit analyzes data on a student's past admissions procedures and can implement the optimal procedure based on that data. For example, the procedure unit collects data on a student's past admissions procedures, and AI analyzes it to implement the optimal procedure. For example, a new procedure is implemented based on procedures that have been successful in the past. The procedure unit also analyzes data on a student's past admissions procedures and automatically implements the optimal procedure for a specific university or program. For example, a procedure is implemented based on data on students who have enrolled at the same university in the past. The procedure unit also uses AI to suggest the optimal procedure based on data on a student's past admissions procedures. For example, it improves any problems that occurred in the past procedures and implements a new procedure. This makes it possible to implement the optimal procedure based on data on a student's past admissions procedures.

[0035] The procedure unit can automatically generate and submit a student's academic record and letter of recommendation during the admissions process. For example, the procedure unit uses AI to automatically generate a letter of recommendation based on the student's academic record and submit it during the admissions process. For example, it generates a highly rated letter of recommendation for a student with excellent grades. The procedure unit also analyzes a student's past academic data and uses AI to automatically generate a transcript, which it submits during the admissions process. For example, for a student who achieved excellent grades in a particular subject, it generates a certificate that highlights the grades in that subject. The procedure unit also uses AI to automatically generate a letter of recommendation and a transcript based on the student's academic record and extracurricular activity data and submit it during the admissions process. For example, for a student who is active in extracurricular activities, it generates a letter of recommendation that highlights those activities. This makes it possible to automatically generate and submit a student's academic record and letter of recommendation during the admissions process.

[0036] The procedure section can also collect the opinions of students' parents and guardians and take them into consideration when carrying out admission procedures. For example, the procedure section provides a questionnaire function to collect opinions from students' parents and guardians, and AI analyzes the results to carry out admission procedures. For example, universities recommended by parents can be given priority. The procedure section also collects information on admission procedures recommended by students' parents and guardians, and based on that, AI suggests the optimal procedure. For example, if parents prioritize safety, safe procedures will be given priority. The procedure section also collects the opinions of parents and guardians, and based on that, AI narrows down the candidates for admission procedures. For example, if parents recommend a specific university, the procedures for that university will be given priority. This allows admission procedures to be carried out taking the opinions of students' parents and guardians into consideration.

[0037] The procedure unit can handle the admissions process, including applying for scholarships and grants that students desire. For example, the procedure unit inputs information about the scholarships and grants that students desire, and the AI ​​then performs the optimal application process based on that information. For example, it automatically generates documents to apply for a specific scholarship. The procedure unit also collects information about the scholarships and grants that students desire, and the AI ​​then suggests the optimal application process based on that information. For example, it recommends scholarships based on the student's grades and financial situation. The procedure unit also automates the scholarship and grant application process, and the AI ​​prepares and submits the necessary documents on behalf of the student. For example, it automatically generates and submits scholarship application documents. This allows students to complete the admissions process, including applying for the scholarships and grants that they desire.

[0038] The processing unit analyzes students' past visa application data and can carry out the optimal visa application procedures based on that data. For example, the processing unit collects students' past visa application data, and AI analyzes it to carry out the optimal application procedures. For example, a new application is made based on application procedures that were successful in the past. The processing unit also analyzes students' past visa application data and automatically carries out the optimal application procedures for specific countries and programs. For example, an application is made based on the data of students who have studied in the same country in the past. The processing unit also uses AI to suggest the optimal application procedures based on students' past visa application data. For example, the AI ​​can improve any issues that arose in the past application and make a new application. This makes it possible to carry out the optimal visa application procedures based on students' past visa application data.

[0039] The processing unit automatically connects information about the student's residence or study abroad destination when applying for a visa, allowing for a smooth application process. The processing unit, for example, automatically connects information about the student's residence or study abroad destination, allowing AI to smoothly process visa applications. For example, it automatically inputs the residence address and details of the study destination. The processing unit also automatically obtains information about the student's residence or study abroad destination when applying for a visa, and generates application documents based on that information. For example, it automatically generates a residence contract and a study abroad acceptance certificate. The processing unit also uses AI to suggest the optimal visa application procedure based on the information about the student's residence or study abroad destination. For example, it automatically generates and submits the necessary documents based on the residence information. This allows the processing unit to automatically connect information about the student's residence or study abroad destination when applying for a visa, allowing for a smooth application process.

[0040] The processing department can also collect the opinions of students' parents and guardians and take them into consideration when processing visa applications. For example, the processing department provides a survey function to collect opinions from students' parents and guardians, and AI analyzes the results to process visa applications. For example, it can prioritize the procedures recommended by parents. The processing department also collects information on visa application procedures recommended by students' parents and guardians, and based on that, the AI ​​suggests the optimal procedure. For example, if parents prioritize safety, it will prioritize the safest procedure. The processing department also collects the opinions of parents and guardians, and based on that, the AI ​​narrows down the candidate visa application procedures. For example, if parents recommend a specific procedure, it will prioritize that procedure. This allows the visa application process to take the opinions of students' parents and guardians into consideration.

[0041] The processing unit can suggest the most appropriate type of visa based on the student's desired length of stay and purpose. For example, the processing unit inputs the student's desired length of stay and purpose, and the AI ​​then suggests the most appropriate type of visa based on that. For example, a student visa is suggested for a one-year study abroad program. The processing unit also automatically selects the type of visa based on the student's length of stay and purpose, and carries out the application process. For example, a short-term stay visa is suggested for a short-term study abroad program. The processing unit also lists the most appropriate visa types based on the student's length of stay and purpose, and handles the application process on their behalf. For example, an intern visa is suggested for an internship. This makes it possible to suggest the most appropriate type of visa based on the student's desired length of stay and purpose.

[0042] The procedure unit can analyze a student's past procedure data and implement the optimal procedure based on that. For example, the procedure unit collects a student's past procedure data, and AI analyzes it to implement the optimal procedure. For example, a new procedure is implemented based on procedures that have been successful in the past. The procedure unit also analyzes a student's past procedure data and automatically implements the optimal method for a specific procedure. For example, a procedure is implemented based on the data of students who have implemented the same procedure in the past. The procedure unit also uses AI to suggest the optimal procedure based on a student's past procedure data. For example, it improves any problems that occurred in the past procedure and implements a new procedure. This makes it possible to implement the optimal procedure based on a student's past procedure data.

[0043] The procedure unit takes into account a student's lifestyle and hobbies, and can carry out the most suitable procedure. For example, the procedure unit inputs information about a student's lifestyle and hobbies, and the AI ​​carries out the most suitable procedure based on that information. For example, for a student who likes to go to bed early and rise early, the AI ​​will carry out a procedure that suits that habit. The procedure unit also takes into account a student's hobbies and interests, and the AI ​​will carry out the most suitable procedure. For example, for a student who likes sports, the AI ​​will prioritize sports-related procedures. The procedure unit also lists the most suitable procedures based on the student's lifestyle and hobbies. For example, for a student who likes cooking, the AI ​​will carry out a procedure for a cooking class. This allows the AI ​​to carry out the most suitable procedure based on the student's lifestyle and hobbies.

[0044] The procedure unit also collects the opinions of students' parents and guardians and can take them into consideration when carrying out other procedures. For example, the procedure unit provides a survey function to collect opinions from students' parents and guardians, and the AI ​​analyzes the results and carries out procedures. For example, it can give priority to procedures recommended by parents. The procedure unit also collects information on procedures recommended by students' parents and guardians, and the AI ​​can suggest the optimal procedures based on that information. For example, if parents place importance on safety, it can give priority to safe procedures. The procedure unit also collects the opinions of parents and guardians, and the AI ​​can narrow down the candidate procedures based on that information. For example, if parents recommend a specific procedure, it can give priority to that procedure. This allows the opinions of students' parents and guardians to be taken into consideration when carrying out other procedures.

[0045] The procedure unit can handle other procedures, including specific services desired by students. For example, the procedure unit inputs the specific services desired by students, and the AI ​​performs the optimal procedures based on that. For example, for students who request on-site support services, the AI ​​performs procedures that include those services. The procedure unit also collects information on the specific services desired by students, and the AI ​​suggests the optimal procedures based on that information. For example, the AI ​​prioritizes procedures that provide on-site support services. The procedure unit also automates procedures that include specific services, and the AI ​​prepares and submits the necessary documents on behalf of the student. For example, the AI ​​automatically generates and submits a contract for on-site support services. This allows the AI ​​to handle other procedures, including those for the specific services desired by students.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The preference analysis unit can also recommend the most suitable study abroad destination based on the student's health condition and allergy information. For example, the preference analysis unit can recommend a study abroad destination with a good selection of medical facilities based on the student's health condition. It can also recommend regions and universities where students can avoid certain foods based on allergy information. Furthermore, the preference analysis unit can recommend study abroad destinations that offer an environment that makes it easy to manage their health based on the student's health condition and allergy information. This makes it possible to recommend the most suitable study abroad destination based on the student's health condition and allergy information.

[0048] The preference analysis unit can also consider a student's hobbies and interests and recommend the most suitable study abroad destination based on those. For example, the preference analysis unit can recommend universities with a wide range of related club activities and circles based on a student's hobbies. It can also recommend universities that are strong in specific fields based on the student's interests. Furthermore, the preference analysis unit can recommend areas with many cultural events and activities based on the student's hobbies and interests. This makes it possible to recommend the most suitable study abroad destination based on the student's hobbies and interests.

[0049] The preference analysis unit can also consider the student's family structure and home environment and recommend the most suitable study abroad destination based on that. For example, the preference analysis unit can recommend regions and universities that are easy for the family to visit based on the student's family structure. It can also recommend universities that provide an environment where the student can study abroad with peace of mind, taking into account the family environment. Furthermore, the preference analysis unit can recommend regions where communication with family is easy based on the student's family structure and home environment. This makes it possible to recommend the most suitable study abroad destination based on the student's family structure and home environment.

[0050] The preference analysis unit can also consider a student's learning style and pace and recommend the most suitable study abroad destination based on that. For example, the preference analysis unit can recommend a university that offers comprehensive individualized instruction based on the student's learning style. It can also recommend a university that offers a flexible curriculum based on the student's learning pace. Furthermore, the preference analysis unit can recommend a university that offers online learning based on the student's learning style and pace. This makes it possible to recommend the most suitable study abroad destination based on the student's learning style and pace.

[0051] The accommodation department can also arrange accommodation that best suits a student's food preferences and food culture, taking into consideration the student's food preferences and food culture. For example, the accommodation department can arrange accommodation that allows students to enjoy a specific food culture based on the student's food preferences. For students with dietary restrictions, the accommodation department can also arrange accommodation that accommodates those restrictions. Furthermore, the accommodation department can arrange accommodation that allows students to enjoy local ingredients and cuisine based on the student's food preferences and food culture. This allows the accommodation department to arrange the best accommodation based on the student's food preferences and food culture.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The preference analysis unit analyzes the student's preferences and conditions. For example, the preference analysis unit analyzes conditions such as major, budget, climate, and culture entered by the student. The preference analysis unit can also analyze the student's past academic performance and extracurricular activity data. Step 2: The recommendation department recommends the most suitable study abroad destination based on the preferences and conditions analyzed by the preference analysis department. For example, the recommendation department may use a generation AI to create a list of study abroad destinations that match the student's preferences. Step 3: The Accommodation Department arranges accommodation based on the study abroad destinations recommended by the Recommendation Department. For example, the Accommodation Department will arrange the most suitable accommodation based on the student's desired accommodation type (dormitory, apartment, homestay, etc.), budget, and location. Step 4: The Processing Department will process the admission procedures and visa application based on the accommodation arranged by the Arrangement Department. For example, the Processing Department will automatically prepare and submit the necessary documents, apply for the entrance exam, and apply for a visa.

[0054] (Example 2) The study abroad support system according to an embodiment of the present invention is a system in which AI handles procedures such as recommendations for the student's desired study abroad destination and preparations for stay and enrollment, etc. As a result, the study abroad support system can automatically recommend the most suitable study abroad destination, arrange accommodation, complete enrollment procedures, and apply for a visa based on the student's wishes and conditions.

[0055] The study abroad support system according to the embodiment includes a preference analysis unit, a recommendation unit, an arrangement unit, and a procedure unit. The preference analysis unit analyzes a student's preferences and conditions. For example, the preference analysis unit analyzes conditions such as the student's major, budget, climate, and culture entered by the student. The preference analysis unit can also analyze the student's past academic performance and extracurricular activity data. The recommendation unit recommends an optimal study abroad destination based on the preferences and conditions analyzed by the preference analysis unit. For example, the recommendation unit uses a generation AI to create a list of study abroad destinations that match the student's preferences. The arrangement unit arranges accommodation based on the study abroad destination recommended by the recommendation unit. For example, the arrangement unit arranges the optimal accommodation based on the student's desired accommodation type (dormitory, apartment, homestay, etc.), budget, and location. The procedure unit performs admission procedures and visa applications based on the accommodation arranged by the arrangement unit. For example, the procedure unit automatically prepares and submits necessary documents, applies for entrance exams, and applies for visas. As a result, the study abroad support system according to the embodiment can automatically recommend study abroad destinations, arrange accommodation, complete admission procedures, and apply for visas based on the student's wishes and conditions.

[0056] The preference analysis unit analyzes students' past academic performance and extracurricular activity data and can recommend the most suitable study abroad destination based on that data. For example, the preference analysis unit imports students' past transcripts and extracurricular activity records into a database, and AI analyzes the data. For example, it can recommend prestigious universities to students with excellent grades, and universities with strong extracurricular activities to students with active extracurricular activities. The preference analysis unit also analyzes students' past academic performance and recommends universities with strong specialties to students who have achieved excellent results in a particular subject. For example, it can recommend prestigious universities for mathematics to students who excel in mathematics. The preference analysis unit also analyzes extracurricular activity data and recommends universities with strong specialties to students who are enthusiastic about a particular sport or club activity. For example, it can recommend universities with strong soccer clubs to students who are active in soccer. This allows the system to recommend the most suitable study abroad destination based on students' past academic performance and extracurricular activity data.

[0057] The preference analysis unit takes into account a student's future career goals and can recommend the study abroad destination that best suits those goals. For example, the preference analysis unit inputs a student's future career goals, and the AI ​​recommends the university that best suits those goals. For example, for a student aiming for international business, it would recommend an international business school. The preference analysis unit also recommends universities that have strong ties with related industries and companies based on the student's career goals. For example, for a student aiming for the IT industry, it would recommend a university that has strong ties with IT companies. The preference analysis unit also takes into account a student's career goals and recommends universities that offer programs and courses that are best suited to those goals. For example, for a student aiming to start a business, it would recommend a university with a comprehensive entrepreneurial support program. This makes it possible to recommend the optimal study abroad destination based on the student's future career goals.

[0058] The preference analysis unit can use the emotion estimation function to recommend the study abroad destination that evokes the most positive emotions in the student. For example, the preference analysis unit analyzes the emotional reactions of students in real time when they view candidate study abroad destinations, and recommends the destination that evokes the most positive emotions. For example, it prioritizes recommending universities where the student smiles. The preference analysis unit also uses the emotion estimation function to analyze emotional data about places the student has visited in the past and events they have experienced, and recommends the most suitable study abroad destination based on that data. For example, if a student evokes positive emotions in a city they have visited in the past, it recommends a university in that city. The preference analysis unit also monitors the student's emotional reactions when viewing information about study abroad destinations, and recommends the destination that evokes the most positive emotions in real time. For example, if a student becomes excited when viewing information about a particular university, it recommends that university. This makes it possible to recommend the study abroad destination that evokes the most positive emotions based on the student's emotions.

[0059] The arrangement department can take into account a student's lifestyle and hobbies and arrange the most suitable accommodation for them. For example, the arrangement department inputs information about a student's lifestyle and hobbies, and the AI ​​arranges the most suitable accommodation based on that. For example, for a student who likes to go to bed early and rise early, the AI ​​can arrange accommodation in a quiet environment. The arrangement department also takes into account a student's hobbies and interests and arranges the most suitable accommodation for them. For example, for a student who likes sports, the AI ​​can arrange accommodation with excellent sports facilities. The arrangement department also lists the most suitable accommodations based on the student's lifestyle and hobbies. For example, for a student who likes to cook, the AI ​​can arrange accommodation with a well-equipped kitchen. This makes it possible to arrange the most suitable accommodation based on a student's lifestyle and hobbies.

[0060] The arrangement department can analyze safety and security information about the destination and arrange the safest place to stay. For example, the arrangement department collects safety and security information about the destination, and AI analyzes it to arrange the safest place to stay. For example, arranging accommodation in an area with a low crime rate. The arrangement department also analyzes security information about the destination in real time, and AI arranges the safest place to stay. For example, selecting a safe place to stay based on recent security information. The arrangement department also collects data about the safety of the destination, and AI arranges the optimal place to stay based on that data. For example, arranging accommodation near a police station or fire station. This allows the safest place to be arranged based on the safety and security information of the destination.

[0061] The arrangement unit can use the emotion estimation function to arrange accommodation that will make the student feel most at ease. For example, the arrangement unit analyzes in real time the emotional reactions of students when they view candidate accommodations, and arranges the accommodation that they feel most at ease at. For example, it prioritizes arranging accommodations where the student shows a relaxed expression. The arrangement unit also uses the emotion estimation function to analyze emotional data when the student views accommodation information, and arranges the optimal accommodation based on that data. For example, it arranges accommodations where the student shows a sense of security. The arrangement unit also monitors the emotional reactions of students when they view accommodation information, and arranges in real time the accommodation that they feel most at ease at. For example, it arranges accommodations where the student shows a relaxed expression. In this way, it is possible to arrange accommodations where the student feels most at ease based on their emotions.

[0062] The procedure unit analyzes data on a student's past admissions procedures and can implement the optimal procedure based on that data. For example, the procedure unit collects data on a student's past admissions procedures, and AI analyzes it to implement the optimal procedure. For example, a new procedure is implemented based on procedures that have been successful in the past. The procedure unit also analyzes data on a student's past admissions procedures and automatically implements the optimal procedure for a specific university or program. For example, a procedure is implemented based on data on students who have enrolled at the same university in the past. The procedure unit also uses AI to suggest the optimal procedure based on data on a student's past admissions procedures. For example, it improves any problems that occurred in the past procedures and implements a new procedure. This makes it possible to implement the optimal procedure based on data on a student's past admissions procedures.

[0063] The procedure unit can automatically generate and submit a student's academic record and letter of recommendation during the admissions process. For example, the procedure unit uses AI to automatically generate a letter of recommendation based on the student's academic record and submit it during the admissions process. For example, it generates a highly rated letter of recommendation for a student with excellent grades. The procedure unit also analyzes a student's past academic data and uses AI to automatically generate a transcript, which it submits during the admissions process. For example, for a student who achieved excellent grades in a particular subject, it generates a certificate that highlights the grades in that subject. The procedure unit also uses AI to automatically generate a letter of recommendation and a transcript based on the student's academic record and extracurricular activity data and submit it during the admissions process. For example, for a student who is active in extracurricular activities, it generates a letter of recommendation that highlights those activities. This makes it possible to automatically generate and submit a student's academic record and letter of recommendation during the admissions process.

[0064] The procedure unit uses the emotion estimation function to carry out the admissions procedures that cause students the least stress. For example, the procedure unit monitors in real time the stress felt by students at each step of the admissions procedures and suggests the least stressful procedures. For example, it prioritizes procedures where the student shows a relaxed expression. The procedure unit also uses the emotion estimation function to analyze emotional data when students view information about the admissions procedures and performs the optimal procedures based on that data. For example, it prioritizes procedures where the student shows a sense of relief. The procedure unit also monitors the emotional reactions felt by students at each step of the admissions procedures and performs procedures where the student shows the most positive emotions in real time. For example, it prioritizes procedures where the student shows a happy expression. This allows the admissions procedures to be carried out with the least stress based on the student's emotions.

[0065] The procedure section can also collect the opinions of students' parents and guardians and take them into consideration when carrying out admission procedures. For example, the procedure section provides a questionnaire function to collect opinions from students' parents and guardians, and AI analyzes the results to carry out admission procedures. For example, universities recommended by parents can be given priority. The procedure section also collects information on admission procedures recommended by students' parents and guardians, and based on that, AI suggests the optimal procedure. For example, if parents prioritize safety, safe procedures will be given priority. The procedure section also collects the opinions of parents and guardians, and based on that, AI narrows down the candidates for admission procedures. For example, if parents recommend a specific university, the procedures for that university will be given priority. This allows admission procedures to be carried out taking the opinions of students' parents and guardians into consideration.

[0066] The procedure unit can handle the admissions process, including applying for scholarships and grants that students desire. For example, the procedure unit inputs information about the scholarships and grants that students desire, and the AI ​​then performs the optimal application process based on that information. For example, it automatically generates documents to apply for a specific scholarship. The procedure unit also collects information about the scholarships and grants that students desire, and the AI ​​then suggests the optimal application process based on that information. For example, it recommends scholarships based on the student's grades and financial situation. The procedure unit also automates the scholarship and grant application process, and the AI ​​prepares and submits the necessary documents on behalf of the student. For example, it automatically generates and submits scholarship application documents. This allows students to complete the admissions process, including applying for the scholarships and grants that they desire.

[0067] The procedure unit uses the emotion estimation function to monitor in real time the emotional reactions of students when they have their admission procedures handled by a proxy, and is able to carry out the optimal procedures. For example, the procedure unit monitors in real time the emotional reactions of students when they have their admission procedures handled by a proxy, and carries out the procedures that show the most positive emotions. For example, it prioritizes procedures where the student shows a happy expression. The procedure unit also uses the emotion estimation function to analyze emotional data when students view information about the admission procedures, and carries out the optimal procedures based on that. For example, it prioritizes procedures where the student shows a sense of relief. The procedure unit also collects emotional data when students have their admission procedures handled by a proxy, and the AI ​​uses that data to suggest the optimal procedures. For example, it prioritizes procedures where the student shows positive emotions. This makes it possible to monitor students' emotional reactions in real time and carry out the optimal admission procedures.

[0068] The processing unit analyzes students' past visa application data and can carry out the optimal visa application procedures based on that data. For example, the processing unit collects students' past visa application data, and AI analyzes it to carry out the optimal application procedures. For example, a new application is made based on application procedures that were successful in the past. The processing unit also analyzes students' past visa application data and automatically carries out the optimal application procedures for specific countries and programs. For example, an application is made based on the data of students who have studied in the same country in the past. The processing unit also uses AI to suggest the optimal application procedures based on students' past visa application data. For example, the AI ​​can improve any issues that arose in the past application and make a new application. This makes it possible to carry out the optimal visa application procedures based on students' past visa application data.

[0069] The processing unit automatically connects information about the student's residence or study abroad destination when applying for a visa, allowing for a smooth application process. The processing unit, for example, automatically connects information about the student's residence or study abroad destination, allowing AI to smoothly process visa applications. For example, it automatically inputs the residence address and details of the study destination. The processing unit also automatically obtains information about the student's residence or study abroad destination when applying for a visa, and generates application documents based on that information. For example, it automatically generates a residence contract and a study abroad acceptance certificate. The processing unit also uses AI to suggest the optimal visa application procedure based on the information about the student's residence or study abroad destination. For example, it automatically generates and submits the necessary documents based on the residence information. This allows the processing unit to automatically connect information about the student's residence or study abroad destination when applying for a visa, allowing for a smooth application process.

[0070] The procedure unit uses the emotion estimation function to carry out the visa application procedure in which the student feels most comfortable. For example, the procedure unit monitors in real time the sense of comfort felt by the student at each step of the visa application procedure and suggests the procedure that gives the student the most comfort. For example, it prioritizes the procedure for which the student shows a relaxed expression. The procedure unit also uses the emotion estimation function to analyze emotional data when the student views information about the visa application procedure and carries out the optimal procedure based on that data. For example, it prioritizes the procedure for which the student shows a sense of comfort. The procedure unit also monitors the emotional reactions felt by the student at each step of the visa application procedure and carries out the procedure for which the student shows the most positive emotion in real time. For example, it prioritizes the procedure for which the student shows a happy expression. This makes it possible to carry out the visa application procedure in which the student feels the most comfortable based on their emotions.

[0071] The processing department can also collect the opinions of students' parents and guardians and take them into consideration when processing visa applications. For example, the processing department provides a survey function to collect opinions from students' parents and guardians, and AI analyzes the results to process visa applications. For example, it can prioritize the procedures recommended by parents. The processing department also collects information on visa application procedures recommended by students' parents and guardians, and based on that, the AI ​​suggests the optimal procedure. For example, if parents prioritize safety, it will prioritize the safest procedure. The processing department also collects the opinions of parents and guardians, and based on that, the AI ​​narrows down the candidate visa application procedures. For example, if parents recommend a specific procedure, it will prioritize that procedure. This allows the visa application process to take the opinions of students' parents and guardians into consideration.

[0072] The processing unit can suggest the most appropriate type of visa based on the student's desired length of stay and purpose. For example, the processing unit inputs the student's desired length of stay and purpose, and the AI ​​then suggests the most appropriate type of visa based on that. For example, a student visa is suggested for a one-year study abroad program. The processing unit also automatically selects the type of visa based on the student's length of stay and purpose, and carries out the application process. For example, a short-term stay visa is suggested for a short-term study abroad program. The processing unit also lists the most appropriate visa types based on the student's length of stay and purpose, and handles the application process on their behalf. For example, an intern visa is suggested for an internship. This makes it possible to suggest the most appropriate type of visa based on the student's desired length of stay and purpose.

[0073] The procedure unit uses the emotion estimation function to monitor students' emotional reactions in real time when their visa applications are processed, allowing for the most optimal application. For example, the procedure unit monitors students' emotional reactions in real time when their visa applications are processed, and performs the application process that shows the most positive emotions. For example, it prioritizes the process where the student shows a happy expression. The procedure unit also uses the emotion estimation function to analyze emotional data when students view visa application information, and performs the optimal application process based on that information. For example, it prioritizes the process where the student shows a sense of relief. The procedure unit also collects emotional data when students have their visa applications processed, and based on that, AI suggests the optimal application process. For example, it prioritizes the process where the student shows positive emotions. This allows for the student's emotional reactions to be monitored in real time, allowing for the most optimal visa application.

[0074] The procedure unit can analyze a student's past procedure data and implement the optimal procedure based on that. For example, the procedure unit collects a student's past procedure data, and AI analyzes it to implement the optimal procedure. For example, a new procedure is implemented based on procedures that have been successful in the past. The procedure unit also analyzes a student's past procedure data and automatically implements the optimal method for a specific procedure. For example, a procedure is implemented based on the data of students who have implemented the same procedure in the past. The procedure unit also uses AI to suggest the optimal procedure based on a student's past procedure data. For example, it improves any problems that occurred in the past procedure and implements a new procedure. This makes it possible to implement the optimal procedure based on a student's past procedure data.

[0075] The procedure unit takes into account a student's lifestyle and hobbies, and can carry out the most suitable procedure. For example, the procedure unit inputs information about a student's lifestyle and hobbies, and the AI ​​carries out the most suitable procedure based on that information. For example, for a student who likes to go to bed early and rise early, the AI ​​will carry out a procedure that suits that habit. The procedure unit also takes into account a student's hobbies and interests, and the AI ​​will carry out the most suitable procedure. For example, for a student who likes sports, the AI ​​will prioritize sports-related procedures. The procedure unit also lists the most suitable procedures based on the student's lifestyle and hobbies. For example, for a student who likes cooking, the AI ​​will carry out a procedure for a cooking class. This allows the AI ​​to carry out the most suitable procedure based on the student's lifestyle and hobbies.

[0076] The procedure unit can use the emotion estimation function to perform procedures that make students feel most at ease. For example, the procedure unit can monitor in real time the sense of security felt by students at each step of a procedure and suggest procedures that show the most sense of security. For example, it can prioritize procedures where students show a relaxed expression. The procedure unit can also use the emotion estimation function to analyze emotional data when students view procedure information and perform optimal procedures based on that data. For example, it can prioritize procedures where students show a sense of security. The procedure unit can also monitor the emotional reactions felt by students at each step of a procedure and perform procedures where students show the most positive emotions in real time. For example, it can prioritize procedures where students show a happy expression. This makes it possible to perform procedures that make students feel the most at ease based on their emotions.

[0077] The procedure unit also collects the opinions of students' parents and guardians and can take them into consideration when carrying out other procedures. For example, the procedure unit provides a survey function to collect opinions from students' parents and guardians, and the AI ​​analyzes the results and carries out procedures. For example, it can give priority to procedures recommended by parents. The procedure unit also collects information on procedures recommended by students' parents and guardians, and the AI ​​can suggest the optimal procedures based on that information. For example, if parents place importance on safety, it can give priority to safe procedures. The procedure unit also collects the opinions of parents and guardians, and the AI ​​can narrow down the candidate procedures based on that information. For example, if parents recommend a specific procedure, it can give priority to that procedure. This allows the opinions of students' parents and guardians to be taken into consideration when carrying out other procedures.

[0078] The procedure unit can handle other procedures, including specific services desired by students. For example, the procedure unit inputs the specific services desired by students, and the AI ​​performs the optimal procedures based on that. For example, for students who request on-site support services, the AI ​​performs procedures that include those services. The procedure unit also collects information on the specific services desired by students, and the AI ​​suggests the optimal procedures based on that information. For example, the AI ​​prioritizes procedures that provide on-site support services. The procedure unit also automates procedures that include specific services, and the AI ​​prepares and submits the necessary documents on behalf of the student. For example, the AI ​​automatically generates and submits a contract for on-site support services. This allows the AI ​​to handle other procedures, including those for the specific services desired by students.

[0079] The procedure unit uses the emotion estimation function to monitor in real time the emotional reactions of students when other procedures are performed on their behalf, and can implement the optimal procedure. For example, the procedure unit monitors in real time the emotional reactions of students when other procedures are performed on their behalf, and implements the procedure that shows the most positive emotion. For example, it prioritizes the procedure where the student shows a happy expression. The procedure unit also uses the emotion estimation function to analyze emotional data when students view information about other procedures, and implements the optimal procedure based on that. For example, it prioritizes the procedure where the student shows a sense of relief. The procedure unit also collects emotional data when students have other procedures performed on their behalf, and the AI ​​suggests the optimal procedure based on that. For example, it prioritizes the procedure where the student shows positive emotion. This makes it possible to monitor the emotional reactions of students in real time and implement the optimal other procedures.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The preference analysis unit can also recommend the most suitable study abroad destination based on the student's health condition and allergy information. For example, the preference analysis unit can recommend a study abroad destination with a good selection of medical facilities based on the student's health condition. It can also recommend regions and universities where students can avoid certain foods based on allergy information. Furthermore, the preference analysis unit can recommend study abroad destinations that offer an environment that makes it easy to manage their health based on the student's health condition and allergy information. This makes it possible to recommend the most suitable study abroad destination based on the student's health condition and allergy information.

[0082] The preference analysis unit can also consider a student's hobbies and interests and recommend the most suitable study abroad destination based on those. For example, the preference analysis unit can recommend universities with a wide range of related club activities and circles based on a student's hobbies. It can also recommend universities that are strong in specific fields based on the student's interests. Furthermore, the preference analysis unit can recommend areas with many cultural events and activities based on the student's hobbies and interests. This makes it possible to recommend the most suitable study abroad destination based on the student's hobbies and interests.

[0083] The preference analysis unit can also consider the student's family structure and home environment and recommend the most suitable study abroad destination based on that. For example, the preference analysis unit can recommend regions and universities that are easy for the family to visit based on the student's family structure. It can also recommend universities that provide an environment where the student can study abroad with peace of mind, taking into account the family environment. Furthermore, the preference analysis unit can recommend regions where communication with family is easy based on the student's family structure and home environment. This makes it possible to recommend the most suitable study abroad destination based on the student's family structure and home environment.

[0084] The preference analysis unit can also consider a student's learning style and pace and recommend the most suitable study abroad destination based on that. For example, the preference analysis unit can recommend a university that offers comprehensive individualized instruction based on the student's learning style. It can also recommend a university that offers a flexible curriculum based on the student's learning pace. Furthermore, the preference analysis unit can recommend a university that offers online learning based on the student's learning style and pace. This makes it possible to recommend the most suitable study abroad destination based on the student's learning style and pace.

[0085] The accommodation department can also arrange accommodation that best suits a student's food preferences and food culture, taking into consideration the student's food preferences and food culture. For example, the accommodation department can arrange accommodation that allows students to enjoy a specific food culture based on the student's food preferences. For students with dietary restrictions, the accommodation department can also arrange accommodation that accommodates those restrictions. Furthermore, the accommodation department can arrange accommodation that allows students to enjoy local ingredients and cuisine based on the student's food preferences and food culture. This allows the accommodation department to arrange the best accommodation based on the student's food preferences and food culture.

[0086] The arrangement unit can also use the student emotion estimation function to arrange the most relaxing accommodation for the student. For example, the arrangement unit analyzes in real time the emotional reactions of the student when looking at candidate accommodations, and arranges the most relaxing accommodation. For example, it prioritizes arranging accommodations where the student shows a relaxed expression. The arrangement unit also uses the emotion estimation function to analyze emotional data when the student views information about accommodations, and arranges the most optimal accommodation based on that. For example, it arranges accommodations where the student shows a sense of relaxation. In this way, it is possible to arrange the most relaxing accommodation based on the student's emotions.

[0087] The arrangement unit can also use the student emotion estimation function to arrange the accommodation that the student is most excited about. For example, the arrangement unit analyzes in real time the emotional reactions of students when they look at candidate accommodations, and arranges the accommodation that they are most excited about. For example, it prioritizes arranging accommodations where the student shows an excited expression. The arrangement unit also uses the emotion estimation function to analyze emotional data when the student views information about accommodations, and arranges the most optimal accommodation based on that. For example, it arranges accommodations where the student shows excitement. In this way, it is possible to arrange the most exciting accommodation based on the student's emotions.

[0088] The procedure unit can also use the student emotion estimation function to carry out the admissions procedures that make the student feel most comfortable. For example, the procedure unit can monitor in real time the sense of comfort the student feels at each step of the admissions procedures and suggest the procedures that make the student feel most comfortable. For example, it can prioritize the procedures in which the student shows a relaxed expression. The procedure unit can also use the emotion estimation function to analyze emotional data when the student views the admissions procedures information and carry out the optimal procedures based on that. For example, it can prioritize the procedures in which the student shows a sense of comfort. This makes it possible to carry out the admissions procedures that make the student feel most comfortable based on the student's emotions.

[0089] The procedure unit can also use the student emotion estimation function to carry out the visa application procedure that will evoke the most positive emotions from the student. For example, the procedure unit can monitor in real time the positive emotions felt by students at each step of the visa application procedure and suggest the procedure that evokes the most positive emotions. For example, it can prioritize the procedure that evokes the most positive emotions from the student. The procedure unit can also use the emotion estimation function to analyze emotional data when students view information about the visa application procedure and carry out the optimal procedure based on that data. For example, it can prioritize the procedure that evokes the most positive emotions from the student.

[0090] The procedure unit can also use the student emotion estimation function to conduct visa application procedures that cause students the least stress. For example, the procedure unit can monitor in real time the stress felt by students at each step of the visa application process and suggest the least stressful procedures. For example, it can prioritize procedures where the student shows a relaxed expression. The procedure unit can also use the emotion estimation function to analyze emotional data when students view information about the visa application process and perform the optimal procedures based on that. For example, it can prioritize procedures where the student shows a sense of relief. This allows the visa application process to be conducted in the least stressful way based on the student's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The preference analysis unit analyzes the student's preferences and conditions. For example, the preference analysis unit analyzes conditions such as major, budget, climate, and culture entered by the student. The preference analysis unit can also analyze the student's past academic performance and extracurricular activity data. Step 2: The recommendation department recommends the most suitable study abroad destination based on the preferences and conditions analyzed by the preference analysis department. For example, the recommendation department may use a generation AI to create a list of study abroad destinations that match the student's preferences. Step 3: The Accommodation Department arranges accommodation based on the study abroad destinations recommended by the Recommendation Department. For example, the Accommodation Department will arrange the most suitable accommodation based on the student's desired accommodation type (dormitory, apartment, homestay, etc.), budget, and location. Step 4: The Processing Department will process the admission procedures and visa application based on the accommodation arranged by the Arrangement Department. For example, the Processing Department will automatically prepare and submit the necessary documents, apply for the entrance exam, and apply for a visa.

[0093] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A preference analysis department that analyzes students' preferences and conditions; a recommendation unit that recommends an optimal study abroad destination based on the preferences and conditions analyzed by the preference analysis unit; an arrangements unit that arranges accommodation based on the study abroad destination recommended by the recommendation unit; a procedure unit that carries out admission procedures and visa applications based on the accommodation arranged by the arrangement unit; A system characterized by:

2. The desire analysis unit Analyzing the student's past academic performance and extracurricular activity data and recommending the most suitable study abroad destination based on that data 2. The system of claim 1.

3. The desire analysis unit Consider the student's future career goals and recommend the study abroad destination that best suits those goals 2. The system of claim 1.

4. The desire analysis unit Recommend the study abroad destination that the student feels most positive about 2. The system of claim 1.

5. The arrangement department Arrange accommodation that best suits the student's lifestyle and hobbies 2. The system of claim 1.

6. The arrangement department Analyze the safety and security information of the destination and arrange the safest destination for the guest.

2. The system of claim 1.

Images