system

Abstract

The system according to this embodiment aims to automate the documents and procedures required for studying abroad, thereby reducing the burden on the user. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a creation unit, and a submission unit. The reception unit allows a user to input information necessary for studying abroad. The analysis unit analyzes the information input by the reception unit and lists the necessary documents and procedures. The creation unit creates the documents listed by the analysis unit and inputs the necessary information. The submission unit automatically submits the documents created by the creation unit and notifies the user of progress.

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 paperwork and procedures required for studying abroad had to be done manually, which presented challenges in terms of time and effort.

[0005] The system according to this embodiment aims to automate the documents and procedures required for studying abroad, thereby reducing the burden on the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a submission unit. The reception unit allows a user to input information necessary for studying abroad. The analysis unit analyzes the information input by the reception unit and lists the necessary documents and procedures. The creation unit creates the documents listed by the analysis unit and inputs the necessary information. The submission unit automatically submits the documents created by the creation unit and notifies the user of the progress. [Effects of the Invention]

[0007] The system according to the embodiment automates the documents and procedures required for studying abroad, thereby reducing the burden on users. [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) In an embodiment of the present invention, the study abroad procedure automation system allows users to enter information necessary for studying abroad. AI analyzes the information, creates a list of required documents and procedures, and automatically creates and submits each document. This system allows users to complete all procedures necessary for studying abroad without any cumbersome procedures. For example, when a user enters information such as the name of the university they are studying at, the duration of their study abroad, and their field of study, the AI ​​analyzes the information and creates a list of required documents, such as a visa application form, letter of recommendation, and transcript. The AI ​​then automatically creates each document and enters the user's personal information and educational background. Finally, the AI ​​automatically submits the documents and notifies the user of their progress. This allows users to complete all procedures necessary for studying abroad without any cumbersome procedures. The study abroad procedure automation system allows users to complete all procedures necessary for studying abroad without any cumbersome procedures.

[0029] The automated system for studying abroad procedures according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a submission unit. The reception unit allows a user to input information necessary for studying abroad. The information input by the user includes, but is not limited to, the name of the university where the user will study, the duration of the study abroad program, and the field of study. The reception unit, for example, stores the information input by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information input by the user and lists the necessary documents and procedures. The analysis unit lists the necessary documents, such as a visa application form, a letter of recommendation, and a transcript. The analysis unit can use AI to analyze the information input by the user and list the necessary documents and procedures. The creation unit automatically creates the listed documents and inputs the necessary information. The creation unit inputs, for example, the user's personal information and educational information, and automatically creates documents, such as a visa application form, a letter of recommendation, and a transcript. The creation unit can use AI to automatically create the listed documents and input the necessary information. The submission unit automatically submits the created documents and notifies the user of progress. The submission unit, for example, submits visa applications to embassies, recommendation letters to universities, and transcripts to destination universities. The submission unit can use AI to automatically submit the created documents and notify the user of the progress. As a result, the study abroad procedure automation system according to the embodiment allows users to complete all procedures required for studying abroad without having to go through any complicated procedures.

[0030] The analysis unit can list the necessary documents and procedures based on the information entered by the user. For example, the analysis unit analyzes the information entered by the user and lists the necessary documents, such as visa applications, letters of recommendation, and transcripts. The analysis unit can use AI to analyze the information entered by the user and list the necessary documents and procedures. For example, the analysis unit lists the necessary documents and procedures based on information entered by the user, such as the name of the university the user will study at, the period of study abroad, and the field of major. This allows the analysis unit to list the necessary documents and procedures based on the information entered by the user.

[0031] The creation unit can automatically create listed documents and input the necessary information. For example, the creation unit can input the user's personal information and educational background information and automatically create documents such as visa applications, letters of recommendation, and academic transcripts. The creation unit uses AI to automatically create listed documents and input the necessary information. For example, the creation unit can input the user's personal information and create a visa application. It can also input the user's educational background information and create a letter of recommendation. It can also input the user's academic performance information and create an academic transcript. In this way, the creation unit can automatically create listed documents and input the necessary information.

[0032] The submission system can automatically submit the created documents and notify the user of their progress. For example, the submission system can submit visa applications to embassies, letters of recommendation to universities, and transcripts to universities abroad. The submission system can use AI to automatically submit the created documents and notify the user of their progress. For example, the submission system can submit visa applications to embassies and notify the user of their progress. It can also submit letters of recommendation to universities and notify the user of their progress. It can also submit transcripts to universities abroad and notify the user of their progress. In this way, the submission system can automatically submit the created documents and notify the user of their progress.

[0033] The analysis unit can not only list the necessary documents and procedures based on the information entered by the user, but also include a collection unit that collects the information necessary to create each document. For example, the analysis unit can analyze the information entered by the user and list the necessary documents such as visa application forms, letters of recommendation, and academic transcripts, but can also include a collection unit that collects the information necessary to create each document. For example, the collection unit can automatically collect the user's passport information and information about the university where the student will study, which are necessary for creating the visa application form, and provide this information to the analysis unit. The collection unit can use AI to automatically collect the information necessary to create each document and provide this information to the analysis unit. For example, the collection unit can automatically collect the user's passport information and provide the information necessary for creating the visa application form. The collection unit can also automatically collect information about the university where the student will study and provide the information necessary for creating the letter of recommendation. Furthermore, the collection unit can also automatically collect the user's academic performance information and provide the information necessary for creating the academic transcript. As a result, the analysis unit can not only list the necessary documents and procedures based on the information entered by the user, but can also include a collection unit that collects the information necessary to create each document.

[0034] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can analyze the user's past input history and suggest the optimal input method.

[0035] The reception desk can customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on the user's areas of interest. The reception desk can use AI to customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on the user's areas of interest. The reception desk can also suggest the optimal duration of study abroad, taking into account the user's current academic year and expected graduation date when the user enters the duration of study abroad. Furthermore, when a user enters their area of ​​specialization, the reception desk can suggest the optimal specialization based on the user's past academic performance and areas of interest. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest when entering information.

[0036] The reception system can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception system will prioritize displaying universities and majors related to that country. The reception system uses AI to prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception system will prioritize displaying universities and majors related to that country. The reception system can also prioritize displaying nearby universities and programs if a user wishes to study in a location close to their current location. Furthermore, if a user is interested in a particular region, the reception system can prioritize displaying information related to that region. In this way, the reception system can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location.

[0037] The reception unit can analyze the user's social media activity when inputting information and suggest related input items. For example, the reception unit can suggest universities and majors that the user frequently mentions on social media as input candidates. The reception unit can use AI to analyze the user's social media activity when inputting information and suggest related input items. For example, the reception unit can suggest universities and majors that the user frequently mentions on social media as input candidates. The reception unit can also suggest study abroad programs and events that the user follows on social media as input candidates. The reception unit can also suggest related input items based on past study abroad experiences and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity when inputting information and suggest related input items.

[0038] The analysis unit can optimize the analysis algorithm by referring to past data during analysis. The analysis unit, for example, suggests the most efficient procedure based on past data on study abroad procedures. The analysis unit can use AI to optimize the analysis algorithm by referring to past data during analysis. For example, the analysis unit suggests the most efficient procedure based on past data on study abroad procedures. The analysis unit can also analyze past user input data and select the optimal analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past success stories. This allows the analysis unit to optimize the analysis algorithm by referring to past data during analysis.

[0039] The analysis unit can apply different analysis methods depending on the category of user input information during analysis. For example, the analysis unit can apply an analysis method that prioritizes a specific procedure based on the university name entered by the user. The analysis unit can also apply an analysis method that prioritizes relevant procedures based on the field of study entered by the user. Furthermore, the analysis unit can apply an analysis method that suggests the optimal procedure based on the duration of study abroad entered by the user. In this way, the analysis unit can apply different analysis methods depending on the category of user input information during analysis.

[0040] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user wishes to study abroad in a specific country, the analysis unit will prioritize analyzing the procedures for that country. The analysis unit can use AI to perform analysis while considering the geographical distribution of users. For example, if a user wishes to study abroad in a specific country, the analysis unit will prioritize analyzing the procedures for that country. The analysis unit can also prioritize analyzing procedures for nearby study abroad destinations if the user wishes to study abroad in a location close to their current location. Furthermore, if a user is interested in a specific region, the analysis unit can prioritize analyzing procedures related to that region. In this way, the analysis unit can perform analysis while considering the geographical distribution of users.

[0041] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest literature on study abroad procedures. The analysis unit can use AI to improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest literature on study abroad procedures. The analysis unit can also refer to literature on past success stories and propose the optimal procedures. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to laws, regulations, and guidelines related to studying abroad. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process.

[0042] The creation unit can select the optimal creation method by referring to the user's past document creation history when creating a document. For example, the creation unit can suggest the optimal document creation method based on documents the user has created in the past. The creation unit can use AI to select the optimal creation method by referring to the user's past document creation history when creating a document. For example, the creation unit can suggest the optimal document creation method based on documents the user has created in the past. The creation unit can also select the most efficient creation method from the user's past document creation history. Furthermore, the creation unit can analyze the user's past document creation history and suggest the optimal template. As a result, the creation unit can select the optimal creation method by referring to the user's past document creation history when creating a document.

[0043] The creation unit can customize the content of the document based on the user's current situation when creating the document. For example, the creation unit suggests optimal document content when the user inputs their current year of school and expected graduation date. The creation unit can use AI to customize the content of the document based on the user's current situation when creating the document. For example, the creation unit suggests optimal document content when the user inputs their current year of school and expected graduation date. The creation unit can also suggest related document content when the user inputs their current field of study. The creation unit can also suggest document content suitable for the study abroad destination when the user inputs their current study abroad destination. This allows the creation unit to customize the content of the document based on the user's current situation when creating the document.

[0044] The creation unit can create optimal documents by taking into account the user's geographical location information when creating documents. For example, if the user wishes to study abroad in a specific country, the creation unit prioritizes creating documents related to that country. The creation unit can use AI to create optimal documents by taking into account the user's geographical location information when creating documents. For example, if the user wishes to study abroad in a specific country, the creation unit prioritizes creating documents related to that country. Furthermore, if the user wishes to study abroad in a location close to their current location, the creation unit can prioritize creating documents related to the surrounding area. Furthermore, if the user is interested in a specific region, the creation unit can prioritize creating documents related to that region. This allows the creation unit to create optimal documents by taking into account the user's geographical location information when creating documents.

[0045] The document creation system can analyze a user's social media activity and suggest document content during the document creation process. For example, it can suggest document content based on universities and majors that users frequently mention on social media. The document creation system can use AI to analyze a user's social media activity and suggest document content during the document creation process. For example, it can suggest document content based on universities and majors that users frequently mention on social media. It can also suggest document content based on study abroad programs and events that users follow on social media. Furthermore, it can suggest relevant document content based on past study abroad experiences and interests that users have shared on social media. In this way, the document creation system can analyze a user's social media activity and suggest document content during the document creation process.

[0046] The submission unit can select the optimal submission method by referring to past submission history at the time of submission. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. The submission unit can use AI to select the optimal submission method by referring to past submission history at the time of submission. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. The submission unit can also select the most efficient submission method from the user's past submission history. Furthermore, the submission unit can analyze the user's past submission history and suggest the optimal submission timing. As a result, the submission unit can select the optimal submission method by referring to past submission history at the time of submission.

[0047] The submission unit can customize the means of submission based on the user's current situation at the time of submission. For example, the submission unit suggests the optimal means of submission when the user inputs their current year of school or expected graduation date. The submission unit can use AI to customize the means of submission based on the user's current situation at the time of submission. For example, the submission unit suggests the optimal means of submission when the user inputs their current year of school or expected graduation date. The submission unit can also suggest a related means of submission when the user inputs their current major field. The submission unit can also suggest a submission means suitable for the destination of study abroad when the user inputs their current study abroad destination. This allows the submission unit to customize the means of submission based on the user's current situation at the time of submission.

[0048] The submission unit can select the optimal submission method at the time of submission by taking into consideration the user's geographical location information. For example, if the user wishes to study abroad in a specific country, the submission unit preferentially selects a submission method related to that country. The submission unit can use AI to select the optimal submission method at the time of submission by taking into consideration the user's geographical location information. For example, if the user wishes to study abroad in a specific country, the submission unit preferentially selects a submission method related to that country. Furthermore, if the user wishes to study abroad near their current location, the submission unit can preferentially select a nearby submission method. Furthermore, if the user is interested in a specific region, the submission unit can preferentially select a submission method related to that region. This allows the submission unit to select the optimal submission method at the time of submission by taking into consideration the user's geographical location information.

[0049] The submission unit can analyze the user's social media activity at the time of submission to suggest a means of submission. For example, the submission unit makes suggestions based on submission methods frequently mentioned by the user on social media. The submission unit can use AI to analyze the user's social media activity at the time of submission to suggest a means of submission. For example, the submission unit makes suggestions based on submission methods frequently mentioned by the user on social media. The submission unit can also make suggestions based on submission platforms and services that the user follows on social media. The submission unit can also suggest related submission means based on past submission experiences and interests shared by the user on social media. This allows the submission unit to analyze the user's social media activity at the time of submission to suggest a means of submission.

[0050] The collection unit can optimize the collection algorithm by referring to past collection data when collecting information. The collection unit, for example, proposes the most efficient collection method based on past information collection data. The collection unit can use AI to optimize the collection algorithm by referring to past collection data when collecting information. For example, the collection unit proposes the most efficient collection method based on past information collection data. The collection unit can also analyze past collection data of users and select the optimal collection algorithm. The collection unit can also optimize the collection algorithm by referring to past success stories. This allows the collection unit to optimize the collection algorithm by referring to past collection data when collecting information.

[0051] The collection unit can collect optimal information by taking into consideration the user's geographical location information when collecting information. For example, if the user wishes to study abroad in a specific country, the collection unit prioritizes collecting information related to that country. The collection unit can use AI to collect optimal information by taking into consideration the user's geographical location information when collecting information. For example, if the user wishes to study abroad in a specific country, the collection unit prioritizes collecting information related to that country. Furthermore, if the user wishes to study abroad in a location close to their current location, the collection unit can also prioritize collecting information about nearby areas. Furthermore, if the user is interested in a specific region, the collection unit can also prioritize collecting information related to that region. This allows the collection unit to collect optimal information by taking into consideration the user's geographical location information when collecting information.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The reception unit may include a verification unit that verifies the accuracy of the information entered by the user before providing it to the analysis unit. The verification unit, for example, verifies whether the information entered by the user is in the correct format and whether all necessary information has been entered. The verification unit can use AI to automatically verify the accuracy of the information entered by the user, and if there is missing or incorrect information, notify the user and prompt them to correct it. This allows the reception unit to verify the accuracy of the information entered by the user and improve the quality of the information before providing it to the analysis unit.

[0054] The analysis unit not only lists the necessary documents and procedures based on the information entered by the user, but also takes into account the user's past study abroad experience and related information to suggest more appropriate procedures. For example, if the user has studied abroad in the past, the analysis unit customizes the necessary documents and procedures based on that experience. Furthermore, if the user has experience in a specific field, the analysis unit can also preferentially suggest procedures related to that field. This allows the analysis unit to suggest more appropriate procedures by taking into account the user's past experience and related information.

[0055] When automatically creating a listed document, the creation unit can customize the format and style of the document according to the user's preferences. For example, the creation unit selects the user's preferred font and layout and creates the document. In addition, if the user prefers a specific format or template, the creation unit can also create the document using that template. This allows the creation unit to customize the format and style of the document according to the user's preferences.

[0056] The submission department not only automatically submits the prepared documents, but also adjusts the content of the documents according to the requirements of the recipient. For example, when submitting a visa application, the submission department adjusts the content of the documents according to the requirements of the recipient embassy. Also, when submitting a letter of recommendation, the submission department can adjust the content of the documents according to the requirements of the recipient university. This allows the submission department to adjust the content of the documents according to the requirements of the recipient, thereby improving the success rate of submission.

[0057] The analysis unit can not only list the required documents and procedures based on the information entered by the user, but also include a collection unit that collects information necessary for creating each document. The collection unit, for example, automatically collects the user's passport information and information about the university the student is studying at, which are necessary for creating a visa application form, and provides this information to the analysis unit. The collection unit can use AI to automatically collect the information necessary for creating each document and provide this information to the analysis unit. For example, the collection unit can automatically collect the user's passport information and provide the information necessary for creating a visa application form. The collection unit can also automatically collect information about the university the student is studying at, and provide the information necessary for creating a letter of recommendation. The collection unit can also automatically collect the user's grade information and provide the information necessary for creating a transcript. As a result, the analysis unit can not only list the required documents and procedures based on the information entered by the user, but also include a collection unit that collects the information necessary for creating each document.

[0058] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the reception unit to analyze the user's past input history and suggest the optimal input method.

[0059] The reception desk can customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on their areas of interest. The reception desk can use AI to customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on their areas of interest. The reception desk can also suggest the optimal duration of study abroad when a user enters the duration of study abroad, taking into account their current academic year and expected graduation date. Furthermore, when a user enters their area of ​​specialization, the reception desk can suggest the optimal specialization based on their past academic performance and areas of interest. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest when entering information.

[0060] The reception desk can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception desk will prioritize displaying universities and majors related to that country. The reception desk can also prioritize displaying nearby universities and programs if the user wishes to study in a location close to their current location. Furthermore, if the reception desk is interested in a particular region, it can prioritize displaying information related to that region. In this way, the reception desk can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception unit inputs the information the user needs to study abroad. The information input by the user includes the name of the university they will be studying at, the period of study abroad, and their field of study. The reception unit stores the information input by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information entered by the reception unit and lists the necessary documents and procedures. The analysis unit lists the necessary documents such as visa applications, letters of recommendation, and transcripts. The analysis unit can use AI to analyze the information entered by the user and list the necessary documents and procedures. Step 3: The creation unit automatically creates the documents listed by the analysis unit and inputs the necessary information. The creation unit inputs the user's personal information and educational history information and automatically creates documents such as visa applications, letters of recommendation, and transcripts. The creation unit can use AI to automatically create the listed documents and input the necessary information. Step 4: The submission department automatically submits the documents created by the creation department and notifies the user of the progress. The submission department submits the visa application to the embassy, ​​the recommendation letter to the university, and the transcript to the host university. The submission department can use AI to automatically submit the created documents and notify the user of the progress.

[0063] (Example 2) In an embodiment of the present invention, the study abroad procedure automation system allows users to enter information necessary for studying abroad. AI analyzes the information, creates a list of required documents and procedures, and automatically creates and submits each document. This system allows users to complete all procedures necessary for studying abroad without any cumbersome procedures. For example, when a user enters information such as the name of the university they are studying at, the duration of their study abroad, and their field of study, the AI ​​analyzes the information and creates a list of required documents, such as a visa application form, letter of recommendation, and transcript. The AI ​​then automatically creates each document and enters the user's personal information and educational background. Finally, the AI ​​automatically submits the documents and notifies the user of their progress. This allows users to complete all procedures necessary for studying abroad without any cumbersome procedures. The study abroad procedure automation system allows users to complete all procedures necessary for studying abroad without any cumbersome procedures.

[0064] The automated system for studying abroad procedures according to the embodiment includes a reception unit, an analysis unit, a creation unit, and a submission unit. The reception unit allows a user to input information necessary for studying abroad. The information input by the user includes, but is not limited to, the name of the university where the user will study, the duration of the study abroad program, and the field of study. The reception unit, for example, stores the information input by the user in a database and provides it to the analysis unit. The analysis unit analyzes the information input by the user and lists the necessary documents and procedures. The analysis unit lists the necessary documents, such as a visa application form, a letter of recommendation, and a transcript. The analysis unit can use AI to analyze the information input by the user and list the necessary documents and procedures. The creation unit automatically creates the listed documents and inputs the necessary information. The creation unit inputs, for example, the user's personal information and educational information, and automatically creates documents, such as a visa application form, a letter of recommendation, and a transcript. The creation unit can use AI to automatically create the listed documents and input the necessary information. The submission unit automatically submits the created documents and notifies the user of progress. The submission unit, for example, submits visa applications to embassies, recommendation letters to universities, and transcripts to destination universities. The submission unit can use AI to automatically submit the created documents and notify the user of the progress. As a result, the study abroad procedure automation system according to the embodiment allows users to complete all procedures required for studying abroad without having to go through any complicated procedures.

[0065] The analysis unit can list the necessary documents and procedures based on the information entered by the user. For example, the analysis unit analyzes the information entered by the user and lists the necessary documents, such as visa applications, letters of recommendation, and transcripts. The analysis unit can use AI to analyze the information entered by the user and list the necessary documents and procedures. For example, the analysis unit lists the necessary documents and procedures based on information entered by the user, such as the name of the university the user will study at, the period of study abroad, and the field of major. This allows the analysis unit to list the necessary documents and procedures based on the information entered by the user.

[0066] The creation unit can automatically create listed documents and input the necessary information. For example, the creation unit can input the user's personal information and educational background information and automatically create documents such as visa applications, letters of recommendation, and academic transcripts. The creation unit uses AI to automatically create listed documents and input the necessary information. For example, the creation unit can input the user's personal information and create a visa application. It can also input the user's educational background information and create a letter of recommendation. It can also input the user's academic performance information and create an academic transcript. In this way, the creation unit can automatically create listed documents and input the necessary information.

[0067] The submission system can automatically submit the created documents and notify the user of their progress. For example, the submission system can submit visa applications to embassies, letters of recommendation to universities, and transcripts to universities abroad. The submission system can use AI to automatically submit the created documents and notify the user of their progress. For example, the submission system can submit visa applications to embassies and notify the user of their progress. It can also submit letters of recommendation to universities and notify the user of their progress. It can also submit transcripts to universities abroad and notify the user of their progress. In this way, the submission system can automatically submit the created documents and notify the user of their progress.

[0068] The analysis unit can not only list the necessary documents and procedures based on the information entered by the user, but also include a collection unit that collects the information necessary to create each document. For example, the analysis unit can analyze the information entered by the user and list the necessary documents such as visa application forms, letters of recommendation, and academic transcripts, but can also include a collection unit that collects the information necessary to create each document. For example, the collection unit can automatically collect the user's passport information and information about the university where the student will study, which are necessary for creating the visa application form, and provide this information to the analysis unit. The collection unit can use AI to automatically collect the information necessary to create each document and provide this information to the analysis unit. For example, the collection unit can automatically collect the user's passport information and provide the information necessary for creating the visa application form. The collection unit can also automatically collect information about the university where the student will study and provide the information necessary for creating the letter of recommendation. Furthermore, the collection unit can also automatically collect the user's academic performance information and provide the information necessary for creating the academic transcript. As a result, the analysis unit can not only list the necessary documents and procedures based on the information entered by the user, but can also include a collection unit that collects the information necessary to create each document.

[0069] The reception unit can estimate the user's emotions and adjust the information input interface based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple and intuitive interface and minimizes input steps. The reception unit can use AI to estimate the user's emotions and adjust the information input interface based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit provides a simple and intuitive interface and minimizes input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This allows the reception unit to adjust the information input interface according to the user's emotions.

[0070] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can also prioritize suggesting input methods that the user has used in the past (voice, text, etc.). Furthermore, the reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, the reception desk can analyze the user's past input history and suggest the optimal input method.

[0071] The reception desk can customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on the user's areas of interest. The reception desk can use AI to customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on the user's areas of interest. The reception desk can also suggest the optimal duration of study abroad, taking into account the user's current academic year and expected graduation date when the user enters the duration of study abroad. Furthermore, when a user enters their area of ​​specialization, the reception desk can suggest the optimal specialization based on the user's past academic performance and areas of interest. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest when entering information.

[0072] The reception unit can estimate the user's emotions and determine the priority of inputs based on the estimated user's emotions. For example, if the user is nervous, the reception unit prompts the user to input information in order of most important information. The reception unit can use AI to estimate the user's emotions and determine the priority of inputs based on the estimated user's emotions. For example, if the user is nervous, the reception unit prompts the user to input information in order of most important information. Furthermore, if the user is relaxed, the reception unit can suggest the order in which to input detailed information. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input information in order of easiest information. In this way, the reception unit can determine the priority of inputs according to the user's emotions.

[0073] The reception system can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception system will prioritize displaying universities and majors related to that country. The reception system uses AI to prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception system will prioritize displaying universities and majors related to that country. The reception system can also prioritize displaying nearby universities and programs if a user wishes to study in a location close to their current location. Furthermore, if a user is interested in a particular region, the reception system can prioritize displaying information related to that region. In this way, the reception system can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location.

[0074] The reception unit can analyze the user's social media activity when inputting information and suggest related input items. For example, the reception unit can suggest universities and majors that the user frequently mentions on social media as input candidates. The reception unit can use AI to analyze the user's social media activity when inputting information and suggest related input items. For example, the reception unit can suggest universities and majors that the user frequently mentions on social media as input candidates. The reception unit can also suggest study abroad programs and events that the user follows on social media as input candidates. The reception unit can also suggest related input items based on past study abroad experiences and interests that the user has shared on social media. This allows the reception unit to analyze the user's social media activity when inputting information and suggest related input items.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit relaxes the analysis criteria and prioritizes simple procedures. The analysis unit can use AI to estimate the user's emotions and adjust the analysis criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit relaxes the analysis criteria and prioritizes simple procedures. The analysis unit can also perform a detailed analysis and suggest the optimal procedure if the user is relaxed. The analysis unit can also prioritize procedures that can be completed quickly if the user is in a hurry. This allows the analysis unit to adjust the analysis criteria according to the user's emotions.

[0076] The analysis unit can optimize the analysis algorithm by referring to past data during analysis. The analysis unit, for example, suggests the most efficient procedure based on past data on study abroad procedures. The analysis unit can use AI to optimize the analysis algorithm by referring to past data during analysis. For example, the analysis unit suggests the most efficient procedure based on past data on study abroad procedures. The analysis unit can also analyze past user input data and select the optimal analysis algorithm. The analysis unit can also optimize the analysis algorithm by referring to past success stories. This allows the analysis unit to optimize the analysis algorithm by referring to past data during analysis.

[0077] The analysis unit can apply different analysis methods depending on the category of user input information during analysis. For example, the analysis unit can apply an analysis method that prioritizes a specific procedure based on the university name entered by the user. The analysis unit can also apply an analysis method that prioritizes relevant procedures based on the field of study entered by the user. Furthermore, the analysis unit can apply an analysis method that suggests the optimal procedure based on the duration of study abroad entered by the user. In this way, the analysis unit can apply different analysis methods depending on the category of user input information during analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. The analysis unit can use AI to estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions.

[0079] The analysis unit can perform analysis while considering the geographical distribution of users. For example, if a user wishes to study abroad in a specific country, the analysis unit will prioritize analyzing the procedures for that country. The analysis unit can use AI to perform analysis while considering the geographical distribution of users. For example, if a user wishes to study abroad in a specific country, the analysis unit will prioritize analyzing the procedures for that country. The analysis unit can also prioritize analyzing procedures for nearby study abroad destinations if the user wishes to study abroad in a location close to their current location. Furthermore, if a user is interested in a specific region, the analysis unit can prioritize analyzing procedures related to that region. In this way, the analysis unit can perform analysis while considering the geographical distribution of users.

[0080] The analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest literature on study abroad procedures. The analysis unit can use AI to improve the accuracy of its analysis by referring to relevant literature during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to the latest literature on study abroad procedures. The analysis unit can also refer to literature on past success stories and propose the optimal procedures. Furthermore, the analysis unit can improve the accuracy of its analysis by referring to laws, regulations, and guidelines related to studying abroad. In this way, the analysis unit can improve the accuracy of its analysis by referring to relevant literature during the analysis process.

[0081] The creation unit can estimate the user's emotions and adjust the document creation method based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit provides a simple and intuitive document creation method. The creation unit can use AI to estimate the user's emotions and adjust the document creation method based on the estimated user's emotions. For example, if the user is feeling stressed, the creation unit provides a simple and intuitive document creation method. Furthermore, if the user is relaxed, the creation unit can provide detailed input options and suggest a customizable document creation method. Furthermore, if the user is in a hurry, the creation unit can prioritize voice input to enable quick document creation. This allows the creation unit to adjust the document creation method according to the user's emotions.

[0082] The creation unit can select the optimal creation method by referring to the user's past document creation history when creating a document. For example, the creation unit can suggest the optimal document creation method based on documents the user has created in the past. The creation unit can use AI to select the optimal creation method by referring to the user's past document creation history when creating a document. For example, the creation unit can suggest the optimal document creation method based on documents the user has created in the past. The creation unit can also select the most efficient creation method from the user's past document creation history. Furthermore, the creation unit can analyze the user's past document creation history and suggest the optimal template. As a result, the creation unit can select the optimal creation method by referring to the user's past document creation history when creating a document.

[0083] The creation unit can customize the content of the document based on the user's current situation when creating the document. For example, the creation unit suggests optimal document content when the user inputs their current year of school and expected graduation date. The creation unit can use AI to customize the content of the document based on the user's current situation when creating the document. For example, the creation unit suggests optimal document content when the user inputs their current year of school and expected graduation date. The creation unit can also suggest related document content when the user inputs their current field of study. The creation unit can also suggest document content suitable for the study abroad destination when the user inputs their current study abroad destination. This allows the creation unit to customize the content of the document based on the user's current situation when creating the document.

[0084] The creation unit can estimate the user's emotions and determine the priority of document creation based on the estimated user's emotions. For example, if the user is nervous, the creation unit prompts the user to create documents starting with the most important one. The creation unit can use AI to estimate the user's emotions and determine the priority of document creation based on the estimated user's emotions. For example, if the user is nervous, the creation unit prompts the user to create documents starting with the most important one. The creation unit can also suggest the order in which detailed documents should be created if the user is relaxed. The creation unit can also prompt the user to create documents starting with the easiest one if the user is in a hurry. This allows the creation unit to determine the priority of document creation according to the user's emotions.

[0085] The creation unit can create optimal documents by taking into account the user's geographical location information when creating documents. For example, if the user wishes to study abroad in a specific country, the creation unit prioritizes creating documents related to that country. The creation unit can use AI to create optimal documents by taking into account the user's geographical location information when creating documents. For example, if the user wishes to study abroad in a specific country, the creation unit prioritizes creating documents related to that country. Furthermore, if the user wishes to study abroad in a location close to their current location, the creation unit can prioritize creating documents related to the surrounding area. Furthermore, if the user is interested in a specific region, the creation unit can prioritize creating documents related to that region. This allows the creation unit to create optimal documents by taking into account the user's geographical location information when creating documents.

[0086] The document creation system can analyze a user's social media activity and suggest document content during the document creation process. For example, it can suggest document content based on universities and majors that users frequently mention on social media. The document creation system can use AI to analyze a user's social media activity and suggest document content during the document creation process. For example, it can suggest document content based on universities and majors that users frequently mention on social media. It can also suggest document content based on study abroad programs and events that users follow on social media. Furthermore, it can suggest relevant document content based on past study abroad experiences and interests that users have shared on social media. In this way, the document creation system can analyze a user's social media activity and suggest document content during the document creation process.

[0087] The submission unit can estimate the user's emotions and adjust the timing of submission based on the estimated user's emotions. For example, if the user is feeling stressed, the submission unit flexibly adjusts the timing of submission to match the user's pace. The submission unit can use AI to estimate the user's emotions and adjust the timing of submission based on the estimated user's emotions. For example, if the user is feeling stressed, the submission unit flexibly adjusts the timing of submission to match the user's pace. The submission unit can also suggest the optimal submission timing when the user is relaxed. The submission unit can also adjust the timing so that the user can submit quickly when the user is in a hurry. This allows the submission unit to adjust the timing of submission according to the user's emotions.

[0088] The submission unit can select the optimal submission method by referring to past submission history at the time of submission. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. The submission unit can use AI to select the optimal submission method by referring to past submission history at the time of submission. For example, the submission unit can suggest the optimal submission method based on the submission method the user has used in the past. The submission unit can also select the most efficient submission method from the user's past submission history. Furthermore, the submission unit can analyze the user's past submission history and suggest the optimal submission timing. As a result, the submission unit can select the optimal submission method by referring to past submission history at the time of submission.

[0089] The submission unit can customize the means of submission based on the user's current situation at the time of submission. For example, the submission unit suggests the optimal means of submission when the user inputs their current year of school or expected graduation date. The submission unit can use AI to customize the means of submission based on the user's current situation at the time of submission. For example, the submission unit suggests the optimal means of submission when the user inputs their current year of school or expected graduation date. The submission unit can also suggest a related means of submission when the user inputs their current major field. The submission unit can also suggest a submission means suitable for the destination of study abroad when the user inputs their current study abroad destination. This allows the submission unit to customize the means of submission based on the user's current situation at the time of submission.

[0090] The submission unit can estimate the user's emotions and determine the priority of submission based on the estimated user's emotions. For example, if the user is nervous, the submission unit urges the user to submit documents in order of most important documents. The submission unit can use AI to estimate the user's emotions and determine the priority of submission based on the estimated user's emotions. For example, if the user is nervous, the submission unit urges the user to submit documents in order of most important documents. Furthermore, if the user is relaxed, the submission unit can suggest the order in which detailed documents should be submitted. Furthermore, if the user is in a hurry, the submission unit can urge the user to submit documents in order of easiest documents. In this way, the submission unit can determine the priority of submission according to the user's emotions.

[0091] The submission unit can select the optimal submission method at the time of submission by taking into consideration the user's geographical location information. For example, if the user wishes to study abroad in a specific country, the submission unit preferentially selects a submission method related to that country. The submission unit can use AI to select the optimal submission method at the time of submission by taking into consideration the user's geographical location information. For example, if the user wishes to study abroad in a specific country, the submission unit preferentially selects a submission method related to that country. Furthermore, if the user wishes to study abroad near their current location, the submission unit can preferentially select a nearby submission method. Furthermore, if the user is interested in a specific region, the submission unit can preferentially select a submission method related to that region. This allows the submission unit to select the optimal submission method at the time of submission by taking into consideration the user's geographical location information.

[0092] The submission unit can analyze the user's social media activity at the time of submission to suggest a means of submission. For example, the submission unit makes suggestions based on submission methods frequently mentioned by the user on social media. The submission unit can use AI to analyze the user's social media activity at the time of submission to suggest a means of submission. For example, the submission unit makes suggestions based on submission methods frequently mentioned by the user on social media. The submission unit can also make suggestions based on submission platforms and services that the user follows on social media. The submission unit can also suggest related submission means based on past submission experiences and interests shared by the user on social media. This allows the submission unit to analyze the user's social media activity at the time of submission to suggest a means of submission.

[0093] The data collection unit can estimate the user's emotions and adjust its information collection methods based on those emotions. For example, if the user is stressed, the data collection unit can provide a simple and intuitive information collection method. The data collection unit can use AI to estimate the user's emotions and adjust its information collection methods based on those emotions. For example, if the user is stressed, the data collection unit can provide a simple and intuitive information collection method. The data collection unit can also provide detailed information collection options and suggest customizable methods if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can provide a method for quickly gathering information. In this way, the data collection unit can adjust its information collection methods according to the user's emotions.

[0094] The collection unit can optimize the collection algorithm by referring to past collection data when collecting information. The collection unit, for example, proposes the most efficient collection method based on past information collection data. The collection unit can use AI to optimize the collection algorithm by referring to past collection data when collecting information. For example, the collection unit proposes the most efficient collection method based on past information collection data. The collection unit can also analyze past collection data of users and select the optimal collection algorithm. The collection unit can also optimize the collection algorithm by referring to past success stories. This allows the collection unit to optimize the collection algorithm by referring to past collection data when collecting information.

[0095] The collection unit can estimate the user's emotions and determine the collection priority based on the estimated user's emotions. For example, if the user is nervous, the collection unit prompts the user to collect information in order of most important information. The collection unit can use AI to estimate the user's emotions and determine the collection priority based on the estimated user's emotions. For example, if the user is nervous, the collection unit prompts the user to collect information in order of most important information. The collection unit can also suggest the order in which to collect detailed information if the user is relaxed. The collection unit can also prompt the user to collect information in order of easiest information if the user is in a hurry. This allows the collection unit to determine the collection priority according to the user's emotions.

[0096] The collection unit can collect optimal information by taking into consideration the user's geographical location information when collecting information. For example, if the user wishes to study abroad in a specific country, the collection unit prioritizes collecting information related to that country. The collection unit can use AI to collect optimal information by taking into consideration the user's geographical location information when collecting information. For example, if the user wishes to study abroad in a specific country, the collection unit prioritizes collecting information related to that country. Furthermore, if the user wishes to study abroad in a location close to their current location, the collection unit can also prioritize collecting information about nearby areas. Furthermore, if the user is interested in a specific region, the collection unit can also prioritize collecting information related to that region. This allows the collection unit to collect optimal information by taking into consideration the user's geographical location information when collecting information. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, creation unit, and submission unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14, and the user inputs information necessary for studying abroad. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and lists the necessary documents and procedures. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates the listed documents and inputs the necessary information. The submission unit, for example, automatically submits the created documents via the communication I / F 44 of the smart device 14 and notifies the user of the progress. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, creation unit, and submission unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214, and the user inputs information necessary for studying abroad. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and lists the necessary documents and procedures. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates the listed documents and inputs the necessary information. The submission unit, for example, automatically submits the created documents via the communication I / F 44 of the smart glasses 214 and notifies the user of the progress. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, creation unit, and submission unit, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314, and the user inputs information necessary for studying abroad. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and lists the necessary documents and procedures. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates the listed documents and inputs the necessary information. The submission unit, for example, automatically submits the created documents via the communication I / F 44 of the headset terminal 314 and notifies the user of the progress. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, creation unit, and submission unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414, and the user inputs the information necessary for studying abroad. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the information input by the user and lists the necessary documents and procedures. The creation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and automatically creates the listed documents and inputs the necessary information. The submission unit, for example, automatically submits the created documents via the communication I / F 44 of the robot 414 and notifies the user of the progress.

[0097] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0098] The reception unit may include a verification unit that verifies the accuracy of the information entered by the user before providing it to the analysis unit. The verification unit, for example, verifies whether the information entered by the user is in the correct format and whether all necessary information has been entered. The verification unit can use AI to automatically verify the accuracy of the information entered by the user, and if there is missing or incorrect information, notify the user and prompt them to correct it. This allows the reception unit to verify the accuracy of the information entered by the user and improve the quality of the information before providing it to the analysis unit.

[0099] The analysis unit not only lists the necessary documents and procedures based on the information entered by the user, but also takes into account the user's past study abroad experience and related information to suggest more appropriate procedures. For example, if the user has studied abroad in the past, the analysis unit customizes the necessary documents and procedures based on that experience. Furthermore, if the user has experience in a specific field, the analysis unit can also preferentially suggest procedures related to that field. This allows the analysis unit to suggest more appropriate procedures by taking into account the user's past experience and related information.

[0100] When automatically creating a listed document, the creation unit can customize the format and style of the document according to the user's preferences. For example, the creation unit selects the user's preferred font and layout and creates the document. In addition, if the user prefers a specific format or template, the creation unit can also create the document using that template. This allows the creation unit to customize the format and style of the document according to the user's preferences.

[0101] The submission department not only automatically submits the prepared documents, but also adjusts the content of the documents according to the requirements of the recipient. For example, when submitting a visa application, the submission department adjusts the content of the documents according to the requirements of the recipient embassy. Also, when submitting a letter of recommendation, the submission department can adjust the content of the documents according to the requirements of the recipient university. This allows the submission department to adjust the content of the documents according to the requirements of the recipient, thereby improving the success rate of submission.

[0102] The analysis unit can not only list the required documents and procedures based on the information entered by the user, but also include a collection unit that collects information necessary for creating each document. The collection unit, for example, automatically collects the user's passport information and information about the university the student is studying at, which are necessary for creating a visa application form, and provides this information to the analysis unit. The collection unit can use AI to automatically collect the information necessary for creating each document and provide this information to the analysis unit. For example, the collection unit can automatically collect the user's passport information and provide the information necessary for creating a visa application form. The collection unit can also automatically collect information about the university the student is studying at, and provide the information necessary for creating a letter of recommendation. The collection unit can also automatically collect the user's grade information and provide the information necessary for creating a transcript. As a result, the analysis unit can not only list the required documents and procedures based on the information entered by the user, but also include a collection unit that collects the information necessary for creating each document.

[0103] The reception unit can estimate the user's emotions and adjust the information input interface based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. The reception unit can use AI to estimate the user's emotions and adjust the information input interface based on the estimated user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable quick information input. This allows the reception unit to adjust the information input interface according to the user's emotions.

[0104] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can use AI to analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display information that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest information that will be used in a specific time period based on the user's past input history. This allows the reception unit to analyze the user's past input history and suggest the optimal input method.

[0105] The reception desk can customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on their areas of interest. The reception desk can use AI to customize input fields based on the user's current situation and areas of interest when entering information. For example, when a user enters the name of a university to study abroad, the reception desk can suggest relevant universities based on their areas of interest. The reception desk can also suggest the optimal duration of study abroad when a user enters the duration of study abroad, taking into account their current academic year and expected graduation date. Furthermore, when a user enters their area of ​​specialization, the reception desk can suggest the optimal specialization based on their past academic performance and areas of interest. In this way, the reception desk can customize input fields based on the user's current situation and areas of interest when entering information.

[0106] The reception unit can estimate the user's emotions and determine the priority of inputs based on the estimated user's emotions. For example, if the user is nervous, the reception unit can prompt the user to input information starting with the most important information. The reception unit can estimate the user's emotions using AI and determine the priority of inputs based on the estimated user's emotions. For example, if the user is nervous, the reception unit can prompt the user to input information starting with the most important information. Furthermore, if the user is relaxed, the reception unit can suggest the order in which to input detailed information. Furthermore, if the user is in a hurry, the reception unit can prompt the user to input information starting with the easiest information. In this way, the reception unit can determine the priority of inputs according to the user's emotions.

[0107] The reception desk can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location. For example, if a user wishes to study in a specific country, the reception desk will prioritize displaying universities and majors related to that country. The reception desk can also prioritize displaying nearby universities and programs if the user wishes to study in a location close to their current location. Furthermore, if the reception desk is interested in a particular region, it can prioritize displaying information related to that region. In this way, the reception desk can prioritize displaying highly relevant input fields when users enter information, taking into account their geographical location.

[0108] The processing flow of the second embodiment will be briefly explained below.

[0109] Step 1: The reception unit inputs the information the user needs to study abroad. The information input by the user includes the name of the university they will be studying at, the period of study abroad, and their field of study. The reception unit stores the information input by the user in a database and provides it to the analysis unit. Step 2: The analysis unit analyzes the information entered by the reception unit and lists the necessary documents and procedures. The analysis unit lists the necessary documents such as visa applications, letters of recommendation, and transcripts. The analysis unit can use AI to analyze the information entered by the user and list the necessary documents and procedures. Step 3: The creation unit automatically creates the documents listed by the analysis unit and inputs the necessary information. The creation unit inputs the user's personal information and educational history information and automatically creates documents such as visa applications, letters of recommendation, and transcripts. The creation unit can use AI to automatically create the listed documents and input the necessary information. Step 4: The submission department automatically submits the documents created by the creation department and notifies the user of the progress. The submission department submits the visa application to the embassy, ​​the recommendation letter to the university, and the transcript to the host university. The submission department can use AI to automatically submit the created documents and notify the user of the progress.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0131] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0138] 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.

[0139] 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.

[0140] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification 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 identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 7, a 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification 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 the same process as the identification processing unit 290 using these models.

[0158] 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.

[0159] 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.

[0160] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0161] 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.

[0162] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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).

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] [Explanation of symbols]

[0182] 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 reception section where users input information necessary for studying abroad; an analysis unit that analyzes the information input by the reception unit and lists the necessary documents and procedures; a creation unit that creates the documents listed by the analysis unit and inputs necessary information; a submission unit that automatically submits the document created by the creation unit and notifies the user of the progress. A system characterized by:

2. The analysis unit Based on the information entered by the user, a list of required documents and procedures is generated.

2. The system of claim 1.

3. The creation unit Automatically create a list of documents and enter the required information 2. The system of claim 1.

4. The submission unit Automatically submit completed documents and notify users of progress 2. The system of claim 1.

5. The analysis unit It not only lists the necessary documents and procedures based on the information entered by the user, but also has a collection section that collects the information necessary to create each document.

2. The system of claim 1.

6. The reception unit Estimates a user's emotions and adjusts the information input interface based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

8. The reception unit As you enter information, customize the input fields based on your current situation and interests 2. The system of claim 1.

9. The reception unit Estimate the user's emotions and prioritize inputs based on the estimated user emotions.

2. The system of claim 1.

10. The reception unit When entering information, the app takes into account the user's geographic location and prioritizes the most relevant input fields.

2. The system of claim 1.

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