system

Abstract

The system according to this embodiment aims to suggest the most suitable university based on students' preferences, academic performance, and areas of interest. [Solution] The system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as the student's desired conditions, academic performance, and areas of interest. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes the most suitable university based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it has not been fully carried out to propose an optimal university based on the desired conditions, academic achievements, and areas of interest of students, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal university based on the desired conditions, academic achievements, and areas of interest of students.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as the desired conditions, academic achievements, and areas of interest of students. The analysis unit analyzes the information collected by the collection unit. The proposal unit proposes an optimal university based on the analysis result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable university based on the student's desired conditions, academic performance, and areas of interest. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

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

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

[0028] (Example of form 1) The career guidance support system according to an embodiment of the present invention is a system that works in cooperation with career guidance teachers to support high school students in choosing the most suitable university. The career guidance support system collects information such as students' desired conditions, academic performance, and areas of interest, and proposes the most suitable university in cooperation with career guidance teachers. In addition, to reduce the burden on career guidance teachers, it is equipped with functions for managing the schedule of regular career counseling and automatically generating feedback reports for students. For example, the career guidance support system collects information such as students' desired conditions, academic performance, and areas of interest. In this process, it collects information entered by students, school grade data, and survey results. For example, if a student enters "I want to go to a science-related university," that information is collected by the career guidance support system. Next, based on the collected information, the career guidance support system performs data analysis. The career guidance support system analyzes students' desired conditions, academic performance, areas of interest, etc., and proposes the most suitable university. For example, the career guidance support system analyzes students' grade data and proposes highly competitive universities for high-achieving students and mid-tier universities for students with average grades. Furthermore, the career guidance support system works in cooperation with career guidance counselors, who then provide advice to students based on the universities suggested by the system. Career guidance counselors provide specific advice to students, using the university information suggested by the system as a reference. For example, a counselor might advise, "This university has a thriving science and engineering research program, which suits your aspirations." The career guidance support system also includes features for managing regular career counseling schedules and automatically generating feedback reports for students. For instance, the system automatically manages counseling schedules, adjusting the dates to suit the convenience of both students and counselors. After each counseling session, the system automatically generates a feedback report and provides it to the student. This system reduces the burden on counselors and supports students in choosing the most suitable university. Even if a counselor is too busy to dedicate sufficient time to individual students, the career guidance support system can provide support, enabling them to offer highly accurate advice to each student.This allows the career guidance support system to collect and analyze information such as students' desired conditions, academic performance, and areas of interest, and then suggest the most suitable universities.

[0029] The career guidance support system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as students' desired conditions, academic performance, and areas of interest. For example, the collection unit collects information entered by students, school performance data, and survey results. For example, if a student enters "I want to go to a science-related university," the collection unit can collect that information. The collection unit can also collect school performance data to understand students' academic performance. Furthermore, the collection unit can collect survey results to identify students' areas of interest. For example, the collection unit saves information entered by students to a database for later analysis. The collection unit automatically acquires school performance data and saves it to a database. The collection unit aggregates survey results to identify students' areas of interest. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes students' desired conditions, academic performance, and areas of interest. For example, the analysis unit analyzes students' performance data and proposes prestigious universities to high-achieving students and mid-tier universities to average-achieving students. The analysis department, for example, analyzes students' areas of interest and suggests universities related to those areas of interest. The analysis department, for example, analyzes students' desired conditions and suggests universities that meet those conditions. For example, the analysis department statistically analyzes students' academic performance data to understand academic trends. The analysis department classifies students' areas of interest using clustering technology and identifies related universities. The analysis department analyzes students' desired conditions using natural language processing technology and extracts universities that meet those conditions. The proposal department suggests the most suitable universities based on the analysis results obtained by the analysis department. For example, the proposal department suggests highly competitive universities for high-achieving students and mid-tier universities for average-achieving students. For example, the proposal department suggests universities related to areas of interest. For example, the proposal department suggests universities that meet desired conditions. For example, the proposal department presents a list of highly competitive universities to high-achieving students. The proposal department provides information on universities related to areas of interest. The proposal department provides detailed information on universities that meet desired conditions. As a result, the career guidance support system according to this embodiment can collect and analyze information such as students' desired conditions, academic performance, and areas of interest, and propose the most suitable university.

[0030] The data collection department collects information such as students' preferences, academic performance, and areas of interest. Specifically, it collects information entered by students, school performance data, and survey results. For example, if a student enters "I want to go to a science-related university," this information can be collected. The data collection department can also collect school performance data to understand students' academic performance. Furthermore, the data collection department can collect survey results to identify students' areas of interest. The data collection department stores the information entered by students in a database for later analysis. School performance data is automatically acquired in conjunction with the school's system and stored in the database. Survey results are collected and compiled through online forms and paper-based questionnaires. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall performance of the system. Furthermore, the data collection department implements encryption technology and access control to ensure data privacy and security, protecting students' personal information. This allows students to provide information with confidence and improves the reliability of the system.

[0031] The Analysis Department analyzes the information collected by the Data Collection Department. Specifically, it analyzes students' desired conditions, academic performance, and areas of interest. For example, it analyzes students' academic performance data and suggests prestigious universities to high-achieving students and mid-tier universities to average-achieving students. It also analyzes students' areas of interest and suggests universities related to those areas. Furthermore, it analyzes students' desired conditions and suggests universities that meet those conditions. The Analysis Department statistically analyzes students' academic performance data to understand academic trends. For example, based on past academic data, it identifies the trends in students' academic ability, their strengths and weaknesses, and suggests appropriate career paths. It classifies students' areas of interest using clustering technology and identifies related universities. For example, if an area of ​​interest is "biology," it lists universities strong in biology. It analyzes students' desired conditions using natural language processing technology and extracts universities that meet those conditions. For example, it analyzes the desired condition of "a university in an urban area with well-equipped research facilities" and identifies the corresponding universities. In addition, the Analysis Department processes data in real time using AI and provides analysis results based on the latest information. The AI ​​uses machine learning algorithms to learn patterns from student data and make more accurate suggestions. This allows the analytics department to quickly and accurately analyze the collected data and suggest the best career path for each student.

[0032] The proposal department proposes the most suitable universities based on the analysis results obtained by the analysis department. Specifically, it proposes top-tier universities to high-achieving students and mid-tier universities to students with average grades. It also proposes universities related to students' areas of interest and universities that meet their desired criteria. For high-achieving students, the proposal department presents a list of top-tier universities. For example, students in the top 10% of grades are proposed to top universities both domestically and internationally. It provides information on universities related to students' areas of interest. For example, students interested in biology are provided with information on universities with active research in biology and related faculties and departments. It provides detailed information on universities that meet the desired criteria. For example, it provides campus information, details of research facilities, and introductions to professors for universities that meet the desired criteria of "a university in an urban area with excellent research facilities." Furthermore, the proposal department incorporates visual and interactive elements to make the proposals easy for students to understand. For example, it provides university campus tour videos and alumni interview videos to help students form a concrete image. The proposal department also collects student feedback and continuously improves the accuracy and effectiveness of its proposals. For example, based on feedback from students who receive proposals, the proposal team can revise the proposals or add new ones. This allows the proposal team to suggest the most suitable career paths to students and support them in their career choices.

[0033] The career guidance support system includes a schedule management unit that manages the schedule of career counseling sessions. The schedule management unit manages the schedule of career counseling sessions. For example, the schedule management unit manages the schedule of career counseling sessions using a calendar function. For example, the schedule management unit notifies users of the schedule of career counseling sessions using a reminder function. For example, the schedule management unit automatically adjusts the schedule of career counseling sessions. For example, the schedule management unit registers the schedule of career counseling sessions in the calendar and adjusts it to suit the convenience of the career guidance teacher and the student. The schedule management unit notifies users of the schedule of career counseling sessions using a reminder function, reminding the career guidance teacher and the student. The schedule management unit automatically adjusts the schedule of career counseling sessions and provides the optimal schedule to suit the convenience of the career guidance teacher and the student. In this way, the burden on career guidance teachers can be reduced by managing the schedule of career counseling sessions. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without using AI. For example, the schedule management department can manage schedules by inputting career counseling schedules into an AI model and using that AI model to output the optimal schedule.

[0034] The career guidance support system includes a report generation unit that automatically generates feedback reports for students. The report generation unit automatically generates feedback reports for students. The report generation unit automatically generates feedback reports that include, for example, grade evaluations and comments. The report generation unit automatically generates feedback reports after career counseling and provides them to students. The report generation unit automatically generates feedback reports and sends them to students via email. For example, the report generation unit automatically generates feedback reports that include grade evaluations and comments after career counseling and provides them to students. The report generation unit automatically generates feedback reports and sends them to students via email. The report generation unit automatically generates feedback reports and provides them to students online. This reduces the burden on career guidance teachers by automatically generating feedback reports for students. Some or all of the above-described processes in the report generation unit may be performed using, for example, AI, or not using AI. For example, the report generation unit can automatically generate reports using an AI model that inputs data after career counseling and outputs feedback reports.

[0035] The data collection unit can collect information entered by students, school performance data, survey results, etc. For example, the data collection unit collects information entered by students. For example, the data collection unit collects school performance data. For example, the data collection unit collects survey results. For example, the data collection unit saves information entered by students to a database for later analysis. The data collection unit automatically acquires school performance data and saves it to a database. The data collection unit aggregates survey results and identifies students' areas of interest. This allows for analysis based on more accurate information by collecting information entered by students, school performance data, survey results, etc. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information entered by students into an AI model and collect information using the AI ​​model.

[0036] The analysis department can analyze students' desired conditions, academic performance, areas of interest, etc. For example, the analysis department can analyze students' desired conditions. For example, the analysis department can analyze students' academic performance. For example, the analysis department can analyze students' areas of interest. For example, the analysis department can analyze students' desired conditions using natural language processing technology and extract universities that match those conditions. The analysis department can statistically analyze students' academic performance to understand performance trends. The analysis department can classify students' areas of interest using clustering technology and identify related universities. In this way, by analyzing students' desired conditions, academic performance, areas of interest, etc., it is possible to suggest more appropriate universities. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input students' desired conditions, academic performance, and areas of interest into an AI model and perform the analysis using the AI ​​model.

[0037] The proposal unit can suggest the most suitable universities based on the analysis results obtained by the analysis unit. For example, the proposal unit may suggest prestigious universities to high-achieving students and mid-tier universities to students with average grades. The proposal unit may suggest universities related to fields of interest. The proposal unit may suggest universities that meet the student's desired criteria. For example, the proposal unit may present a list of prestigious universities to high-achieving students. The proposal unit may provide information on universities related to fields of interest. The proposal unit may provide detailed information on universities that meet the student's desired criteria. This allows the proposal unit to provide students with highly accurate advice by suggesting the most suitable universities based on the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may suggest universities using an AI model that inputs the analysis results obtained by the analysis unit and outputs the most suitable universities.

[0038] The data collection unit can analyze a student's past career counseling history and select the most suitable information collection method. For example, the data collection unit can select the most suitable method based on information collection methods the student has used in the past. For example, the data collection unit can prioritize the selection of effective information collection methods from a student's past career counseling history. For example, the data collection unit can analyze a student's past career counseling history and select the most efficient information collection method. In this way, the optimal information collection method can be selected by analyzing a student's past career counseling history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a student's past career counseling history into an AI model and use an AI model that outputs the optimal information collection method to select an information collection method.

[0039] The data collection unit can filter information based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize collecting highly relevant information based on the student's current learning status. For example, the data collection unit can filter out unnecessary information based on the student's areas of interest. For example, the data collection unit can select and collect appropriate information according to the student's learning progress. This allows for the collection of highly relevant information by filtering based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's current learning status and areas of interest into an AI model and perform filtering using the AI ​​model.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location during information gathering. For example, the data collection unit can prioritize the collection of information on nearby universities based on the student's current location. For example, the data collection unit can collect relevant information based on the region of the student's desired university. For example, the data collection unit can prioritize the collection of information on universities that are easily accessible based on the student's geographical location. In this way, by considering the student's geographical location, the data collection unit can prioritize the collection of highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the student's geographical location as input and outputs highly relevant information.

[0041] The data collection unit can analyze students' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can collect relevant university information based on students' interests on social media. For example, the data collection unit can prioritize collecting information on universities that students follow. For example, the data collection unit can analyze students' social media activities and collect information on areas of interest. In this way, relevant information can be collected by analyzing students' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes students' social media activities as input and outputs relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a concise analysis on information of low importance. The analysis unit adjusts the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a statistical analysis algorithm to information about academic performance. For example, the analysis unit can apply a clustering algorithm to information about areas of interest. For example, the analysis unit can apply a regression analysis algorithm to information about desired conditions. By applying different analysis algorithms depending on the category of information, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may analyze older information as needed. The analysis unit may determine the order of analysis based on when the information was collected. This enables efficient analysis by determining the priority of analysis based on when the information was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the information collection time as input and outputs the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit adjusts the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0046] The proposal department can adjust the level of detail of its proposals based on the importance of the universities. For example, the proposal department will provide detailed proposals for universities of high importance, and concise proposals for universities of low importance. The proposal department adjusts the depth of its proposals according to the importance of the universities. This allows for efficient proposals by adjusting the level of detail based on the importance of the universities. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes the importance of universities as input and outputs the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the university category. For example, the proposal unit might apply a proposal algorithm that emphasizes research achievements to science and engineering universities. For example, it might apply a proposal algorithm that emphasizes employment achievements to humanities universities. For example, it might apply a proposal algorithm that emphasizes artwork evaluation to art universities. By applying different proposal algorithms depending on the university category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that takes the university category as input and outputs an appropriate proposal algorithm.

[0048] The proposal department can determine the priority of proposals based on the submission deadlines of universities. For example, the proposal department will prioritize proposals from universities with approaching deadlines. For example, the proposal department will postpone proposals from universities with later deadlines. The proposal department will determine the order of proposals based on the submission deadlines of universities. This allows for efficient proposals by prioritizing proposals based on the submission deadlines of universities. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes the submission deadlines of universities as input and outputs a priority order for proposals.

[0049] The proposal department can adjust the order of proposals based on the relevance of the universities. For example, the proposal department will prioritize proposing universities with high relevance. For example, the proposal department will postpone proposing universities with low relevance. The proposal department adjusts the order of proposals based on the relevance of the universities. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the universities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes the relevance of universities as input and outputs the order of proposals.

[0050] The schedule management unit can provide an optimal schedule by referring to the student's past schedule history. For example, the schedule management unit can suggest the optimal time slot based on the student's past schedule history. For example, the schedule management unit can prioritize suggesting effective time slots from the student's past schedule history. For example, the schedule management unit can analyze the student's past schedule history and provide the most efficient schedule. In this way, an optimal schedule can be provided by referring to the student's past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can provide a schedule using an AI model that takes the student's past schedule history as input and outputs an optimal schedule.

[0051] The schedule management unit can provide an optimal schedule by taking into account the student's device information. For example, if the student is using a smartphone, the schedule management unit sets the schedule in conjunction with the smartphone's calendar. For example, if the student is using a tablet, the schedule management unit provides a schedule optimized for a larger screen. For example, if the student is using a smartwatch, the schedule management unit provides a concise and highly visible schedule. In this way, by taking into account the student's device information, the optimal schedule can be provided. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can provide a schedule using an AI model that takes the student's device information as input and outputs an optimal schedule.

[0052] The report generation unit can provide the optimal report by referring to the student's past feedback history. For example, the report generation unit selects the optimal report format based on the student's past feedback history. For example, the report generation unit prioritizes providing the report format that was effective based on the student's past feedback history. For example, the report generation unit analyzes the student's past feedback history and provides the most efficient report. In this way, the optimal report can be provided by referring to the student's past feedback history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide a report using an AI model that takes the student's past feedback history as input and outputs the optimal report.

[0053] The report generation unit can provide an optimal report by taking into account the student's device information. For example, if the student is using a smartphone, the report generation unit will provide a report that is optimized for the smartphone's screen size. For example, if the student is using a tablet, the report generation unit will provide a report optimized for a larger screen. For example, if the student is using a smartwatch, the report generation unit will provide a concise and highly readable report. In this way, by taking into account the student's device information, the optimal report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide a report using an AI model that takes the student's device information as input and outputs an optimal report.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The career guidance support system can also include a learning style analysis unit that analyzes students' learning styles. This unit collects and analyzes information about students' learning methods and learning environments. For example, if a student prefers online learning, it can collect that information and suggest universities suitable for online learning. If a student prefers group learning, it can suggest universities with many group learning opportunities. Furthermore, if a student prefers practical learning, it can suggest universities with abundant opportunities for practical training and internships. This allows the system to suggest the most suitable university based on the student's learning style.

[0056] The career guidance support system can also include a career goal analysis unit that analyzes students' future career objectives. This unit collects and analyzes information about students' future occupations and careers. For example, if a student aspires to be a doctor, it can collect that information and suggest medical universities. If a student aspires to be an engineer, it can suggest engineering universities. Furthermore, if a student aspires to be an artist, it can suggest art universities. This allows the system to suggest the most suitable university based on the student's future career goals.

[0057] The career guidance support system can also include a learning progress monitoring unit that monitors students' learning progress in real time. This unit grasps students' learning status in real time and provides appropriate advice according to their progress. For example, if a student feels they are falling behind, the system can collect this information and suggest revising their learning plan. Similarly, if a student achieves their goals, the system can collect this information and suggest the next steps. Furthermore, if a student is experiencing difficulties in their studies, the system can collect this information and provide support. This allows for the provision of appropriate advice based on students' learning progress.

[0058] The career guidance support system can also include a learning environment analysis unit that analyzes students' learning environments. This unit collects and analyzes information about students' learning environments. For example, if a student studies at home, it can collect that information and suggest universities suitable for home study. If a student studies in a library, it can suggest universities with well-equipped libraries. Furthermore, if a student studies in a cafe, it can suggest universities where cafes are available. This allows the system to suggest the most suitable university based on the student's learning environment.

[0059] The career guidance support system can also include a learning history analysis unit that analyzes students' learning history. This unit collects and analyzes information about students' past learning history. For example, it can collect and analyze subjects and grades students have previously taken. It can also collect and analyze extracurricular activities and projects students have participated in. Furthermore, it can collect and analyze the history of career counseling sessions students have received. This allows the system to suggest the most suitable universities based on the student's learning history.

[0060] The career guidance support system may also include a learning objective setting unit that sets students' learning goals. This unit collects and sets information related to the student's learning goals. For example, if a student aims for a high score in a particular subject, it can collect that information and set a learning goal. Similarly, if a student aims to be accepted into a specific university, it can collect that information and set a learning goal. Furthermore, if a student aims to acquire a specific skill, it can collect that information and set a learning goal. This allows the system to propose an optimal learning plan based on the student's learning goals.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects information such as students' preferences, academic performance, and areas of interest. For example, it collects information entered by students, school grade data, and survey results. If a student enters "I want to go to a science-related university," the data collection unit can collect that information. It can also collect school grade data to understand students' academic performance. Furthermore, it can collect survey results to identify students' areas of interest. The collected information is stored in a database and used for analysis later. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes students' desired conditions, academic performance, and areas of interest. It analyzes academic performance data to suggest prestigious universities to high-achieving students and mid-tier universities to average-achieving students. It also analyzes students' areas of interest and suggests universities related to those areas. It analyzes desired conditions and suggests universities that meet those conditions. This involves using statistical analysis, clustering techniques, and natural language processing techniques. Step 3: The proposal department proposes the most suitable universities based on the analysis results obtained by the analysis department. For example, it may suggest highly competitive universities to high-achieving students and mid-tier universities to students with average grades. It may also suggest universities related to the student's areas of interest or that meet their desired criteria. Specifically, it may provide a list of highly competitive universities, information on universities related to the student's areas of interest, and detailed information on universities that meet their desired criteria.

[0063] (Example of form 2) The career guidance support system according to an embodiment of the present invention is a system that works in cooperation with career guidance teachers to support high school students in choosing the most suitable university. The career guidance support system collects information such as students' desired conditions, academic performance, and areas of interest, and proposes the most suitable university in cooperation with career guidance teachers. In addition, to reduce the burden on career guidance teachers, it is equipped with functions for managing the schedule of regular career counseling and automatically generating feedback reports for students. For example, the career guidance support system collects information such as students' desired conditions, academic performance, and areas of interest. In this process, it collects information entered by students, school grade data, and survey results. For example, if a student enters "I want to go to a science-related university," that information is collected by the career guidance support system. Next, based on the collected information, the career guidance support system performs data analysis. The career guidance support system analyzes students' desired conditions, academic performance, areas of interest, etc., and proposes the most suitable university. For example, the career guidance support system analyzes students' grade data and proposes highly competitive universities for high-achieving students and mid-tier universities for students with average grades. Furthermore, the career guidance support system works in cooperation with career guidance counselors, who then provide advice to students based on the universities suggested by the system. Career guidance counselors provide specific advice to students, using the university information suggested by the system as a reference. For example, a counselor might advise, "This university has a thriving science and engineering research program, which suits your aspirations." The career guidance support system also includes features for managing regular career counseling schedules and automatically generating feedback reports for students. For instance, the system automatically manages counseling schedules, adjusting the dates to suit the convenience of both students and counselors. After each counseling session, the system automatically generates a feedback report and provides it to the student. This system reduces the burden on counselors and supports students in choosing the most suitable university. Even if a counselor is too busy to dedicate sufficient time to individual students, the career guidance support system can provide support, enabling them to offer highly accurate advice to each student.This allows the career guidance support system to collect and analyze information such as students' desired conditions, academic performance, and areas of interest, and then suggest the most suitable universities.

[0064] The career guidance support system according to this embodiment comprises a collection unit, an analysis unit, and a proposal unit. The collection unit collects information such as students' desired conditions, academic performance, and areas of interest. For example, the collection unit collects information entered by students, school performance data, and survey results. For example, if a student enters "I want to go to a science-related university," the collection unit can collect that information. The collection unit can also collect school performance data to understand students' academic performance. Furthermore, the collection unit can collect survey results to identify students' areas of interest. For example, the collection unit saves information entered by students to a database for later analysis. The collection unit automatically acquires school performance data and saves it to a database. The collection unit aggregates survey results to identify students' areas of interest. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes students' desired conditions, academic performance, and areas of interest. For example, the analysis unit analyzes students' performance data and proposes prestigious universities to high-achieving students and mid-tier universities to average-achieving students. The analysis department, for example, analyzes students' areas of interest and suggests universities related to those areas of interest. The analysis department, for example, analyzes students' desired conditions and suggests universities that meet those conditions. For example, the analysis department statistically analyzes students' academic performance data to understand academic trends. The analysis department classifies students' areas of interest using clustering technology and identifies related universities. The analysis department analyzes students' desired conditions using natural language processing technology and extracts universities that meet those conditions. The proposal department suggests the most suitable universities based on the analysis results obtained by the analysis department. For example, the proposal department suggests highly competitive universities for high-achieving students and mid-tier universities for average-achieving students. For example, the proposal department suggests universities related to areas of interest. For example, the proposal department suggests universities that meet desired conditions. For example, the proposal department presents a list of highly competitive universities to high-achieving students. The proposal department provides information on universities related to areas of interest. The proposal department provides detailed information on universities that meet desired conditions. As a result, the career guidance support system according to this embodiment can collect and analyze information such as students' desired conditions, academic performance, and areas of interest, and propose the most suitable university.

[0065] The data collection department collects information such as students' preferences, academic performance, and areas of interest. Specifically, it collects information entered by students, school performance data, and survey results. For example, if a student enters "I want to go to a science-related university," this information can be collected. The data collection department can also collect school performance data to understand students' academic performance. Furthermore, the data collection department can collect survey results to identify students' areas of interest. The data collection department stores the information entered by students in a database for later analysis. School performance data is automatically acquired in conjunction with the school's system and stored in the database. Survey results are collected and compiled through online forms and paper-based questionnaires. The data collection department centrally manages this data and can link with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and proposal departments. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection department to collect data efficiently and effectively, improving the overall performance of the system. Furthermore, the data collection department implements encryption technology and access control to ensure data privacy and security, protecting students' personal information. This allows students to provide information with confidence and improves the reliability of the system.

[0066] The Analysis Department analyzes the information collected by the Data Collection Department. Specifically, it analyzes students' desired conditions, academic performance, and areas of interest. For example, it analyzes students' academic performance data and suggests prestigious universities to high-achieving students and mid-tier universities to average-achieving students. It also analyzes students' areas of interest and suggests universities related to those areas. Furthermore, it analyzes students' desired conditions and suggests universities that meet those conditions. The Analysis Department statistically analyzes students' academic performance data to understand academic trends. For example, based on past academic data, it identifies the trends in students' academic ability, their strengths and weaknesses, and suggests appropriate career paths. It classifies students' areas of interest using clustering technology and identifies related universities. For example, if an area of ​​interest is "biology," it lists universities strong in biology. It analyzes students' desired conditions using natural language processing technology and extracts universities that meet those conditions. For example, it analyzes the desired condition of "a university in an urban area with well-equipped research facilities" and identifies the corresponding universities. In addition, the Analysis Department processes data in real time using AI and provides analysis results based on the latest information. The AI ​​uses machine learning algorithms to learn patterns from student data and make more accurate suggestions. This allows the analytics department to quickly and accurately analyze the collected data and suggest the best career path for each student.

[0067] The proposal department proposes the most suitable universities based on the analysis results obtained by the analysis department. Specifically, it proposes top-tier universities to high-achieving students and mid-tier universities to students with average grades. It also proposes universities related to students' areas of interest and universities that meet their desired criteria. For high-achieving students, the proposal department presents a list of top-tier universities. For example, students in the top 10% of grades are proposed to top universities both domestically and internationally. It provides information on universities related to students' areas of interest. For example, students interested in biology are provided with information on universities with active research in biology and related faculties and departments. It provides detailed information on universities that meet the desired criteria. For example, it provides campus information, details of research facilities, and introductions to professors for universities that meet the desired criteria of "a university in an urban area with excellent research facilities." Furthermore, the proposal department incorporates visual and interactive elements to make the proposals easy for students to understand. For example, it provides university campus tour videos and alumni interview videos to help students form a concrete image. The proposal department also collects student feedback and continuously improves the accuracy and effectiveness of its proposals. For example, based on feedback from students who receive proposals, the proposal team can revise the proposals or add new ones. This allows the proposal team to suggest the most suitable career paths to students and support them in their career choices.

[0068] The career guidance support system includes a schedule management unit that manages the schedule of career counseling sessions. The schedule management unit manages the schedule of career counseling sessions. For example, the schedule management unit manages the schedule of career counseling sessions using a calendar function. For example, the schedule management unit notifies users of the schedule of career counseling sessions using a reminder function. For example, the schedule management unit automatically adjusts the schedule of career counseling sessions. For example, the schedule management unit registers the schedule of career counseling sessions in the calendar and adjusts it to suit the convenience of the career guidance teacher and the student. The schedule management unit notifies users of the schedule of career counseling sessions using a reminder function, reminding the career guidance teacher and the student. The schedule management unit automatically adjusts the schedule of career counseling sessions and provides the optimal schedule to suit the convenience of the career guidance teacher and the student. In this way, the burden on career guidance teachers can be reduced by managing the schedule of career counseling sessions. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without using AI. For example, the schedule management department can manage schedules by inputting career counseling schedules into an AI model and using that AI model to output the optimal schedule.

[0069] The career guidance support system includes a report generation unit that automatically generates feedback reports for students. The report generation unit automatically generates feedback reports for students. The report generation unit automatically generates feedback reports that include, for example, grade evaluations and comments. The report generation unit automatically generates feedback reports after career counseling and provides them to students. The report generation unit automatically generates feedback reports and sends them to students via email. For example, the report generation unit automatically generates feedback reports that include grade evaluations and comments after career counseling and provides them to students. The report generation unit automatically generates feedback reports and sends them to students via email. The report generation unit automatically generates feedback reports and provides them to students online. This reduces the burden on career guidance teachers by automatically generating feedback reports for students. Some or all of the above-described processes in the report generation unit may be performed using, for example, AI, or not using AI. For example, the report generation unit can automatically generate reports using an AI model that inputs data after career counseling and outputs feedback reports.

[0070] The data collection unit can collect information entered by students, school performance data, survey results, etc. For example, the data collection unit collects information entered by students. For example, the data collection unit collects school performance data. For example, the data collection unit collects survey results. For example, the data collection unit saves information entered by students to a database for later analysis. The data collection unit automatically acquires school performance data and saves it to a database. The data collection unit aggregates survey results and identifies students' areas of interest. This allows for analysis based on more accurate information by collecting information entered by students, school performance data, survey results, etc. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information entered by students into an AI model and collect information using the AI ​​model.

[0071] The analysis department can analyze students' desired conditions, academic performance, areas of interest, etc. For example, the analysis department can analyze students' desired conditions. For example, the analysis department can analyze students' academic performance. For example, the analysis department can analyze students' areas of interest. For example, the analysis department can analyze students' desired conditions using natural language processing technology and extract universities that match those conditions. The analysis department can statistically analyze students' academic performance to understand performance trends. The analysis department can classify students' areas of interest using clustering technology and identify related universities. In this way, by analyzing students' desired conditions, academic performance, areas of interest, etc., it is possible to suggest more appropriate universities. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input students' desired conditions, academic performance, and areas of interest into an AI model and perform the analysis using the AI ​​model.

[0072] The proposal unit can suggest the most suitable universities based on the analysis results obtained by the analysis unit. For example, the proposal unit may suggest prestigious universities to high-achieving students and mid-tier universities to students with average grades. The proposal unit may suggest universities related to fields of interest. The proposal unit may suggest universities that meet the student's desired criteria. For example, the proposal unit may present a list of prestigious universities to high-achieving students. The proposal unit may provide information on universities related to fields of interest. The proposal unit may provide detailed information on universities that meet the student's desired criteria. This allows the proposal unit to provide students with highly accurate advice by suggesting the most suitable universities based on the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit may suggest universities using an AI model that inputs the analysis results obtained by the analysis unit and outputs the most suitable universities.

[0073] The data collection unit can estimate a student's emotions and adjust the timing of information collection based on the estimated emotions. For example, if a student is stressed, the data collection unit may delay information collection until the student is relaxed. If a student is excited, the data collection unit may immediately begin information collection and prioritize information in areas of interest. If a student is tired, the data collection unit may resume information collection after the student has rested. By adjusting the timing of information collection based on the student's emotions, more effective information collection becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit may input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0074] The data collection unit can analyze a student's past career counseling history and select the most suitable information collection method. For example, the data collection unit can select the most suitable method based on information collection methods the student has used in the past. For example, the data collection unit can prioritize the selection of effective information collection methods from a student's past career counseling history. For example, the data collection unit can analyze a student's past career counseling history and select the most efficient information collection method. In this way, the optimal information collection method can be selected by analyzing a student's past career counseling history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input a student's past career counseling history into an AI model and use an AI model that outputs the optimal information collection method to select an information collection method.

[0075] The data collection unit can filter information based on the student's current learning status and areas of interest. For example, the data collection unit can prioritize collecting highly relevant information based on the student's current learning status. For example, the data collection unit can filter out unnecessary information based on the student's areas of interest. For example, the data collection unit can select and collect appropriate information according to the student's learning progress. This allows for the collection of highly relevant information by filtering based on the student's current learning status and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the student's current learning status and areas of interest into an AI model and perform filtering using the AI ​​model.

[0076] The data collection unit can estimate a student's emotions and prioritize the information to collect based on the estimated emotions. For example, if a student is excited, the data collection unit will prioritize collecting information in areas of interest. If a student is relaxed, the data collection unit will collect a wide range of information to increase their options. If a student is stressed, the data collection unit will prioritize collecting only important information. This allows for more effective information collection by prioritizing the information to collect based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0077] The data collection unit can prioritize the collection of highly relevant information by considering the student's geographical location during information gathering. For example, the data collection unit can prioritize the collection of information on nearby universities based on the student's current location. For example, the data collection unit can collect relevant information based on the region of the student's desired university. For example, the data collection unit can prioritize the collection of information on universities that are easily accessible based on the student's geographical location. In this way, by considering the student's geographical location, the data collection unit can prioritize the collection of highly relevant information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes the student's geographical location as input and outputs highly relevant information.

[0078] The data collection unit can analyze students' social media activities and collect relevant information during the information gathering process. For example, the data collection unit can collect relevant university information based on students' interests on social media. For example, the data collection unit can prioritize collecting information on universities that students follow. For example, the data collection unit can analyze students' social media activities and collect information on areas of interest. In this way, relevant information can be collected by analyzing students' social media activities. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that takes students' social media activities as input and outputs relevant information.

[0079] The analysis unit can estimate students' emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a student is relaxed, the analysis unit provides detailed analysis results. If a student is in a hurry, the analysis unit provides concise analysis results that get straight to the point. If a student is excited, the analysis unit provides analysis results using visually appealing graphs or charts. This allows for more effective analysis results by adjusting the presentation of the analysis based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a concise analysis on information of low importance. The analysis unit adjusts the depth of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the importance of the information as input and outputs the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a statistical analysis algorithm to information about academic performance. For example, the analysis unit can apply a clustering algorithm to information about areas of interest. For example, the analysis unit can apply a regression analysis algorithm to information about desired conditions. By applying different analysis algorithms depending on the category of information, more appropriate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the category of information as input and outputs an appropriate analysis algorithm.

[0082] The analysis unit can estimate students' emotions and adjust the length of the analysis based on the estimated emotions. For example, if a student is in a hurry, the analysis unit provides a short, concise analysis. For example, if a student is relaxed, the analysis unit provides a detailed analysis. For example, if a student is excited, the analysis unit provides the analysis using visually appealing graphs or charts. By adjusting the length of the analysis based on the student's emotions, more effective analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0083] The analysis unit can determine the priority of analysis based on when the information was collected. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may analyze older information as needed. The analysis unit may determine the order of analysis based on when the information was collected. This enables efficient analysis by determining the priority of analysis based on when the information was collected. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the information collection time as input and outputs the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. The analysis unit adjusts the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can perform analysis using an AI model that takes the relevance of information as input and outputs the order of analysis.

[0085] The suggestion unit can estimate a student's emotions and adjust the way it presents its suggestions based on those emotions. For example, if a student is relaxed, the suggestion unit will provide detailed suggestions. If a student is in a hurry, the suggestion unit will provide concise suggestions that get straight to the point. If a student is excited, the suggestion unit will use visually appealing graphs or charts to present its suggestions. By adjusting the way suggestions are presented based on the student's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0086] The proposal department can adjust the level of detail of its proposals based on the importance of the universities. For example, the proposal department will provide detailed proposals for universities of high importance, and concise proposals for universities of low importance. The proposal department adjusts the depth of its proposals according to the importance of the universities. This allows for efficient proposals by adjusting the level of detail based on the importance of the universities. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can make proposals using an AI model that takes the importance of universities as input and outputs the level of detail of the proposals.

[0087] The proposal unit can apply different proposal algorithms depending on the university category. For example, the proposal unit might apply a proposal algorithm that emphasizes research achievements to science and engineering universities. For example, it might apply a proposal algorithm that emphasizes employment achievements to humanities universities. For example, it might apply a proposal algorithm that emphasizes artwork evaluation to art universities. By applying different proposal algorithms depending on the university category, more appropriate proposals can be made. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can make proposals using an AI model that takes the university category as input and outputs an appropriate proposal algorithm.

[0088] The suggestion unit can estimate a student's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if a student is in a hurry, the suggestion unit will make a short, concise suggestion. If a student is relaxed, the suggestion unit will make a detailed suggestion. If a student is excited, the suggestion unit will use visually appealing graphs or charts in its suggestion. By adjusting the length of the suggestion based on the student's emotions, more effective suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0089] The proposal department can determine the priority of proposals based on the submission deadlines of universities. For example, the proposal department will prioritize proposals from universities with approaching deadlines. For example, the proposal department will postpone proposals from universities with later deadlines. The proposal department will determine the order of proposals based on the submission deadlines of universities. This allows for efficient proposals by prioritizing proposals based on the submission deadlines of universities. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes the submission deadlines of universities as input and outputs a priority order for proposals.

[0090] The proposal department can adjust the order of proposals based on the relevance of the universities. For example, the proposal department will prioritize proposing universities with high relevance. For example, the proposal department will postpone proposing universities with low relevance. The proposal department adjusts the order of proposals based on the relevance of the universities. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the universities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can make proposals using an AI model that takes the relevance of universities as input and outputs the order of proposals.

[0091] The schedule management unit can estimate students' emotions and adjust their schedules based on those emotions. For example, if a student is feeling stressed, the schedule management unit can adjust their schedule to a time when they can relax. If a student is excited, the schedule management unit can immediately schedule a career counseling session. If a student is tired, the schedule management unit can adjust their schedule after they have rested. This allows for more effective schedule management by adjusting schedules based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the schedule management unit may be performed using AI, or not using AI. For example, the schedule management unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0092] The schedule management unit can provide an optimal schedule by referring to the student's past schedule history. For example, the schedule management unit can suggest the optimal time slot based on the student's past schedule history. For example, the schedule management unit can prioritize suggesting effective time slots from the student's past schedule history. For example, the schedule management unit can analyze the student's past schedule history and provide the most efficient schedule. In this way, an optimal schedule can be provided by referring to the student's past schedule history. Some or all of the above processes in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can provide a schedule using an AI model that takes the student's past schedule history as input and outputs an optimal schedule.

[0093] The schedule management unit can estimate students' emotions and prioritize schedules based on those estimated emotions. For example, if a student is excited, the schedule management unit will immediately schedule a career counseling session. If a student is relaxed, the schedule management unit will suggest a wide range of time slots. If a student is stressed, the schedule management unit will prioritize only important schedules. This allows for more effective schedule management by prioritizing schedules based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the schedule management unit may be performed using AI or not. For example, the schedule management unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The schedule management unit can provide an optimal schedule by taking into account the student's device information. For example, if the student is using a smartphone, the schedule management unit sets the schedule in conjunction with the smartphone's calendar. For example, if the student is using a tablet, the schedule management unit provides a schedule optimized for a larger screen. For example, if the student is using a smartwatch, the schedule management unit provides a concise and highly visible schedule. In this way, by taking into account the student's device information, the optimal schedule can be provided. Some or all of the above processing in the schedule management unit may be performed using AI, for example, or without AI. For example, the schedule management unit can provide a schedule using an AI model that takes the student's device information as input and outputs an optimal schedule.

[0095] The report generation unit can estimate a student's emotions and adjust the report's presentation based on the estimated emotions. For example, if a student is relaxed, the report generation unit provides a detailed report. If a student is in a hurry, the report generation unit provides a concise report that gets straight to the point. If a student is excited, the report generation unit provides a report using visually appealing graphs and charts. This allows for the provision of more effective reports by adjusting the report's presentation based on the student's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI or not. For example, the report generation unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0096] The report generation unit can provide the optimal report by referring to the student's past feedback history. For example, the report generation unit selects the optimal report format based on the student's past feedback history. For example, the report generation unit prioritizes providing the report format that was effective based on the student's past feedback history. For example, the report generation unit analyzes the student's past feedback history and provides the most efficient report. In this way, the optimal report can be provided by referring to the student's past feedback history. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide a report using an AI model that takes the student's past feedback history as input and outputs the optimal report.

[0097] The report generation unit can estimate students' emotions and prioritize reports based on the estimated emotions. For example, if a student is excited, the report generation unit will prioritize reports on topics of interest. If a student is relaxed, the report generation unit will provide reports containing a wide range of information. If a student is stressed, the report generation unit will prioritize providing only essential information. This allows for the provision of more effective reports by prioritizing reports based on students' emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using AI or not. For example, the report generation unit can input student emotion data into a generative AI and have the generative AI perform emotion estimation.

[0098] The report generation unit can provide an optimal report by taking into account the student's device information. For example, if the student is using a smartphone, the report generation unit will provide a report that is optimized for the smartphone's screen size. For example, if the student is using a tablet, the report generation unit will provide a report optimized for a larger screen. For example, if the student is using a smartwatch, the report generation unit will provide a concise and highly readable report. In this way, by taking into account the student's device information, the optimal report can be provided. Some or all of the above processing in the report generation unit may be performed using AI, for example, or without AI. For example, the report generation unit can provide a report using an AI model that takes the student's device information as input and outputs an optimal report.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The career guidance support system can also include a learning style analysis unit that analyzes students' learning styles. This unit collects and analyzes information about students' learning methods and learning environments. For example, if a student prefers online learning, it can collect that information and suggest universities suitable for online learning. If a student prefers group learning, it can suggest universities with many group learning opportunities. Furthermore, if a student prefers practical learning, it can suggest universities with abundant opportunities for practical training and internships. This allows the system to suggest the most suitable university based on the student's learning style.

[0101] The career guidance support system can also include a career goal analysis unit that analyzes students' future career objectives. This unit collects and analyzes information about students' future occupations and careers. For example, if a student aspires to be a doctor, it can collect that information and suggest medical universities. If a student aspires to be an engineer, it can suggest engineering universities. Furthermore, if a student aspires to be an artist, it can suggest art universities. This allows the system to suggest the most suitable university based on the student's future career goals.

[0102] The career guidance support system can further estimate students' emotions and adjust the timing of career counseling based on those estimates. For example, if a student is feeling stressed, the counseling session can be scheduled for a time when they can relax. If a student is agitated, the counseling session can begin immediately. Furthermore, if a student is tired, the counseling session can resume after they have rested. By adjusting the timing of career counseling based on students' emotions, more effective career counseling becomes possible.

[0103] The career guidance support system can also include a learning progress monitoring unit that monitors students' learning progress in real time. This unit grasps students' learning status in real time and provides appropriate advice according to their progress. For example, if a student feels they are falling behind, the system can collect this information and suggest revising their learning plan. Similarly, if a student achieves their goals, the system can collect this information and suggest the next steps. Furthermore, if a student is experiencing difficulties in their studies, the system can collect this information and provide support. This allows for the provision of appropriate advice based on students' learning progress.

[0104] The career guidance support system can further estimate the student's emotions and adjust the content of the feedback report based on those emotions. For example, if the student is relaxed, a detailed feedback report can be provided. If the student is in a hurry, a concise feedback report focusing on the key points can be provided. Furthermore, if the student is agitated, a feedback report using visually appealing graphs and charts can be provided. In this way, by adjusting the content of the feedback report based on the student's emotions, more effective feedback can be provided.

[0105] The career guidance support system can also include a learning environment analysis unit that analyzes students' learning environments. This unit collects and analyzes information about students' learning environments. For example, if a student studies at home, it can collect that information and suggest universities suitable for home study. If a student studies in a library, it can suggest universities with well-equipped libraries. Furthermore, if a student studies in a cafe, it can suggest universities where cafes are available. This allows the system to suggest the most suitable university based on the student's learning environment.

[0106] The career guidance support system can further estimate students' emotions and adjust the content of career counseling based on those emotions. For example, if a student is relaxed, detailed career counseling can be provided. If a student is in a hurry, concise career counseling can be provided. Furthermore, if a student is agitated, visually engaging materials can be used during the career counseling session. By adjusting the content of career counseling based on students' emotions, more effective career counseling becomes possible.

[0107] The career guidance support system can also include a learning history analysis unit that analyzes students' learning history. This unit collects and analyzes information about students' past learning history. For example, it can collect and analyze subjects and grades students have previously taken. It can also collect and analyze extracurricular activities and projects students have participated in. Furthermore, it can collect and analyze the history of career counseling sessions students have received. This allows the system to suggest the most suitable universities based on the student's learning history.

[0108] The career guidance support system can further estimate students' emotions and adjust the method of career counseling based on those emotions. For example, if a student is relaxed, face-to-face counseling can be conducted. If a student is in a hurry, online counseling can be conducted. Furthermore, if a student is agitated, group counseling can be conducted. By adjusting the method of career counseling based on students' emotions, more effective career counseling becomes possible.

[0109] The career guidance support system may also include a learning objective setting unit that sets students' learning goals. This unit collects and sets information related to the student's learning goals. For example, if a student aims for a high score in a particular subject, it can collect that information and set a learning goal. Similarly, if a student aims to be accepted into a specific university, it can collect that information and set a learning goal. Furthermore, if a student aims to acquire a specific skill, it can collect that information and set a learning goal. This allows the system to propose an optimal learning plan based on the student's learning goals.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects information such as students' preferences, academic performance, and areas of interest. For example, it collects information entered by students, school grade data, and survey results. If a student enters "I want to go to a science-related university," the data collection unit can collect that information. It can also collect school grade data to understand students' academic performance. Furthermore, it can collect survey results to identify students' areas of interest. The collected information is stored in a database and used for analysis later. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes students' desired conditions, academic performance, and areas of interest. It analyzes academic performance data to suggest prestigious universities to high-achieving students and mid-tier universities to average-achieving students. It also analyzes students' areas of interest and suggests universities related to those areas. It analyzes desired conditions and suggests universities that meet those conditions. This involves using statistical analysis, clustering techniques, and natural language processing techniques. Step 3: The proposal department proposes the most suitable universities based on the analysis results obtained by the analysis department. For example, it may suggest highly competitive universities to high-achieving students and mid-tier universities to students with average grades. It may also suggest universities related to the student's areas of interest or that meet their desired criteria. Specifically, it may provide a list of highly competitive universities, information on universities related to the student's areas of interest, and detailed information on universities that meet their desired criteria.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, schedule management unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information entered by students, school performance data, survey results, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes students' desired conditions, academic performance, areas of interest, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable university. The schedule management unit is implemented by the control unit 46A of the smart device 14 and manages the schedule for career counseling. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates feedback reports for students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, schedule management unit, and report generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information entered by students, school performance data, survey results, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes students' desired conditions, academic performance, areas of interest, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable university. The schedule management unit is implemented by the control unit 46A of the smart glasses 214 and manages the schedule for career counseling. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates feedback reports for students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, schedule management unit, and report generation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information entered by students, school performance data, survey results, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes students' desired conditions, academic performance, areas of interest, etc. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the most suitable university. The schedule management unit is implemented by the control unit 46A of the headset terminal 314 and manages the schedule for career counseling. The report generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically generates feedback reports for students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, schedule management unit, and report generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects information entered by students, school performance data, survey results, etc. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes students' desired conditions, academic performance, areas of interest, etc. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the most suitable university. The schedule management unit is implemented by, for example, the control unit 46A of the robot 414 and manages the schedule for career counseling. The report generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically generates feedback reports for students. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The collection department gathers information such as students' desired conditions, academic performance, and areas of interest, An analysis unit analyzes the information collected by the aforementioned collection unit, The system comprises a proposal unit that suggests the most suitable university based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features. (Note 2) It has a scheduling department that manages the schedule for career guidance consultations. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a report generation unit that automatically generates feedback reports for students. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is The system collects information entered by students, school grade data, survey results, and other data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze students' preferences, academic performance, areas of interest, etc. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Based on the analysis results obtained by the aforementioned analysis unit, we propose the most suitable university. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the students' emotions and adjust the timing of information gathering based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze students' past career counseling history to select the most suitable information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on students' current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When gathering information, prioritize collecting highly relevant information, taking into account the students' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, analyze students' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is We estimate the students' emotions and adjust the way the analysis is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is Adjust the level of detail in the analysis based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Apply different analysis algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Estimate the students' emotions and adjust the length of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Prioritize analysis based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, We estimate the students' emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the university. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, Apply different proposed algorithms depending on the university category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, Estimate the students' emotions and adjust the length of the proposal based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, Prioritize proposals based on the university's submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, Adjust the order of proposals based on the relevance of the universities. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned schedule management unit, The system estimates students' emotions and adjusts schedules based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned schedule management unit, We provide the optimal schedule by referring to the student's past schedule history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned schedule management unit, The system estimates students' emotions and prioritizes schedules based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The aforementioned schedule management unit, We provide the optimal schedule, taking into account the students' device information. The system described in Appendix 2, characterized by the features described herein. (Note 29) The report generation unit, We estimate the students' emotions and adjust the way the report is written based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The report generation unit, Provide the most suitable report by referring to the student's past feedback history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The report generation unit, Estimate students' emotions and prioritize reports based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The report generation unit, We provide optimal reports that take into account students' device information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department gathers information such as students' desired conditions, academic performance, and areas of interest, An analysis unit analyzes the information collected by the aforementioned collection unit, The system comprises a proposal unit that suggests the most suitable university based on the analysis results obtained by the aforementioned analysis unit. A system characterized by the following features.

2. It has a scheduling department that manages the schedule for career guidance consultations. The system according to feature 1.

3. It includes a report generation unit that automatically generates feedback reports for students. The system according to feature 1.

4. The aforementioned collection unit is The system collects information entered by students, school grade data, survey results, and other data. The system according to feature 1.

5. The aforementioned analysis unit is Analyze students' preferences, academic performance, areas of interest, etc. The system according to feature 1.

6. The aforementioned proposal section is, Based on the analysis results obtained by the aforementioned analysis unit, we propose the most suitable university. The system according to feature 1.

7. The aforementioned collection unit is We estimate the students' emotions and adjust the timing of information gathering based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze students' past career counseling history to select the most suitable information gathering method. The system according to feature 1.

9. The aforementioned collection unit is When gathering information, filtering is performed based on students' current learning status and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is The system estimates students' emotions and prioritizes the information to collect based on those estimated emotions. The system according to feature 1.

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